Tropical and Extratropical Air-Sea Interactions: Modes of Climate Variations 9780128181560, 9780128181577

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Tropical and Extratropical Air
Sea Interactions

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Tropical and Extratropical Air
Sea Interactions Modes of Climate Variations

Edited by

Swadhin Kumar Behera Application Laboratory, Research Institute for Value-Added-Information Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan; University of Tokyo, Tokyo, Japan

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-818156-0 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

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Contents List of contributors Foreword Preface 1.

xiii xv xvii

Introduction to atmosphere and ocean variability and air
sea interactions

1

SWADHIN KUMAR BEHERA

2.

1.1 Introduction

1

1.2 Atmospheric heat budget

3

1.3 Atmosphere and ocean circulations

6

1.4 Ocean circulation, upwelling, and climate variations

10

1.5 Summary

14

References

15

Impact of atmosphere
ocean interactions on propagation and initiation of boreal winter and summer intraseasonal oscillations

17

TIM LI AND TIANYI WANG

2.1 Introduction

17

2.2 Observed characteristics of tropical intraseasonal oscillation and intraseasonal sea surface temperature anomaly

19

2.2.1 Observed intraseasonal oscillation
sea surface temperature anomaly relationship

19

2.2.2 Cause of the intraseasonal sea surface temperature anomaly

22 v

vi

Contents

2.3 Impact of air
sea interaction on tropical intraseasonal oscillation

3.

28

2.3.1 Role of air
sea interaction in affecting overall intraseasonal oscillation variance

28

2.3.2 Impact of air
sea interaction on intraseasonal oscillation propagation

31

2.3.3 Role of ocean feedback in Madden
Julian oscillation initiation

44

2.3.4 Theoretical air-sea interaction frameworks on intraseasonal timescale selection

50

2.4 Summary and concluding remark

52

Acknowledgments

54

References

54

Air
sea interaction in tropical Pacific: The dynamics of El Niño/Southern Oscillation

61

SWADHIN KUMAR BEHERA, TAKESHI DOI AND JING-JIA LUO

3.1 Introduction

61

3.2 El Niño/Southern Oscillation theory

63

3.2.1 Sporadic mode

65

3.2.2 Oscillatory mode

65

3.2.3 Asymmetry

67

3.3 Diversity and flavors

69

3.4 Teleconnection

72

3.5 Predictability

75

3.6 Decadal and future climate

81

3.7 Summary

83

Acknowledgments

85

References

85

Contents vii

4.

The El Niño Modoki

93

SHAMAL MARATHE AND ASHOK KARUMURI

5.

4.1 What El Niño Modoki is?

93

4.2 Debate

96

4.3 Distinctions and nonlinearities

97

4.4 Teleconnections

101

4.5 Climate change

105

4.6 Summary

106

Acknowledgment

108

References

108

Air
sea interactions in tropical Indian Ocean: The Indian Ocean Dipole

115

SWADHIN KUMAR BEHERA, TAKESHI DOI AND J. VENKATA RATNAM

6.

5.1 Introduction

115

5.2 Indian Ocean Dipole as a phenomenon: the unique event of 2019

117

5.3 Indian Ocean Dipole and Indian summer monsoon rainfall

122

5.4 Indian Ocean Dipole interactions with ENSO and ENSO Modoki

125

5.5 Other teleconnections

127

5.6 Indian Ocean Dipole predictions

130

5.7 Indian Ocean Dipole in future climate

132

5.8 Summary

133

Acknowledgment

133

References

134

The Indo-western Pacific Ocean capacitor effect

141

YU KOSAKA, YUHEI TAKAYA AND YOUICHI KAMAE

6.1 Introduction

141

viii

Contents

6.2 Mechanism and predictability 6.2.1 The wind-evaporation-sea surface temperature feedback in the tropical western North Pacific

143

6.2.2 The Indian Ocean capacitor

145

6.2.3 The Indo-western Pacific Ocean capacitor mode

148

6.2.4 Seasonal predictions

150

6.3 Climate impacts

7.

142

152

6.3.1 The Pacific-Japan pattern

152

6.3.2 Extremes in Southeast and East Asia

154

6.3.3 South Asia

157

6.4 Long-term modulations

158

6.4.1 Historical changes

158

6.4.2 Future changes

160

6.5 Summary

161

Acknowledgment

162

References

162

The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections

171

INGO RICHTER AND HIROKI TOKINAGA

7.1 Introduction

171

7.2 Data description and definition

172

7.3 Climatological annual cycle of the equatorial Atlantic

173

7.4 Dynamical and thermodynamical elements of equatorial Atlantic variability

176

7.4.1 Introduction

176

7.4.2 Composite evolution of the Atlantic zonal mode

178

7.4.3 Phase locking

181

7.4.4 Negative Atlantic zonal mode events—symmetry

181

7.4.5 Atlantic Niño II

182

Contents

7.4.6 Noncanonical Atlantic zonal mode events

182

7.4.7 Thermodynamic Atlantic zonal mode

184

7.4.8 Initiation of Atlantic zonal mode events

185

7.5 Linkage to tropical Atlantic variability

186

7.5.1 Link to the meridional mode

186

7.5.2 Link to the Benguela Niño

187

7.6 Relations of equatorial Atlantic variability to terrestrial precipitation and remote basins

188

7.6.1 Impact on tropical precipitation

188

7.6.2 Impact of the Atlantic zonal mode on El Niño-Southern Oscillation

189

7.6.3 Impact of El Niño-Southern Oscillation on the Atlantic zonal mode

190

7.7 Representation of equatorial Atlantic variability in global climate models

8.

ix

191

7.7.1 Mean state biases

191

7.7.2 Errors in the simulated variability

192

7.8 Prediction of equatorial Atlantic variability

194

7.9 Low-frequency modulation of equatorial Atlantic variability and the impact of climate change

195

7.10 Summary and open questions

196

7.10.1 Summary

197

7.10.2 Open questions

197

7.10.3 Ways forward

199

Acknowledgment

200

References

200

The Ningaloo Niño/Niña: Mechanisms, relation with other climate modes and impacts

207

TOMOKI TOZUKA, MING FENG, WEIQING HAN, SHOICHIRO KIDO AND LEI ZHANG

8.1 Introduction

207

x

Contents

8.2 Mechanisms

9.

208

8.2.1 Remote oceanic forcing

208

8.2.2 Remote atmospheric forcing

209

8.2.3 Local ocean-atmosphere coupled feedback

210

8.2.4 Thermodynamics

212

8.3 Relations with other climate modes

212

8.4 Impacts

214

8.5 Conclusion

214

Acknowledgment

216

References

216

Interannual-to-decadal variability and predictability in South Atlantic and Southern Indian Oceans

221

YUSHI MORIOKA, FRANCOIS ENGELBRECHT AND SWADHIN KUMAR BEHERA

9.1 South Atlantic and Indian Ocean subtropical dipoles

221

9.2 Predictability of the subtropical dipoles

225

9.3 Decadal variability over the South Atlantic and Southern Indian Oceans

226

9.4 Predictability of the South Atlantic and Indian Ocean decadal variability

230

9.5 Summary

232

References

233

10. The other coastal Niño/Niña—the Benguela, California, and Dakar Niños/Niñas

237

PASCAL OETTLI, CHAOXIA YUAN AND INGO RICHTER

10.1 Introduction

237

10.2 The upwelling regions and their variability

238

10.2.1 Benguela system

238

10.2.2 Baja California system

247

10.2.3 Dakar system

249

Contents

xi

10.3 Representation of coastal Niños in climate models

252

10.4 Future of the upwelling regions

255

10.5 Summary and outlook

256

Acknowledgment

258

References

258

11. Impacts of strong warm ocean currents on development of extratropical cyclones through the warm and cold conveyor belts: A review

267

HIDETAKA HIRATA AND MASAMI NONAKA

11.1 Introduction

267

11.2 Role of warm currents in the maintenance of baroclinicity

270

11.3 Role of moisture and heat supply from warm currents in cyclone development

273

11.4 The Kuroshio and Kuroshio Extension, and their variability

282

11.5 Summary and conclusion

284

11.5.1 Summary

284

11.5.2 Open questions

285

11.5.3 Conclusion

286

Acknowledgment

286

References

286

Useful resources

295

Index

299

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List of Contributors Swadhin Kumar Behera

Application Laboratory, Research Institute for Value-Added-Information Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Takeshi Doi

Application Laboratory, Research Institute for Value-AddedInformation Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Francois Engelbrecht

Global Change Institute, University of the Witwatersrand, Johannesburg, South Africa

Ming Feng

CSIRO Oceans and Atmosphere, Indian Ocean Marine Research Centre, Crawley, WA, Australia

Weiqing Han

Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, United States

Hidetaka Hirata

Faculty of Geo-Environmental Sciences, Rissho University,

Kumagaya, Japan

Youichi Kamae

Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan

Ashok Karumuri

University Centre for Earth and Space Sciences, University of Hyderabad, Hyderabad, India

Shoichiro Kido

Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, Tokyo, Japan

Yu Kosaka

Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

Tim Li International Pacific Research Center and Department of Atmospheric Sciences, School of Ocean and Earth Science and Technology, University of Hawai‘i at Ma
noa, Honolulu, HI, United States

Jing-Jia Luo

Institute for Climate and Application Research, Nanjing University of Information Science and Technology, Nanjing, China

xiii

xiv

List of Contributors

Shamal Marathe

Center for Climate Change Research, Indian Institute of Tropical Meteorology, Pune, India

Yushi Morioka

Application Laboratory, Research Institute for Value-AddedInformation Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Masami Nonaka

Application Laboratory, Research Institute for Value-AddedInformation Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Pascal Oettli Application Laboratory, Research Institute for Value-AddedInformation Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan J. Venkata Ratnam

Application Laboratory, Research Institute for ValueAdded-Information Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Ingo Richter Application Laboratory, Research Institute for Value-AddedInformation Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan Yuhei Takaya Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan Hiroki Tokinaga

Research Institute for Applied Mechanics, Kyushu University, Kasuga, Japan

Tomoki Tozuka

Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, Tokyo, Japan

Tianyi Wang

International Pacific Research Center and Department of Atmospheric Sciences, School of Ocean and Earth Science and Technology, University of Hawai‘i at Ma
noa, Honolulu, HI, United States

Chaoxia Yuan Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China Lei Zhang Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, United States

Foreword The climate variation has a significant impact on the welfare of the human society since it is directly related to abnormal weather and extreme phenomena. Hence, it is important to understand the real nature of the climate variation for developing mitigation and adaptation measures for human security and sustainability. It is particularly the case for the developing countries that are vulnerable to those impacts. Recent observational and modeling studies have advanced our understanding of the modes of climate variations. For example, the TOGA program was successful in clarifying some of the underlying mechanisms of the El Niño/Southern Oscillation (ENSO) and helped in extending the ENSO prediction skills. Because of the advancement in the dynamical prediction system, the El Niño is now predictable at least several seasons ahead. In contrast, predicting climate variations rooted in the Indian Ocean was quite a challenge because of the richness of interactions among phenomena with different time and space scales. Nevertheless, the prediction skills of the basin’s climate impact on the surrounding regions and other parts of the world have progressed extremely after the discovery of the Indian Ocean Dipole (IOD) and the subsequent high research activities. This book has discussed all those topics on tropical climate variations and beyond, like some of the recently discovered coastal Niños/Niñas as well as the subtropical and midlatitude climate variability and predictability. I had the pleasure to introduce these new topics to my young colleagues after the seminal work on IOD, and I am glad that most of them are now world-leading scientists in their respective research areas. I am glad that my colleagues have carefully reviewed all those research topics and updated them with the latest information. Since the chapters are opulent with in-depth information ranging from oceanatmosphere dynamics to numerical modeling and climate predictability, I am sure the book will come in handy not only for the young researchers but also for the established professionals in the field. Toshio Yamagata1,2 Emeritus Professor, the University of Tokyo 2 Principal Research Scientist, Application Laboratory, JAMSTEC E-mail: [email protected] http://www.jamstec.go.jp/res/ress/yamagata/ 1

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Preface Climate variability and change have huge impacts on the global socioeconomic conditions. From agriculture to human health, the human society is now facing enormous challenges owing to the extreme climate events that frequently appear with higher intensities under the stress of global warming. It is not the first time that the planet has seen such changes. The world has experienced the vagaries of climate extremes and climate change in all its pasts. The only difference now, unlike our ancestors, is that we are in an opportune time when the climate science has advanced rapidly and the scope of its scientific exploration has increased manifold in the past few decades. Routine weather observations including satellite observations and advancement in telecommunication made it easier for the development of effective weather prediction systems. Those together with the progresses in ocean observations have also helped us to monitor and understand modes of climate variations like the El Niño/ Southern Oscillation (ENSO). We have also developed better insights on the behavior of mean climate system and processes that are helping to maintain the mean ocean and atmospheric heat budget, global circulations, etc. In the meantime, advances in computational sciences have helped us to develop state-of-the-art numerical models and reliable climate prediction systems. The present generations of global climate models are able to reliably predict climate variations, especially the tropical climate variations like ENSO and Indian Ocean Dipole, several seasons ahead with skills not far behind that of the weather forecasts that are done with a lead time of a few days. Recent studies have also helped us in discovering new modes of climate variations in subtropical and coastal regions as mentioned in the foreword of Prof. Toshio Yamagata. Those are shown to be extremely important not only for the climate but also for the marine and terrestrial ecosystems of those regions. An attempt is made in this book to review the present status of all those research studies. Links to available resources are also provided at the end of the book for further research in these areas. While we have developed a lot of understanding on the air
sea interactions of tropical and subtropical climate phenomena, the research in mid-latitude air
sea interactions is not that advanced. Nevertheless, we have tried to bring one such topic for the discussions in the book to provide a flavor of what is happening at this frontier. I hope the studies made by the leading experts in those areas of climate research will help us to establish a base for understanding and predicting the present climate. A better understanding of the present climate system will also help us to reduce model biases and associated errors in the projections of future climate. Swadhin Kumar Behera Yokohama, May 19, 2020

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1 Introduction to atmosphere and ocean variability and air
sea interactions Swadhin Kumar Behera APPLICAT ION LABORATORY, RE SEARCH INSTITUTE FOR VALUE-ADDED-INFORM ATION GENE RATION, JAP AN AGENCY FOR MARINE-EARTH SCIENCE AND TECHNOLOGY, YOKOHAMA, JAPAN

1.1 Introduction Earth’s climate system is an interactive system consisting of five major components: the atmosphere, the hydrosphere, the cryosphere, the land surface, and the biosphere. The interaction is very complex as is the internal processes within each of those five components. Atmosphere is one of the major components of the system that has been observed for centuries even before the inventions of weather instruments. In fact, the science of it, known as the meteorology, said to come from the observations of meteors in evening skies. One of the early documentations of the atmospheric science was that of the Aristotle’s treatise Meteoroligca said to be written around 340 BCE. It was notably one of the early documents that brought the subject of meteorology closer to the scientific discussions. Actual scientific observations and analysis of the atmospheric elements started much later in time, that is, after the inventions of Galileo’s air thermometer and Torricelli’s barometer in 17th century. The observations taken with those early instruments helped to understand the vertical distribution of temperature and pressure in the atmosphere, for example, the observation of the temperature and pressure decrease with height (in the troposphere, the lower atmosphere). Besides, those instruments also helped in picking up early signs of local day-to-day atmospheric variations such as dreary-weather or fair-weather conditions associated with lower and higher than normal barometric pressure, respectively. The observation of weather phenomena progressed rapidly after launching of the World Weather Watch program led by the World Meteorological Organization, which brought the satellite observations besides the many other atmospheric parameters observed in weather stations. At the same time, advancement in computational field helped in the development of numerical modeling for weather predictions using advanced computers. Climate, the topic of the discussions in the present book, is a compilation of the weather states over a place for quite long period of time, for example, over a period of 30 years. Since Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00013-7 © 2021 Elsevier Inc. All rights reserved.

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Tropical and Extratropical Air
Sea Interactions

FIGURE 1–1 Schematic representation of the annual cycle of Earth’s rotation around the Sun causing the seasons. The seasons mentioned in the diagram refer to Northern Hemisphere ones.

most regions over Earth experience changes from season to season (Fig. 1
1), an average must be taken, for instance, over January of many different years to observe a climatological condition for January and so on. Also, in addition to these climatologies of, for example, rainfall and temperature averages for a particular month or even a day (the 30-year average of daily data), the climate of a region could also describe the probability of extreme events, such as a major rainfall event occurring in July to August in India, the range of variations of temperature that typically occur during January to March in Tokyo, or the number of hurricanes that typically hit the American coasts in the hurricane season. Classical climatology provides classifications and descriptions of the various climate regimes found in different parts of the world. For example, the Mediterranean climate that is a very pleasant climate with warm and dry summers and cool but not so wet winters as is seen in some of the regions (though not limited to) around the Mediterranean Sea from which it takes its name. Similarly, there are extreme climate regimes like the Desert climate, which refers to very dry climate, and the Tropical climate (discussed in length in this book) that refers to extremely hot and wet conditions. So, climate varies from place to place, depending on latitude, distance to the sea, vegetation, presence or absence of mountains, or other geographical factors. Climate varies also in time, within a season, season to season, year to year, decade to decade, or on much longer timescales like the Ice age and the global warming (referred to as climate change) that we are experiencing now. The natural climate change that Earth has experienced from Ice age to global warming has taken a new turn now since human activities are inducing such changes, which is referred to as anthropogenic climate change. However, all the chapters in this book discuss about the climate variations of timescales ranging from a few months to a decade.

Chapter 1 • Introduction to atmosphere and ocean variability and air
sea interactions

3

This book discusses those variations with a particular emphasis on the interaction between two of the major components of the climate system, namely, the atmosphere and the ocean. Both components are extremely complex in their own respects with many internal processes that maintain their inherent variability while contributing to the maintenance of the Earth’s heat budget as discussed in the following section.

1.2 Atmospheric heat budget The variation in the atmosphere is mainly driven by the thermal gradient. The major source of this thermal energy is Sun, which directly and indirectly (e.g., through the oceans) influences the atmospheric heat budget. First, the radiative energy-related heat budget is discussed here. The other form of the energy, called the turbulent energy is discussed in Section 1.4. The incoming solar radiation (in short wave) from the Sun goes through several processes after entering the Earth’s atmosphere and only about 51% of the total solar radiation reaches the Earth’s surface (Fig. 1
2). Diffusion and absorption by atmospheric molecules and clouds reduce the incoming energy. In addition, a considerable amount of energy is also reflected back from the ocean and land surfaces. In total, 30% of the energy is reflected from Earth’s surface and atmosphere back to the outer space. The actual energy absorbed in the upper layers of the ocean may vary within wide limits according to the nature of the surface, season, time of the day, and cloudiness. Since Earth is a black body, it also radiates back the energy albeit in infrared radiation. This happens after reaching an equilibrium between thermal emission emitted from the sea surface (ground surface as well) and from that of the atmosphere above it (Fig. 1
2) through a complex exchange of radiations. Let us estimate the temperature of Earth considering the black body radiation and Stefan
Boltzmann law. In this theory, the power emitted per unit area is given by σT4, where T is the uniform absolute temperature of Earth and σ is the Stefan
Boltzmann constant. Since the power is emitted in all directions from a total surface area 4πa2 (“a” being the radius of Earth), the total power emitted by Earth is 4πa2σT4. This outgoing radiation should be equal to the incoming radiation from Sun if Earth is in a thermal equilibrium. The incoming solar radiation can be estimated by the formula (1 2 A) Fsπa2 based on the total solar irradiance (or the solar constant) Fs on a disk and the energy reflected back (albedo) A. So, the balance between incoming and outgoing radiation will be used to estimate Earth’s equilibrium temperature T: ð1 2 AÞFsπa2 5 4πa2 σT 4 Or T 5

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4 ð1 2 AÞFs 4σ

By inserting the values of A (0.3), Fs (1370 W m22), and σ (5.67 3 1028 W m22 K24), we will obtain T  255K (around 218 C). This is obviously lower than the observed global mean temperature, which is about 288K (B15 C). It is clear that the above black body radiation model is missing

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Tropical and Extratropical Air
Sea Interactions

FIGURE 1–2 Typical pathways of energy transfer in the global energy budget. Solar radiation is either reflected by the Earth’s surface or clouds or absorbed by the atmosphere or surface. The surface exchange (mostly turbulent) includes sensible heat and latent heat associated with conduction and evaporation. Some part of terrestrial (infrared) radiation emitted from the Earth’s surface is absorbed by the atmosphere and clouds, some part escapes to outer space, and some other radiated back to the surface by clouds and greenhouse gases.

out some aspect that is inherent in the Earth’s atmosphere. In fact, similar differences (actually, a large difference) in estimates and actual temperature can also be found for Venus. The layer of atmosphere is what we have missed out in the above calculation using the radiative equilibrium assumptions. What if we have a layer of gases, which allow Sun’s shortwave radiations to pass through but selectively restrict the Earth’s longwave radiation to escape? And in fact, that is what is seen in case of Earth and Venus. The Earth’s dry atmosphere is composed mainly of nitrogen (78.1%), oxygen (20.9%), and argon (0.93%). These gases have only limited interaction with the incoming solar radiation and have almost no interaction with the outgoing longwave radiation emitted by the Earth. However, the dry atmosphere also has a number of trace gases, such as carbon dioxide, methane, nitrous oxide, and ozone. These gases, with a total of less than 0.1% by volume, play an essential role in the Earth’s energy budget. This is because of their ability to absorb and emit the Earth’s longwave radiation, unlike the nitrogen and the oxygen. Because of their ability to absorb and emit longwave radiation, while allowing solar radiation to pass through, these gases are called the greenhouse gases (since they just act like greenhouses in farming). It may be noted here that the water vapor in a moist atmosphere also acts like a greenhouse gas. By choosing right ratio of transmission (of solar radiation) and absorption (of longwave radiation) we can reach a radiative equilibrium that is just right to yield a global mean surface temperature closer to

Chapter 1 • Introduction to atmosphere and ocean variability and air
sea interactions

5

FIGURE 1–3 Monthly sea surface temperature anomaly averaged over the globe relative to a 1982
2011 climatology derived from Characteristics of Global Sea Surface Temperature Analysis Data (COBE SST; Japan Meteorological Agency, 2006). The dark solid line is a 7-year running mean applied on the monthly anomalies.

what is observed. But any change in that well-balanced ratio, for example, an increase or decrease in the amount of greenhouse gases, is bound to disrupt Earth’s thermal equilibrium and we will end up having a warmer or a cooler global temperature. At the moment, it is believed that our anthropogenic activities are adding up more greenhouse gases leading to the global warming depicted in the SST anomalies (Fig. 1
3). On the other hand, some dust particles (usually from volcanic eruptions) and aerosols released to the atmosphere could act in an opposite way to cut down the solar radiation leading to Ice age, as it has happened in the past. Coming back to the recent historical records of SST anomalies, shown in Fig. 1
3, we find low-frequency variations in the global monthly mean time series on interannual-to-decadal timescales on top of the long-term global warming trend. We will focus on those interannual to decadal variations for most part of the discussions in this book in addition to the air
sea interactions on intra-seasonal timescales as reviewed by Li and Wang (2020) in Chapter 2, Impact of Atmosphere
Ocean Interactions on Propagation and Initiation of Boreal Winter and Summer Intraseasonal Oscillations, of this book. The net incoming solar energy (i.e., after subtracting the part of the solar energy that is reflected back to space from the incoming solar radiation) varies between equator and poles. Tropical regions, being closer to the Sun, receive much of the solar radiations throughout the year, whereas the mid- and high-latitude regions (beyond 30 N and 30 S) receive much less energy compared to tropics with a large seasonal variation. On the other hand, the outgoing longwave radiation (Earth’s radiation) varies much less as a function of latitude. This is because polar regions though receive very little incoming solar radiation because of the atmospheric and oceanic transports they remain warm enough to emit quite large amounts of outgoing longwave radiation. As a result of this meridional difference in the distribution of the incoming and outgoing radiations, tropics become the net gainer, whereas the mid- and high-latitudes become net looser of the radiative energy.

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Tropical and Extratropical Air
Sea Interactions

FIGURE 1–4 Zonally averaged global sea surface temperature (SST) climatology for the period 1982
2011 ( C) derived from Characteristics of Global SST Analysis Data (COBE SST; Japan Meteorological Agency, 2006).

The tropics receive about 60 W m22 more incoming solar radiation than outgoing longwave radiation and the higher latitudes almost loose about 100 W m22. This is reflected in the equator-to-pole distribution of zonally averaged global SST (Fig. 1
4). We see that the 24 C isoline comes closer to 30 N in August and closer to 30 S in February with a 2 months lag from the solar cycle, and peak radiations in June and December, respectively. Fig. 1
4 also shows the seasonal migration of the warm temperatures to the summer hemisphere (albeit less pronounced in the Southern Hemisphere owing to more ocean areas with large heat capacity). It may be noted here that the ocean currents and associated meridional overturning, especially in the North Atlantic Ocean, explain the northward displacement of the intertropical convergence zone from the equator during most part of the year. The tropical regions are considerably warmer than the mid- and high-latitudes and that difference in heating basically drives the atmospheric and oceanic circulations that in turn help in maintaining the Earth’s energy balance. Because of those circulations and associated energy transports, the Earth is approximately in energetic balance in annual average and the net energy loss in the polar regions is roughly balanced with the net energy gain in the tropical regions. In fact, if it were not those heat transports and heat storage in the ocean the gradients between poles and equator would be so huge that we cannot imagine a stable climate regime as we have now.

1.3 Atmosphere and ocean circulations Like any other fluid, atmosphere and ocean follow the general fluid dynamics; air/water flows from higher pressure/height to lower pressure/height. The net gain of heat in the equatorial region discussed in the previous section causes air to expand and generate a

Chapter 1 • Introduction to atmosphere and ocean variability and air
sea interactions

7

higher height at the top of the troposphere (lower part of the atmosphere; please refer to Andrews, 2010; Marshall and Plumb, 2008) in that region. Similarly, net loss in heat at the polar regions compresses the air and to generate lower height in those regions. Keeping everything else aside, this difference in the tropospheric height would create a pressure gradient favoring air to blow (water to flow) from equatorial region to the polar regions. The displaced air at the top of the troposphere in equatorial region must be replenished by rising air from the ground to keep a stable circulation. As Earth’s gravity pulls the air and water toward the center of the Earth, the highest pressure in air and water is seen at the ground/bottom level. As a result, pressure decreases upward in both atmosphere and ocean. Like pressure, density (a function of pressure, temperature, and to some extent moisture for air and salinity for water) also decreases with height and in a stable stratification lighter air/water stays above denser air/water. This stratification can be disturbed by heating at the ground level and/or turbulence. The heating at the ground (through the net radiative fluxes discussed earlier) will lighten (and hence make the air buoyant) the otherwise denser air at the ground compared to air above. The buoyant air will rise adiabatically until it becomes stable with respect to its environment. The stability of the rising air with respect to its environment will be determined by the lapse rate of rising air parcel’s temperature compared to the lapse rate of the environment. Depending on the amount of water vapor in the air parcel, either dry or moist (by including the effect of water vapor) adiabatic lapse rates (cf. Andrews, 2010; Marshall and Plumb, 2008) would be applied to determine the stability of the air. A typical moist adiabatic lapse rate is around 6K km21 as opposed to a dry adiabatic lapse rate of 10K km21 (which would usually be the environmental lapse rate where the air surrounding the parcel is dry). So, when the moist parcel is lifted in the air its temperature will be higher than the surrounding air due to this difference in the lapse rate. However, this will change with height as the moistures in the parcel get condensed out and the parcel reverts to an almost dry adiabatic lapse rate. An adiabatic cooling of a moist air parcel will saturate the water vapor inside the parcel at some height leading to cloud formation. The latent heat released in the process will further increase the buoyancy to the parcel favoring instability. This is what is generally observed in equatorial/tropical regions. The rising air then travels to higher latitudes but because of the rotation of the Earth and the associated Coriolis force (cf. Holton, 2004; Pond and Pickard 1978; Vallis, 2006), the air traveling to higher latitudes in the Northern (Southern) Hemisphere turn to its right (left). Air heated in the tropics rises and moves poleward with high potential temperature (the temperature the air would have if it were brought back down to ground level while conserving energy), and as it cools to a lower potential temperature, it sinks while releasing the heat to the environment in its path. So, instead of traveling all the way to pole, the air will descend around the subtropical region and the descending air will then travel back to the equator at the surface level. A zonally averaged circulation will look like a meridional overturning cell known as the Hadley cell (Fig. 1
5). The poleward traveling air in the Hadley cell will experience greater rotation as it moves to higher latitudes due to Earth’s sphericity. Therefore the traveling air must spin faster relative to the Earth’s surface. Further owing to the rise in Coriolis effect, the winds become more zonal

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FIGURE 1–5 Schematic of major features of the atmospheric circulation without the considerations of the effects of zonal variations due to land-sea contrasts and asymmetries around the equator and other latitudes. Hadley cell, Ferrel cell, and Polar cell are the schematic representations of zonally averaged meridional overturning circulations.

leading to the development of subtropical/mid-latitude westerly jets in the upper troposphere (Fig. 1
6). These jets are pronounced in winter seasons in both hemispheres and are very important for weather and climate variations. Especially, several chapters (Behera et al., 2020a, Chapter 3: Air
Sea Interaction in Tropical Pacific: The dynamics of El Niño/Southern Oscillation (ENSO); Marathe and Ashok, 2020, Chapter 4: The El Niño Modoki; Behera et al., 2020b, Chapter 5: Air
Sea Interactions in Tropical Indian Ocean: The Indian Ocean Dipole; Kosaka et al., 2020, Chapter 6: The Indo-Western Pacific Ocean Capacitor Effect; Richter and Tokinaga, 2020, Chapter 7: The Atlantic Zonal Mode: Dynamics, Thermodynamics, and Teleconnections) in this book discuss their role in atmospheric teleconnections in which the signal from tropical region is projected onto the jets and gets trapped in the associated Rossby wave path for the tropical signal to travel all around the globe. The Northern Hemisphere jet is not as continuous as its southern counterpart but in some extreme years they are uninterrupted enough to carry the signal around the globe (e.g., Behera et al., 2020b, Chapter 5: Air
Sea Interactions in Tropical Indian Ocean: The Indian Ocean Dipole of this book). The sinking air in the subtropical region heats the atmosphere and gets dry, depriving the region of moisture. Hence, most of the deserts are found in these regions. Also, the accumulation of air mass leads to the formation of surface highpressure systems in those regions, some of which are important for subtropical climate variation over oceans (Morioka et al., 2020; Chapter 9: Interannual-to-decadal variability and predictability in South Atlantic and southern Indian Oceans, of this book). When the air

Chapter 1 • Introduction to atmosphere and ocean variability and air
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9

FIGURE 1–6 A 200-hPa zonal wind climatologies for June to August (upper) and December to February (lower) derived from National Centers for Environmental Prediction (NCEP) reanalyses (Kalnay et al., 1996). Wind speed exceeding 30 m s21 is shaded to demarcate the upper troposphere jet stream.

returns toward the equator from subtropics at the lower level they start turning to their right/left owing to the Coriolis and surface frictional forces leading to easterly trade winds in both hemispheres (Fig. 1
5). On the poleward side the traveling air from the subtropics reaches the poles through two other weak meridional overturning circulations, namely Ferrel cell and Polar cell (Fig. 1
5). Those are somewhat related to the residual circulations resulting from averaging many transient weather disturbances seen in the mid- and high-latitudes as weather fronts (Fig. 1
5). Those fronts help to defuse meridional temperature gradient between the tropics and the polar regions by transporting cold air equatorward and warm air poleward, generally accomplishing poleward heat transports. Those transients also help in transporting moisture from the tropics to higher latitudes besides the heat and the momentum. The rising air around 60 N and 60 S travels poleward in the Polar cell to subside near the polar regions. The return flow at the surface meets the westerlies of the Ferrel cell to complete the circulation.

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1.4 Ocean circulation, upwelling, and climate variations The situation is very different in the oceans as compared to the atmosphere. Since oceans are heated from the top (in addition to freshening by rainwater in some regions), the lighter water stays at top, that is, opposite of the atmosphere. This is referred to as stable stratification and a lot of energy will be needed to break such a stratification and to generate convections. The buoyancydriven circulations are mainly in the areas of deep-water formation near the Arctic and the Antarctic where cold dense (and hence heavier) waters sink sometime all the way to the bottom and then spread toward the equator. This global circulation meanders through ocean basins interacting with topography and eventually get upwelled to surface via ocean mixing/diffusion as part of a global thermohaline circulation. But the timescale of that kind of circulation is very long with the order of a thousand-year and is not covered in this book. In the subtropical regions where there is a strong atmospheric subsidence at the descending branch of the Hadley cell, discussed in the previous section, the dry surface air and the cloud-free conditions promote strong evaporations and the evaporated water vapors are transported to the equator by the surface easterlies (Fig. 1
5). However, due to the loss of the fresh water at the ocean surface, the upper oceans get denser with heavier saline water. These regions then tend to produce intermediate waters where the salty water sinks to intermediate levels of about 700 m (e.g., pronounced in northeast Pacific). Those intermediate waters then travel to the equator in the subsurface to get upwelled and provide a mechanism for the decadal variation of the ENSO discussed in Chapter 3, Air
Sea Interaction in Tropical Pacific: The dynamics of ENSO, of this book (Behera et al., 2020a). Most surface circulations in the oceans are wind-driven, the winds in the tropics and the subtropics (Fig. 1
5) give rise to the large upper ocean circulations called the subtropical gyres (Fig. 1
7). The gyres have the equatorial westward currents driven by easterly winds and strong western boundary currents like the Kuroshio in the northwest Pacific and the Gulf Stream in the northwest Atlantic. In the interior, currents are strongly influenced by the rate of change of the zonal wind with latitude and the currents are set by the rate of change of the wind and Coriolis force with latitude through a process called the Sverdrup balance (Pond and Pickard, 1978; Gill, 1982; Vallis 2006). Hence, in the subtropical gyres, currents flow slowly equatorward in most parts of the basins (Fig. 1
5). These gyres help to carry heat on the western boundaries by the strong western boundary currents in both hemispheres and bring cold waters from the higher latitudes to tropics in the interior and eastern boundaries of the oceans (Fig. 1
7). Oceans also play an important role in the heat budget of the atmosphere and its heat transport. In the long term, the convergence and divergence produced by oceanic heat transports provide sources and sinks of heat for the atmosphere and partly shape the mean climate of the Earth. The oceans also exchange gases, water vapor, suspended particles, momentum, and energy with the atmosphere at the air
sea interface. These exchanges are often associated with the friction at the sea surface and the associated turbulences caused by the surface winds. Hence, these kinds of heat exchanges are often categorized as turbulent energy to differentiate them from the category of radiative energy discussed in Section 1.2 of this chapter.

Chapter 1 • Introduction to atmosphere and ocean variability and air
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11

FIGURE 1–7 Schematic representation of major features of the ocean circulations as depicted in the streamlines of ocean currents at 55 m for August (upper) and February (lower) climatologies derived from NCEP ocean reanalysis (Behringer and Xue, 2004). Red and blue arrows indicate warm and cold currents respectively carrying heat from tropics to extratropics and vice versa.

The exchange of fluxes and the ocean 2 atmosphere coupling mostly happen through small-scale processes. The associated coupling depends on the air 2 sea differences in several ocean and atmospheric variables (e.g., temperature, current, and wind speeds) leading to a broad range of spatio-temporal variability. Oceans play a very important role through the evaporation of water at the sea surface leading to the transfer of the latent heat to the atmosphere. In higher latitudes, sensible heat transfer from ocean to atmosphere also plays a big role in the heat budget. On the other hand, atmosphere transfers mechanical energy to the ocean through wind-driven waves, currents, oceanic turbulences, etc. These exchanges are important for the tropical 2 extratropical heat transfers, the regional climate variations, and the global climate. The surface wind induces a force on the ocean called wind stress and its effect reduces with the depth owing to greater friction. So, the effect of the wind stress is pronounced at the surface. Together with the Coriolis force (due to Earth’s rotation) and the friction (by neglecting other terms), the wind stress causes water to upwell through a process called the Ekman pumping. The direction of the wind-induced ocean currents also changes with the depth changing from around 45 degrees, to the right in the Northern Hemisphere (opposite in the Southern Hemisphere), at the surface to opposite of the wind direction at the bottom of the Ekman layer (the depth up to which the wind stress is felt) because of the effect of Coriolis and frictional forces (Fig. 1
8). So, when integrated over the depth of the Ekman layer the direction of the water mass transport becomes perpendicular to the wind direction (to the right

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FIGURE 1–8 Schematic representation of ocean currents in an Ekman layer with Ekman transport to the right of the wind direction in the Northern Hemisphere.

in the Northern Hemisphere and to the left in the Southern Hemisphere). Because of this, especially along the eastern boundaries of the oceans the equatorward winds move upper ocean water away from the coasts causing cooler subsurface water to upwell from below to replenish the surface water moving offshore. The equatorial region, especially in the eastern Pacific, also has the upwelling because the trade winds on either side of the equator drive surface water away from the equator to draw the cooler subsurface water from below (Fig. 1
9). The coastal upwelling as explained earlier causes interesting zonal differences in the ocean basins. As the subsurface waters are cooler than the surface waters, the SSTs in the upwelling regions are cooler. This is very clearly seen in the eastern boundaries of tropical Pacific and Atlantic Oceans along the Western American coasts and the Western African coasts (Fig. 1
9). While both western and eastern sides of these basins receive similar amount of solar radiation, the SSTs in eastern sides of the basins are much cooler than the western sides because of those upwelling. In the Indian Ocean, the upwelling is manifested seasonally in response to the reversal of monsoon winds through the year. Upwelling is pronounced on the western side of the basin during summer monsoon season due to upwelling favorable winds along the Somali coast. This important zonal difference in the mean SST and their interannual variations (with reduction/enhancement of the upwelling) are the basis on which the important modes of climate variations develop as discussed in several chapters of this book. For example, Chapter 3, Air
Sea Interaction in Tropical Pacific: The dynamics of ENSO, (Behera et al., 2020a) discusses

Chapter 1 • Introduction to atmosphere and ocean variability and air
sea interactions

13

FIGURE 1–9 Seasonal climatologies of sea surface temperature (SST) and surface winds for June to August (upper) and December to February (lower) derived from Characteristics of Global SST Analysis Data (COBE SST; Japan Meteorological Agency, 2006) and NCEP reanalysis (Kalnay et al., 1996) for the period of 1982
2011.

the ENSO phenomenon of the tropical Pacific, Chapter 4, The El Niño Modoki (Marathe and Ashok, 2020), discusses the ENSO Modoki phenomenon of the tropical Pacific, Chapter 5, Air
Sea Interactions in Tropical Indian Ocean: The Indian Ocean Dipole (Behera et al., 2020b), discusses the Indian Ocean Dipole phenomenon, Chapter 6, The Indo-Western Pacific Ocean Capacitor Effect (Kosaka et al., 2020), discusses the IPOC mode, and Chapter 7, The Atlantic Zonal Mode: Dynamics, Thermodynamics, and Teleconnections (Richter and Tokinaga, 2020), discusses the Atlantic Niño/Niña of the tropical Atlantic Ocean. The coastal upwellings leading to large-scale climate phenomena discussed earlier are prominent in the tropical regions. Several other regional modes of climate variations are found to be associated with coastal upwelling in subtropical/mid-latitude regions. These regional modes of variations have huge impacts on the marine ecosystem of those regions. For example, the Benguela Niño discussed in Chapter 10, The Other Coastal Niño/Niña—The Benguela,

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California and Dakar Niños/Niñas, of this book (Oettli et al., 2020) affects the Benguela Current Large Marine Ecosystem (BCMLE). The seasonal upwelling in the region maintains the BCMLE in normal years but anomalous warming during Benguela Niño years badly affects the ecosystems and the associated socio-economy. Similarly, the Ningaloo Niño/Niña discussed in Chapter 8, The Ningaloo Niño/Niña: Mechanisms, Relation with Other Climate Modes and Impacts, of this book (Tozuka et al., 2020) affect the marine ecosystem off Western Australia besides affecting the climate of Australia. The other such regional modes are the California Niño/Niña and Dakar Niño/Niña discussed in Chapter 10, The Other Coastal Niño/ Niña—The Benguela, California, and Dakar Niños/Niñas, of this book (Oettli et al., 2020), and recently discovered Chile Niño/Niña (Xue et al., 2020). Away from the coast, the wind stress curl plays an important role in determining the upwelling/Ekman pumping of the interior oceans. Also, the wind
evaporation
SST feedbacks in the tropical northwest Pacific give rise to an interesting interbasin climate mode together with the Indian Ocean. Kosaka et al. (2020) discussed this IPOC mode in Chapter 6, The IndoWestern Pacific Ocean Capacitor Effect, of this book. The IPOC mode is shown to affect South, Southeast, and East Asia through modulating occurrence of heat waves, heavy rains, and tropical cyclones. In the subtropics, the variations in mixed layer depth in addition to the wind
evaporation
SST feedback help to develop subtropical climate modes. Such variations in the subtropics of southern Atlantic, southern Indian Ocean, and southern Pacific give rise to Subtropical Dipole modes that are linked to rainfall variations in southern Africa and Australia. Morioka et al. (2020) review these modes in Chapter 9, Interannual-to-decadal variability and predictability in South Atlantic and southern Indian Oceans, of this book. The air
sea interaction in the mid-latitudes is not well understood. The sharp horizontal gradient in SST fronts near the warm western boundary currents, such as the Gulf Stream and the Kuroshio, provides ample scopes for air
sea interactions in those regions. The Rossby radius of deformation (Gill, 1982), that is, the length scale at which rotational effects become as important as buoyancy or gravity wave effects in the evolution of the flow, in the mid-latitudes is smaller compared to that in the tropics. This means that the scales of air
sea interactions are also smaller in the mid-latitudes compared to that in the tropics. Hirata and Nonaka (2020) review recent studies in Chapter 11, Impacts of Strong Warm Ocean Currents on Development of Extratropical Cyclones Through the Warm and Cold Conveyor Belts: A Review, of this book to shed light on the role of SST fronts in development of extratropical cyclones, some of which are associated with extreme rainfall/snowfall over the adjacent landmasses.

1.5 Summary The atmosphere and oceans play an important role in managing the heat budget of the climate system. The associated processes play important roles not only in transporting the heat and momentum but also in developing modes of climate variations that are so important for terrestrial and marine ecosystems. Some of those key processes are broadly discussed in this chapter. From the physical point of view, the interaction between the atmosphere and the ocean is a nonlinear process fundamental to the inherent processes in both systems.

Chapter 1 • Introduction to atmosphere and ocean variability and air
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15

The winds blowing over the surface of the ocean transfer momentum and mechanical energy to the water generating waves and currents, while the ocean gives off energy through latent heat, sensible heat, and the longwave radiations. This chapter broadly introduces and links those processes to the climate variations discussed in subsequent chapters. The human society is facing a challenge from the anthropogenic global warming, which has not only affected the climate of the earth but modes of climate variations and extreme events. While projecting the future climate is still a challenge for climate models, we have achieved great successes in predicting seasonal-to-interannual climate variations. For example, prediction of El Niño by some of the state-of-the-art climate models has brought seasonal-to-interannual climate predictions closer to realm of weather predictions. This was possible because of the advancement in observations, which were sustained for several decades, and rapid progresses in climate modeling. The sustained efforts will help us in one day to achieve greater success in projecting the future climate that is so important for our wellbeing. In the meantime, we also need to focus more on advancing our understanding of the many facets of our versatile climate system. This book is intended to introduce some of those progresses with resources to develop those researches further.

References Andrews, D.G., 2010. An Introduction to Atmospheric Physics. Cambridge University Press. Behera, S.K., Doi, T., Luo, J.J., 2020a. In: Behera, S.K. (Ed.), Air
Sea Interaction in Tropical Pacific: The Dynamics of ENSO. Elsevier, in press. Behera, S.K., Doi, T., Ratnam, J.V., 2020b. In: Behera, S.K. (Ed.), Air
Sea Interactions in Tropical Indian Ocean: The IOD. Elsevier, in press. Behringer, D.W., and Xue, Y., 2004. Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. In: Eighth Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, AMS 84th Annual Meeting, Washington State Convention and Trade Center, Seattle, WA, 11
15. Gill, A.E., 1982. Atmosphere
Ocean Dynamics. Academic Press. Hirata, H., Nonaka, M., 2020. In: Behera, S.K. (Ed.), Impacts of Strong Warm Ocean Currents on Development of Extratropical Cyclones Through the Warm and Cold Conveyer Belts: A Review. Elsevier, in press. Holton, J.R., 2004. An Introduction to Dynamic Meteorology. Academic Press, Burlington, MA. Japan Meteorological Agency, 2006. Characteristics of Global Sea Surface Temperature Analysis Data (COBESST) for Climate Use. Monthly Report on Climate System Separated, vol. 12, p. 116. Kalnay, E., coauthors, 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437
471. Kosaka, Y., Takaya, Y., Kamae, Y., 2020. The Indo-western Pacific Ocean capacitor effect. In: Behera, S.K. (Ed.), Tropical and Extratropical Air
Sea Interactions. Elsevier, in press. Li, T., Wang, T., 2020. In: Behera, S.K. (Ed.), Impact of Atmosphere
Ocean Interactions on Propagation and Initiation of Boreal Winter and Summer Intraseasonal Oscillations. Elsevier, in press. Marathe, S., Ashok, K., 2020. In: Behera, S.K. (Ed.), The El Niño Modoki, Tropical and Extratropical Air
Sea Interactions. Elsevier, in press. Marshall, J., Plumb, R.A., 2008. Atmosphere, Ocean and Climate Dynamics: An Introductory Text, vol. 93. Academic Press.

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Morioka, Y., Engelbrecht, F., Behera, S., 2020. In: Behera, S.K. (Ed.), Interannual-to-Decadal Variability and Predictability in South Atlantic and Southern Indian Oceans, Tropical and Extratropical Air-Sea Interactions. Elsevier, in press. Oettli, P., Yuan, C., Richter, I., 2020. In: Behera, S.K. (Ed.), The Other Coastal Niño/Niña—The Benguela, California and Dakar Niños/Niñas, Tropical and Extratropical Air
Sea Interactions. Elsevier, in press. Pond, S., Pickard, G., 1978. Introductory Dynamic Oceanography. Pergamon Press, Oxford. Richter, I., Tokinaga, H., 2020. In: Behera, S.K. (Ed.), The Atlantic Zonal Mode: Dynamics, Thermodynamics, and Tele-Connections. Elsevier, in press. Tozuka, T., Feng, M., Han, W., Kido, S., Zhang, L., 2020. In: Behera, S.K. (Ed.), The Ningaloo Niño/Niña: Mechanisms, Relation with Other Climate Modes and Impacts, Tropical and Extratropical Air
Sea Interactions. Elsevier, in press. Vallis, G.K., 2006. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press. Xue, J., Luo, J.-J., Yuan, C., Yamagata, T., 2020. Discovery of Chile Niño/Niña. Geophys. Res. Lett. 47, e2019GL086468. Available from: https://doi.org/10.1029/2019GL086468.

2 Impact of atmosphere
ocean interactions on propagation and initiation of boreal winter and summer intraseasonal oscillations Tim Li, Tianyi Wang I NT ERNATIONAL PACIFIC R E SEARCH CENT ER AND DEP ART MENT O F AT MOS PHERIC SCIE NCE S, SCHOOL OF OCEAN AND EARTH SCIENCE AND TECHNOLOGY, UNIVERS ITY OF HAWAI‘I AT MA
NOA, HONOLULU, HI, UNITED STATES

2.1 Introduction The tropical intraseasonal oscillation (ISO) exhibits pronounced seasonality in propagation (Madden, 1986; Wang and Rui, 1990; Madden and Julian, 1994; Jones et al., 2004; Zhang and Dong, 2004; Kikuchi et al., 2012) and initiation (Jiang and Li, 2005; Wang et al., 2006; Zhao et al., 2013; Li et al., 2015). While the boreal winter ISO, firstly discovered by Madden and Julian (1971, 1972) (hereafter Madden-Julian oscillation or MJO), is well known as equatorially trapped eastward-propagating convective anomalies, with a zonal wave number-1 structure and a principal period of 40
50 days (Salby and Hendon, 1994; Hendon and Salby, 1994), the boreal summer intraseasonal oscillation (hereafter BSISO) is characterized by pronounced northward propagation over the tropical Indian Ocean (TIO) and South China Sea (SCS) (Yasunari, 1979; Jiang et al., 2004; Li et al., 2018, 2020) and northwestward propagation over tropical western North Pacific (WNP) (Li and Wang, 2005; Zhang et al., 2020). Various theories have been proposed to understand the eastward propagation of MJO. Currently widely accepted theories for the eastward propagation include boundary layer east-west moisture asymmetry (Hsu and Li, 2012) and column-integrated moist static energy (MSE) tendency asymmetry (Sobel and Maloney, 2013). These two theories comprised two schools of thinkings of the moisture mode framework. The former emphasizes the planetary boundary layer (PBL) moistening and its destabilizing effect to the east of MJO convection (Hsu and Li, 2012), which is caused by the phase leading of PBL convergence (Wang and Li, 1994; Hendon and Salby, 1994). The latter Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00011-3 © 2021 Elsevier Inc. All rights reserved.

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excludes the PBL moistening effect but emphasizes the zonal asymmetry of columnintegrated MSE tendency, which is primarily induced by meridional MSE advection in lower troposphere (Kim et al., 2014) and vertical MSE advection in upper troposphere associated with stratiform clouds (Wang et al., 2017). It has been shown that the asymmetric PBL moistening is partially caused by air
sea interaction process (Hsu and Li, 2012). The northward propagation of BSISO is caused by both the internal atmospheric dynamics (e.g., the vertical shear mechanism proposed by Jiang et al., 2004) and atmosphere
ocean interactions (Kemball-Cook and Wang, 2001; Fu et al., 2003). Warm sea surface temperature anomalies (SSTAs) were observed in front of the northward-propagating BSISO convection (Wang et al., 2006). This warm SSTA may exert a significant effect on PBL convergence and anomalous moistening process (e.g., Wang et al., 2018). In this review, we will describe specific air
sea interaction processes through which intraseasonal SSTA feeds back to ISOs. The distinctive propagation characteristics between boreal winter and summer ISOs are regulated by the seasonal cycle of the mean state over the warm pool region. A theoretical study by Li (2014) showed that in the presence of northern winter mean state in which the thermal equator shifts slightly south of the equator, Kelvin waves are unstable, while Rossby waves are stable, and as a result the convectively coupled Kelvin
Rossby wave couplet associated with MJO moves eastward along the mean The Inter Tropical Convergence Zone (ITCZ). However, in the presence of northern summer mean state in which the thermal equator shifts northward over the Maritime Continent beyond the Rossby radius of deformation, Kelvin waves are damped, while Rossby waves become unstable, which promotes northward propagation. The initiation of ISO convection over TIO also exhibits a clear seasonality. While the initiation location in boreal winter is located over southwestern IO (Zhao et al., 2013), it is more confined over central equatorial IO in boreal summer (Wang et al., 2006). It is likely that the distinctive difference in the mean SST determines the difference of the initiation locations between boreal summer and winter (Zhang et al., 2019). In this review we will discuss a possible route through which the intraseasonal SSTA may be responsible for the MJO initiation over western IO. The overall objective of this paper is to reveal specific processes through which intraseasonal SSTAs, which are induced by atmospheric ISOs, feed back to the ISOs to affect their propagation, initiation, and strength. The remaining part of this chapter is organized as the following. In Section 2.2, we describe the observed characteristics of ISO propagation and initiation and ISO convection
SSTA relationship in boreal winter and summer, respectively. In addition, the cause of the intraseasonal SSTA variability is discussed. In Section 2.3, we focus on examining the role of air
sea interaction in modulating the ISO overall strength, propagation, and initiation, respectively. The specific processes of the SSTA feedback and the quantitative measurement of air
sea interaction effect will be emphasized. A summary and concluding remark are given in the last section.

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

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2.2 Observed characteristics of tropical intraseasonal oscillation and intraseasonal sea surface temperature anomaly 2.2.1 Observed intraseasonal oscillation
sea surface temperature anomaly relationship Pronounced seasonality of the tropical ISO is illustrated in Fig. 2
1, which shows the standard deviations of 20
80-day filtered rainfall anomalies and ISO propagation vectors in boreal summer and winter, respectively. In boreal winter, regions of strong ISO variance are confined over the equatorial area with the maximum amplitude shifting slightly southward, whereas a clear northward shifting to the Asian summer monsoon (ASM) region can be found in boreal summer, so that northern IO/Bay of Bengal (BoB) and WNP become two major intraseasonal convective activity centers. The most striking contrast between winter and summer ISO propagation is dominant eastward versus northward movement (Fig. 2
1). The boreal winter ISO (i.e., MJO) is generally characterized by equatorial trapped eastward-propagating convective anomalies with a zonal wave number-1 structure (Fig. 2
2). Maximum rainfall associated with the MJO is observed over the tropical Indian and West Pacific Oceans, where the mean SST is high. The western equatorial IO is a key region of the MJO initiation (Zhao et al., 2013). Compare to the MJO, the BSISO has a more complicated propagating pattern that varies with regions and timescales. While independent northward-propagating events and northwestward

FIGURE 2–1 The standard deviation of 20
80-day filtered CPC Merged Analysis of Precipitation rainfall anomalies (shaded) during 1979
98 and propagation vectors for boreal (A) summer (May
October) and (B) winter (December
April). From Li, T., 2014, Recent advance in understanding the dynamics of the Madden-Julian oscillation. J. Meteorol. Res. 28, 1
33. doi:10.1007/s13351-014-3087-6.

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FIGURE 2–2 Longitude-height schematic diagram along the equator illustrating the fundamental large-scale features of the MJO through its life cycle (from top to bottom). Cloud symbols represent the convective center, arrows indicate the zonal circulation, and curves above and below the circulation represent perturbations in the upper tropospheric and sea level pressure. From Madden, R.A., Julian, P.R., 1972. Description of global-scale circulation cells in the tropics with a 40-50 day period. J. Atmos. Sci. 29, 1109
1123. doi:10.1175/1520-0469(1972) 029 , 1109:DOGSCC . 2.0.CO;2. Reproduced by McPhaden.

propagation were identified sometimes (Lau and Chan, 1986; Nitta, 1987; Wang and Rui, 1990; Fukutomi and Yasunari, 1999; Hsu and Weng, 2001; Lee et al., 2013), the majority of the northward-propagating BSISO events are accompanied with the eastward propagating signal over the Indo-western Pacific warm pool (around 78%, Lawrence and Webster, 2002). Fig. 2
3 presents a composite life cycle of the BSISO with use of modern satellite data such as TMI precipitation rate and SST anomalies. The positive rainfall anomaly is initiated over the equatorial central IO and then migrates northeastward with a northwest-southeastoriented rain band structure over the TIO and Maritime Continent. The ISO signal finally dissipates over the subtropics. A striking feature in Fig. 2
3 is the organized SSTA variation associated with the northeastward-propagating rainfall anomaly throughout the BSISO life cycle. It can be found that spatially, a warm SSTA appears in front of the enhanced precipitation, and the latter leaves a negative SSTA behind. The appearance of warm SSTAs before the initiation is also noteworthy. Albeit small discrepancies exist, previous studies confirmed that a general feature describing the relationship between the tropical ISO convection and associated SSTAs

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

21

Composite life cycle of QMO rainrate (contour) & SST (shading)

FIGURE 2–3 Composite eight-phase life cycle of the boreal summer quasimonthly oscillation. Green denotes positive precipitation rate (The Tropical Rainfall Measuring Mission's/Microwave Imager) anomalies starting from 2 mm day21 with contour intervals of 3 mm day21; lavender shows negative anomalies starting from 22 mm day21. The major features of positive rainfall anomalies are highlighted using black solid lines; dashed lines indicate negative anomalies. Shading represents SSTAs in units of degree Celsius. Only statistically significant rainfall and SST anomalies at 90% confidence level are shown. From Wang, B., Webster, P., Kikuchi, K., Yasunari, T., Qi, Y., 2006. Boreal summer quasi-monthly oscillation in the global tropics. Clim. Dyn. 27, 661
675. doi:10.1007/s00382006-0163-3.

was a near-quadrature phase relation, as revealed in Figs. 2
4 and 2
5. That is, an enhanced (suppressed) convection leads a cold (warm) SSTA and a cold (warm) SSTA leads a suppressed (enhanced) convection, by about a quarter of the oscillation cycle. The intraseasonal SST variability over the TIO-western Pacific exhibits pronounced seasonality, which has a close relation with the intraseasonal convective activities (Fig. 2
6). In boreal winter, the strongest intraseasonal SST variability is located over the southern IO thermocline dome region (along 5
10 S) (Saji et al., 2006), where the MJO variability is strong. In boreal summer, intraseasonal SST variability is significantly weakened along the equator but enhanced over the northern IO and WNP areas where BSISO convections are more active. It has been reported that in addition to the seasonal change, there exists contrast between the 10
20-day and 30
60-day intraseasonal SST variability (Cao et al., 2017). The dominant intraseasonal SSTA pattern displays a tilted southwest-northeast band from the SCS to the subtropical WNP in summer, with a larger value in the subtropical WNP on the 10
20-day timescale and in the SCS on the 30
60-day timescale. The dominant SST pattern during

22

Tropical and Extratropical Air
Sea Interactions

FIGURE 2–4 Hovmöller map along 5 S of anomalies of OLR (contoured, contour interval 10 W m22) and SST (shaded,  C) reconstructed from the leading two eigenmodes of intraseasonally filtered OLR. The period from October 1, 1992 to April 15, 1993 is shown. OLR, Outgoing Longwave Radiation. From Hendon, H.H., Glick, J., 1997. Intraseasonal air-sea interaction in the tropical Indian and Pacific Oceans. J. Clim. 10, 647
661. doi:10.1175/15200442(1997)010 , 0647:IASIIT . 2.0.CO;2.

winter resembles that during summer, but with a larger value in the SCS. The SSTAs show obvious northwestward and northward propagations in the SCS
WNP region on the 10
20day and 30
60-day timescales, respectively, while a southward propagation on both timescales has been detected during winter.

2.2.2 Cause of the intraseasonal sea surface temperature anomaly Fig. 2
5 reveals clear in-phase relationships among the ISO convection, net surface heat flux anomaly and intraseasonal SSTA tendency throughout the BSISO life cycle, and the temporal evolutions of the key variables are shown in Fig. 2
9. This indicates that the intraseasonal

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

23

FIGURE 2–5 Hovmöller diagram of the composite raw pentad anomalies along the section of 110
135 E for (A) OLR (shaded) and SST (contour, interval: 0.1K), and (B) SST tendency (shaded) and downward net heat flux (contour, interval: 10 W m22). The abscissa is the lead/lag time in days with positive (negative) values indicating the days after (before) Day 0. The composites are based on the selected stronger BSISO events over the WNP area for 1985
2009. Day 0 refers to the moment when the anomalous convection is the most enhanced at around 15 N. OLR, Outgoing Longwave Radiation. From Wang, T., Yang, X., Fang, J., Sun, X., Ren, X., 2018. Role of air-sea interaction in the 30
60-day boreal summer intraseasonal oscillation over the Western North Pacific. J. Clim. 31, 1653
1680. doi:10.1175/JCLI-D-17-0109.1.

SSTAs are primarily caused by ISO-induced surface heat flux anomalies. Figs. 2
7 and 2
8 show schematic diagrams illustrating how boreal winter and summer ISOs induce the SSTAs. The cumulus cloud cover associated with the active ISO convection always leads to a local reduction of the insolation (as indicated by the in-phase relationship between the red line and gray area in Fig. 2
9A). Meanwhile, the convection induced low-level westerly anomaly in situ, either as a Gill-type Kelvin wave response for MJO or off-equatorial Rossby wave response for BSISO, which increases the near-surface wind speed in the presence of the background westerly that prevails over the ITCZ or ASM region (as indicated by the magenta line in Fig. 2
9B). The surface evaporation is thus enhanced with a slight lag. Through the

24

Tropical and Extratropical Air
Sea Interactions

(A)

(B)

FIGURE 2–6 The standard deviation of 20
80-day filtered SSTAs during 1985
2009 for boreal (A) summer (May
October) and (B) winter (December
April).

two major heat flux processes, a significant SST signal is generated. Due to the large heat capacity of the ocean, the SSTA response is delayed (blue bars in Fig. 2
9), which explains the near-quadrature phase relationship between the convection and the SSTA. Many previous studies suggested that the surface heat flux anomaly is the primary factor determining the intraseasonal SST variability, especially over the BoB and western Pacific warm pool region during boreal summer. First, the horizontal gradient of SST is small over these regions. As a result, the horizontal advection is generally negligible in the ocean mixed layer heat budget (Shinoda and Hendon, 1998, 2001; Duncan and Han, 2009; Parampil et al., 2010; Raj et al., 2016; Girishkumar et al., 2017). Second, due to freshwater fluxes from rivers and monsoon precipitations, a shallow mixed layer is formed because of the surface freshwater cap. A thick “barrier layer” that refers to the layer between the halocline and the thermocline (Fig. 2
10) inhibits the communication between surface and colder subthermocline water (Lukas and Lindstrom, 1991; Sprintall and Tomczak, 1992; Anderson et al., 1996; Vinayachandran et al., 2002; Bosc et al., 2009; Girishkumar et al., 2011; Li et al., 2017a).

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

25

FIGURE 2–7 Schematic diagram showing the magnitude and phase relationship relative to the convective anomaly of surface fluxes and SST variations produced by the canonical MJO. The asymmetric zonal scale of the cloudy windy phase and suppressed calm phase as well as the eastward phase speed (4 m s21) of the joint atmosphere
ocean disturbance across the warm pool are indicated. Typical extrema of surface fluxes and SST over the lifecycle of the MJO are shown for the Western Pacific. From Shinoda, T., Hendon, H.H., 1998. Mixed layer modeling of intraseasonal variability in the tropical Western Pacific and Indian Oceans. J. Clim. 11, 2668
2685. doi:10.1175/1520-0442(1998)011 , 2668:MLMOIV . 2.0.CO;2.

Under these conditions, a one-dimensional (1D) heat balance driven by surface heat flux anomalies dominates the intraseasonal SST change. A quasiperiodic surface flux perturbation associated with the ISO also expects a near-quadrature relationship between the convection and SSTA. Such a 1D balance was supported by previous observational studies for MJO (e.g., Shinoda and Hendon, 1998; Zhang and Mcphaden, 2000; Shinoda and Hendon, 2001) and BSISO (e.g., Sengupta and Ravichandran, 2001; Sengupta et al., 2001; Roxy and Tanimoto, 2007; Parampil et al., 2010; Li et al., 2016). In addition to the heat flux forcing, upper-ocean dynamics such as vertical entrainments and horizontal advection may also play a role. Recently, a quantitative diagnosis of the mixed layer temperature budget associated with the BSISO over the BoB area was carried out using the latest RAMA mooring data (Girishkumar et al., 2017). The result indicated that the entrainment cooling made a substantial contribution to the mixed layer cooling tendency at the developing phase of the active convection, while the net surface heat flux anomaly is weakly positive. However, for the peak of the active phase, opposite situations occur. Previous observational studies (e.g., Godfrey et al., 1999; Schiller and Godfrey, 2003) found that in the existence of the shallow mixed layer and thick barrier layer, the shortwave radiation that penetrates below the shallow mixed layer favors the development of a temperature

26

Tropical and Extratropical Air
Sea Interactions

FIGURE 2–8 Schematic of air
sea interaction in the northward propagation of convective anomalies associated with the ISO during boreal summer in the Indian and Western Pacific Oceans. Dark vertical lines indicate the mid-troposphere vertical velocity anomaly. The cloud indicates deep precipitating convection. DSWRF is downward shortwave radiative flux and Lq is the latent heat of vaporization times the humidity anomaly q. The boxes represent the approximate locations of anomalies relative to the convection. Solid boxes indicate a positive anomaly and dashed boxes a negative anomaly. Circles indicate the direction of the 850 mb zonal wind anomaly with the ⨂ (⨀) representing easterlies (westerlies). From Kemball-Cook, S., Wang, B., 2001. Equatorial waves and air
sea interaction in the boreal summer intraseasonal oscillation. J. Clim. 14, 2923
2942. doi:10.1175/1520-0442(2001)014 , 2923:EWAASI . 2.0.CO;2.

inversion; thus the enhanced vertical mixing at the peak of the active convection could even partly compensate the mixed layer heat loss by surface heat fluxes. The conclusion above implies that the upper-ocean dynamical processes may play different roles at different stages of the ISO development. In addition to the direct effects of vertical entrainments and horizontal advections, the change of the mixed layer depth (MLD) due to wind stress forcing was also detected (e.g., Li et al., 2016, 2017a,b), which alters the sensitivity of the intraseasonal SSTA response and could be regarded as an indirect effect of atmospheric forcing. To sum up, there is clear observational evidence indicating a significant near-quadrature phase relationship between the ISO convection and intraseasonal SST variations. An enhanced convection leads a cold SSTA, while a cold SSTA leads a suppressed convection. The net surface heat flux variation is in phase with the SSTA tendency, indicating that the intraseasonal SSTAs are caused by atmospheric ISO forcing. Surface shortwave radiative flux anomaly and surface latent heat flux (LHF) anomaly are two major heat flux terms that affect the SSTA. Due to the existence of the barrier layer, the net surface heat flux anomaly dominates the mixed layer heat budget in most cases. The upper-ocean dynamical processes forced by ISO-induced wind stress

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

27

(A)

(B)

FIGURE 2–9 Temporal evolutions of the composite raw anomalies spatially averaged in the WNP area, for (A) downward net heat flux (Qnetk, the black dotted-dashed line) and its four components, that is, the upward latent heat flux (LHFm, the green line), the upward sensible heat flux (SHFm, the yellow line), the upward longwave radiative flux (LWFm, the brown line), and the downward shortwave radiative flux (SWFk, the red line); (B) upward water vapor flux (WVFmLHFm=Le , green dotted-dashed line) and its three components, that is, 0 the anomalous sea-air humidity difference related term ρc e UΔq (cyan line), the anomalous near-surface wind 0 0 0 0 speed-related term ρc e U Δq (magenta line), and the nonlinear interaction term ρc e ðU Δq Þ (light yellow line). For comparison, the corresponding composite SST (blue bars) and OLR (gray areas) anomalies are displayed, and the black cross-dashed lines in (B) denote the vertical diffusion moistening rates (derived from Climate Forecast System Reanalysis diabatic heating products) in the atmospheric boundary layer (vertically integrated from surface to 850 hPa). A curve (bar/area) in the figure matches the ordinate with the same color, or with the black if the colored one is not provided. The abscissas are same as those in Fig. 2
5. OLR, Outgoing Longwave Radiation. Modified from Wang, T., Yang, X., Fang, J., Sun, X., Ren, X., 2018. Role of air-sea interaction in the 30
60-day boreal summer intraseasonal oscillation over the Western North Pacific. J. Clim. 31, 1653
1680. doi:10.1175/JCLI-D-17-0109.1.

28

Tropical and Extratropical Air
Sea Interactions

FIGURE 2–10 Schematic diagram showing the Lukas
Lindstrom “barrier layer” theory. During a strong wind burst, the surface mixed layer extends down to the top of the thermocline. Following the wind burst, the additional buoyancy from precipitation and strong surface heating act to form a relatively warm and fresh thin surface layer. Below this thin layer is a strong halocline, which effectively decouples surface forcing from deeper waters. Further heating is trapped by vertical mixing above the barrier formed by the halocline. From Anderson, S.P., Weller, R.A., Lukas, R.B., 1996. Surface buoyancy forcing and the mixed layer of the Western Pacific warm pool: observations and 1D model results. J. Clim. 9, 3056
3085.doi:10.1175/1520-0442(1996)009 , 3056:SBFATM . 2.0.CO;2.

occasionally become important. The relative role of the dynamic and thermodynamic processes requires further observational and modeling investigations.

2.3 Impact of air
sea interaction on tropical intraseasonal oscillation 2.3.1 Role of air
sea interaction in affecting overall intraseasonal oscillation variance One way to estimate how air
sea interaction affects the overall ISO variance is through idealized (e.g., coupled versus uncoupled) modeling studies. Using a hybrid atmosphere-ocean coupled model, Fu et al. (2003) showed that the coupled simulation significantly enhanced the intensity of the BSISO, comparing with the simulations with the atmosphere-only model (Fig. 2
11). The atmosphere-only model was unable to reproduce the same strength BSISO variability in the coupled simulation, even when it is forced by daily SST output from the coupled model (Fig. 2
11B). Given that these experiments produced nearly identical mean states, and the daily SST output from the coupled model contains the intraseasonal signal, the discrepancies shown in Fig. 2
11B arose primarily from the effect of air
sea interaction. It is thus speculated that the intraseasonal air
sea interaction could increase the overall ISO variability by about 30%.

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

29

FIGURE 2–11 Wavenumber
frequency spectrum from (A) the coupled run, (B) the difference between the coupled run and the daily run (forced with daily SST from coupled run), and (C) the difference between the coupled run and the mean run (forced with climatological monthly SST from coupled run). The contour interval is 3 (mm day21)2 in (A) but 1 (mm day21)2 in (B) and (C). Yellow (orange) shaded areas in (B) and (C) represent the significance larger than 75% (95%). From Fu, X., Wang, B., Li, T., Mccreary, J.P., 2003. Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J. Atmos. Sci. 60, 1733
1753. doi:10.1175/1520-0469(2003)060 , 1733:CBNIOA . 2.0.CO;2.

30

Tropical and Extratropical Air
Sea Interactions

An important feature in the coupled model is the near-quadrature phase relation between intraseasonal atmospheric convection and SST (Fig. 2
12A). This differs markedly from the stand-alone atmospheric model in which the atmospheric convection is almost in phase with the underlying SST on the intraseasonal timescale (Fig. 2
12B). This is because in the coupled model there is a two-way interaction between the atmosphere and ocean, that is, on the one hand, a warm SSTA leads to an enhanced convection due to strengthened PBL convergence and surface evaporation, and on the other hand, the enhanced convection leads to the reduction of the SSTA due to the decrease of downward shortwave radiation. This is in

FIGURE 2–12 Latitude
time plots of intraseasonal rainfall rate (mm day21; shaded) and SST ( C; contours, interval 0.05 C) averaged between 65 E and 95 E for (A) coupled run and (B) daily run. From Fu, X., Wang, B., Li, T., Mccreary, J.P., 2003. Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J. Atmos. Sci. 60, 1733
1753. doi:10.1175/1520-0469(2003)060 , 1733: CBNIOA . 2.0.CO;2.

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

31

contrast to the stand-alone atmospheric model where the SSTA always acts a forcing to the atmosphere. Further numerical model studies by Fu and Wang (2004a,b) with improved experimental designs confirmed the role of air
sea interaction in producing stronger BSISO variability and a more realistic convection
SST phase relation. However, the conclusion above is model dependent. Unlike the significant reduction found in Fu et al. (2003), the simulated ISO variabilities in stand-alone atmospheric models could be comparable to those in coupled models (e.g., Zheng et al., 2004; Sharmila et al., 2013), or even greater (e.g., Pegion and Kirtman, 2008). Albeit large discrepancies existed in the simulated ISO intensity, all the models produced a near-quadrature phase relation between intraseasonal convection and SST in the coupled version and an in-phase relation in the atmosphere-only models. The results above point out that there is no consensus on the role of air
sea interaction in affecting the overall ISO variance. The inconsistency among the models may attribute to the diversity of the atmosphere-only model performance in reproducing the observed ISO structure and propagation characteristics. For instance, Miyakawa et al. (2014) reported that the super high
resolution atmosphere-only model NICAM can produce a realistic MJO simulation, while a comparison made by Jiang et al. (2015) found that the CNRM model shows a great contrast in MJO propagation between coupled and uncoupled experiments. It is speculated that air
sea coupling may improve simulated ISOs only if an atmospheric model on its own can reproduce reasonable ISO signals (e.g., Zhang, 2005). Many state-of-art models still have difficulty in reproducing the eastward propagation of MJO (e.g., Lin et al., 2006; Zhang et al., 2006; Kim et al., 2009; Sabeerali et al., 2013; Zhao et al., 2014; Jiang et al., 2015; Wang et al., 2017).

2.3.2 Impact of air
sea interaction on intraseasonal oscillation propagation 2.3.2.1 Impact on eastward propagation in boreal winter Fig. 2
13 illustrates the composite zonal-vertical distribution of MJO-filtered moisture and its phase relationship with the MJO convection [represented by a negative Outgoing Longwave Radiation (OLR) center]. While in the middle troposphere the maximum moisture anomaly is colocated with the MJO convection, in the PBL there is a clear zonal asymmetry in the perturbation moisture field; that is, a positive (negative) center is located to the east (west) of the OLR center. Because of this asymmetry, the maximum moisture content line tilts eastward and downward (Sperber, 2003; Kiladis et al., 2005). To demonstrate how the PBL moisture asymmetry affects the MJO growth and evolution via atmospheric destabilization, the vertical profile of the intraseasonal equivalent potential tempera0 ture (θe ) was examined by Hsu and Li (2012). As shown in the top panel of Fig. 2
14, a signifi0 cant increase of low-level θe is found, consistently with PBL moistening, to the east of the MJO 0 convection. If defining a convective instability parameter as the difference of θe between the PBL (850
1000 hPa) and the middle troposphere (400
500 hPa), one may find that the atmosphere is more (less) potentially unstable to the east (west) of the MJO convective center (bottom panel

32

Tropical and Extratropical Air
Sea Interactions

FIGURE 2–13 (Upper panel) Zonal-vertical distributions of 0
10 S averaged MJO-filtered specific humidity (contour, 1024 kg kg21) and specific humidity tendency (shading, 10210 kg kg21 s21). (Bottom panel) Zonal distributions of 0
10 S averaged MJO-filtered OLR (blue dashed line, W m22), OLR tendency (blue solid line, 1026 W m22 s21) and column-integrated specific humidity tendency (red line, 1027 kg m22 s21) during the active phase of MJO in the eastern IO. From Hsu, P., Li, T., 2012. Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden
Julian oscillation. J. Clim. 25, 4914
4931. doi:10.1175/JCLI-D-11-00310.1.

0

FIGURE 2–14 As in Fig. 2
13, but for MJO-filtered equivalent potential temperature (θe , upper panel) and the 0 convective instability index (bottom panel), which is defined as the difference of θe at the PBL and the middle 0 0 troposphere (i.e., 1000 2 850-hPa averaged θe minus 500 2 400-hPa averaged θe ). Unit: K. From Hsu, P., Li, T., 2012. Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden
Julian oscillation. J. Clim. 25, 4914
4931. doi:10.1175/JCLI-D-11-00310.1.

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

33

of Fig. 2
14). Therefore a phase leading of a positive low-level moisture anomaly may set up a relatively unstable stratification and generate a favorable environment for potential development of new convection to the east of the MJO convective center. Hsu and Li (2012) further conducted a moisture budget analysis to reveal the cause of the PBL moistening in front of MJO convection. The diagnosis result showed that the largest positive contribution is anomalous vertical moisture advection, that is, the advection of the mean moisture (which has a maximum at the surface and decays exponentially with height) by anomalous ascending motion, the latter of which is associated with the PBL convergence. This indicates that the boundary layer convergence and associated ascending motion play an important role in moistening the PBL to the east of deep convection. Physically, two factors may contribute to the moistening in PBL. The first factor is the boundary layer convergence, and the second factor is the surface evaporation. The diagnosis by Hsu and Li (2012) shows that while the low-level convergence shows a significantly eastward shift to the MJO convection (Fig. 2
15D), the surface evaporation tends to decrease to the east of MJO convection (Fig. 2
16). Here the surface evaporation fields derived from the ERA-40, the OAFlux, and the bulk formula all show a consistent result with a decreased (increased) evaporation to the east (west) of the MJO convection. This indicates that the

(A)

(C)

(B)

(D)

FIGURE 2–15 Zonal-vertical distributions of 0
10 S averaged MJO-filtered (A) zonal wind (m s21), (B) meridional wind (m s21), (C) vertical velocity (pa s21), and (D) divergence (1026 s21) for the MJO active phase in the eastern IO. The triangles indicate the MJO convective center. From Hsu, P., Li, T., 2012. Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden
Julian oscillation. J. Clim. 25, 4914
4931. doi:10.1175/JCLI-D-11-00310.1.

34

Tropical and Extratropical Air
Sea Interactions

FIGURE 2–16 Zonal variations of 0
10 S averaged MJO-filtered OLR (blue, leftmost vertical axis, W m22), SST (red, left vertical axis, K), LHF (green, rightmost vertical axis, W m22), and zonal wind (black, right vertical axis, m s21) for the MJO active phase in the eastern IO. The LHF is based on the ensemble average of the ERA-40, OAFlux and bulk formula. From Hsu, P., Li, T., 2012. Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden
Julian oscillation. J. Clim. 25, 4914
4931. doi:10.1175/JCLI-D-11-00310.1.

boundary layer convergence is a major process that causes the observed phase leading of PBL moisture. The decrease of the surface evaporation or upward LHF is attributed to the decrease of the near-surface wind speed. In the southeastern IO where the mean westerly flow prevails, the intraseasonal easterly (westerly) to the east (west) of MJO convection suppresses (enhances) the near-surface wind speed and thus leads to a suppressed (enhanced) LHF. The change of the LHF further leads to the change in SST (Jones and Weare, 1996; Shinoda et al., 1998; Araligidad and Maloney, 2008). As shown in Fig. 2
16, a warm (cold) SSTA due to the weaker (stronger) near-surface evaporation is observed to the east (west) of MJO convection. How does the warm SSTA contribute to the eastward propagation? According to Lindzen and Nigam (1987), a warm SSTA may induce a boundary layer convergence through the change of the boundary layer temperature and pressure. However, it is not clear to what extent the observed PBL convergence is contributed by the underlying SSTA. The effect of air
sea interactions on MJO eastward propagation has been mentioned by previous studies (e.g., Sperber et al., 1997; Waliser et al., 1999; Fu et al., 2003; Li et al., 2008), but specific processes that contribute to the eastward propagation are not clear. Here we intend to address the following two important questions: through what physical processes the phase-leading PBL convergence is generated, and to what extent does the warm SSTA in front of the convection contribute to the boundary layer convergence? Fig. 2
17 shows a schematic diagram illustrating key processes that contribute to the phase leading of the boundary layer convergence. For simplicity, this schematic diagram displays an equatorially symmetric feature, although in reality the circulation may shift slightly south of the equator in boreal winter. Firstly, the mid-tropospheric heating associated with MJO deep convection induces a baroclinic free-atmosphere response, with a Kelvin (Rossby) wave response to the east (west) of the convective center. The anomalous low pressure at top of the PBL

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

35

FIGURE 2–17 Schematic diagram of boundary layer convergence induced by free-atmospheric wave dynamic and SSTA. Cloud stands for the MJO convection with heating, Solid (dashed) gyres with HK (LK) and HR (LR) indicate the high (low) pressure anomaly associated with Kelvin and Rossby waves response to convection, respectively; red and blue shadings denote the positive and negative SSTAs, respectively; solid green arrows indicates the anomalous ascending motion; dashed green arrows represent the boundary layer convergence; and ps and pe are pressure levels at the bottom and top of the PBL, respectively. From Hsu, P., Li, T., 2012. Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden
Julian oscillation. J. Clim. 25, 4914
4931. doi:10.1175/JCLI-D-11-00310.1.

associated with the Kelvin wave response may induce a convergent flow in the boundary layer, while a PBL divergence may occur to the west of the convective center between two Rossby wave gyres. Thus the first convergence-generation process is associated with the mid-tropospheric heating and equatorial wave responses to the heating. The second generation process is associated with the SSTA forcing. As a warm SSTA is generated to the east of the MJO convection, the warm SSTA may drive boundary layer flows through induced hydrostatic effect on sea level pressure (Lindzen and Nigam, 1987). Therefore the convergence in the atmospheric boundary layer may be connected to the underlying positive SSTA and associated SSTA gradients to the east of the MJO convection. To quantitatively examine the relative roles of the SSTA gradient induced pressure gradient force and the heating induced free-atmospheric wave dynamics in determining the PBL convergence, we diagnose the boundary-layer momentum budget equation developed by Wang and Li (1993). The PBL momentum equation in Wang and Li (1993) states 0

0

0

f k 3 V B 1 EV B 5 2 rφe 1

R ðps 2 pe Þ 0 rTs ; 2 pe

(2.1)

where a prime denotes the MJO component, f is the Coriolis parameter, k is the unit vector in the vertical direction, V B denotes the vertically averaged horizontal wind in the boundary layer, r is the horizontal gradient operator, φe denotes the geopotential at the top of the boundary layer, R is the gas constant of air, ps and pe are pressures at the bottom and top of the PBL, respectively, Ts is the surface temperature, and E is the friction coefficient and is equal to 1025 s21. The first term in the right-hand side of Eq. (2.1) represents the

36

Tropical and Extratropical Air
Sea Interactions

free-atmospheric wave effect. The second term in the right-hand side of Eq. (2.1) represents the SSTA forcing effect. To test the sensitivity of the result to the boundary layer depth, two different PBL depths, 1000
850 and 1000
700 hPa, are applied. The vector form of Eq. (2.1) may be decomposed into two scalar equations for the zonal and meridional components, with the sum of two linear forcing terms. Once the zonal and meridional wind components are derived, the PBL divergence can be readily solved with either of individual forcing terms. Fig. 2
18 reveals the diagnosis results for the PBL convergence. It turns out that the free atmospheric wave effect in response to the MJO heating plays a major role in determining the boundary layer convergence. It accounts for 90% and 75% of the total boundary layer convergence in the case of pe 5 850 and pe 5 700, respectively. The warm SSTA induced by decreased LHF ahead of MJO convection, on the other hand, also plays a role. It contributes about 10%
25% to the observed boundary layer convergence. Since the PBL convergence is a major factor affecting the moisture asymmetry, the result above suggests that both the heating-induced equatorial wave response and the underlying SSTA contribute to the eastward propagation of MJO. The effects of air
sea coupling on the eastward-propagating boreal winter intraseasonal oscillation (MJO) are investigated by comparing a fully coupled and a partially decoupled IO experiment using the SINTEX-F coupled model (Li et al., 2017c). Air
sea coupling over the TIO significantly enhances the intensity of the eastward propagations of the MJO along the 5
10 S zonal areas. The zonal asymmetry of the SSTA is responsible for the enhanced eastward propagation. A positive SSTA appears to the east of the MJO convection, which results in the boundary layer moisture convergence and positively feeds back to the MJO convection. In addition, the air
sea interaction effect on the eastward propagation of the MJO is related to the interannual variations of the TIO. In general, air
sea coupling enhances (reduces) the eastward-propagating spectrum during the negative (positive) IO dipole mode. Such phase dependence is attributed to the role of the background mean westerly in

FIGURE 2–18 (From left to right) Total boundary layer convergence averaged over (130 E
150 E, 0
10 S) induced by both the free-atmospheric wave dynamic and SSTA, and relative contributions of wave dynamic and SSTA effect in the case of pe 5 850 hPa (filled bars) and pe 5 700 hPa (hollow bars). Unit is 1026 s21. From Hsu, P., Li, T., 2012. Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden
Julian oscillation. J. Clim. 25, 4914
4931. doi:10.1175/JCLI-D-11-00310.1.

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

37

affecting the wind
evaporation
SST feedback. Air
sea coupling (decoupling) enhances (reduces) the zonal asymmetry of the low-level specific humidity and thus the eastward propagation spectrum of the MJO.

2.3.2.2 Impact on northward propagation in boreal summer Resembling the eastward-propagating MJO, a warm SSTA locates to the north, that is, spatially in front of the BSISO convection due to the near-quadrature relationship, which implies the potential impact of the air
sea interaction on the northward propagation of the BSISO. Satellite observations also confirmed the preconditioning of the anomalous boundary layer moistening in the BSISO (Fu et al., 2006; Yang et al., 2008). As shown in Fig. 2
19B
D when the BSISO convection is propagating northward along the longitudinal band analyzed in figure, while the maximum moisture anomaly of the entire troposphere tends to appear where the rainfall is maximized, a clear northward extension of the anomalous moistening in the boundary layer is detected where the underlying SSTA is warmer. However, unlike in the MJO case in Hsu and Li (2012), the surface convergence is almost collocated with the

(A)

(C)

(C)

(D)

FIGURE 2–19 Composite height-latitude plots of specific humidity (g kg21) anomalies averaged over 85
95 E along with composite rainfall (black-dashed line, unit: 20 mm day21), SST (black-solid line, unit:  C), and surface convergence (green-solid line, unit: 5 3 1026 s21) anomalies at (A) 22 pentads, (B) 21 pentad, (C) 0 pentad, and (D) 11 pentad. From Fu, X., Wang, B., Tao, L., 2006. Satellite data reveal the 3-D moisture structure of tropical intraseasonal oscillation and its coupling with underlying ocean. Geophys. Res. Lett. 33, L3705. doi:10.1029/ 2005GL025074.

38

Tropical and Extratropical Air
Sea Interactions

convection in this case, so that it cannot fully explain the observed boundary layer moistening ahead. In addition, it is worth noticing that the anomalous sea-air temperature and humidity difference terms in the bulk formula (the latter is indicated by the cyan line in Fig. 2
9B) are highly related to the SSTA and have similar magnitudes of the near-surface wind speed
related terms, indicating that the SSTA moistening effect via evaporation might not be negligible. Through which specific processes and to what extent does the SSTA contribute to the northward propagation of BSISO? What are the major differences in comparison with the eastward-propagating MJO? Following Hsu and Li (2012), a low-level moisture budget diagnosis method incorporating bulk formula and boundary-layer momentum budget [Eq. (2.1)] was developed by Wang et al. (2018). Relative contributions of the atmospheric internal dynamics and SSTA forcing on the BSISO northward propagation via moisture processes were investigated. The anomalous moisture budget equation may be written as @q0 @ðωqÞ0 Q0 5 2 ðVUrqÞ0 2 ðqrU VÞ0 2 2 2; @t @p Le

(2.2)

where q is the specific humidity, t the time, V is the horizontal wind vector, p is the pressure, ω is the vertical pressure velocity, Q2 is the atmospheric apparent moisture sink, Le is the latent heat of evaporation/condensation, and a prime denotes the intraseasonal component. There are two terms on the right-hand side of Eq. (2.2) directly related to the oceanic feed0 back processes. The first is the horizontal convergence of the moisture ½2 ðqr  VÞ  in which the SSTA gradient influences the surface pressure and the low-level circulation (Hsu and Li, 0 2012). The second is the diabatic processes ½2Q2 =Le  in which the sea-air humidity difference influences the surface evaporation and further the vertical moisture diffusion in the PBL. For the first term, the vertically-integrated horizontal convergence of the moisture in the PBL associated with the BSISO is mainly determined by the interaction between anomalous circulation and background moisture field (figure not shown), that is, 2g 21

ð pe



 2ðqrU VÞ0 dp  2 g 21

ps

ð pe

ð 2qrUV0 Þdp;

(2.3)

ps

where g is the acceleration of gravity, and a bar denotes the low-frequency background. If we apply the PBL momentum equation [Eq. (2.1)], the right-hand side of Eq. (2.3) has the following form: 2g 21

ð pe ps

0

 0  0 ð 2qrUV0 Þdp 5 2 qc Datm 1 Dssta

(2.4)

where D indicates the vertically averaged horizontal convergence anomaly in the PBL, with subscripts “atm” and “ssta” represent the free-atmospheric wave dynamics and SSTA gradient
induced components, respectively; both are the diagnosed results according to Eq. (2.1). q c is equivalent to the vertically-integrated background water vapor field in the

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

39

slab boundary layer model; it can be obtained with the rest terms in Eq. (2.4) since they are all calculable. In order to explicitly represent the surface evaporation in the moisture budget equation, the apparent moisture sink is replaced by the Climate Forecast System Reanalysisderived diabatic heating products: 0

2

Q2  LHRMR0 1 VDFMR0 Le

(2.5)

where LHRMR represents the total condensational moistening rate and VDFMR is the vertical diffusion moistening rate. The vertical integration of the latter in the PBL can be related to the surface evaporation: 2g 21

ð pe

ðVDFMR0 Þdp 5 2 g 21

ps

ð pe ps

 @  WVFðpÞ0 dp 5 WVFðps Þ0 2 WVFðpe Þ0  WVFðps Þ0 @p

(2.6)

where WVFðpÞ indicates the upward water vapor flux anomaly at level p, and its value at the top of the boundary layer ðpe Þ is nearly zero. The surface upward water vapor flux anomaly 0 WVFðps Þ is calculated based on the bulk formula: WVFðps Þ0  ρce U 0 Δq 1 ρce UΔq0

(2.7)

where ρ is the density of the air, ce is the turbulent exchange coefficient, U is the wind speed near the sea surface, and Δq is the specific humidity differences between the sea surface h i 0 0 0 and near-surface air. The nonlinear interaction term ρce ðU Δq Þ is neglected in Eq. (2.7) since it’s small, as shown in Fig. 2
9B. By combining the above equations, the final form of  0 the vertically-integrated moisture tendency anomaly in the PBL q_ can be written as 2 3 ð pe 0 0 @q @ðωqÞ 4 2ðVU rqÞ0 2 q_ 0  2 g 21 dp  2 g 21 1 LHRMR0 5dp @p ps @t ps   2 qc D0atm 1 D0ssta 1 ρce U 0 Δq 1 ρce UΔq0 ð pe

0

(2.8)

0

in which 2q c Dssta and ρce U Δq are attributed to the oceanic forcing, and the rest terms on the right-hand side of Eq. (2.8) represent the atmospheric effects. In this case, the relative contributions of the atmosphere and ocean to the low-level moisture budget can be quantitatively diagnosed. Focusing on the stage when the SSTAs are the warmest in the cycle of the BSISO over WNP, a meridional section (Fig. 2
20A) shows that both sensible and latent heat flux anomalies with positive (upward) signs can be clearly detected to the north of the convectively enhanced area (denoted by negative OLR anomalies). They are resulted from the underlying 0 0 warm SSTA forcing since ΔT and Δq -related terms in the bulk formula have larger positive values than the turbulent flux anomalies. In the existence of the upward turbulent heat flux

40

Tropical and Extratropical Air
Sea Interactions

(A)

(B)

(C)

FIGURE 2–20 Composite raw anomalies along 110
135 E averaged over Days 212 to 28, for (A) OLR (gray areas), SST (blue bars), potential temperature difference between 1000 and 850 hPa (brown dashed line), upward sensible heat flux (orange dotted-dashed line) and the anomalous sea
air temperature difference
related term (orange solid line), upward water vapor flux (green dotted-dashed line), and the anomalous sea-air humidity difference related term (green solid line); (B) altitude-latitude diagrams of vertical diffusion heating rate; and (C) vertical diffusion moistening rate. Lead/lag days and Day 0 are same as those defined in Fig. 2
5. Modified from Wang, T., Yang, X., Fang, J., Sun, X., Ren, X., 2018. Role of air-sea interaction in the 30
60-day boreal summer intraseasonal oscillation over the Western North Pacific. J. Clim. 31, 1653
1680. doi:10.1175/JCLI-D-17-0109.1.

anomalies, positive low-level vertical diffusion heating (Fig. 2
20B) and moistening (Fig. 2
20C) anomalies extend to the north of the convectively enhanced area, and so is the unstable stratification (denoted by vertical gradient of θe in Fig. 2
20A). Recalling Fig. 2
16, which describes the equatorial eastward-propagating MJO, a warm SSTA to the east of the MJO convective center is collocated with a negative LHF anomaly, which is an opposite situation comparing with the BSISO case shown in Fig. 2
20A. As the SSTA is mainly driven by

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

41

the surface heat flux anomaly, one may wonder why the feedbacks of the SSTA act to reverse the latter, rather than weaken or partially compensate it. Actually, the SSTA is almost equally driven by the shortwave radiation and turbulent heat flux anomalies (Fig. 2
9A), whereas only the latter is directly modulated by the SSTA feedback. Besides, the reversing of the sensible and LHF anomalies takes place at the transition stages of the BSISO convection, when the near-surface wind speed anomaly is small and the SSTA is large and tends to play a dominant role. Furthermore, the ASM region has a higher background wind speed, which makes the turbulent heat flux more sensitive to the sea-air temperature/humidity difference change (Kanemaru and Masunaga, 2014). In addition to altering the atmospheric thermal dynamic status, the SSTA gradient effect on the PBL convergence in BSISO is diagnosed with Eq. (2.1). The result (Fig. 2
21, note that the latitudes in the figure are relative to the convective center) shows that the SSTA gradient tends to offset the PBL convergence anomaly over the convective center (Fig. 2
21A) and enhances the PBL convergence to the north where SST is warmer (Fig. 2
21B,C). Particularly, over the area right in front of the active convection center (Fig. 2
21B), the SSTA gradient contributes more than half of the total PBL convergence anomaly, which is much larger than that calculated in the MJO case by Hsu and Li (2012). The distinctive spatial structures between the MJO and BSISO may be responsible for the difference. For the MJO, PBL convergence ahead of the convection is primarily associated with the freeatmospheric Kelvin wave response. For the BSISO, anomalous subsidence due to vertically overturning circulation partially offsets anomalous PBL convergence leading off-equatorial Rossby wave response and the background easterly shear effect (Jiang et al., 2004); as a result an area-averaged divergence anomaly appears north of the BSISO convection (Fig. 2
21C). Therefore the warm SSTA associated with the BSISO tends to play a more important role in regulating the PBL convergence, compared to free-atmospheric processes. Taking into consideration all the related physical processes, the diagnosed result of lowlevel moisture budget based on Eq. (2.8) for BSISO over the WNP is shown in Fig. 2
22. The total diagnosed moisture tendency anomalies [i.e., sum of the right-hand side terms of 0 0 Eq. (2.8), denoted by q_ atm1ssta ] are close to the observations (denoted by q_ obs ), indicating the reliability of the method. The diagnosed result highlights that the oceanic forcing (denoted 0 by q_ ssta ) contributes to the positive low-level moistening anomalies to the north of the con0 vective center, while the net atmospheric effects (denoted by q_ atm ) are negative. The two oceanic forcing processes have comparable positive contribuitionas, and the SSTA gradient
induced low-level moisture convergence is found to be slightly larger. In the existence of the low-level moistening, a shallow convection anomaly is detected to the north of the deep convection center based on the Climate Forecast System Reanalysis diabatic heating products (figure not shown), which forms a smooth transition from boundary layer moistening to shallow convection and then to deep convection, as discovered in Yang et al. (2008) who utilized satellite observations. As illustrated in the schematic diagram (Fig. 2
23), the SSTA feedback with a nearquadrature spatial relation provides an alternative mechanism for the northward propagation of the BSISO convection. One of the key processes is the triggering of shallow convection

42

Tropical and Extratropical Air
Sea Interactions

(A)

(B)

(C)

FIGURE 2–21 (A) Composite raw anomalies along 110
135 E averaged over Day 212 to 28 for horizontal convergence in the boundary layer over the regions of 22.5 to 2.5 relative to the convection center. From left to 0 right, diagnosed free-atmospheric wave dynamics induced term (Datm ), diagnosed SSTA gradient
induced term 0 0 0 (Dssta Þ, diagnosed total anomaly (Datm1ssta Þ, and the observed anomaly (Dobs Þ. The purple and yellow bars indicate the boundary layer depths of 1000
850 and 1000
700 hPa, respectively. Figures (B) and (C) are the same as (A), but for regions with relative latitudes of 5
10 and 10
15 . Lead/lag days and Day 0 are same as those defined in Fig. 2
5. From Wang, T., Yang, X., Fang, J., Sun, X., Ren, X., 2018. Role of air-sea interaction in the 30
60-day boreal summer intraseasonal oscillation over the Western North Pacific. J. Clim. 31, 1653
1680. doi:10.1175/JCLI-D17-0109.1.

over warm SSTA ahead of the deep convection. The shallow convection is the first stage or an indication of the deep convection developing in the tropical ISO, since it transports moisture upward and the deep convection is then triggered when the mid-tropospheric moisture

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

43

FIGURE 2–22 Composite raw anomalies along 110
135 E averaged over Day 212 to 28 for moisture budget in the low-level atmosphere (1000
700 hPa) over the regions with relative latitudes of 5
10 (green bars) and 0 10
15 (yellow bars). From left to right, the SSTA gradient
induced horizontal moisture convergence ( 2 q c Dssta ), 0 the net oceanic effect contributed to local moisture tendency q_ ssta , the net atmospheric effect contributed to local 0 0 moisture tendency (q_ atm ), sum of the net atmospheric and oceanic effects (q_ atm1ssta ), and the observed local 0 moisture tendency (q_ obs ). Lead/lag days and Day 0 are same as those defined in Fig. 2
5. From Wang, T., Yang, X., Fang, J., Sun, X., Ren, X., 2018. Role of air-sea interaction in the 30
60-day boreal summer intraseasonal oscillation over the Western North Pacific. J. Clim. 31, 1653
1680. doi:10.1175/JCLI-D-17-0109.1.

FIGURE 2–23 Schematic diagram of the air
sea interaction in the BSISO over the WNP, for (A) an active convection induced warm SSTA in the northern side of the convection, and (B) the feedback of the warm SSTA on the convection, which provides an alternative mechanism for the northward propagation of the convection. From Wang, T., Yang, X., Fang, J., Sun, X., Ren, X., 2018. Role of air-sea interaction in the 30
60-day boreal summer intraseasonal oscillation over the Western North Pacific. J. Clim. 31, 1653
1680. doi:10.1175/JCLI-D-17-0109.1.

44

Tropical and Extratropical Air
Sea Interactions

reaches its maximum (Kemball-Cook and Weare, 2001; Kikuchi and Takayabu, 2004; Benedict and Randall, 2007; Zhang and Song, 2009). The diagnoses of the surface heat flux and convergence and moisture budget above reveal some different aspects in comparison with the eastward-propagating MJO during its peak phase over the eastern IO (Hsu and Li, 2012). To the east of the MJO convective center where the mean westerly flow prevails, the total upward LHF anomaly is negative due to the intraseasonal easterly anomaly. The warm SSTA resulted from lack of evaporation plays a supporting role in the low-level moisture convergence through induced hydrostatic effect on the sea level pressure (Lindzen and Nigam, 1987), and the relative contribution of it is small (about 10%
25%). For the northward-propagating BSISO over the WNP, the warm SSTA to the north of the active convection acts to reverse the sign of the preexisting downward sensible/LHF anomaly caused by the lower surface wind speed and contributes to a positive lowlevel convergence anomaly at the same time. On the other hand, the free-atmospheric wave effect accounts for less than half of the total convergence anomaly over the convectively enhanced region with underlying warm SSTA, but leads to a divergence anomaly over the convectively suppressed region to the north. The moisture budget diagnosis by Wang et al. (2018) suggested that the SST feedback is a main process that causes the phase leading of PBL moisture to the north of the BSISO convection, which conflicts with some previous studies suggesting that the ocean feedback played a secondary role in the BSISO propagation (e.g., Jiang et al., 2004; Chou and Hsueh, 2010; Demott et al., 2013). The disagreement is attributed to different methods used. For instance, in the previous studies only part of the oceanic feedback processes is examined, and the apparent moisture sink-related moisture loss in the atmosphere is ignored. In Wang 0 et al. (2018), the vertical moisture flux term ½2@ðωqÞ =@p is treated as the atmospheric process but vertical velocity here is related to anomalous convergence influenced by the SSTA. Thus the diagnosis by Wang et al. (2018) may overestimate the SSTA effect. A more careful diagnosis is needed. Although the diagnoses of PBL divergence and moisture budget by Wang et al. (2018) are taken over the WNP area, similar conclusions are expected over the northern IO, as the 90degree phase leading of the warm SSTA is detected over both the areas (Wang et al., 2006), and the background circulations are regulated by ASM. In this case, both the vertical shear mechanism (i.e., leading of PBL convergence) and air
sea interaction are responsible for the northward propagation of BSISO. It is also speculated that diversities may exist due mainly to the different background status, particularly the prevailing directions of the monsoon flow.

2.3.3 Role of ocean feedback in Madden
Julian oscillation initiation Satellite observations reveal a seasonal contrast of intraseasonal SST variability over TIO. A strongest intraseasonal SST variability was found over the southern IO thermocline dome region (at 5
10 S) in boreal winter (Fig. 2
6B). Fig. 2
24 illustrates the annual cycle of strength of the intraseasonal SST variability along the thermocline dome latitudinal zone. The longitude-time profile highlights the seasonal dependence of the intraseasonal SSTA over the southern IO. The

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

45

FIGURE 2–24 Standard deviation of the 25
90-day band-pass-filtered SST averaged over 5 2 12.5 S, computed based on TMI data during the period of 1998 2 2005. A 90-day moving window is applied throughout the calendar year. From Li, T., Tam, F., Fu, X., Tian-Jun, Z., Wei-Jun, Z., 2008. Causes of the intraseasonal SST variability in the tropical Indian Ocean. Atmos. Ocean. Sci. Lett. 1, 18
23. doi:10.1080/16742834.2008.11446758.

SSTA amplitude over this latitudinal band is strongest (weakest) during late boreal winter (summer). It is worth noticing that the change of the SSTA amplitude from boreal winter to summer reaches 40%
50% over the most part of the basin, despite the fact that the summer and winter ISO forcing have comparable amplitudes in the region. Whereas the climatological thermocline dome over the southern IO explains the preferred location for the occurrence of the maximum SST variability, but it cannot explain the seasonal dependence feature as the thermocline dome appears all year around with a minor annual variation. Li et al. (2008) investigated the cause of the seasonal-dependent intraseasonal SST variability, using an intermediate ocean model (Wang et al., 1995) forced by daily surface wind stress and heat flux products from the ERA-40 reanalysis for the period of 1987
2001. The model was spun up from the rest with a 10-year integration based on the climatological annual cycle forcing. Then the model is forced by ERA-40 daily reanalysis products, which include winds at 10 m, net longwave and shortwave radiation at the surface, and 2 m air temperature for the period of 1987
2001. A bulk formula is used to calculate the surface LHF, with the surface specific humidity qa being related to the model SST according to an empirical relation: qa 5 ð0:972 3 SST 2 8:92Þ 3 1023 (see Wang et al., 1995). To mimic the high-frequency wind effect, a minimum wind speed of 4 m s21 is imposed within 5 N
5 S when the heat flux is calculated. Pentad mean model outputs are archived for further analyses.

46

Tropical and Extratropical Air
Sea Interactions

Fig. 2
25 plots the standard deviation of the model 25
90-day band-pass filtered SST fields in December, January and February (DJF) and June, July and August (JJA). During boreal winter, a zonally elongated band of strong intraseasonal SST responses can be found along 10 S, with the maximum amplitude of about 0.3 C. In contrast, such a feature is completely absent in boreal summer simulations. Instead, the strongest SST response occurs in the northern part of the Arabian Sea and the BoB, and also over the equatorial eastern IO. In general, the features of the simulated SST variability are consistent with where the strongest intraseasonal activity of the zonal wind and OLR is located. The seasonal contrast of the intraseasonal SST variability from the model simulation agrees well with the observed in the general patter. However, the model simulation appears to strongly underestimate intraseasonal SST variability during JJA if compared with Fig. 2
6A, particularly over the southern IO (also reported in Vaid and Modi, 2009). This is due mainly to the relatively weak wind forcing (compared to the QuikSCAT product) and the overestimation of the MLD in the model. Therefore caution is needed in interpreting the model results. Given that the model is capable of simulating the winter-summer asymmetry of the intraseasonal SST variability, we further investigate specific processes that give rise to the asymmetry. The overall SST response in boreal summer is much weaker than that in boreal winter, even though the wind stress and convection perturbations associated with the ISO have comparable amplitudes. To demonstrate the relative role of the ocean dynamics and heat flux processes in causing the seasonal asymmetric response, a mixed layer heat budget analysis is performed. Fig. 2
26 shows the regressed SST tendency terms associated with the anomalous shortwave radiation, LHF, entrainment cooling, and horizontal advection, respectively. They are regressed based on the same ISO indices defined previously, averaged over the lag of pentad 21 to pentad 0. In boreal winter during the westerly phase of the ISO, enhanced convection across the basin causes SST cooling due to reduced shortwave radiation (Fig. 2
26A). At the same time, the wind anomalies reinforce the background wind, leading to stronger total wind speed and thus stronger surface evaporation and ocean entrainment/vertical mixing. The in-phase relationship

FIGURE 2–25 The standard deviation patterns of the simulated 25
90-day band-pass filtered SST during (A) DJF and (B) JJA. Contour interval is 0.02 C, starting from 0.2 C. DJF, December, January and February; JJA, June, July and August. From Li, T., Tam, F., Fu, X., Tian-Jun, Z., Wei-Jun, Z., 2008. Causes of the intraseasonal SST variability in the tropical Indian Ocean. Atmos. Ocean. Sci. Lett. 1, 18
23. doi:10.1080/16742834.2008.11446758.

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

47

FIGURE 2–26 Anomalies of (A and B) the net shortwave radiation (shading) and OLR (contour, interval: 2.5 W m22), (C and D) the surface latent heat flux (shading) and wind stress (arrow), (E and F) temperature tendency due to ocean entrainment (shading) and wind stress (arrows), and (G and H) horizontal temperature advection in DJF (left panel) and JJA (right panel). See scale bar at bottom for SST tendency terms. Here the shortwave radiation and latent heat flux terms have been divided by the climatological mean mixed layer depth and water density and specific heat to reflect the same temperature tendency units as the entrainment and advection terms. DJF, December, January and February; JJA, June, July and August. From Li, T., Tam, F., Fu, X., Tian-Jun, Z., Wei-Jun, Z., 2008. Causes of the intraseasonal SST variability in the tropical Indian Ocean. Atmos. Ocean. Sci. Lett. 1, 18
23. doi:10.1080/16742834.2008.11446758.

between the two major heat flux terms and the entrainment results in a strong cooling tendency near 10 S in boreal winter (Fig. 2
26A,C,E), while the SST tendency due to horizontal advection is weak (Fig. 2
26G). Therefore the background mean flow plays an important role in producing a strong SST tendency associated with the passing of the ISO in DJF over the southern IO. During boreal summer, the phase relationship between various tendency terms is markedly different from that in winter. Westerly wind anomalies over the southeastern IO produce a warming effect along 5
10 S (see Fig. 2
26D,F). This is because, during JJA, the

48

Tropical and Extratropical Air
Sea Interactions

mean surface wind is easterly south of the equator, implying the reduction of the total wind speed and hence less evaporation and entrainment. On the other hand, enhanced convection leads to SST cooling due to decreased shortwave radiation (Fig. 2
26B). Thus the shortwave cooling is opposing the anomalous warming due to the evaporation and entrainment. Furthermore, anomalous horizontal temperature advection opposes the latent heat and entrainment effect along 5 S (Fig. 2
26H). This is because the westerly wind stress causes eastward and northward surface currents that advect cold water from both the west and the south, leading to a cooling tendency over the region. The mutual cancellation among the tendency terms leads to a weak SST response in JJA over the region. The annual variation of the MLD is an additional factor that enhances the summer-winter SST asymmetry. Both the observed and simulated MLDs attain a minimum (maximum) during December
March (August
September). The magnitude of the mean MLD in boreal summer is about twice as large as that in boreal winter. This implies that even given the same anomalous heat flux forcing, the SST variability could be smaller (greater) in boreal summer (winter) due to the deeper (shallower) mixed layer. Fig. 2
27 shows the evolution of the model SSTA, along with wind stress and OLR anomalies, during a composite ISO cycle over the IO for the DJF (left panels) and JJA (right panels) seasons. The time series of the 25
90-day band-pass filtered zonal wind stress, averaged over the domain of 5
12.5 S, 50
65 E, is chosen as an index to characterize the ISO cycle during DJF. For the JJA season, the wind stress averaged over 75
90 E, 2.5
10 S is used. In Fig. 2
27, the SST, wind stress and OLR signals are all regressed onto the ISO indices. First, we discuss the evolution of the SSTA in DJF. In pentad 21 (figure not shown), westerly wind stress and negative OLR signals associated with an active phase of ISO appear in the TIO. Cold SSTA at about 55 E, 10 S appears in pentad 0 (Fig. 2
27A). Since the background wind is westerly in the vicinity of this latitude, a positive zonal wind stress anomaly increases the evaporative cooling in the region. This gives rise to the in-phase relationship between the anomalous surface shortwave radiation and the LHF, and together they act with the ocean entrainment to strongly cool the model SST at this phase of the ISO. As a result, in pentad 11, the cold SSTA is enhanced over the western to central IO along B10 S (see Fig. 2
27C). One pentad later (Fig. 2
27E), signals of suppressed convection appear at about 55 E, collocated with the cold SST response. The positive OLR anomaly subsequently expands eastward and grows (Fig. 2
27G). It is noteworthy that suppressed convection tends to develop over the location where cold SST is already present for 5
10 days, as inferred from the sequence of charts from pentad 11 to 13. This phase relationship between the SST and convection implies a delayed two-way air
sea interaction scenario for the ISO; that is, on the one hand, an ocean cooling is induced by the wet (westerly) phase of the ISO through combined cloud radiative forcing and surface evaporation/ocean vertical mixing, and on the other hand, the so-induced western IO cold SSTA in turn initiates a subsequent dry (easterly) phase of the ISO. Thus air
sea interactions play an important role in the reinitiation of the ISO over the western IO during boreal winter. Next, we examine the evolution of the wind stress, convection, and SST associated with ISO in boreal summer. In pentad 22 (figure not shown), enhanced convection first appears

Chapter 2 • Atmosphere-ocean interactions in tropical ISO

49

FIGURE 2–27 Regression coefficients of the simulated SSTA (shading) and observed wind stress (arrow) and OLR (red contours for positive and green contours for negative values, starting from 6 5 W m22, interval: 2.5 W m22) anomalies for the lag of (A and B) 0, (C and D) 11, (E and F) 12, and (G and H) 13 pentads, based on data from the DJF (left panels) and JJA (right panels) periods. Wind stress and SST (OLR) anomalies being shown are above the 95% (90%) significance level. DJF, December, January and February; JJA, June, July and August. From Li, T., Tam, F., Fu, X., Tian-Jun, Z., Wei-Jun, Z., 2008. Causes of the intraseasonal SST variability in the tropical Indian Ocean. Atmos. Ocean. Sci. Lett. 1, 18
23. doi:10.1080/16742834.2008.11446758.

over the eastern equatorial IO. This is followed by further development of convection and the appearance of surface westerlies one pentad later. In pentad 0 when the westerlies are still strong, the maximum convection anomaly starts to propagate northward to the BoB (see Fig. 2
27B). In the next three pentads (Fig. 2
27D,F,H), suppressed convective anomalies gradually move eastward, as easterly wind stress anomalies replace the original westerly wind stress anomalies in the eastern IO. This picture of summertime ISO evolution is consistent with the one portrayed by Kemball-Cook and Wang (2001), Fu et al. (2003), and Jiang et al. (2004).

50

Tropical and Extratropical Air
Sea Interactions

To sum up, observed intraseasonal SST variability in TIO showed a striking asymmetry between boreal summer and winter. In boreal winter, the strongest SSTAs are found over the latitudinal band of 5
10 S. In boreal summer, SST signals are almost absent south of the equator. A 21/2-layer intermediate ocean model was used to study the cause of the SST asymmetry. Forced by daily ERA-40 reanalysis, the model is able to capture the seasonal contrast of the intraseasonal SSTA. The winter-summer asymmetry of the SSTA response over the southern IO may be understood as follows. During DJF, mean surface westerlies prevail along the ITCZ (5
10 S) across the IO basin. When the ISO surface wind is westerly (easterly), enhanced (reduced) evaporative cooling results. Combined with the fact that the ISO westerly is nearly in phase with convection, the LHF and shortwave radiation anomalies therefore tend to be in phase. Anomalies of entrainment and the LHF also have the same sign. These three ISO forcing terms, having comparable magnitudes, act together to produce strong SST responses over the southern IO in boreal winter. During JJA, the mean surface wind becomes easterly in the same region. This makes the anomalous incoming shortwave radiation against the LHF and ocean entrainment. Meanwhile, there is negative horizontal temperature advection just south of the equator, due to the northward Ekman drift during the westerly phase of the ISO. Thus there is mutual cancellation among the temperature tendency terms. As a result, the SST response is weak in boreal summer. This seasonal asymmetry of the SST response is further amplified by the annual cycle of the MLD, which is shallower (deeper) in the boreal winter (summer). Thus different from internal atmospheric dynamic mechanisms such as boundary convergence (e.g., Matthews, 2000; Seo and Kim, 2003; Jiang and Li, 2005) or anomalous moisture advection (Zhao et al., 2013; Li et al., 2015), here we present an air
sea interaction mechanism for MJO initiation, that is, significant SSTAs caused by the passing of an active ISO may have a delayed effect on initiation of a suppressed phase ISO, and vice versa. This ocean forcing mechanism appears most effective in boreal winter when the intraseasonal SST variability is strongest. It becomes less effective in boreal summer when the intraseasonal SST variability at the equatorial IO is weak.

2.3.4 Theoretical air-sea interaction frameworks on intraseasonal timescale selection Wang and Xie (1998) developed a simple theoretical model to understand atmosphere
ocean interaction over the warm pool. The coupled model consists of a Gill type atmospheric model and a reduced gravity upper ocean model with explicit treatment of ocean mixed layer physics and thermodynamic coupling that are essential for the warm pool regime. In contrast to the cold tongue basic state, which favors an unstable The El Niño-Southern Oscillation mode, the warm pool regime (moderate mean surface westerlies and deep thermocline) is conducive for higher-frequency (intraseasonal timescale) coupled unstable modes. The wind
mixed layer interaction through entrainment/evaporation plays a central role in the warm pool instability. The cloud-radiation feedback enhances the instability, whereas the ocean wave dynamics have little impact. The thermodynamic coupling between

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51

the atmosphere and ocean mixed layer results in a positive SSTA leading convection, which provides eddy available potential energy for growing coupled mode. The characteristics of the eastward-propagating coupled mode of the warm pool system compare favorably with the large-scale features of the observed MJO. This suggests that, in addition to atmospheric internal dynamic instability, the ocean mixed layer thermodynamic processes interacting with the atmosphere may play an active role in sustaining the MJO by destabilizing atmospheric moist Kelvin waves, slowing down phase propagation and setting up a preferred intraseasonal timescale. A simple air
sea interaction model was constructed by Jiang et al. (2004) to investigate the instability of the northward-propagating BSISO mode. The key processes in the atmospheric model involve the interaction between barotropic and baroclinic modes under background vertical shear and perturbation moisture variation in the PBL. The SSTA, primarily affected by surface LHF anomaly, can feed back to the atmospheric convective heating. An eigenvalue analysis indicates that the northward propagation of the BSISO is an unstable mode of the summer mean flow in the Indo-western Pacific monsoon region. The most unstable mode has a wavelength of about 2500 km. While the internal atmospheric processes such as the background vertical shear mechanism and the moisture asymmetry contribute significantly to the northward propagation of BSISO, the air
sea interaction also plays an important role. Recently, a 1D theoretical framework for local air
sea coupling in the BSISO over the North IO was constructed by Zhang et al. (2018). In the ocean mixed layer component of their model, the oceanic advection and entrainment terms as well as the intraseasonal variation of MLD are neglected. Incorporating the bulk formula and linear relations between the precipitation and shortwave radiation/surface wind speed anomalies, the atmospheric forced SSTA tendency formula is established. The oceanic feedback is empirically written as a linear relation between the precipitation tendency and SST anomalies according to the observed near-quadrature relation. With Newtonian damping processes, a 1D local air
sea coupling model in the form of a damped oscillating system is thus constructed: d2 δP dδP 1 αβδP 5 0; 1λ dt 2 dt

(2.9)

where t is the time, δP is the precipitation anomaly, λ indicates the Newtonian damping process of the surface LHF anomaly associated with SSTA, α is proportional to the sum of the linear coefficients relating the precipitation and shortwave radiation anomalies γ 1 and the precipitation and surface wind speed anomalies γ 2 , and β is the linear coefficient relating the SST and precipitation tendency anomalies. pffiffiffiffiffiffi The damped oscillating system [Eq. (2.9)] has an intrinsic frequency αβ , if the damping term is neglected. Obviously, the period of the oscillation of the local air
sea coupling model is determined by multiple physical processes, including the rainfall-shortwave radiation relation, the convection-surface wind speed relation, and SST-precipitation tendency relation. The sensitivity of the period to variations of these parameters (Fig. 2
28) reveals that the period decreases with γ 1 , γ 2 , and β, and increases with MLD. In their model, higher

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FIGURE 2–28 (A) Local air
sea coupling model period (day) predicted by the model with varying parameter β and mean-state mixed layer depth (H) and fixed parameters γ1 and γ 2. Black stars represent period values with β 5 5 and H 5 25 (50) m, which are determined based on observations. (B) Period with varying γ1 and γ 2, and β 5 5 and H 5 25 m. From Zhang, L., Han, W., Li, Y., Maloney, E.D., 2018. Role of North Indian Ocean air-sea interaction in summer monsoon intraseasonal oscillation. J. Clim. 31, 7885
7908. doi:10.1175/JCLI-D-17-0691.1.

values of γ 1 and γ 2 indicate stronger atmospheric forcing and favors a larger SST tendency, and a higher MLD slows down the SST variation. Therefore the intrinsic period of the local air
sea coupling model is positively correlated to the persistency of the SSTA. As discussed in Wang et al. (2018), the SSTA provides a negative feedback in the oscillation cycle, and such a negative feedback is always delayed due to the large heat capacity of the ocean mixed layer. It is thus speculated that the air
sea interaction is capable of providing an extra source of the low-frequency oscillation in which the persistency of the SSTA could affect the timescale of the oscillation, in addition to the atmospheric internal dynamics. Given the importance of the SSTA persistency, it is further suggested that the upper-ocean dynamics also plays an essential role in the air
sea interaction excited oscillation cycle, since the MLD is another key factor in determining the SSTA tendency in addition to the surface heat flux exchange. Using OGCM simulations, Li et al. (2017a,b) found that the strong wind stress forcing during the active convection phase deepens the mixed layer and thus leads to attenuated mixed layer temperature response to surface heat flux forcing. On the other hand, the overall effect of the freshwater flux of the monsoon rainfall acts to increase the mixed layer response and enhance the intraseasonal SST variability. It appears that a more delayed but stronger negative feedback would be provided by the air
sea interaction under these conditions.

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2.4 Summary and concluding remark In this review, we firstly describe the pronounced seasonality of the tropical ISO in boreal winter and summer, and the observed phase relation between intraseasonal convection and SST. The boreal winter ISO (i.e., MJO) is characterized by equatorial trapped eastwardpropagating convective anomalies, with a zonal wave number-1 structure and a period of 30
60 days. The BSISO, on the other hand, is mainly active over TIO and SCS with dominant northward propagation and active over tropical WNP with pronounced northwestward propagation. In accompany with active ISO convection and large-scale atmospheric circulation, significant intraseasonal SST variations are observed. A near-quadrature phase relation between the ISO convection and intraseasonal SSTA exists in both temporal and spatial domains. At a fixed location, an enhanced (suppressed) convection leads a cold (warm) SSTA and a cold (warm) SSTA leads a suppressed (enhanced) convection, both by about a quarter of the oscillation cycle. At a given time, a warm SSTA appears in front of ISO convection by a 90degree phase, regardless of season. The principal causes of the intraseasonal SSTA are discussed. A 1D heat budget analysis indicates that the intraseasonal SSTA in the warm ocean is primarily caused by surface heat flux anomalies. The ISO convection acts to modulate the surface heat flux anomalies through atmospheric processes. An enhanced ISO convection appears to increase the cumulus cloud cover and reflect/absorb a great portion of solar radiation, thus reducing the amount of the shortwave radiation reaching the sea surface. Meanwhile, the enhanced convection results in a low-level westerly burst with a slight lag, which accelerates the near-surface wind speed in the existence of the background westerly that prevails over the wintertime ITCZ or ASM region, thus enhancing the surface evaporation (upward LHF). Via the above two major processes, the ISO perturbs the net surface heat flux and results in significant SST variation. The upper-ocean dynamics (including vertical entrainment and horizontal advection), on the other hand, plays a secondary role in affecting the intraseasonal SSTA, particularly over the regions where a thick barrier layer inhibits the communication between surface and colder subthermocline water. The air
sea interaction may play an important role in modulating the ISO propagation. It is observed that there is an eastward (northward) shift of PBL perturbation moisture relative to the MJO (BSISO) convective center. Such a moisture shift destabilizes the atmosphere in front of the convection and favors the eastward (northward) propagation of the MJO (BSISO). The role of the underlying warm SSTA in the formation of the PBL moisture asymmetry is quantitatively diagnosed. For MJO, Hsu and Li (2012) revealed that PBL convergence is a major factor affecting the moisture asymmetry, and the SSTA contributes about 10%
25% to the observed PBL convergence through SSTA-induced pressure gradient force. The surface evaporation, on the other hand, is reduced to the east of the MJO convection, due to reduced wind speed. For BSISO, Wang et al. (2018) diagnosed the low-level moisture budget with a method incorporating bulk formula and boundary-layer momentum budget equation. The result showed that the warm SSTA acts to moisten the PBL via increasing both

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the surface evaporation and the PBL convergence simultaneously. Thus in comparison with the MJO, the surface evaporation in front of the BSISO convection is enhanced due to the warm SSTA forcing, and the SSTA plays a more important role in inducing the PBL convergence. The different SSTA forcing effects between the winter and summer are possibly attributed to distinctive circulation structures associated with the MJO and BSISO, with the former (latter) being dominated by Kelvin (Rossby) wave response. A possible scenario through which atmosphere
ocean interactions influence the initiation of the MJO in northern winter is introduced. It is noted that the intraseasonal SST variability in TIO exhibits a striking contrast between summer and winter. A strong intraseasonal SST variability appears in boreal winter over the equatorial IO, and during boreal summer the SST variability in the region is much weaker. Such a seasonal contrast is primarily caused by the seasonal change of mean zonal wind (Li et al., 2008). Based on an oceanic mixed layer heat budget analysis, Li et al. (2008) demonstrated that in boreal winter when mean surface westerlies prevail along the ITCZ (5
10 S), enhanced surface LHF during ISO active phase is in phase with the reduced shortwave radiative flux, leading to a significant cooling of surface ocean. The so-generated strong cold SSTA generates a delayed ocean feedback to help initiate an opposite phase of the MJO in the western IO as the preceding active-phase MJO has moved away from the equatorial IO. Such a process becomes much weaker in boreal summer due to the following two reasons. Firstly, a much weaker intraseasonal SSTA is generated because surface LHF and shortwave radiation anomalies now offset each other in boreal summer when the mean easterly is pronounced in the region. Secondly, due to the onset of the monsoon, the mean SST in western IO is too cold to support the initiation of the ISO convection. There is uncertainty in measuring the impact of air
sea interaction in the overall strength of the ISO. Some modeling studies suggested that air
sea coupling may enhance the ISO variance, while others showed an opposite result. The inconsistency may arise from the atmospheric model performance in capturing the observed ISO structure. It is argued that ocean feedback may not help much if the atmospheric component of the coupled model is not able to reproduce realistic three-dimensional wind structures or eastward/northward phase propagation of the MJO/BSISO. Further in-depth investigations are needed to address this open issue.

Acknowledgments This work was supported by the NSF AGS-1643297, NOAA NA18OAR4310298, and NSFC grant 41875069. This is SOEST contribution number 10909 and IPRC contribution number 1430.

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4134. Available from: https://doi.org/ 10.1175/JCLI3202.1.

3 Air
sea interaction in tropical Pacific: The dynamics of El Niño/Southern Oscillation Swadhin Kumar Behera1, Takeshi Doi1, Jing-Jia Luo2 1

APPLICAT ION LABORATORY, RE SEARCH INSTITUTE FOR VALUE-ADDED-INFORM ATION GENE RATION, JAP AN AGENCY FOR MARINE-EARTH SCIENCE AND TECHNOLOGY,

Y O K O HAM A , JA P A N 2 INSTITUTE FOR CLIMATE AND AP PLICATION R ESEARCH, NANJING UNIVERS ITY O F INFORMATION SCIENCE AND TECHNO LOGY, NANJING, CHINA

3.1 Introduction El Niño is referred to the abnormal ocean warming that develops once in a few years in the eastern tropical Pacific Ocean. The warming covers a large part of central tropical Pacific and linked to variations in the atmospheric circulations and oceanic conditions. While we know the phenomenon for its unusual warming, the original concept was drawn from the observation of unusual ocean currents in the coastal regions off Peru. For example, as recounted in Philander (1990), researcher from Peru reported in the year 1891 a countercurrent flowing from north to south near the coast. This was noticed on several different occasions. Its appearance along the Peruvian coast was concurrent with rains in regions where it did not rain normally (cf. Pezet, 1896). These initial reports remain uninvestigated until systematic ocean observations were made available. Interestingly though the atmospheric component of the ocean
atmosphere system, known as the Southern Oscillation, was discovered at the beginning of the 20th century while investigating the persistent failures of Indian summer monsoon rainfall (Walker, 1924). Except for El Niño years, sea surface temperatures (SSTs) off Peru and tropical South America are normally cooler than its western counterpart even though both sides are in the same latitude band receiving almost the same amount of incoming solar radiation. Trying to explain that zonal difference, Bjerknes (1969) hypothesized that the east-west gradient in the SSTs is coupled to surface wind and zonal atmospheric circulation, known as the Walker circulation (in recognition of the Southern Oscillation discussed earlier), in a feedback loop. In a normal year, the large SST gradient across the equatorial Pacific drives the easterlies that cool the temperature in eastern tropical Pacific through upwelling and evaporation. The cool and dry air blows westward and on reaching the western Pacific gets heated, in the absence of the Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00005-8 © 2021 Elsevier Inc. All rights reserved.

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upwelling, and gets moist as well through the evaporation over the warm water. The moist air rises on the west giving rise to atmospheric convection/rainfall and upon reaching the upper troposphere travels eastward to complete the Walker circulation and constitute the atmospheric component, the Southern Oscillation (Walker, 1924), of the El Niño/Southern Oscillation (ENSO) cycle. Bjerknes noted that an intensified Walker circulation would help to increase the east-west temperature contrast causing even stronger Walker circulation leading to stronger upwelling and intense cooling in the eastern Pacific. This stronger than normal condition is referred to as La Niña (Fig. 3
1, right panel), which is opposite of El Niño in which the Bjerknes feedback weakens leading to weaker equatorial easterlies, weaker Walker circulation, weaker upwelling, and warmer SST (Fig. 3
1, left panel) in the eastern Pacific. The canonical picture of ENSO based on a variety of observations is basically consistent with this Bjerknes hypothesis. Interestingly, Bjerknes’ mechanism while explaining the two favored states of the system, that is, El Niño and normal/La Niña (La Niña is basically an amplification of the normal state as explained earlier), does not clarify why there is an oscillation between them. That was basically explained by the equatorial ocean dynamics (Wyrtki, 1975) involving the depth of the thermocline or the amount of warm water above the thermocline. The changes in the depth of this warm layer associated with ENSO are a consequence of wind-driven ocean dynamics by which the wind and SST changes in the ENSO cycle are tightly locked together. Based on the observed data, Wyrtki (1975) tried to understand the dynamic response of the equatorial Pacific Ocean to the wind forcing prior to an El Niño. To analyze the ocean preconditions, he compiled an extensive record of sea level data collected from a network of tide gauges spread around the tropical Pacific. He postulated that the El Niño is not a response to the weakening of the easterlies in the eastern Pacific but rather due to the buildup of warm water in the equatorial western Pacific in years prior to an El Niño. This is a process associated with the strengthening of the easterly trade winds in the tropical Pacific in a

FIGURE 3–1 Schematic of the atmospheric and oceanic conditions associated with El Niño (left) and La Niña (right) events. Bluish shades mean cooler sea surface temperature and reddish shades mean warmer sea surface temperature.

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La Niña-like state. When the winds relax in the central Pacific, the accumulated warm waters pass to the eastern Pacific as internal equatorial Kelvin waves. This ocean process causes the surface warming leading to a Bjerknes-type positive feedback in which the winds weaken, more warm waters flow east, SSTs warm, and El Niño develops. The positive ocean
atmosphere feedback of Bjerknes type amplifies small initial perturbations into large anomalies and that eventually evolve as an El Niño event (Fig. 3
1, left panel). As it was recognized in several other studies that followed Wyrtki’s early work, the equatorial ocean dynamics play a vital role in the ENSO dynamics and its variability. The equatorial Kelvin and Rossby waves constitute the ocean dynamics and carry the signal from one end of the equatorial Pacific to the other end. The delay in their phase speeds to a great extent explains the periodicity of the phenomena and the oscillation between the two phases. Lack of observations did not help the progress in the understanding of these dynamics. This was also the reason why the 1982
83 El Niño could not be detected until it was largely developed. The intensity of that event and its large-scale global impact motivated the research community to explore the underlying processes. This led to the 10-year international Tropical Ocean-Global Atmosphere (TOGA) program (1985
94) to study and predict ENSO. The observation greatly advanced our understanding of ENSO by focusing on the interaction between the tropical Pacific Ocean and atmosphere (e.g., see ENSO overviews by Philander, 1990; Neelin et al., 1998; Wang and Picaut, 2004). The success led to the foundation of the sustained observation of the tropical Pacific through the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) observational network (McPhaden, 1995; Ando and Kuroda, 2002; Smith et al., 2019).

3.2 El Niño/Southern Oscillation theory Much of the early studies following the 1982
83 El Niño event investigated the equatorial ocean dynamics by simulating the coupled tropical ocean
atmosphere system with models of varying complexity (e.g., Philander et al., 1984; Yamagata, 1985; Cane and Zebiak, 1985; Philander, 1990; McCreary and Anderson, 1991; Neelin et al., 1998; Chang et al., 2006). One of the key components in these studies was the role of the oceanic Kelvin and Rossby waves (e.g., McCreary, 1983). Since the propagation speeds of similar atmospheric waves are far greater than these oceanic waves, the adjustment time scale of the tropical atmosphere to changes in SST is much shorter (10 days or less) than the adjustment time scale of the equatorial ocean to changes in wind stress (around six months). The short adjustment time of the atmosphere supports the assumption that the atmosphere is in a statistical equilibrium with the SST on time scales longer than a few months. Therefore, it is natural to expect that the memory of the system that determines ENSO dynamics primarily resides in the ocean. The free oceanic Kelvin and Rossby waves (Fig. 3
2A) are slower than their atmospheric counterpart but not completely explain the ENSO oscillation and its periodicity. For example, the Kelvin waves typically take less than 3 months to cross the basin from western end to the eastern end, and if we consider the off-equatorial Rossby wave of one-third the speed of

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FIGURE 3–2 Schematic diagrams showing westward propagating Rossby waves and eastward propagating equatorial Kelvin waves (A) and in case of 1996
1998 El Niño/La Niña transitions (B
E) as revealed from sea surface height anomalies. DR, DK, UR, and UK mean downwelling Rossby, downwelling Kelvin, upwelling Rossby, and upwelling Kelvin waves, respectively.

the Kelvin waves they will take about 9 months to return from eastern to western Pacific (e.g., McCreary, 1983). Hence the free oceanic Kelvin waves and Rossby waves can repeat the cycle in less than a year. This is depicted in the sequence of events with downwelling and upwelling Kelvin and Rossby waves to complete a typical cycle (Fig. 3
2B
D). Downwelling off-equatorial Rossby waves propagate westward to reflect back to the equator on western boundary and then the reflected waves turn into downwelling equatorial Kelvin waves to carry the warm water to eastern Pacific. Once El Niño is established, a reverse cycle begins with upwelling Rossby and Kelvin waves to return to a La Niña state. This could explain the typical cycle of one event but cannot explain complete ENSO variability, which is not so regular. The ENSO cycle is also protracted in the real world because of the ocean
atmosphere interactions that significantly modulate the free oceanic waves. The Bjerknes feedback can destabilize these waves, giving rise to unstable coupled modes that resemble the slow westward propagating oceanic Rossby mode and the eastward propagating oceanic Kelvin mode. In fact, the coupling between the atmosphere and ocean generates a breed of modes whose characteristics depend on the time scale of dynamical adjustment of ocean relative to the time scale of SST anomaly, which is related to the air
sea coupling. Stability analysis of a simple ENSO model linearized around a given mean state shows a rich variety of structures of the coupled modes on a parameter space. However, the ENSO aperiodicity observed in the natural nonlinear system of the real world does not agree with the oscillatory modes of these linearized frameworks. Therefore, we may broadly divide the

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ENSO mechanisms into two categories and that the reality corresponds to a blend of these two possibilities (e.g., Philander and Fedorov, 2003). In the first category, ENSO is considered as a sporadic event associated with stochastic forcing or noise such as westerly wind bursts (WWBs) generally associated with the intraseasonal oscillations. In the second category, ENSO is considered as a self-sustained, unstable, and naturally oscillatory mode of the coupled ocean
atmosphere system. We will discuss a few examples of both the categories here.

3.2.1 Sporadic mode Wyrtki’s (1975) study based on the analysis of the sea level data suggests that the El Niño is a result of the warm water build-up in the western Pacific. This build-up and its subsequent discharges are to a great extent dependent on equatorial surface winds. The intensity and periodicity of the easterlies will determine how quickly the warm water builds up and any relaxation of those winds will trigger a favorable condition for the El Niño development; the accumulated warm water in the western Pacific would rush eastward in the form of equatorial downwelling Kelvin waves to initiate an El Niño event. The triggering could also be a surge of WWBs around the dateline. As the intensity, frequency, and duration of the wind events (both easterlies and relaxation in it or westerly bursts) could be very random (and externally forced), it is expected that those are responsible for the aperiodicity in the ENSO variability. Hence, ENSO can be characterized as a random mode triggered by stochastic atmospheric/oceanic forcing (e.g., Lau 1985; Penland and Sardeshmukh, 1995; Moore and Kleeman, 1999; Philander and Fedorov, 2003; Kessler 2002; Behera et al., 2013; Wang et al., 2016). This view of ENSO requires the presence of noise for the event triggering and the noise could be generated by external atmospheric forcing such as WWBs (e.g., Gebbie et al., 2007) and the oceanic disturbances such as the tropical instability waves (e.g., An, 2008). There are other forms of noises, besides the WWBs, such as the easterly wind surge that explains unusual termination of an evolving El Niño-like that of 2014 (Larson and Kirtman, 2015; Min et al., 2015; Hu and Fedorov, 2017; Chiodi and Harrison, 2017). The presence of such noises in a variety of forms and intensity explains why each El Niño could be distinct and so difficult to predict (e.g., Landsea and Knaff, 2000; Philander and Fedorov, 2003; Levine et al., 2016; Newman and Sardeshmukh, 2017). Wyrtki’s concept not only brought the discussions on the role of noises in ENSO variability but also modified the earlier idea that the coastal warmings near Peru are mostly local and associated with local ocean circulations. The earlier concept of the locally generated events could have two implications; one that the coastal processes were random and the second that the coastal warming was associated with a larger basin-wide mechanism wherein the Bjerknes ocean
atmosphere feedback process sustains the events.

3.2.2 Oscillatory mode Bjerknes (1969) hypothesized that interactions between the atmosphere and the equatorial eastern Pacific Ocean are part of a large-scale basin-wide mechanism that explains the El Niño and La Niña states. For example, an initial positive SST anomaly in the equatorial

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eastern Pacific reduces the east-west SST gradient, weakens trade winds around the equator and weakens the Walker circulation that further reinforces the positive SST anomaly. This positive ocean
atmosphere feedback leads the equatorial Pacific to an El Niño state. As discussed earlier, this is a “never-ending” process in Bjerknes mechanism and another negative feedback is required to turn the El Niño state to a neutral or La Niña state. Thus the coupled mode most relevant to ENSO appears to reside in a parameter regime where the time scales associated with the local air
sea interaction are comparable to the dynamical adjustment time of the whole tropical Pacific. The evolution of the coupled mode in this parameter regime can be described in two phases. During the development phase, the Bjerknes positive feedback dominates and causes the anomalies to grow. However, during the decay phase, an adjustment process of equatorial oceanic waves is needed through a negative feedback. Rossby wave packets carry offequatorial thermocline anomalies of opposite sign, to the equatorial anomaly generated by the Bjerknes feedback, to western boundary at which the waves are reflected into equatorial Kelvin waves and the thermocline anomalies propagate eastward (Fig. 3
2) along the equator (e.g., McCreary, 1983). They then counteract the Bjerknes positive feedback and cause the system to turn from warm to cold states and back again (e.g., Chang et al., 2006; Tozuka and Yamagata, 2003; Tozuka et al., 2005). The time scale that is associated with the ocean wave adjustment imparts the “memory” of the coupled system that is essential for the oscillations in this ENSO paradigm. The above description of ENSO physics is based on a linear and deterministic framework. It offers a basic understanding of the evolution and duration, as well as oscillatory nature of ENSO events. However, the detailed features of ENSO events can vary greatly from event to event including when and where the initial warming starts and whether the initial signal propagated eastward or westward. There are several hypotheses that are used to explain the cause of ENSO irregularity in such an oscillatory paradigm. Those can be broadly grouped into two general categories as explained in the followings. “Delayed oscillator” is the most widely used hypothesis of ENSO variability. The underlying dynamics is emphasized in this mechanism and it assumes that the western Pacific is an inactive region for air
sea interaction and the ocean wave reflection is unimportant at the eastern boundary. Thus, it emphasizes the significance of wave reflection at the ocean western boundary. The delayed negative feedback is by free Rossby waves generated in the eastern Pacific coupling region. These off-equatorial ocean waves propagate to the western boundary and get reflected to equator to return to eastern boundary as equatorial Kelvin waves to reverse the Niño3 SST anomalies in the eastern Pacific coupling region (Fig. 3
2). This is the process in which the perturbation is initiated in the eastern Pacific and helps the oscillation between the two phases. Suarez and Schopf (1988) introduced it by considering the effects of equatorially trapped oceanic waves. Battisti and Hirst (1989) formulated and derived a version of this conceptual delayed oscillator model and argued that this delayed oscillator model could explain most of the important aspects of the numerical model of Zebiak and Cane (1987) that was used in early El Niño predictions.

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“Recharge
discharge oscillator” is the other hypothesis that is used to explain the ENSO oscillation. This hypothesis builds on Wyrtki’s original idea of warm water build-up (Wyrtki, 1975, 1985), that is, prior to El Niño upper ocean heat content or warm water volume builds up over the western tropical Pacific. This is treated as a “charging or recharging” phase. When El Niño develops, the divergence of Sverdrup transport associated with equatorial central Pacific westerly wind anomalies cause discharge of equatorial heat content. This discharge of equatorial heat content leads to a transition phase in which the entire equatorial Pacific thermocline depth becomes anomalously shallow and this allows anomalous cold waters to be pumped into the surface layer by climatological upwelling, leading to the cold phase or La Niña. This is treated as a “discharge” phase. After the discharge and occurrence of La Niña, warm water starts building-up again in the recharge phases leading to next El Niño phase. The time taken for each of these two phases is not necessarily the same every time, leading to the irregularity in the ENSO variability. It is the recharge
discharge process that makes the coupled ocean
atmosphere system oscillate on interannual time scales. Jin (1997a,b) formulated and derived this recharge
discharge oscillator model. It argues the importance of nonlinearity (Timmermann et al., 2003) that arises from strong air
sea feedback in an unstable dynamic regime. In this regime, not only ENSO can be described as a self-sustained oscillator, but also it can interact nonlinearly with either the annual cycle (Jin et al., 1994; Tziperman et al., 1994; Chang et al., 1994; Wang et al., 1999) or other coupled modes (e.g., Mantua and Battisti, 1995) giving rise to deterministic chaos. The loss of predictability in this regime is primarily due to the inaccurate initial conditions. Although these oscillator modes are said to capture ENSO variability, a stable/damped ENSO cycle cannot be self-sustained without external noise forcing (Penland and Sardeshmukh, 1995; Flügel and Chang, 1996; Kleeman and Moore, 1997; Thompson and Battisti, 2001; Flügel et al., 2004) as discussed in case of the sporadic mode (Section 3.2.1). Weather noise generated by the internal dynamics of the atmosphere plays a fundamental role in not only giving rise to ENSO irregularity (e.g., Chang et al., 2006) but also maintaining ENSO variance. In between these two extreme paradigms lies the hypothesis of a mixed category, which assumes ENSO to be self-sustained (due to weak nonlinearity) and periodic (Battisti, 1988; Schopf and Suarez, 1988; Jin, 1997a; Kirtman, 1997) driven by stochastic noises and the predictability comes from the oscillatory nature of the dominant mode (Chen et al., 2004) while the loss of predictability is primarily due to noises (Philander and Fedorov, 2003).

3.2.3 Asymmetry The theories discussed above capture the typical features of ENSO, the dynamics, the air
sea interactions, and the transitions from one phase to the other. But the asymmetries exhibited in spatial structure, amplitude, and evolution of El Niño and La Niña cannot be explained in a linear framework. SST anomalies associated with El Niños are stronger and cover wider areas in the tropical Pacific compared to that associated with La Niña (Fig. 3
3), resulting in positive skewness of interannual SST variations. Most El Niños and La Niñas are initiated in boreal spring and peak in boreal winter. After the mature phase, most El Niños

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FIGURE 3–3 Detrended anomalies of SST ( C) and 850 hPa wind for November
February derived from NOAA NCEP Global SST analysis (Reynolds et al., 2002) and NCEP/NCAR reanalysis dataset (Kalnay et al., 1996), respectively. The upper panel is for 1997/98 El Niño event and the bottom panel is for 1988/89 La Niña event, the two strongest events on record since 1982.

decay rapidly by next summer (with some exception like 2014
15 discussed later) whereas La Niñas generally persist through the following year and often reintensify in the subsequent winter (Fig. 3
4). This temporal asymmetry is said to be because of the nonlinear response of atmospheric deep convection to SSTs. Okumura and Deser (2010) suggested that the displacement of the center of the anomalous precipitation farther west during La Niña compared to El Niño favors strong anomalous easterly winds over much of the western Pacific and eastern Pacific allowing the La Niñas to often persist for longer periods. El Niño exhibits a similar structure with reversed sign as the atmospheric deep convection shift eastward but the westerlies in the western Pacific decay with the easterly favorable signal arriving from the Indian Ocean. The same Indian Ocean mechanism (westerly signal in this case) appears to work during La Niña but the local precipitation changes sustain the easterlies over the western Pacific. Thus, the enhanced duration of zonal wind anomalies in the western Pacific during La Niña compared to El Niño often prolongs La Niña conditions for more than a year (Okumura and Deser, 2010; Okumura et al., 2011).

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FIGURE 3–4 Time series of Niño3 (solid bars) and ENSO Modoki Index (EMI, green line) seasonally averaged for November
February months, and DMI (blue line) averaged for August
November months. All time series are normalized with their respective standard deviations.

Significant asymmetries are also observed between amplitudes of El Niños and La Niñas. For example, the amplitudes of Niño3 index for the super El Niños reach three times of its standard deviation (Fig. 3
4) while the amplitudes of strongest La Niñas are half of those amplitudes. This asymmetry in the amplitude indicates the nonlinearity in the ENSO variability. To address this nonlinearity issue, several hypotheses are proposed from seasonal to interdecadal scales. Those include nonlinear dynamic heating (Jin and An, 1999; Jin et al., 2003; An, 2009; Kim and An, 2020), nonlinear thermocline feedback (DiNezio and Deser, 2014), the atmospheric nonlinearity involved in convection (Kang and Kug, 2002), the nonlinearity related to wind stress response to changes in the zonal SST gradient (Liang et al., 2012), biological feedbacks on ENSO (Marzeion et al., 2005), tropical instability waves (Vialard et al., 2001), multiplicative (i.e., state dependent) noise forcing (Lengaigne et al., 2004; Eisenman et al., 2005; Gebbie et al., 2007; Chen et al., 2015), and the Indian Ocean influences (Behera and Yamagata 2003; Izumo et al., 2010; Okumura and Deser, 2010; Behera et al., 2020, Chapter 5 of this book; Kosaka et al., 2020, Chapter 6 of this book). Levine et al. (2016) examined the ENSO state influence on the fetch and/or wind speed of WWBs that in turn create asymmetric ENSO response. They found that the state dependence of WWB magnitude on ENSO and ENSO asymmetry hold in both the observations and results of 21 coupled climate models.

3.3 Diversity and flavors An El Niño typically lasts for a year and a La Niña sometime lasts for several years (for example that of 2010
13, Fig. 3
4), as discussed earlier. They are seasonally phase-

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locked and usually develop in boreal summer and peak in boreal winter. However, all El Niño/La Niña events are not similar to each other. A large diversity is observed in their evolutions, seasonality, intensity, spatial pattern, and periodicity. For example, there are a variety of El Niños observed in last few years alone. The 2014 event, predicted to be a major El Niño, unexpectedly went down early in its evolution but reappeared again in early 2015 with renewed intensity (Fig. 3
5). The 2015 event went on to be one of the super El Niños with comparable strength to that of 1982 and 1997 events (Fig. 3
4). There was another quite interesting El Niño event that evolved in 2009 prior to the 2015 El Niño. This event saw warm SST anomalies spread across the equatorial Pacific with not so warm SST anomalies in the eastern Pacific but with most of the warm SST anomalies in the central Pacific (Fig. 3
5), giving an impression that the event was a blend of canonical El Niño and the El Niño Modoki (discussed later in this section). All these have made us to realize the diversity (Capotondi et al., 2015) as well as the spatio-temporal complexity (Timmermann et al., 2018) of ENSO.

FIGURE 3–5 Detrended anomalies of SST ( C) and 850 hPa wind for November
February derived from NOAA NCEP Global SST analysis (Reynolds et al., 2002) and NCEP/NCAR reanalysis dataset (Kalnay et al., 1996), respectively. The upper panel is for 2015 El Niño event and the bottom panel is for 2009 blended El Niño Modoki event.

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The ENSO diversity is linked to several factors. The nonlinearity embedded in the atmospheric response to SST anomalies in the tropical Pacific could give rise to a variety of ENSO behaviors, especially their intensities. For example, a significantly larger SST anomaly tendency appears in super El Niños as compared to the regular El Niños (Chen et al., 2017) during the onset phase in April
May, when the positive SST anomaly starts to develop. A mixed-layer heat budget analysis in Chen et al. (2017) suggested that the tendency difference between super and regular El Niños grows primarily from the difference in zonal advective feedback and the associated zonal current anomaly. This is attributed to the difference in the thermocline depth anomaly over the off-equatorial western Pacific prior to the onset phase owing to difference in the wind stress curl anomaly, which is mainly regulated by the anomalous SST and precipitation over the Maritime Continent and equatorial Pacific. The nonlinear relationship between the atmospheric deep convection and underlying SST gives rise to diversity and complexity in ENSO behavior (reviewed in Okumura, 2019). The SST anomalies and the air
sea interaction are also dependent on the climatological SSTs of the basin. Those background SSTs vary both spatially and temporally and to a large extent play a role in determining the SST anomalies, associated atmospheric deep convection and the corresponding evolution of El Niño. Besides the tropical Pacific, SST variations in the neighboring basins could also play a role in determining the spatial and temporal behavior changes in the ENSO. For example, it is suggested that the enhanced warming in tropical Indian and Atlantic Oceans during recent decades favors stronger easterlies in the western Pacific via the atmospheric bridge and that might have contributed to the La Niña-like state (with enhanced east-west Walker circulation) in the Pacific (Luo et al., 2012; McGregor et al., 2014; reviewed in Cai et al., 2019). The emergence of these understandings on the diverse ENSO characteristics, the ENSO events are now categorized at least in two major types. Earlier studies based on the composites of “canonical El Niño” (Rasmusson and Carpenter, 1982) suggest a pattern in which the SST warming peaks in the equatorial eastern Pacific. However, emergence of a slightly different kind of SST anomaly pattern after the 1980s has brought out another form of ENSO now popularly known as the “El Niño Modoki/La Niña Modoki (ENSO Modoki)” (Ashok et al., 2007; Weng et al., 2007; Ashok and Yamagata, 2009; Behera and Yamagata, 2018). Other terminologies are also used to describe the phenomenon; e.g., “Trans- Niño” (Trenberth and Stepaniak, 2001), “Dateline El Niño” (Larkin and Harrison, 2005), “Central Pacific El Niño” (Kao and Yu, 2009; Yeh et al., 2009), and “Warm Pool El Niño” (Kug et al., 2009). Different from canonical El Niños, the El Niño Modokis (Ashok et al., 2007) are characterized by warm central Pacific flanked by cool eastern and western Pacific leading to different global teleconnections from that of the canonical El Niño (Ashok et al., 2007; Weng et al., 2007, 2009a,b; Wang and Hendon, 2007; Cai and Cowan 2009; Taschetto and England, 2009; Pradhan et al., 2011; Kim et al., 2012). Opposite patterns are observed during La Niña Modoki. A typical pattern of El Niño Modoki is seen in the boreal summer of 2004 and a typical pattern of La Niña Modoki is observed in the boreal winter of 2000
01. The seasonal phase locking is not so strong in ENSO Modoki, and events are seen in boreal summer and winter seasons either as a sequence of events or evolving only in either one of the seasons. Please refer to Marathe and Ashok, 2020 (Chapter 4 of this book) for a latest review on this phenomenon.

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3.4 Teleconnection The ENSO impact on global SST and atmospheric circulations were discussed in several previous studies. In addition to affecting adjacent landmasses of the tropical Pacific, ENSO affects remote regions of the globe through atmospheric teleconnections even modulating extreme weather events. Early studies presented the evolution of SST, surface wind, and rainfall anomalies during major El Niño events based on the observations available at that time (Rasmusson and Carpenter, 1982) and the seasonality of the teleconnections (Trenberth et al., 1998). Fig. 3
6 shows a schematic diagram of such El Niño teleconnections in boreal summer. One typical pattern of the teleconnection is depicted in the seasonal surface temperature anomalies as a cold “boomerang” pattern surrounding the warm anomalies in the eastern tropical Pacific. The boomerang pattern persists through the boreal winter (Fig. 3
7). A similar boomerang pattern is seen in the rainfall anomalies above the corresponding boomerang in SST anomalies for both seasons. But there are some differences seen in other parts of the world between the two seasons. The Indian subcontinent experiences a warm and dry season during boreal summer while southern Africa experiences such a condition in boreal winter/austral summer. A cooler summer is generally expected over some parts of Japan but the winter gets warmer during El Niño and vice versa. Western Pacific and Maritime Continent experience drier conditions during El Niño owing to the downdrafts of the anomalous Walker circulation associated with the event (depicted in the schematics of Fig. 3
1). At this time, wetter than normal conditions are

FIGURE 3–6 Schematic of the El Niño-related boreal summer surface temperature and rainfall anomalies. The blue (orange) color means colder (warmer) than normal temperature, and clouds (hashed areas) indicate a wetter (drier)-than-normal condition.

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FIGURE 3–7 Boreal winter partial regression patterns of anomalous SST over the oceans and skin temperature over land (upper panel) and GPCP rainfall and surface wind (lower panel) for Niño3 after regressing out ENSO Modoki associations. The values labeled in all the regression patterns and for the remaining figures are actually the values per standard deviation of the index being regressed. SST is from HadISST (Rayner et al., 2003), skin temperature and wind fields are from the NCEP/NCAR Reanalysis (Kalnay et al., 1996), and the rainfall data are from the Global Precipitation Climatology Project (GPCP) Version 2 Combination data (Adler et al., 2003). Adapted from Weng, H., Behera, S.K., Yamagata, T., 2009a. Anomalous winter climate conditions in the Pacific rim during recent El Niño Modoki and El Niño events. Clim. Dyn., 32, 663
674.

seen east of the dateline owing to the updrafts of the anomalous Walker circulation. Western coasts of the United States also receive higher rainfall associated with the El Niño teleconnections. In addition to the anomalous tropical Walker circulation, we find extratropical teleconnection pathways in north Pacific and south Pacific that help to carry the signal to far away regions from the equatorial eastern Pacific. The extratropical teleconnection pattern is associated with the stationary Rossby wave trains, which are triggered by anomalous

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atmospheric convection over the tropical Pacific. The teleconnection pattern appears as a series of positive and negative geopotential height anomalies that extend northwards and southwards into the mid-latitudes on both sides of the equator. In north Pacific it is known as the Pacific-North American (PNA) pattern. The boreal summer cold anomalies in eastern United States and Canada are the outcome of such a teleconnection. The teleconnection is even extended to northern Europe (Fig. 3
6). Interestingly the situation reverses in the winter time for most parts of northern United States and western Europe (Fig. 3
7). PNA is most pronounced in winter season (e.g., Wallace and Gutzler, 1981). It is mainly because the upper tropospheric westerly jets are stronger during winter and hence, they provide a better wave-guide for the stationary Rossby waves and for the tropical signals to propagate in higher latitudes. In south Pacific, the corresponding teleconnection is known as the Pacific-South American (PSA) pattern (e.g., Mo and Ghil, 1987). The wave-guide here is most pronounced in austral winter (June
August) and as a result of the teleconnection we notice warmer and wetter conditions in parts of South America (Fig. 3
6). Besides PSA teleconnection, floods over west coasts of Peru and droughts in the Amazon as well as north-east of South America are directly caused by the anomalous Walker circulation associated with El Niño. The rainfall anomaly distribution in Australia and adjacent regions is very sensitive to the displacement of the south Pacific Convergence Zone and its adjacent dry zone to its southwest. The eastern Australia and northern New Zealand are drier in El Niño years. The rainfall anomaly in Australia and New Zealand is also influenced by the extratropical teleconnections more pronounced during austral winter (June
August). ENSO is also linked to typhoon (tropical cyclone) variability in the northwestern Pacific. It is suggested that the typhoon genesis region shifts eastward (westward) during El Niño (La Niña) years (Chan, 2000; Wang and Chan, 2002) the lifetime of typhoon in El Niño years is longer (Camargo and Sobel, 2005). It is noted that TCs are usually more active during the canonical El Niño years as compared to the La Niña years. However, the suggested relationships are not consistent. Typhoon frequency has also been found to be related to ENSO Modoki phenomenon; a greater number of typhoons are reported in the western-north Pacific during positive ENSO Modoki years (Wang et al., 2013). A more recent study found that the frequencies of typhoons in the subtropical and tropical western-north Pacific appear to be connected to different remote forcing that are generally linked to canonical ENSO and the combined effect of canonical ENSO, ENSO Modoki, and Philippines
Taiwan Oscillations (Chang et al., 2019). Over the Atlantic basin, El Niño suppresses Atlantic hurricane activity (Bell and Chelliah, 2006) through enhanced vertical wind shear. The teleconnections discussed here are mostly focused on El Niños. The teleconnections and influences associated with La Niñas are mostly opposite of El Niños. However, these teleconnections are inherently nonlinear among the events and asymmetries in the teleconnections are also observed between El Niño and La Niña events as already discussed in the previous section while discussing the asymmetries in their inherent properties. Further, oceanic and atmospheric mean states outside the tropics also modulate the teleconnections. The character of ENSO as well as the ocean mean state has changed since the 1990s and

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those have resulted in changes in ENSO teleconnections in terms of precipitation and temperature in various parts of the globe (Yeh et al., 2018; Cai et al., 2020). For example, we have started observing more ENSO Modokis in recent decades and the teleconnections arising from ENSO Modokis are sometimes very different from that of ENSOs (Ashok et al., 2007; Weng et al., 2007, 2009a,b). Very recently, Doi et al. (2020) showed that El Niño Modoki in the tropical Pacific Ocean was a key to successfully predicting the 2019 super Indian Ocean Dipole phenomenon. Besides affecting weather and climate of various regions of the world through atmospheric teleconnections and atmospheric bridges, El Niño could affect ocean
atmosphere conditions through oceanic pathways. One such pathway is the passage of cold/warm waters through the Indonesian throughflow from the western Pacific associated with El Niño/La Niña. These waters then propagate along the coast of Australia as coastal Kelvin waves and sometimes induce nonlocally formed “Ningaloo Niña/Niño” (Feng et al., 2013; Kataoka et al., 2014; Tozuka et al., 2020, reviewed it in Chapter 9 of this book). The other oceanic pathway connects ENSO with the nonlocally formed California Niño/Niña (Yuan and Yamagata, 2014; Oettli et al., 2020 reviewed it in Chapter 10 of this book)

3.5 Predictability Statistical models were generally used for ENSO forecasts in the past. That concept changed considerably with the availability of intermediately complex dynamical models in the late 1980s. Confidence on the dynamical prediction system has grown over the years following the success of 1986
87 El Niño prediction with a simple coupled model (Cane et al., 1986). With the availability of high-performance computing environment in the 1990s, sophisticated coupled general circulations models (CGCMs) were developed and employed in the ENSO forecasts. Significant progresses have been made since then in the ENSO prediction using these CGCMs by operational and research centers for real-time ENSO forecasts. Multiple dynamical and statistical models are now routinely used to make forecasts on a monthly basis (e.g., http://iri.columbia.edu/climate/ENSO/index.html; Graham et al., 2011). A majority of existing forecast systems exhibited useful prediction skills of ENSO up to two or three seasons ahead (e.g., Barnston et al., 1999; Palmer et al., 2004; Jin et al., 2008; Xue et al., 2011). This together with advancements in in situ and satellite observations has changed the perspective on the monitoring and predictions of ENSO compared to the 1980s when the 1982 El Niño was not even noticed until quite late in its development. ENSO forecasts based on a multimodel prediction system is one of the strategies used to extract better prediction skill from a variety of available models. These models/prediction systems differ from one another in the way they treat different model physics, initialization process, and prediction strategies. Since the atmospheric systems are chaotic, each of these models is often integrated with a bunch of different initialization strategies to yield an ensemble of forecasts of a single model system. Several forecasts are produced from different initial states and each of these ensemble members then contributes to an ensemble mean forecast of one particular model/prediction system. When several such models/predictions

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systems are combined together in a prediction system, it is called a multimodel ensemble (MME) prediction system. Jin et al. (2008) and Wang et al. (2009) reported Niño 3.4 (averaged over 5 S-5 N, 120
170 W) forecasts based on an MME of 13 coupled models that participated in Climate Prediction and its Application to Society (CliPAS) and DEMETER (Palmer et al., 2004) projects. Their results showed that ENSO can be predicted at 6-month lead with an anomaly correlation coefficient (ACC) skill score of 0.86 for the MME predictions. Also, it was noted that the predictions initiated on August 1st had a higher ACC skill score of about 0.9. Jin et al. (2008) further found that the forecast skill depends strongly on season, ENSO phases, and ENSO intensity and that a stronger El Niño or La Niña is more predictable than weak events or neutral conditions. The multimodel forecasts are further aided by adopting deep-learning approach. Ham et al. (2019) have recently shown skillful ENSO forecasts for lead times of up to one and a half years using such an approach. To circumvent the limited amount of observation data, they used transfer learning to train a convolutional neural network (CNN) first on historical simulations and subsequently on the reanalysis data from 1871 to 1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Niño3.4 index of the CNN model was found to be much higher than that of any of the single dynamical forecast system. They also found that the CNN model is better at predicting the detailed zonal distribution of SSTs. The dynamical forecast systems are still preferable since those predict not only the ENSO state but also the teleconnections that are at least dynamically consistent. Some of the individual models/prediction systems have done quite well in the predictions of ENSO if not better than the MME forecasts. Here we discuss one of those models: The JAMSTEC climate prediction system that was built on the basis of the SINTEX-F CGCM (cf. Luo et al., 2003, 2005a; Masson et al., 2005). The first version of Scale Interaction Experiment-Frontier Research Center for Global change, Version 1 (SINTEX-F1) model has the atmospheric component (ECHAM4.6) with a horizontal resolution of 1.1 (T106) and 19 vertical levels. Its oceanic component (OPA8) has a relatively coarse resolution of a 2 Mercator horizontal mesh but enhanced up to 0.5 near equator in meridional direction. It has 31 levels in vertical of which 20 lies in the top 500-m. Heat, water and momentum fluxes across the air
sea interface are exchanged every 2 hours without any corrections using a standardized coupler of OASIS2. Sea ice cover is relaxed toward observed monthly climatology in the absence of an exclusive sea ice model. The SINTEX-F1 model realistically simulates the ENSO and IOD variability (Yamagata et al., 2004; Luo et al., 2005a). A simple initialization approach is adopted in its prediction system by assimilating only observed SSTs in a coupled way and using three coupling physics for the surface wind stress and heat fluxes [please refer to Luo et al. (2005b) for the details]. This prediction system has been used in realtime forecast experiments and demonstrated excellent performance in forecasting ENSO and IOD and their related climate anomalies over the globe (updated monthly at www.jamstec.go.jp/ frsgc/research/d1/iod/e/seasonal/outlook.html). The SINTEX-F1 has predicted (Fig. 3
8) almost all interannual ENSO events (El Niño events of 1982/83, 1986/87, 1991/92, 1997/98, and 2002/03 and La Niña events of 1984
86, 1988/89, 1995/96, 1999
2001, 2005/06, 2007
09, and 2010
12) up to 12 months ahead

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FIGURE 3–8 SINTEX-F1 predicted Niño3.4 SST anomalies. The black curve is the observed (www.esrl.noaa.gov/psd/ data/gridded/data.noaa.oisst.v2.html) and color curves are 9-member ensemble mean retrospective forecasts at 6, 12, 18, and 24 months lead. Results are smoothed with 5-month running mean. Adapted from Luo, J.-J., Lee, J.-Y., Yuan, C., Sasaki, W., Masson, S., Behera, S., et al., 2016. Current status of intraseasonal-seasonal-to-interannual prediction of the Indo-Pacific climate, Chapter 3. In: Behera, S., Yamagata, T. (Eds.), World Scientific Series on AsiaPacific Weather and Climate, vol. 7.

(Luo et al., 2005b, 2008). The 9-memmber ensemble prediction system could not predict the weak warm event of 1990/91 and the weak cold event of 2003/04. The early termination of the 2014 event was also not predicted well. Moreover, prediction of the El Niños appeared to be less skillful in the 2000s, compared to those in previous decades, which was also found in multimodel real-time forecasts (Barnston et al., 2012; Wang et al., 2010). This could be associated with changes in the tropical Pacific; the El Niño-related warming and rainfall anomalies tend to stay together with much weaker westerly anomalies in the central Pacific in recent decades (Luo et al., 2012). Also, the lead-lag relation between the equatorial warm water volumes and ENSO events has weakened in the last decade (Horii et al., 2012). In addition to the ENSO index, SINTEX-F1 has also realistically predicted the spatial structure of the SST anomalies and the rainfall anomalies (including some of the global teleconnections) associated with the ENSO events (Luo et al., 2016). Theoretically, ENSO is suggested to be predictable beyond 1 year lead time because of the self-sustained nature of the tropical Pacific coupled ocean
atmosphere system and its quasibiennial oscillation feature (Yamagata, 1985; Yamagata and Masumoto, 1989). Recently, some potential sources of ENSO predictability beyond 1-year lead are found not only in the tropical Pacific but also in the other basins. Planton et al. (2018) showed that the western equatorial Pacific heat content is the best oceanic predictor, in particular for La Niña. Izumo et al. (2010) suggested that the Indian Ocean Dipole in boreal autumn can trigger El Niño by modulating surface wind anomalies over the western equatorial Pacific in the following spring and summer, which could help to predict El Niño events with a 13- to 15-month lead time. Park et al. (2018) showed that the Atlantic Warm Pool can help ENSO development

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with a 17-month lead time. Also, DiNezio et al. (2017) showed the multiyear La Niña is potentially predictable 18- to 24-months in advance using perfect model forecasts performed with free-coupled GCM experiments. DiNezio et al. (2018) showed that forecast initialization at the peak of a strong El Niño event leads to skillful predictions of subsequent 2-year La Niña event using the retrospective prediction by a dynamical system. SINTEX-F1 predicted several ENSO events on real time over the past three decades even at lead times of up to 1.5
2 years (Fig. 3
8). It predicted the 1997/98 super El Niño quite well about 1.5-year ahead. Most previously coupled GCMs had difficulties to predict it beyond 6 months lead time (e.g., Barnston et al., 1999). The long-lasting La Niña events in 1984
86, 1999
2001, 2007
09, and 2010
12 were also predicted well at 1.5
2 years lead time. The SINTEX-F1 model has also successfully predicted ENSO across the so-called “spring barrier.” It predicted the Niño3.4 index with correlations above B0.5 across the first spring barrier but failed during the second one (Fig. 3
9 upper panel). Nevertheless, the prediction

FIGURE 3–9 Upper panel shows the Niño3.4 SST anomaly correlations between the observations and nine-member ensemble mean predictions up to 24 lead months. These are shown as a function of start month and lead time. Lower panel shows the corresponding root mean square errors. The red line denotes one standard deviation of observed Niño3.4 SST index. Adapted from Luo, J.-J., Lee, J.-Y., Yuan, C., Sasaki, W., Masson, S., Behera, S., et al., 2016. Current status of intraseasonal-seasonal-to-interannual prediction of the Indo-Pacific climate, Chapter 3. In: Behera, S., Yamagata, T. (Eds.), World Scientific Series on Asia-Pacific Weather and Climate, vol. 7.

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skill rebounds after crossing the first spring barrier and peaks in subsequent boreal winter, coinciding with usual ENSO peaks (Luo et al., 2008). Moreover, predictions initiated on 1 April and 1 May show ACC skills of up to 0.8 even at 1-year lead with low RMSE (Fig. 3
9 lower panel). This success is rooted in the correct prediction of subsurface signal in the equatorial Pacific (Luo et al., 2005b, 2008), which provides key memory for the development of the following ENSO event. On an average, ENSO prediction skill for the period 1982
2012 reached about 0.6 (0.4) at 14 (24) months lead (Fig. 3
9) with root mean square errors smaller than 0.8 C. The SINTEX-F1 model also predicted the ENSO Modoki quite well even at lead times of up to 2 years (Fig. 3
10). The prediction skills of both types of ENSO are well above the persistence levels at all lead times (cf. the solid and dashed lines in Fig. 3
10). For the two indices, the SINTEX-F1 model prediction shows high skill (.0.6) that is up to about 12 months ahead and medium skill of about 0.3 up to 2 years ahead. Interestingly, the Niño3.4 index—that captures a mixture of the canonical and Modoki-type ENSO—has higher prediction skill than either of the ENSO indices at both short and long lead times despite its relatively low persistence (black dashed line in Fig. 3
10). The possibilities of improving the forecasts were explored further by using a second generation of the SINTEX-F model and by improvements in model cores and data assimilations. The SINTEX-F2 has higher spatial resolutions in the ocean and has a realistic sea ice model besides improvements in the atmospheric component (ECHAM5), ocean component (OPA9), the coupler, and the subsurface data assimilations (Doi et al., 2016, 2017). This

FIGURE 3–10 ACC skill scores of Niño3 SST anomaly (red lines), ENSO Modoki index (blue lines), and Niño3.4 SST anomaly (black lines) based on persistence (dashed lines) and nine-member ensemble mean prediction of the SINTEXF1 model for the period 1982
2012. Adapted from Luo, J.-J., Lee, J.-Y., Yuan, C., Sasaki, W., Masson, S., Behera, S., et al., 2016. Current status of intraseasonal-seasonal-to-interannual prediction of the Indo-Pacific climate, Chapter 3. In: Behera, S., Yamagata, T. (Eds.), World Scientific Series on Asia-Pacific Weather and Climate, vol. 7.

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model with subsurface data assimilations showed general improvements in the prediction though it did not improve the prediction skills of the Niño3.4 and EMI up to one-year lead time (Fig. 3
11). Since the skill scores were already high for the SINTEX-F1 system it was not surprising to find that the SINTEX-F2 did not improve the skills further on shorter lead times but the prediction skills beyond one-year lead time were partly improved by the SINTEX-F2 (Fig. 3
11).

FIGURE 3–11 (A) Seasonally stratified ACC for monthly Niño3.4 up to 24-month lead (x-direction) along a fixed start time (y-direction) in the reforecast of 1983
2015 by the SINTEX-F1 system (nine-member ensemble mean). Values lower than the persistence (lag auto-correlation of observation) are shown by hatching. (B) Same as (A), but for the EMI. (C and D) Same as (A and B), but for the SINTEX-F2
3DVAR system (12-member ensemble mean).

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The question remained if a larger ensemble system with SINTEX-F2 could improve the probability forecasts of ENSO. It is generally expected that the prediction performance based on a 10-member ensemble system will be less efficient as compared to that of a 100-member ensemble system, when predicting the occurrence of rare and extreme climate events. This is because a 10-member system means that there are fewer chances of capturing extreme ends of the observed probability distributions. Doi et al. (2019) tested a large-ensemble (of about 100 members) retrospective seasonal forecast for 1983
2015 using SINTEX-F2 for the first time in the climate prediction world using a single dynamical prediction system. Prediction of extremely strong ENSO events is significantly improved in this larger ensemble system. This experiment also indicated that a 10-member ensemble could not perform as well as a 100-member ensemble when predicting the occurrence of 15% extreme events (the tail part of the distribution) of ENSO because only about 1 or 2 members (approximately 10%
20% of 10) have a chance to be nearer to the observations. They went on to suggest that about 50 members will be ideal to capture the extreme El Niños based on the results of their prediction system.

3.6 Decadal and future climate The ENSO variability on decadal scales has been extensively studied in the past following the seminal work of Nitta and Yamada (1989). Torrence and Webster (1999) applied a 15-year running variance to SSTA data and found that the frequency and amplitude of ENSO events change on interdecadal time scales. It is also suggested that the tropical decadal variation influences the global climate as well as the ENSO period. Several physical mechanisms were proposed for the decadal ENSO variability. Gu and Philander (1997) proposed a tropical
extratropical interaction, in which the extratropical atmosphere responds to the tropical warming (cooling) with an increased (decreased) surface westerlies and associated cooling (warming) of the extratropical ocean through an increased (decreased) latent heat flux. The relatively cool (warm) extratropical surface water arrives in the tropical thermocline approximately 12 years later, reversing the warming (cooling) and initiating a relatively cool (warm) period resembling a decadal ENSO-like behavior. Other mechanisms proposed are the extratropical decadal forcing of the oceanic meridional subtropical cell (Kleeman et al., 1999), the delayed negative feedback arising from the tropical oceanic Rossby wave propagation (Knutson and Manabe, 1995), the tropical stochastic atmospheric forcing (Kirtman and Schopf, 1998), and the nonlinearity of the tropical coupling system (Timmermann and Jin, 2002). Besides the north Pacific link to the decadal ENSO-like variability, several studies also suggested such a link through the subsurface signals that move from the south Pacific to the equatorial region (e.g., Luo and Yamagata, 2001; Luo et al., 2003 and references therein). Luo and Yamagata (2001) have shown that the south Pacific subsurface signals are largely forced by the teleconnection in the Southern Hemisphere. Based on the model results from a long integration of SINTEX-F1, Luo et al. (2003) further found that an anomalous cyclonic (anticyclonic) circulation appears in the

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south Pacific, which is tilted in a southeast-northwest direction, in response to the decadal warm (cold) SST anomalies in the tropical Pacific. This results in anomalous upward (downward) Ekman pumping along the northeastern edge of the anomalous circulation, shallowing (deepening) the oceanic thermocline there. Such an external source of heat content tends to slowly discharge/recharge the tropical ocean on the decadal time scale. The decadal ENSO-like variability (Zhang et al., 1997) is often shown to be closely linked to the Pacific decadal variability (PDV), which again is used to explain several low-frequency variations in the Pacific Ocean. There is much discussion about whether it is separable from ENSO. Nevertheless, the ENSO-like PDV pattern captures the largest fraction of lowfrequency variability in the Pacific basin on time scales longer than 8 years (Wang et al., 2016). Temporal variations of these decadal variations are characterized by relatively cold conditions in the tropical Pacific during 1910
1925 and 1947
1976, 1999
2013 and warm conditions during 1926
1945 and 1977
1998, mostly coinciding the decades that saw a greater number of La Niñas and El Niños, respectively (Fig. 3
4). The asymmetry between El Niño and La Niña amplitudes, discussed in Section 3.2.3, could be another factor to explain the decadal variation in the tropical Pacific. The ENSO characteristic in the changing climate is the other topic of discussions recently, beyond the decadal-scale variability. It is yet unclear how ENSO frequency and amplitude might change in response to anthropogenic global warming despite extensive modeling and observational studies, some of which have even used long time series of paleo data. Earlier studies have suggested some possible changes in ENSO behavior looking at the zonal SST gradient in the tropical Pacific. But it is difficult to say if the global warming will cause a stronger zonal SST gradient. For example, the gradient could be weakened due to warming of the eastern Pacific caused by several factors; cloud feedbacks (Meehl and Washington, 1996), evaporation feedbacks (Knutson and Manabe, 1995), weakening of the Walker circulation (Vecchi and Soden, 2007), etc. On the other hand, increased stratification could enhance the cooling effect of eastern Pacific upwelling (Clement et al., 1996; Seager and Murtugudde, 1997). Therefore, it is unclear whether the SST gradient will strengthen or weaken in the future owing to lack of our understanding of the climate sensitivity to global warming and how these processes are going to behave in the future climate. Moreover, the background state of the tropical Pacific is projected to change in response to the global warming. ENSO behavior is closely linked to this mean state. The projected mean state changes are expected to modify ENSO’s amplifying and damping feedback processes (Cai et al., 2015). North and South Pacific Meridional Modes (NPMM and SPMM) are known precursors of El Niño
Southern Oscillation (ENSO) and Tropical Pacific decadal variability (TPDV). However, the relative importance of these precursors and the time scale on which they impact the tropics remain unclear. Using Community Earth System Model experiments, Liguori and Di Lorenzo (2019) found that the absence of NPMM leads to a significant reduction of the tropical interannual variability (B35%) while the absence of the SPMM has no appreciable impact on ENSO but significantly reduces the TPDV (B30%). While the relative importance of the NPMM and SPMM may be model dependent, the stochastic atmospheric

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variability in the extratropics that energizes the meridional modes emerges as a key source of TPDV in their results. A weakening in the Walker circulation, if it happens as is projected, is expected to weaken equatorial Pacific Ocean currents and increase the warming in the eastern equatorial Pacific through the Bjerknes feedback (in the mean state). This could lead to a more El Niño-like perpetual state in the tropical Pacific. However, the recent observations suggest an opposite case with a stronger Walker circulation and a La Niña-like state (Luo et al., 2012), which were not reproduced by most climate model simulations (Luo et al., 2018). Such divergences in the ENSO phase and amplitude are also seen in model results. In a recent study, Chen et al. (2017) show that the fundamental factor that controls the divergent projections of ENSO amplitude change is the change of climatological mean Pacific subtropical cell, whose strength determines the meridional structure of ENSO perturbations and thus the anomalous thermocline response to the wind forcing. From the results based on 20 coupled general circulation models that participated in the Coupled Model Intercomparison Project phase-5, it is noted that the change in the thermocline response is a key factor regulating the strength of Bjerknes, thermocline, and zonal advective feedbacks. Those ultimately lead to the divergence in ENSO amplitudes. Therefore, model uncertainties remain one of the big challenges in the climate models’ abilities to realistically simulate the present-day mean climate state besides ENSO properties. Most climate models have huge biases in the tropical convections and clouds in the eastern Pacific off the South American coast (leading to serious issues in the long-term cloud-radiative feedback processes there). The extent to which these biases are a source of uncertainty is yet to be tested. Therefore, all our efforts must be directed to obtain a model projection that is consistent with our physical and theoretical understanding based on the observations owing to our limitations to achieve a significant reduction in the model uncertainties.

3.7 Summary ENSO remains as one of the dominant modes of climate variations affecting the global climate and environment on several time scales. Initiated with the international TOGA program, the tropical Pacific observing system essentially provides the backbone of ENSO monitoring for decades now. In addition to improving the understanding of ENSO variability, the monitoring system has helped the ENSO predictability. Over the years, several hypotheses were proposed to explain an individual cycle of the ENSO, that is, from a neutral/La Niña state to an El Niño state and back to a neutral/La Niña state. One of the early views hypothesized by Bjerknes (1969) combines the processes in the eastern tropical Pacific Ocean with the tropical atmosphere through a positive feedback process leading to growth of one part of the cycle. It, however, does not explain a process through which the growing mode is terminated and another part of the cycle is initiated. The early work of Wyrtki (1975) tried to throw some light on this by bringing the role of ocean dynamics through the warm water volume that can essentially turn around the ENSO from one state to the other through recharge (build-up) and discharge (release) processes.

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The recharge
discharge processes are not necessarily oscillatory as one would expect from the aperiodic variation of ENSO and its sporadic occurrences. The sporadic part of the ENSO variability is thought to be associated with the stochastic weather noises internal to the atmosphere besides the state-dependent noise. Nevertheless, the nonlinearity in the system has the potential to give rise to the sporadic behavior in ENSO occurrences. In between these two paradigms of oscillatory and sporadic ENSO variability there exists a mixed category, which assumes ENSO to be self-sustained and periodic while driven by stochastic noises. The predictability of the system comes from the oscillatory nature of the dominant mode while the loss of predictability is primarily due to noises. The other aspect of ENSO is the asymmetries it has exhibited in the spatial structure, amplitude, and evolution of El Niño and La Niña. Stronger SST anomalies associated with El Niños cover wider areas in the tropical Pacific compared to that associated with La Niña resulting in positive skewness of interannual SST variations. The amplitudes of the Niño3 index for strong El Niños are higher than that of the La Niñas whereas latter events are usually long-lived (often more than a year) compared to former events. This temporal asymmetry is partly explained by the nonlinear response of atmospheric deep convection to SSTs. The ENSO impact on global weather and climate has been discussed in many studies. It affects remote regions of the globe through atmospheric teleconnections in addition to directly affecting adjacent landmasses by bringing changes in seasonal Walker and Hadley circulations. It even modulates the extreme weather events such as tropical cyclones, heat waves, and floods. In some regions, the ENSO influence changes over the year from either dry/warm condition to wet/cool condition between the summer and winter seasons. The teleconnections and influences associated with La Niñas are generally opposite of El Niños. However, these teleconnections are inherently nonlinear and asymmetries in the teleconnections are also observed between El Niño and La Niña events. In addition to the tropical conditions in the basin, oceanic, and atmospheric mean states of neighboring basins as well as in extratropical regions modulate the teleconnections. The oceanic teleconnection is not studied as much as its atmospheric counterpart. Recent studies indicate the role of ENSO’s oceanic pathways through the Indonesian throughflow in giving rise to some of the nonlocally formed Ningaloo Niño/Niña off Western Australia besides directly affecting nonlocally formed California Niño/Niña off Baja California. The character of ENSO has changed in recent decades. The changes in the mean state of the basin as well as the corresponding changes in the neighboring basins are affecting the ENSO behavior. It is yet unclear how ENSO frequency and amplitude might change in response to anthropogenic global warming despite extensive modeling and observational studies. For example, models are projecting a weaker Walker circulation that would lead to a warmer eastern Pacific and an El Niño-like state. However, observations in the past decade show an opposite situation. Most climate models have huge biases in the tropical convections and clouds in the eastern Pacific off the South American coast leading to serious issues in the long-term cloud-radiative feedback processes there. The extent to which these biases would contribute to the projections remains uncertain. While the changes in ENSO behavior in changing climate remain still a debate, in recent decades, we have started observing a

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different type of variability called the ENSO Modoki, which has the center of actions in the central Pacific flanked by opposite signals on either side of the Pacific. This change in ENSO behavior affects not only its spatial and temporal characteristics within the basin but also outside of it including atmospheric and oceanic teleconnections. Considering its large socio-economic impacts, major emphasis has been laid on the predictability of ENSO. Multiple dynamical and statistical models are now routinely used to make ENSO forecasts on monthly basis and most of these forecast systems exhibit useful prediction skills up to two or three seasons ahead. This together with the advancements in in situ and satellite observations has changed the perspective of the ENSO predictions as compared to the 1980s when the 1982 El Niño was not even detected until late in its development. While MME prediction systems have shown greater skills in the ENSO predictions, some of the individual models have done exceptionally well especially at longer lead times. SINTEX-F1 and SINTEX-F2 are two such models, which are discussed extensively in this chapter. These individual models have shown excellent prediction skills not only on seasonal scales but also on lead times of up to 18 months. These new results are certainly heartening for us to make the short-term adaptation and mitigation plans while we keep improving models and our skills to understand and project future changes in ENSO.

Acknowledgments We thank the reviewer for very useful comments.

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4 The El Niño Modoki Shamal Marathe1, Ashok Karumuri2 1

CENTER FOR C LIMATE CHANGE RESEARCH, INDIAN INSTIT UTE OF T ROPICAL ME TEO ROL OGY, P U NE , IN DI A 2 UNI VE RS I TY C E NT R E FO R EAR TH AND S PAC E SC I ENC ES , UNI V ER S I TY O F HY DERAB AD, HY DE R AB AD, I NDI A

4.1 What El Niño Modoki is? The El Niño-Southern Oscillation (ENSO; Rasmusson and Carpenter, 1982), the most prominent tropical ocean-atmosphere coupled phenomenon, is known to influence the global climate extensively (e.g., Ropelewski and Halpert, 1987; Keshavamurty, 1988). Several publications during the end of the first decade of the current century (Ashok et al., 2007; Kao and Yu, 2009; Kug et al., 2009; Yeh et al., 2009; Lee and McPhaden, 2010; Freund et al., 2019; Rodrigues et al., 2019) propose that the frequency of canonical El Niños (ELs), with the highest warming confined in eastern Pacific (EP), has decreased since late 1970s as compared to the period from 1950s to mid-1970s. Interestingly, Freund et al. (2019)’s conclusions are mainly derived from the analysis of a variety of coral-derived observational datasets. Ashok et al. (2007) have identified it, through application of methods such as the empirical orthogonal function (EOF)/complex EOF and composite analyses on the tropical Pacific (TP) sea surface temperature anomaly (SSTA), a hitherto undocumented event, with largest warming in the central TP flanked by cooler SSTA on both sides of TP and with its own different teleconnections. They have observed that the phenomenon has been occurring with increased frequency since late 1970s (Fig. 4
1) and called it as El Niño Modoki (EM). The essential characteristic of this phenomenon is the persistence of the largest SSTA in the central TP through more than three successive seasons, for example, boreal summer through following boreal winter (Ashok et al., 2007). This is not like the canonical ENSO cases, wherein the tropical Central Pacific (CP) signal hosts a propagating temporary signal prior to 1980s, called as “Transniño” in a pioneering work by Trenberth and Stepaniak (2001). Historically, such a structural difference of the events such as the 2004 and 1994 was also noticed by Donguy and Dessier (1983) and Meyers et al. (2006). Hints for occurrence of the remarkable SSTA pattern in the TP that is distinct from canonical ENSO and its unique impacts are also found in former works (e.g., Weare et al., 1976; Meyers et al., 1999; Keshavamurty, 1982; Navarra et al., 1999). The principal mode of an EOF analysis performed on the TP Ocean SST yields the familiar canonical EL structure with maximum SSTA in the EP (see Fig. 4
2A, Rasmusson and Carpenter, 1982; Trenberth, 1997). This mode (Behera et al., 2020a have reviewed it in Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00009-5 © 2021 Elsevier Inc. All rights reserved.

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FIGURE 4–1 Schematic of EM conditions in the TP. From Ashok K., Yamagata T., 2009. The El Niño with a difference. Nature. 461, 481
484. https://doi.org/10.1038/461481a.

Chapter 3, Air
Sea Interaction in Tropical Pacific: The Dynamics of El Niño-Southern Oscillation, of this book) accounts for around half the overall variance of Pacific Ocean SST, depending on the dataset used and the time analyzed. The second mode associated with the EM explains about 11% of the variance (see Fig. 4
2B). Importantly, the lead and lag correlations between the two principal components are moderate for the post-1976 period, unlike the earlier period. Ashok et al. (2007) proposed a new index named El Niño Modoki Index (EMI), which mainly takes into account the zonal gradients of SSTA among the eastern, western, and central TP. EMI 5 ½SSTAA
0:5 3 ½SSTAB
0:5 3 ½SSTAC

(4.1)

The square bracket in above equation represents the area-averaged SSTA over each of the regions A (165 E
140 W, 10 S
10 N), B (110 W
70 W, 15 S
5 N), and C (125 E
145 E, 10 S
20 N), respectively. Studies such as Kug et al. (2009), Yu et al. (2010), Singh and Delcroix (2013), Behera and Yamagata (2010), Kug et al. (2010), Ren and Jin (2013), Marathe et al. (2015), and Ashok et al. (2017) also support the inference that EM phenomenon is in fact a different type of event as compared to the EL. In addition to the separate SSTA signatures, the teleconnection of the EM phenomenon during diverse seasons, and evolution, have also been recognized as different from that of the canonical ENSOs (e.g., Keshavamurty, 1982; Navarra et al., 1999; Larkin and Harrison, 2005a,b; Kumar et al., 2006; Meyers et al., 2006; Wang and Hendon, 2007; Weng et al., 2007, 2009; Ashok et al., 2007, 2009; Yeh et al., 2009; Taschetto and England, 2009; Cai and Cowan, 2009; Taschetto et al., 2009, 2010; Kim et al., 2009, 2012; Trenberth and Smith, 2009; Chen and Tam, 2010; Gierach et al., 2012; Preethi et al., 2015; Narayanasetti et al., 2016; Dogar et al., 2019).

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(A)

(B)

FIGURE 4–2 (A) EOF1 mode and (B) EOF2 mode of TP SSTA (1979
2014).

In addition to the canonical EL and EM classification to discriminate the types of TP phenomena, numerous other names have been introduced. For example, Larkin and Harrison (2005a) introduced “Dateline ENSO” represented by the Niño3.4 index and stated that it captures more EL seasons apart from those typically agreed. Kao and Yu (2009) distinguish two types of EL as the CP and EP type, represented by SST structures associated with Niño4 index and Niño1 1 2 index, respectively. Yu et al. (2012) chose relative strength of the CP and EP SSTA as a deciding factor. Kug et al. (2009) and Yeh et al. (2009) categorize two different flavors/types of ENSO as the cold tongue and warm pool ENSOs, based on Niño3 and Niño4 index, respectively. The requirement of constant maximum warming in the central TP during three seasons to be cataloged as an EM, distinguishes the nomenclature of Ashok et al. (2007) from the other definitions. Majority of the SST-based indices, discriminate the Modoki-type phenomenon either by the area-averaged SSTA in the CP, or consider the east and west SSTA gradients from the CP. Further, Ren and Jin (2011) and Takahashi et al. (2011) bring in nonlinear SST indices that are combinations of CP and EP SSTA, and propose nonlinear indices to represent the types of ENSO.

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The Modoki terminology is distinct in the sense of persistence of the maximum SSTA in the CP through at least three seasons, typically, boreal summer through winter. Weng et al. (2007) illustrate that the use of a common index such as the SSTA in Niño3.4 region may be less helpful in distinguishing the phenomena. Yu et al. (2012) display, in general, an excellent agreement for the boreal winter season, among the EMI and EP/CP indices for the 1950
2010 period. Hu et al. (2016) introduced two new indices, namely Niño3b and Niño4b and concluded that the climate teleconnections (i.e., land surface temperature and precipitation) associated with each index are distinct in each season, suggesting that the two indices are potentially useful for monitoring the two types of EL and associated climate anomalies. Zhang et al. (2019) developed a unified complex ENSO index, which can characterize and distinguish EP and CP types of ENSO simultaneously. Based on the different features of EP and CP ENSO in regions N3 and N4, they constructed the complex plane of N3 1 N4 and N3 2 N4. The EL type could be determined by the sign of N3 1 N4 and N3 2 N4.

4.2 Debate Several studies disputed the bimodal classification suggested by these earlier studies and propose a continuum distribution with two extremes (see review by Capotondi et al., 2014). Takahashi et al. (2011), Takahashi and Dewitte (2016) argue that EMs and most of the canonical ELs cannot be distinguished in terms of their nonlinear evolution. As the discovery of the Modoki was partly based on the EOF analysis (e.g., Ashok et al., 2007; Kao and Yu, 2009), Lian and Chen (2012) express doubt about the validity of spatial pattern of Modoki. They claim that the orthogonality constraint of an EOF analysis leads to Modoki pattern but actually it is an artifact. Rotational EOF analysis (REOF) may be used as an alternative to EOF for a more reliable outcome. The results from the EOF technique will lead to orthogonality and degeneracy problems (e.g., Behera et al., 2003) if not used cautiously, and exclusive of further confirmation. The REOF method is a probable alternate that can sometimes help a physical explanation, especially when it is impossible to do so using the EOF analysis (Jolliffe, 1989; Von Storch and Zwiers, 1999; Navarra and Simoncini, 2010); this involves transforming the EOF into a nonorthogonal linear basis. The method also helps when the EOF modes are not well separated (Jolliffe, 1989; Von Storch and Zwiers, 1999; Navarra and Simoncini, 2010). On the other hand, application of REOF technique does not always guarantee that one would get a physically meaningful outcome (Behera et al., 2003; Jolliffe, 1989). Jolliffe (1995) has discussed numerous other drawbacks of the REOF technique. Therefore due caution is also essential to decide when to use the REOF method. In summary, either of the techniques needs to be used with due knowledge of the restrictions associated with the particular method. Marathe et al. (2015) utilized this REOF technique for the 1979
2010 period and found that the distinctions proposed between the Modoki and canonical EL evolution are valid. Importantly, Ashok et al. (2007) actually highlight the drawbacks of the EOF analysis, and to reconfirm their results, they present a composite analysis of the SSTA and similar analyses of precipitation and temperature anomalies

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to isolate teleconnections. Further, the variances of the leading three EOF patterns of TP SSTA for the 1979
2004 for the study period of Ashok et al. (2007) are well separated as per North et al.’s (1982) thumb rule. In addition, as shown by them, different lead/lag correlations among the leading two principal components, obtained from the EOF analysis of the post-1978 TP SSTA, are statistically significant but modest, that is, 0.43. In addition, a complex EOF analysis was also carried out by Ashok et al. (2007) to ascertain the distinct evolution.

4.3 Distinctions and nonlinearities Saji et al. (2018), based on experiments with a simple 15-layer model, suggest “a self-limiting behavior inherent to ENSO” dynamics. They attribute this as a consequence of the atmospheric Kelvin wave response, which develops to the east of ENSO’s convective anomalies, dampens sea surface temperature (SST) variations in the EP, thereby preventing super ELs from developing through TP dynamics alone. Interestingly, Saji et al. (2018) suggest that the EP-intensified super ELs, such as in 1972, 1982, and 1997 result from the interaction of an EL and a positive Indian Ocean Dipole (IOD) (also see Behera et al., 2020b, Chapter 5: Air
Sea Interactions in Tropical Indian Ocean: The Indian Ocean Dipole, of this book). They attribute the large SSTA during the 2015 EL, however, to have been likely enhanced by strong decadal variability. Interestingly, an argument was made that the distinction of two types of ELs was due to the inclusion of severe ELs as canonical ELs, of the 1982 and 1997 (Takahashi et al., 2011), along with issues such as the small length of the observed datasets, the continuum of the highest heating in the TP, etc. (e.g., Capotondi et al., 2014; Giese and Ray, 2011; Lian and Chen, 2012; Takahashi et al., 2011). For example, in addition to Takahashi et al. (2011), quite a few new papers such as by Giese and Ray (2011), Johnson (2013) and L’Heureux et al. (2012) have raised problems about the distinctness. Giese and Ray (2011), for instance, warn that the studies that demonstrate that there are two types of EL are based on comparatively short records. Johnson (2013) cautions about the limitations of linear methods in distinguishing between various phenomena. L’Heureux et al. (2012), based on the examination of post-1949 SST datasets, express a doubt on the usefulness of principal component analysis to give a physical explanation of the positive SST trends but Marathe et al. (2015) using REOF technique demonstrated that the EM events are not an artifact associated with the orthogonality constraint and the EOF technique. Newman et al. (2011), carrying out a multivariate study of 42-year observed SST data, propose that ENSO “flavors” are the result of differing combinations of two primarily orthogonal spatial structures that are precursors to canonical and Modoki events of both signs. These precursors can be excited by random weather forcing and later result in SSTA intensification mainly through surface or thermocline feedbacks, respectively. Newman et al. (2011) state that the recent multidecadal increment in the number of EM events comparative to canonical EL events is consistent with multivariate red noise and hence with stationary statistics. Essentially, Newman et al. (2011) also propose

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that while these two ENSO types may be randomly initiated that their observed differences characterize actual dynamical differences in which the leading physical processes depend on preliminary conditions, and result into Modoki ENSOs that may intensify less but also endure more than the canonical ENSOs. Using the Simple Ocean Data Assimilation data for the period 1871
2008, Giese and Ray (2011) suggest that the distribution of the location of EL is random about a central longitude of about 140 W, and is Gaussian. In this background, Marathe et al. (2015), through a multidataset study, show that the distinction of EL types is not a statistical artifact because of the EOF analysis or due to classifying the extreme ELs such as the 1982 and 1997 as ELs. Their analysis shows that there is an uncertainty across the observations-based SST datasets in this regard. Importantly, among other things, they demonstrate that a variety of physical processes that contribute to Bjerknes feedback, and in various thermodynamic processes as well, are distinct between the two types. Marathe et al. (2015) surmise that even if the distribution were unimodal, the difference between the evolution and teleconnections of two types of ELs stems out from the persistence of maximum warm SSTA in central TP for more than a few seasons during the occurrence of an EM, as compared to a much faster transniño signal associated with a canonical EL (Trenberth and Stepaniak, 2001). A number of previous papers by Kug et al. (2009, 2010), McPhaden (2012), Jadhav et al. (2015), and Behera and Yamagata (2010) also document the distinct background and triggering mechanisms, and dynamical and thermodynamical processes that result into different manifestations of the EL types. Based on the observational data and model experiments Lian et al. (2014) documented that westerly wind bursts could be the cause for the development of ELs and for the irregularity and extremes of EMs. Jadhav et al. (2015), through an analysis of reanalyzed/observational datasets and by conducting sensitivity experiments with an ocean general circulation model experiments, show that a unique combination of the strength of anomalous westerlies (e.g., Dewitte et al., 2012) and background conditioning just prior to the occurrence of an EL decide the kind of the ENSO type. Dewitte et al. (2012) propose that the distinct seasonal evolution of EM and EL event is associated to an air-sea mode in the far-EP peaking in Austral summer. Many studies have reported the distinct physical processes that distinguish the EMs from the canonical Els (Behera et al., 2020a, reviewed in Chapter 3: Air
Sea Interaction in Tropical Pacific: The Dynamics of El Niño-Southern Oscillation, of this book). Ashok et al. (2007) emphasize that the wind-induced thermocline variations within the TP are responsible for increasing frequency of the EMs in the recent period. Interestingly, Ashok et al. (2007) also document that in case of EMs, the coupled Kelvin wave from the central TP does not propagate beyond the eastern TP (Xiang et al., 2013). Kug et al. (2009, 2010) suggest that the discharge process during the EM is weak because of its spatial structure of SSTA in the central equatorial Pacific, whereas canonical EL exhibits strong equatorial heat discharge poleward and thus the dynamical feedbacks control the phase transition from a warm event to a cold event. According to Yu et al. (2010), local processes in the central TP associated with the subtropics-related interannual variability play a major role in the EM evolution.

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The distinct recharge oscillator mechanisms between ELs and EMs are discussed by Ren and Jin (2013) and Singh and Delcroix (2013). Marathe et al. (2015) analyzed three parameters such as air-sea coupling strength, wind-thermocline coupling strength, and thermoclinesubsurface temperature coupling strength, which contribute to dynamic coupling strength. They explored that dynamic coupling strength is comparatively dominant for canonical EL events as compared to EM. For EM, dynamic coupling strength is maximum during the initial stages and decreases thereafter. In a similar fashion they studied three parameters that come under thermodynamic coupling strength, for example, SST-latent heat flux (LHF) feedback, SST-shortwave radiation (SWR) feedback, and SST-sensible heat flux (SHF) feedback. Results reveal that SST-LHF feedback is stronger for EL and SST-SHF feedback is dominant in case of EM. However, the SST-SWR feedback is almost similar for the two types of EL. Similar study by Chen et al. (2018) also presents the contrasting characteristics of cloud-radiative feedbacks to the EL and EM. The maximum SSTA of the EL is located in the far-EP. However, the maximum responses of the shortwave and longwave cloud-radiative forcing (SWCRF and LWCRF) to the EL warming are centered near the dateline, showing 70-degree westward shift relative to the maximum SSTA center of EL. In contrast, the maximum responses of the SWCRF and LWCRF to the EM warming show only slight westward shift relative to the maximum SSTA center. Chylek et al. (2018) analyzed monthly tropical near-surface air temperature and Mauna Loa Observatory carbon dioxide (CO2) data within 1960
2016 to identify different carbon cycle responses for two EL types. Significant differences were found between the two types of EL events with respect to time delay of the CO2 rise rate that follows the increase in tropical near-surface air temperatures caused by EL events (Valsala et al., 2014). Their results provide at least a partial support to the existence of two EL types (Yeh et al., 2014; Capotondi et al., 2014) with some distinct characteristics, rather than a single EL phenomenon with a continuum of changing parameters. Based on the monthly LHF from the OAFlux dataset, the monthly SSTs from the Hadley Centre Sea ice and Sea Surface Temperature dataset, and the monthly atmospheric data from National Center for Environmental Prediction/National Center for Atmospheric Research dataset, Liu and Zhou (2018) concludes that distinct differences exist during the mature event phase, with oceanic forcing dominating the equatorial CP during EM events and the area immediately south of the equatorial EP during canonical EL events. In addition, both types of ENSO events suggest the increasing influence of oceanic forcing over the equatorial EP during ENSO event evolutions. Moreover, by using EOF analysis of the monthly TP subsurface ocean temperature anomalies from 1979 to 2015, three leading modes are detected in the TP subsurface temperature. The first mode has a dipole pattern, with warming in the EP and cooling in the western Pacific, and is closely related to traditional EL. The second mode has a monopole pattern, with only warming in the CP subsurface. The third mode has a zonal tripole pattern, with warming in the off-equatorial CP and cooling in the far-EP and western Pacific. Ashok et al. (2017) found similar results using composite method. The second and third modes are both related to EM (see Fig. 4
3). In addition, while EMs involve interannual dynamics, predominantly from a linear sense, the background trend in the TP SST in the last few decades seems to favor

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FIGURE 4–3 First three EOF modes of the TP subsurface temperature anomalies from 1979 to 2015.

the occurrence of Modokis through strengthening of the CP warming. The Modoki signal during the year of 2002 can be construed as one such example (e.g., Ashok et al., 2007, 2012). In fact, the decadal background signal in the TP SST facilitated the manifestation of hitherto unobserved basin-wide warming during the boreal summer of 2009, and later in 2014 (Ashok et al., 2012; Jadhav et al., 2015). Nonlinearities in the ENSO evolution, from the context for different types of ENSO, have been studied by various researchers, in addition to Takahashi et al. (2019), Takahashi and

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Dewitte (2016), etc., as discussed earlier. Johnson (2013) performed self-organizing map (SOM) analysis along with a statistical distinguishability test and determined nine unique patterns that represent the types of ENSO, which include EL, EM, and mixed ENSO patterns. Further, Ren and Jin (2011), using two nonlinear indices, not only suggest that the ELs are different from EM but also successfully demonstrate the nonlinear distinctions in propagation characteristics of the two EL types. These studies provide a substantial support to the argument that the types of the EL are largely different, whereas Kug and Ham (2011) indicate that two types of La Niña (LN) are less distinct than the warm events. Using the Hadley Centre Sea ice and Sea Surface Temperature, Simple Ocean Data Assimilation reanalysis, and various other observed and reanalyzed datasets for the period 1950
2010, Ashok et al. (2017) explored nonlinearities in the subsurface evolutional distinctions between EL and LN types from a few seasons before the onset. Cluster analysis carried out over both boreal summer and winter suggests that while the warm-phased events of both types are separable, several cold-phased events are clustered together. Further, a joint SOM analysis indicates that the evolutionary paths of EM and EL are, broadly, different. Subsurface temperature analysis of EL and EM shows that during an EL, warm anomaly in the west spreads eastward along the thermocline and reaches the surface in the east in March-May of year(0); however, during an EM, warm anomaly already exists in the central TP and then reaches the surface in the east in September-November of year(0). Importantly, Ramesh and Murtugudde (2013) suggest that the earliest lead signal of an EL can be seen 2 years earlier. A composite of subsurface temperature anomalies shows differences in the location of the coldest anomaly as well as evolution at 90% confidence level from a two-tailed Student’s t-test. Thus the EL flavor distinction is potentially predictable at longer leads. Challenges, of course remain, in lead prediction of the distinct teleconnections prior to a season or so (Wang and Hendon, 2007; Jeong et al., 2012; Lee et al., 2018), but can be potentially surmountable with more intense observations as well as better modeling and assimilation.

4.4 Teleconnections There is already a substantial literature that documents that the teleconnections of the two types of EL are different from one another in various regions and seasons. We present here as examples a few results from the composited rainfall anomalies, computed from the climate research unit (CRU) rainfall datasets (Fig. 4
4). The composites for EM (canonical EL) are obtained by averaging rainfall anomalies over 1967
68, 1977
78, 1991
92, 1994
95, 2002
03, and 2004
05 (1957
58, 1965
66, and 1972
73). The importance of the results lies in the fact that we omit the two extreme events, namely 1982
83 and 1997
98 events from the list of canonical ELs that are composited. During the EL summers, the northwestern and northeast Canada receives above-normal precipitation (Fig. 4
4A), while EMs introduce an opposite effect in the northwest (Fig. 4.4B). Similar differences are seen south of the Great Lakes in the United States (Behera et al., 2020a, also reviewed it in Chapter 3: Air
Sea Interaction in Tropical

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FIGURE 4–4 Composite JJA rainfall anomalies (mm/month) based on Climate Research Unit monthly dataset during (A) ELs and (B) EMs. (C and D) Same as (A) and (B), respectively, but for the DJF season. The values above 80% significance from a Student’s two-tailed t-test are hatched with violet color.

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Pacific: The Dynamics of El Niño-Southern Oscillation, of this book). While anomalously deficit rainfall occurs, in general, in the northern hemispheric region of the South America during EL summers, a zone of anomalously surplus precipitation extends from southern Columbia to Ecuador and Northern Peru during EMs. Significantly opposite anomalies are associated with each type of EL in the subtropical and extratropical South America, between 20 S and 28 S. These results are in general agreement with Weng et al. (2007), Ashok et al. (2007), and Yu et al. (2012). In northern Europe, anomalously surplus (deficit) rainfall during boreal summer is observed in Germany and Poland. EM summers are also associated with anomalously surplus rainfall west of the Caspian Sea, and in contrast, surplus rainfall in Spain; the canonical EL does not apparently have these impacts. EL and EM impacts are also opposite in the Middle East, and in southern Africa, (e.g., Preethi et al., 2015). The Sahel region is prone to negative rainfall anomalies relatively more in association with the EMs. Interestingly, while studies suggest a relatively weaker association of canonical EL with the droughts in India during summer (e.g., Kumar et al., 2006; Ashok and Saji, 2007), omission of the 1997 and 1982, two canonical ELs that cooccur with the positive IOD, results in a very strong negative anomaly in rainfall. This in confirmation with earlier studies that the cooccurring positive IODs reduce the deficit rainfall signal associated with the canonical EL (e.g., Ashok et al., 2001; Ashok and Saji, 2007). EM events, on the other hand, are associated with below-normal rainfall in the peninsular India and the foothills of Himalayas, while they are associated with anomalously surplus rainfall along the monsoon trough, in agreement with Ashok et al. (2007, 2019). During ELs, a broad region from Myanmar through eastern China to Northern central China receives reduced summer rainfall but experience exactly opposite conditions during EMs, except in some portions of coastal China. Japan experiences a strong deficit in rainfall during EM summers, in contrast to the anomalously surplus summer rainfall during the canonical ELs. These findings for the East Asia are in broad conformation with the earlier studies such as Weng et al. (2007), Ashok et al. (2007), Kim et al. (2012), Amat and Ashok (2018), Preethi et al. (2015), Pradhan et al. (2011), Feng et al. (2011), Li et al. (2019), Deng et al. (2019), Cai and Cowan (2009), Taschetto et al. (2009), and Wang and Hendon (2007). EL summers are associated with anomalously positive (negative) rainfall in northern (eastern and southern) Australia. Notably, during EM summers, entire Australia suffers below-normal rainfall. During canonical EL (EM) winters, significant positive (negative) precipitation anomalies are seen in the eastern Canada (Fig. 4
4C and D). Even when the impacts are apparently similar, they differ by slight shift in location and intensity; such as the anomalously positive rainfall in the southeastern United States/northeast Mexico that is seen during the canonical ELs as compared to a similar signal in southwestern United States/northwestern Mexico during the EMs (e.g., Weng et al., 2009; Larkin and Harrison, 2005a; Ashok et al., 2007; Yu and Zou, 2013; Tedeschi et al., 2013). While the dry signature associated with the canonical EL is prominent in the tropical South America, the impact of Modoki is more concentrated in the central tropics and subtropics. During the EMs, we see strong negative rainfall anomalies of

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subtropical southern Africa (Preethi et al., 2015). India receives above-normal rainfall during Modoki winters. The rainfall patterns over India and China during canonical ELs are entirely opposite to the signals during EMs. Australia suffers from deficit rainfall conditions during EL and receives normal rainfall to some amount during Modokis. The teleconnection patterns based on the Global Precipitation Climatology Project V2 dataset are also agreeing with that of the CRU. According to Garfinkel et al. (2013), long model integrations suggest that the responses to the two types of EL are similar in both the extratropical troposphere and stratosphere. Both of them lead to a deepened North Pacific low and a weakened polar vortex, and the effects are stronger in late winter than in early winter. By analyzing the observed events during December-February of the years from 1982/83 to 2010/11, Ratnam et al. (2014) investigated remote effects modulating the austral summer precipitation over southern Africa during EL/EM. Based on the composite analyses, it is found that southern Africa experiences significantly below-normal rainfall during EM event compared to EL events. Preethi et al. (2015) also catalog the distinct impacts of the ENSOs, ENSO Modokis, and IOD events on the climates of Africa. During these later events, precipitation anomalies are not so significant although southern Africa as a whole receives below-normal precipitations. The different Walker circulation anomalies of the two ELs result in the distinct patterns in space and time of below-normal precipitation and drought conditions across India and Southeast Asia (Hernandez et al., 2015). Thus their study provides the foundation for explaining mechanisms that can lead to sustained decadal drought patterns throughout monsoon Asia and it confirms the importance of the ENSO characteristics in determining spatiotemporal anomalies of the Asian monsoon system. Recent study by Magee et al. (2017) documents that ENSO Modoki is found to significantly modulate the location of tropical cyclone (TC) genesis, whereby EM and La Niña Modoki lead to a north/south modulation of TC activity (Chen and Tam, 2010; Pradhan et al., 2011; Kim et al., 2009). Interestingly, it seems that the influence of ENSO Modoki on TC activity is greatest in the second half of the TC season February March April. This is an important result as the influence of canonical ENSO on TCs weakens during this time. Dogar et al. (2019) reveal that like conventional ENSO, ENSO Modoki also induces considerable impact over the North Pacific (Atlantic) region and initiates strong Pacific North American (North Atlantic Oscillation, NAO) like response. ENSO Modoki-induced negative/positive NAO-like response and associated changes in Southern Europe and North Africa get significantly strong following increased intensity of EL/La Niña Modoki in the boreal winter. ENSO Modoki magnitude significantly affects tropical and high latitude circulation cells. The positive phase of ENSO (EL) overall strengthens Hadley Cell and a reverse is true for LN phase. The strengthening and weakening of Hadley Cell, associated with ENSO Modoki, induce significant effects over South Asian and Intertropical Convergence Zone (ITCZ) convective regions of Africa through modification of ITCZ/monsoon circulation system. The distinct impacts of the canonical and Modoki ENSOs are also seen in the frequency of typhoons, with studies suggesting that the typhoons in the western TP anomalously increase during EMs (e.g., Chen and Tam, 2010; Pradhan et al.,

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2011). Interestingly, the cooccurring IOD events are suggested to modulate the impacts of the ENSO drivers on the extremality of the typhoons (Pradhan et al., 2011). Such a scale interactions warrant further study for improvement of weather prediction. In addition to this, Doi et al. (2020) reported a kind of teleconnection where Modoki is linked to IOD triggering. The strongest positive IOD event of 2019 was predicted a few seasons ahead with the help of preexisting EM in the TP using a quasireal-time ensemble seasonal prediction system based on the Scale Interaction Experiment-Frontier climate model.

4.5 Climate change The character of ENSO as well as the ocean mean state has altered since the 1990s, which could be because of either natural variability or anthropogenic forcing, or their combined influences (Yeh et al., 2018). By examining control simulations of Kiel Climate Model, run for 4200 years with the present values of greenhouse gases, Yeh et al. (2011) found an evidence of the increasing intensity as well as occurrence frequency of the so-called EM events since the 1990s. This increase of EM during recent decades in the observational estimates could be a part of natural variability in the tropical climate system. On similar lines Ashok et al. (2012) after analyzing observed data and model experiments caution that any further rise in global warming may cause more basin-wide warm events instead of EL, along with more intense LNs and EMs. Li et al. (2017) claim that by suppressing the SSTA growth of EL in the eastern equatorial Pacific, the cold tongue mode (second EOF mode of the SSTA in the TP) contributes to more frequent occurrence of EM under global warming. However, as inferred from CMIP5 models that best capture both EL types, SST variability will become weaker in the future climate, while no robust change of EM-SST is found. Models also reach no consensus on the future change of relative frequency from CP to EP EL (Xu et al., 2017). Freund et al. (2019) from their 400-year reconstruction of EP and CP events gives a valuable long-term context to observational studies by showing that the shift toward more frequent CP events in recent decades and a tendency toward more extreme EP events is unusual relative to the past four centuries. A review by Yeh et al. (2018) sums up the research on the potential teleconnections of the ENSO types in the background of global warming. The study of Di Lorenzo et al. (2010) suggests that the SSTA associated with EM force changes in the extratropical atmospheric circulation and which in turn drives the decadal fluctuations of the North Pacific Gyre Oscillation. Given that EM events could become more frequent with increasing levels of greenhouse gases in the atmosphere, they infer that the North Pacific Gyre Oscillation may play an increasingly important role in shaping Pacific climate and marine ecosystems in the 21st century. Analyzing CMIP4 data, Kwon et al. (2013) documented that linear relationship of ENSO
Pacific Decadal Oscillation (PDO) could be enhanced with the frequent occurrence of EM events. According to Wang et al. (2015) when ENSO and PDO are in phase, the EL/LN-induced dry/wet anomalies are not only intensified over the canonical regions influenced by a typical ENSO event but also expand poleward. If ENSO and the PDO are out of phase, then the associated dry/wet anomaly is dampened or

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disappears. Kao et al. (2018) investigated that decadal variation of spring (February-April) rainfall in northern Taiwan and southern China was significantly related to the PDO during the 20th century but subsequently weakened and now since 1980, the effect of EM-SST has increased, especially during the transition period from the termination of a warm PDO phase to a cold phase in the late 1990s. The ability of real-time forecast for ENSO has not improved gradually and has even decreased during the 21st century (Barnston et al., 2012; Tang et al., 2018). ENSO prediction presently has skill only within half a year in advance; in addition, substantial uncertainties remain. The most important difficulty lies in the so-called “spring predictability barrier” (SPB) occurring in ENSO prediction (Webster and Yang, 1992; Webster, 1995; Zheng and Zhu, 2010). In fact, the emergence of EM has increased the complexity of ENSO prediction (Zheng and Yu, 2017). In ENSO forecasting, prediction of EL types has attracted more attention. The diversity of the ENSO types and a lesser understanding of EM limit the skill of models to simulate and even forecast the ENSO events. Using the monthly mean data of the preindustrial control (“pi-Control”) runs from several coupled model outputs in CMIP5 experiments, Hou et al. (2019) showed that forecast for EM suffered from summer predictability barrier, whereas those for EL are largely interfered by SPB (Wang and Hendon, 2007, Jeong et al., 2012). The preliminary errors most repeatedly causing predictability barrier for EM and EL are revealed and they highlight that the initial sea temperature accuracy in the Victoria mode region in the North Pacific is vital for better forecast of the strength of the EM, whereas that in the subsurface layer of the west equatorial Pacific and the surface layer of the southeast Pacific is of more concern for better forecast of the structure of EM. However, for EL, the former is indicated to change the structure of the event, whereas the latter is shown to be more effective in forecast of the intensity of the event. Interestingly, Pal et al. (2020) were capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times using nonlinear machine learning algorithm called support vector regression, though the amplitude of the EMI is underestimated for the strong events.

4.6 Summary This chapter overviews the EM and its climate impacts including the mechanisms for both types of ENSO, distinctions and similarities among two EL types, the characteristics of EM, the EM-associated teleconnection patterns, the effects of EM on global climate, and related climate features observed and projected for future climate. The proposed mechanisms for developing either type of ENSO might be westerly wind bursts, unique combination of anomalous westerlies, and background conditions or air-sea mode in the far-EP peaking in austral summer. Wind-induced thermocline variations within the TP are supposed to be responsible for increasing frequency of the EMs in the recent period. The discharge process during the EM is weak because of its spatial structure of SSTA in the central equatorial Pacific. Whereas canonical EL exhibits strong equatorial heat discharge poleward and thus the dynamical feedbacks control the phase transition from a warm event to a cold event. Local processes in the central TP associated with the subtropics-related interannual variability play a major role in the EM evolution. The two types of EL differ in a variety of

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physical processes that contribute to Bjerknes feedback, and to various thermodynamic processes as well. Dynamic coupling strength is comparatively dominant for canonical EL events as compared to EM. For EM dynamic coupling strength is maximum during the initial stages but decreases thereafter. Results reveal that SST-wind evaporation feedback is stronger for EL and SST-SHF feedback is dominant in case of EM. However, the SST-SWR feedback is almost similar for the two flavors of EL. Similar study presents the contrasting characteristics of cloud-radiative feedbacks to the EL and EM. However, the maximum responses of SWCRF and LWCRF to the EL warming are centered near the dateline, showing 70-degree westward shift relative to the maximum SSTA center of EL. In contrast, the maximum responses of the SWCRF and LWCRF to the EM warming show only slight westward shift relative to the maximum SSTA center. Teleconnections of both EL and EM across the world show either contrasting features, or the same type of feature with different intensity. For example, during EL summers, northwestern and northeastern Canada receive above-normal precipitation, while EMs introduce an opposite effect in the northwest. While rainfall is anomalously deficit, in general, in the Northern Hemispheric part of South America during EL summers, a zone of anomalously surplus rainfall extends from southern Columbia to Ecuador and Northern Peru during EMs. The EL and EM effects are also opposite in the Middle East, and southern Africa. Interestingly, while studies suggest a relatively weaker association of canonical EL with the droughts in India during summer, omission of the 1997 and 1982, two canonical EL that cooccur with the positive IOD, results in a very strong negative anomaly in rainfall. EM events, on the other hand, are associated with below-normal rainfall in the peninsular India and the foothills of Himalayas, while they are associated with anomalously surplus rainfall along the monsoon trough. During ELs, a broad region from Myanmar through eastern China to Northern central China receives reduced summer rainfall but experiences exactly opposite conditions during EMs, except in some portions of coastal China. Japan experiences a strong deficit in rainfall during EM summers, in contrast to the anomalously surplus summer rainfall during the canonical ELs. EL summers are associated with anomalously positive (negative) rainfall in northern (eastern and southern) Australia whereas during EM summers, entire Australia suffers from below-normal rainfall. ENSO Modoki is found to significantly modulate the location of TC genesis, whereby EM and La Niña Modoki lead to a north/south modulation of TC activity. Interestingly, it seems that the influence of ENSO Modoki on TC activity is greatest in the second half of the TC season (February March April). This is an important result as the influence of canonical ENSO on TCs weakens during this time. The amplitude of ENSO Modoki significantly affects tropical and high latitude circulation cells. EL in general strengthens the Hadley Cell and opposite is true for LN. ENSO Modoki-induced strengthening and weakening of Hadley Cell produce significant impact over South Asian and African ITCZ convective regions through modification of ITCZ/monsoon circulation system. All these conclusions indicate that EM is equally important and challenging for the operational climate prediction community. Climate models show large uncertainties in the future projections of ENSO characteristics such as ENSO intensity, frequency, and even flavor. It is still unclear if magnitude of ELs will increase or decrease and if ELs will occur more or less frequently. If the state-of-the-art

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models are unable to simulate EL correctly, then there is no question of asking about EM. The variety of the ENSO types and a lesser knowledge of EM variability limit the skill of models to simulate and even forecast the ENSO events. Nevertheless, climate models as well as paleo-model results agree that EM will occur more frequently than EL in the global warming and there will be large tendency of having extreme EL events. This review further confirms that nonlinearities are involved in the evolution of EL and EM. In summary, even if the two types of ELs are part of a continuum based on the strength, these two types would be at the far ends of a diverse continuum (e.g., Capotondi et al., 2014), making the distinctions between these two kinds of ELs, and their distinct teleconnections (e.g., Weng et al., 2009, 2007; Yeh et al., 2009; Taschetto et al., 2009), worth studying. In the spirit of the statement given by Toshio Yamagata in Ashok et al. (2007) “We believe that identifying a unique phenomenon with the most appropriate definition, just as new species in biology, is important to promote further research. This is a key issue of the present study,” it does not matter whether we call Modokis as a distinct phenomenon or a type/flavor of ENSO. The important thing is to catalog the distinct evolutions and impacts of each ENSO types, along with the universal similarities across them, and harness the knowledge for better predictions. Even Seow (2018) showcased in a review paper that the discovery of EM in the 2000s was a major progress toward strengthening our understanding of Earth’s climate variability across specific parts of the world and better predictions of EM will help people to prepare for its climatic repercussions.

Acknowledgment We thank the reviewer for constructive comments. We also thank Prof. Toshio Yamagata for initiating the ENSO Modoki research and his continued guidance.

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5 Air
sea interactions in tropical Indian Ocean: The Indian Ocean Dipole Swadhin Kumar Behera, Takeshi Doi, J. Venkata Ratnam APPLICAT ION LABORATORY, RE SEARCH INSTITUTE FOR VALUE-ADDED-INFORM ATION GENE RATION, JAP AN AGENCY FOR MARINE-EARTH SCIENCE AND TECHNOLOGY, YOKOHAMA, JAPAN

5.1 Introduction Earth’s climate and environmental systems are very much affected by the modes of climate variations mostly originating from the tropical oceans. The El Niño/Southern Oscillation (ENSO) of the tropical Pacific is historically recognized as one such phenomenon that profusely influences the socioeconomic conditions of many parts of the world, especially in the developing regions. These climate phenomena also influence the processes in the neighboring ocean basins. For example, the Indian Ocean variability has been thought to be dominantly influenced by ENSO variability that produces a basin-wide warm/cold mode in the sea surface temperature (SST). As discussed in Kosaka et al. (2020, Chapter 6: The Indo-Western Pacific Ocean Capacitor Effect, of this book), this basin-wide mode extends the ENSO effect on East Asia through the Indo-western Pacific Ocean capacitor mode. Here we will discuss the other mode in the tropical Indian Ocean, known as the Indian Ocean Dipole (IOD) mode, that emerges through the air
sea interactions within the Indian Ocean basin and gives rise to global teleconnections. The IOD was discovered a couple of decades ago (Saji et al., 1999; Yamagata et al., 2004), while trying to understand the unusual ocean-atmosphere conditions of the tropical Indian Ocean during 1994 (Vinayachandran et al., 1999; Behera et al., 1999). The variability in the basin is dominated by seasonally reversing monsoon winds. The southwest monsoon winds that dominate the annual cycle produce strong upwelling along the Somali coast. During the transitions of spring and fall, the equatorial westerly winds become stronger and generate the strong equatorial currents known as the Yoshida
Wyrtki jet, which transports the warm waters to the east. In 1994, however, the Yoshida
Wyrtki jet was weaker than normal. While investigating, Vinayachandran et al. (1999) found that the weakening in the jet was actually accompanied by stronger than normal easterly winds in the equatorial region. The coastal Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00001-0 © 2021 Elsevier Inc. All rights reserved.

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regions of Java and Sumatra were affected even more by the southeasterly winds that caused intense upwelling there. The associated SST cooling caused by this dynamic process was further enhanced by evaporative cooling (Behera et al., 1999), giving rise to the cold eastern pole of the positive IOD (pIOD). Behera et al. (1999) further suggested that the moisture transported from the cold pole, associated with anomalous subsidence and moisture divergence there, enhances convections over the monsoon trough region of the Indian subcontinent. That later explained some of the above normal monsoon years in India despite the coevolution of El Niños in the tropical Pacific (Ashok et al., 2001). Investigating the eastern Indian Ocean conditions together with an associated warm pole in the western Indian Ocean during the 1994 anomalous positive event led to the discovery of the IOD (Saji et al., 1999; Yamagata et al., 2004). It was found that the east-west dipole pattern in SST anomalies in the tropical Indian Ocean was coupled with the changes in the surface wind during IOD events. For example, during the peak phase of the pIOD events easterly wind anomalies replace the westerlies when SST is cooler in the east and warmer in the west (Fig. 5
1). These winds are associated with the anomalous Walker circulation (Yamagata et al., 2003) and all these ocean and atmosphere conditions suggest that a Bjerknes-type air
sea interaction operates in the tropical Indian Ocean (Bjerknes, 1969). Several other studies have also discussed this Indian Ocean-coupled phenomenon (Webster et al., 1999; Murtugudde et al., 2000; Feng et al., 2001; Vinayachandran et al., 2002; Xie et al., 2002; Guan et al., 2003; Ashok et al., 2003; Annamalai et al., 2003; Rao and Behera 2005) using observed data and ocean/atmosphere model simulations (Iizuka et al., 2000; Gualdi et al., 2003; Behera et al., 2003; Lau and Nath, 2003; Cai et al., 2005). Most of these studies clearly show the dominant role of the intrinsic air
sea interactions in the Indian Ocean for the development of the IOD. The unique events that were most discussed are the pIODs of 1961, 1994, 2007, and the most recent

FIGURE 5–1 Schematic of the atmospheric and oceanic conditions associated with a positive IOD event. Bluish shades mean cooler SST and reddish shades mean warmer SST. After Behera, S.K., 2019. The Indo-Pacific climate dynamics and teleconnections with a special emphasis on the Indian summer monsoon rainfall. Mausam 70 (1), 87
110.

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pIOD event of 2019. All these events developed without El Niño in the Pacific, putting aside the possibility that these events are ENSO-dependent. Especially the 2019 event has settled the debate to a great extent. In the following sections, we discuss the salient features of the phenomenon and its global teleconnections by reviewing some of the important studies from the past.

5.2 Indian Ocean Dipole as a phenomenon: the unique event of 2019 The IOD was discovered as an ocean-atmosphere coupled phenomenon in the Indian Ocean (Saji et al., 1999). There are typical events that demonstrate the intrinsic ocean-atmosphere variability in the basin, giving rise to the IOD events. Here we show a typical example of the pIOD event in 2019 (Fig. 5
2), which developed independent of El Niño. A clear dipole is seen in the SST anomalies with cold anomalies near Sumatra coast and warm anomalies off Somali coast. The associated easterly wind anomalies in the tropical region clearly demonstrate the typical ocean-atmosphere coupling observed during pIOD events. For a comparison with this pIOD, we have shown the corresponding anomalies of a typical nIOD that occurred in 2016 (Lu et al., 2018). As seen in the lower panel of Fig. 5
2, the SST anomalies in the nIOD event are opposite of the pIOD event with strong westerly wind anomalies on the tropical region.

FIGURE 5–2 Detrended anomalies of SST ( C) and 850 hPa wind (m s21) for August-November derived from NOAA NCEP Global SST analysis (Reynolds et al., 2002) and NCEP/NCAR reanalysis dataset (Kalnay et al., 1996), respectively. The upper panel is for 2019 pIOD and the bottom panel is for 2016 nIOD. NCEP, National Centers for Environmental Prediction; NCAR, National Center for Atmospheric Research; NOAA, National Oceanic and Atmospheric Administration.

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The 2019 pIOD event evolved in May with cold SST anomalies off Sumatra coast and by August it developed into a stronger event (Fig. 5
3). The upwelling favorable winds off Sumatra helped the cold pole to develop and intensify as the easterly wind anomalies on the equator helped the warm pole to develop later in the western Indian Ocean. The amplitude of the dipole mode index (DMI) at that time was close to 1.5 C (Fig. 5
3), which was equal to or higher than that of the most of the previously observed super IODs (like that of 1961, 1994, and 1997) at this stage of the development. The event then peaked in October with a DMI greater than 2 C (Fig. 5
3). The seasonal average of the DMI for the August-November months was the highest among all the events (Fig. 5
4) in the recent past starting from the

FIGURE 5–3 Time series of DMI (dark bars) and equatorial zonal wind index (light-gray bars) for 2019 pIOD (left) and 2016 nIOD (right) events. The DMI is derived from NOAA NCEP Global SST analysis by taking difference between SST anomalies over the western equatorial Indian Ocean (60 E
80 E, 10 S
10 N) and eastern equatorial Indian Ocean (90 E
110 E, 10 S
Equator) as defined in Saji et al. (1999). The equatorial zonal wind index (80 E
110 E, 5 S
5 N) is derived from 850 hPa zonal wind anomalies of NCEP/NCAR reanalyses dataset. The time series are normalized by their standard deviations, 0.51 and 1.88, respectively. NCEP, National Centers for Environmental Prediction; NCAR, National Center for Atmospheric Research; NOAA, National Oceanic and Atmospheric Administration.

FIGURE 5–4 Time series of seasonally averaged DMI (solid bars), Niño3 (green line), and ENSO Modoki Index (EMI, blue line) for August-November months. All time series are normalized with their respective standard deviations.

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year 1982, from when quality SST data were made available owing to the advent of satellite observations. The 2019 pIOD event succeeded two consecutive pIODs observed in 2017 and 2018 (Fig. 5
4). This is one of the rare occasions when three pIODs were observed consecutively. The other occasion was during 2006
08 when such triple consecutive pIODs occurred (Behera et al., 2008). In addition, we had two consecutive pIODs in 2011
12. In fact, we have seen nine pIODs in the last 14 years and this asymmetry in the IOD phase (Cai et al., 2013) indicates a shift in the Indian Ocean conditions favoring more pIODs in the coming decades. Coupled with the oceanic conditions, the atmospheric convections and rainfalls were reduced over Indonesia, parts of Southeast Asia and Australia (Fig. 5
5) during the 2019 pIOD. However, the East African region and the Indian subcontinent received higher than normal rainfall. Opposite rainfall anomalies are seen in most of these regions (Fig. 5
5, lower panel) in response to the oceanic conditions associated with an nIOD (Fig. 5
2, lower panel) during 2016. One of the most important aspects of the 2019 pIOD event is that it evolved without an El Niño in tropical Pacific Ocean. However, a closer look at the upper panel of Fig. 5
2 suggests the possibility of having an El Niño Modoki in the tropical Pacific. Although this oceanic condition is not well coupled with the rainfall anomalies (Fig. 5
5, upper panel), it is quite possible that the surface wind anomalies associated with the El Niño Modoki might have influenced the 2019 pIOD. Doi et al. (2020) reported such a possibility for the first time while trying

FIGURE 5–5 Detrended anomalies of rainfall for August-November derived from CPC CAMS-OPI v0208 monthly gridded precipitation dataset (Janowiak and Xie, 1999). The upper panel is for 2019 pIOD, and the lower panel is for 2016 nIOD. CAMS-OPI, Climate Anomaly Monitoring System-Outgoing Long-wave Radiation Precipitation Index; CPC, Climate Prediction Center.

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to explain the reason behind the success of the prediction of 2019 pIOD, which was predicted from November 2018 by the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) coupled model prediction system. They suggested that a unique development of pIOD preceded by an El Niño Modoki might have helped to increase the lead time of the 2019 pIOD prediction. Like the surface ocean, the subsurface ocean responds to the equatorial winds through the oceanic adjustment process. This was seen in the pIOD of 2019 and nIOD of 2016. During the pIOD event, the thermocline swells near the Sumatra coast owing to the upwelling favorable winds there (Fig. 5
6, left panel). Likewise, the downwelling favorable winds suppress the thermocline on the western Indian Ocean. Opposite thermocline conditions prevail during an nIOD event (Fig. 5
6, right panel). Therefore the subsurface ocean conditions during the IOD events give rise to a subsurface dipole (Rao et al., 2002) with opposite signs during the contrasting phases of IOD. The subsurface adjustment depicted in the dipole structure of the sea-level anomalies (Fig. 5
6) resulted from the propagation of oceanic Rossby and Kelvin waves (Rao et al., 2002; Feng and Meyers, 2003; Yamagata et al., 2004; Rao and Behera, 2005). The wind stress curl associated with the pIOD forces the westward propagating downwelling long Rossby waves north of 10 S as suggested by Rao and Behera (2005). Also, it is shown that the interannual variation of the Seychelles Dome is more closely linked to the IOD than to the ENSO (Tozuka et al., 2010). In contrast, the ENSO influence dominates the south of 10 S of the southern Indian Ocean as suggested by Xie et al. (2002). The Rossby and Kelvin waves play a dominant role in not only shaping the subsurface ocean but also the turnaround of the IOD. Rao et al. (2002) found that the downwelling Rossby waves propagating to the Somali coast during pIOD events pass to the equator as coastal Kelvin waves and then reflect eastward at the equator toward the Sumatra coast. After reaching the Sumatra coast, these equatorial downwelling Kelvin waves reduce the upwelling there and help in turning around the pIOD

FIGURE 5–6 Same as Fig. 5
5 but for sea-level anomalies derived from the NCEP global ocean data assimilation system (Behringer and Xue, 2004). The left panel is for 2019 pIOD, and the right panel is for 2016 nIOD. NCEP, National Centers for Environmental Prediction.

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event to an nIOD event. This turnaround processes rooted in the ocean dynamics could at least partly explain the quasibiennial nature of the IOD variability. Nevertheless, as discussed later, the changes in the Indian Ocean, likely due to global warming, are favoring evolution of more positive types of IOD and hence its variability is losing the biennial flavor owing to those temporal asymmetries. The IOD also exhibits spatial asymmetry leading to the development of a flavor of IOD. Endo and Tozuka (2016) have identified it as IOD Modoki, analogous to the ENSO Modoki in the tropical Pacific (Ashok et al., 2007; Weng et al., 2007; Ashok and Yamagata, 2009); Chapter 4, The El Niño Modoki, of this book (Marathe and Ashok, 2020). The characteristics of Modoki IODs have a close resemblance with the canonical IODs discussed earlier except for that the warm (cold) SST anomalies are confined to the central tropical Indian Ocean, instead of spreading to the western Indian Ocean, during positive (negative) IOD Modoki events. The eastern pole of the IOD Modoki remains same as that of the canonical IOD. Nevertheless, these two types of IOD have different impacts on rainfall, especially over East Africa, since they produce different types of circulation anomalies and teleconnection. Therefore it is important sometimes to treat them separately (Endo and Tozuka, 2016). The initiations and terminations of IODs are of great interest. Several studies indicate the presence of a favorable mechanism in the eastern Indian Ocean that triggers IOD events. For example, in case of the pIOD event, cold SST anomalies, anomalous southeasterlies, and suppression of convection work together in a feedback loop (e.g., Saji et al., 1999; Behera et al., 1999) to trigger an event. However, a few alternatives were also proposed: atmospheric pressure variability in the eastern Indian Ocean (e.g. Gualdi et al., 2003; Li et al., 2003), favorable changes in winds in relation to the Pacific ENSO and the Indian monsoon (e.g., Annamalai et al., 2003), oceanic conditions of the Arabian Sea related to the Indian monsoon (Prasad and McClean, 2004), and influences from the southern extratropical region (e.g., Lau and Nath, 2004). It has also been found from observed data that the equatorial winds in the Indian Ocean are related to variations in pressure and trade winds of the southern Indian Ocean (Hastenrath and Polzin, 2004). Feng et al. (2014) suggested that the Indian Ocean subtropical dipole (Behera and Yamagata, 2001; Morioka et al., 2020, Chapter 9: Interannualto-Decadal Variability and Predictability in South Atlantic and Southern Indian Oceans, of this book) could help the development of the equatorial easterly anomalies through the modulation of the Mascarene high and Indian monsoon trough leading to some of the IOD events. All these studies fall short on one or the other event to answer the failure (or success) of IOD evolution in spite of favorable (or unfavorable) preconditions (e.g., Behera et al., 2006, 2008), for example, no IOD formation in 1979 (Gualdi et al., 2003) and the aborted IOD event of 2003 (Rao and Yamagata 2004). There is also observational evidence that wind and subsurface temperature in the Indian Ocean have signals that could lead to the SST variations associated with IOD (e.g., Horii et al., 2008). The IOD terminations are seen to be linked with intraseasonal oscillations (ISOs)/ Madden
Julian Oscillations originating in the tropical Indian Ocean. Rao and Yamagata (2004) and Rao et al. (2007) have observed strong 30
60-day oscillations of equatorial zonal

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winds prior to the termination of all IOD events, except for the event of 1997. The strong westerlies associated with the ISO excite anomalous downwelling Kelvin waves that deepen the thermocline in the eastern Indian Ocean and terminate the coupled feedback processes, as discussed by Fischer et al. (2005) for the 1994 pIOD event. Similarly, anomalously high ISO activity in the northern summer might explain the aborted pIOD event of 1974 (Gualdi et al., 2003).

5.3 Indian Ocean Dipole and Indian summer monsoon rainfall ENSO has been considered as a major factor in driving the Indian summer monsoon rainfall (ISMR) variability (e.g., Rasmussen and Carpenter, 1983; Behera et al., 2020, review it in Chapter 3: Air
Sea Interaction in Tropical Pacific: The Dynamics of El Niño/Southern Oscillation, of this book). However, that perception has changed in recent years with the observation of a weakening in the ENSO
ISMR relationship (e.g., Kumar et al., 1999) and as the newly discovered IOD emerged as a new driver of ISMR (Behera et al., 1999; Ashok et al., 2001, 2004; Anil et al., 2016; Behera, 2019). Investigating the pIOD event of 1994, Behera et al. (1999) observed that the moisture divergence in the southeast Indian Ocean helped to maintain the monsoon rainfall over India and several parts of southeast Asia. This was further confirmed by Ashok et al. (2001) with a long record of data. They also observed that the emergence of this newfound relationship between IOD and ISMR has in fact reduced the ENSO impacts on the monsoon in recent decades. Behera (2018) noted that the relationship between the Southern Oscillation Index, atmospheric component of ENSO, and ISMR has dramatically changed over the last century. The correlation between them was close to 1 during the first decade of the 20th century as compared to a trivial relationship that we observed during the last decade (Fig. 5
7) of the century. This explains the observed normal monsoon rainfalls over India during some of the strong El Niño years such as 1997, and as recently as of 2006, that cooccurred with pIODs. Our understanding of the historical evolutions of ENSO
ISMR and IOD
ISMR relationships is limited owing to a lack of long-term instrumental observations. Some of the recent studies tried to overcome this issue by applying proxy observations. The historical IOD time series were extracted from the coral records (e.g., Abram et al., 2008; Kayanne et al., 2006). Nakamura et al. (2009), based on the coral records from the Malindi Marine Park in Kenya, suggested that the influence of the ENSO has decreased over the western Indian Ocean in recent decades. The decadal IOD variability related to the warming trend in the western Indian Ocean has raised the mean SST to a threshold value that encourages tropical convections. Those processes and associated changes in the Walker circulation, linked to the global warming, set the preconditions in the mean state to trigger frequent pIOD events and intense short rains in East Africa. Such a decadal variability in the IOD is also discussed in a few previous studies based on recent data derived from ocean reanalysis data and coupled model simulation results (e.g., Ashok et al., 2004; Tozuka et al., 2007).

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FIGURE 5–7 The time series of Southern Oscillation Index (SOI) and Indian summer monsoon rainfall (ISMR) for (A) first and (B) last decades of the 20th century. After Behera, S.K., 2018. Variability and predictability of climate linked to extreme events. World Scientific Series on Asia-Pacific Weather and Climate, vol. 10, pp. 17
32. 10.1142/ 9789813235663-0002.

Abram et al. (2008) based on a coral dipole index have actually found that there has been an exceptional increase in the frequency and strength of IOD events during the 20th century. In addition to the changes in the western Indian Ocean, they suggested that the mode shift in the IOD is associated with enhanced upwelling in the eastern Indian Ocean. The frequent occurrences of the IOD in turn have reduced the ENSO impacts on the Indian subcontinent. The coral indices, therefore, represents an enhanced coupling of the Indian Ocean with the Indian summer monsoon. The ENSO
ISMR correlation was significantly high during early part of the 20th century when the IOD variability was low (Fig. 5
7). This situation led to the discovery of the Southern Oscillation to predict mega-droughts over the Indian subcontinent (Walker, 1924). In recent times, however, mega-droughts and famines are very rare thanks to the increased variability in the Indian Ocean through frequent occurrences of pIODs. These previous studies have in general outlined the influence of the IOD on the Indian summer monsoon, especially suggesting the diminishing role of ENSO in ISMR variability. However, they did not explain if there are any differences in the impacts of IOD on ISMR during opposite phases of the IOD (Fig. 5
8A and B). Behera and Ratnam (2018) discussed those differences in a recent study bringing out the regional symmetries/asymmetries arising from both phases of IOD teleconnections to India. They found that the ISMR response may not necessarily be spatially coherent to both phases of IOD as one would logically infer from other such interactions like the ENSO
ISMR relationship. In contrast to such a logical inference, the region of seasonal monsoon trough in centralwestern part of India actually found to have a symmetric response to both phases of the IOD

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FIGURE 5–8 June-September composite anomalies of SST (shaded) and 850 hPa wind for (A) pIOD and (B) nIOD. The corresponding anomalies for rainfall and 850 hPa divergent wind are shown in (C) and (D). Shown values exceed 90% of confidence level using a two-tailed t-test. Adapted from Behera, S.K., Ratnam, J.V., 2018. Quasiasymmetric response of the Indian summer monsoon rainfall to opposite phases of the IOD. Sci. Rep. 8. doi: 10.1038/s41598-017-18396-6.

with above normal rainfall. Nevertheless, asymmetric responses are observed south and east of that monsoon trough region (Fig. 5
8C and D). These symmetric and asymmetric responses arise from the differences in the teleconnections and moisture distributions over India in response to opposite IOD signals in the tropical Indian Ocean (Fig. 5
8A and B). The anomalous moisture transports to India associated with a pIOD strengthen the monsoon trough and rainfall as discussed in earlier studies (Behera et al., 1999; Ashok et al., 2001; Anil et al., 2016) through an intensified monsoon-Hadley circulation. However, rainfalls are below normal to the south and to the north of the trough. The pIOD teleconnection gives rise to a distinct meridional tripolar pattern in the rainfall anomalies over India. The situation is different during nIODs when the atmospheric responses produce a regional Walker circulation and the moisture distribution favors moisture divergence (convergence) in the eastern (western) part of India. This gives rise to a zonal dipole in the rainfall anomalies with abundant rainfall on the western part and scanty rainfall on the eastern part (Fig. 5
8D). The resulted regional asymmetry is a unique feature associated with

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the ISMR response to IOD. This needs further investigations and modeling studies for better ISMR predictions. However, Behera and Ratnam (2018) have found that such an asymmetric response is not simulated well by coupled general circulation models (GCMs). They tried to simulate the symmetric/asymmetric responses by using a series of regional model experiments with different physical parameterization schemes. It is found that a few combinations of the model parameterization schemes could realistically reproduce the asymmetric response to the opposite phases of IOD. Because of diversity (again not necessarily asymmetric or symmetric to each other) in the parameterization schemes, it is difficult to ascertain what combination would be important for the coupled GCMs engaged in realtime predictions.

5.4 Indian Ocean Dipole interactions with ENSO and ENSO Modoki The interaction among ENSO, IOD, and monsoon is already discussed in the previous section. Here we will discuss the interaction between ENSO and IOD. Though their interaction is not necessarily always linear, some of the previous studies actually discussed their interaction based on linear relationship. For example, the correlation between the time series of Niño3 and DMI, representing ENSO and IOD, is around 0.55 for August-November season (Fig. 5
4). This often leads to the misinterpretation that IODs are generally ENSOdependent (e.g., Baquero-Bernal et al., 2002; Dommenget and Latif, 2002) even though that correlation value only represents about 30% of covariability between the two indices. The correlation does not mean a causal relationship by default and covariability only indicates cooccurrences of some IODs with ENSOs. However, as seen in Fig. 5
4, there are several IOD events that occurred in absence of ENSO, especially the pIODs of 1994, 2011, 2012, 2018, and 2019 occurred without El Niño. The independent nature of the ocean-atmosphere variations is also demonstrated in some of the previous studies. By drawing the composite plots with ocean and atmosphere variables such as the SST, heat content, wind, and rainfall it was noted that pure IOD (in the absence of ENSO variability) and pure ENSO (in the absence of IOD variability) events do show the independent characteristic of both phenomena (Yamagata et al., 2004; Behera et al., 2006). Lack of long-time series of observed data sometimes raises the concern of the statistical robustness of the composites of pure IOD and pure ENSO events. This is why simulated results from coupled GCMs are used to support the robustness of independent signals. The inferences drawn from these long records of the simulated data corroborated the inferences drawn from the limited observed data (cf. Behera et al., 2006). Also, these coupled GCMs became excellent tools to investigate intrinsic nature of pure IOD and pure ENSO by conducting model sensitivity experiments to mimic the real world but in different circumstances in which the ocean and atmosphere are decoupled selectively in one or the other basin. Those experiments clearly showed the intrinsic nature of coupling processes, within each basin, in the developments of pure IOD or pure ENSO events (Behera et al., 2006).

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These results to a great extent are not model-dependent as several other studies (Iizuka et al., 2000; Yu et al., 2002; Fischer et al., 2005; Gualdi et al., 2003; Yamagata et al., 2004; Lau and Nath, 2003; Cai et al., 2005; Behera et al., 2006) have also reported intrinsic IOD variability in the Indian Ocean. In a recent study Ng et al. (2018) examined the intrinsic nature of the IOD using the Community Earth System Model Large Ensemble (CESM-LE) system. They used 35 ensemble members for the present-day climate in addition to the future scenarios to determine how internal variability influences properties of the IOD and their response to a warmer climate in future. It is found that small differences, which are caused by internal climate variability, in the mean thermocline depth of the Indian Ocean generate significant variations in IOD amplitude, skewness in addition to the zonal gradient in SST climatology. The 30% cooccurrences between IOD and ENSO, however, do indicate possible interaction between the two phenomena during the years when both phenomena simultaneously occur in adjacent basins. Interestingly, in Fig. 5
4 we did not find any El Niño event (except for the unusual event of 2009) that occurred without pIOD during this period since 1982; the El Niños of 1982, 1987, 1997, 2006, and 2015 cooccurred with pIODs (Fig. 5
4). This suggests that pIODs do play a role in El Niño developments, especially for the strengthening of the El Niño amplitudes. Therefore Luo et al. (2010), using a state-of-the art coupled model, found that improvement in the seasonal prediction of the Indian Ocean climate variability makes the El Niño prediction more skillful. The zonal Walker circulation is thought to act as an atmospheric bridge connecting the Pacific with the Indian Ocean. Through this atmospheric bridge IOD influences the Darwin pressure variation and hence the Southern Oscillation (Behera and Yamagata, 2003). This relationship was also extended to ocean variability and the air
sea interaction in the tropical Pacific by including the associated ocean dynamics (Izumo et al., 2010). It was found that the signals originating from the nIOD in the Indian Ocean lead to the formation of El Niño in the subsequent year, thereby aiding the long-lead predictability of El Niño. The interactions between IOD and ENSO not only affect their amplitudes but also the periodicity of their inherent variations. Based on the results of a coupled GCM sensitivity experiments, Behera et al. (2006) concluded that the interannual IOD variability is dominantly biennial in the absence of ENSO like variability in the tropical Pacific; that is, when the tropical Pacific Ocean was decoupled from the global atmosphere in their noENSO sensitivity experiment compared to all global coupled control experiment. Conversely when the ocean and atmosphere are decoupled in the tropical Indian Ocean (noIOD experiment), the ENSO variability was protracted to a 5
6-year periodicity (Fig. 5
9). The interaction between ENSO Modoki and IOD is not as much researched as with that of ENSO. Having its origin in the tropical Pacific and connected to the Indian Ocean through the atmospheric bridge, a possible interaction between ENSO Modoki and IOD cannot be ruled out. The ENSO Modoki, which is said to be an ENSO flavor, has distinct characteristics from that of ENSO (please refer to the review of Marathe and Ashok, 2020, Chapter 4: The El Niño Modoki of this book). Especially, the El Niño (La Niña) Modoki is characterized by warm (cold) SST anomalies in the central Pacific flanked by cold (warm) SST anomalies on

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FIGURE 5–9 Global wavelet spectrum of the DMI (left) derived from the noENSO experiment (solid line), control experiment (dashed lines marked by circles), and observation (dashed line marked by squares). Global wavelet spectrum of Niño3 index (right) derived from noIOD experiment (solid line), control experiment (dashed lines marked by circles), and observation (dashed line marked by squares). Adapted from Behera, S.K., Luo, J.-J., Masson, S., Rao, S.A., Sakuma, H., Yamagata, T., 2006. A CGCM study on the interaction between IOD and ENSO. J. Clim. 19, 1688
1705.

either side of the tropical Pacific. The simultaneous correlation between the DMI and ENSO Modoki Index is 0.42 for the August-November season. This simultaneous correlation coefficient is slightly lower than that between the IOD and ENSO time series. Nevertheless, several IOD events cooccurred with ENSO Modokis. For example, one of the strong pIOD events that happened in 1994 was actually accompanied by an El Niño Modoki event. The most recent pIOD of 2019, which is believed to be the strongest on record, was also accompanied by an El Niño Modoki. The other pIODs that accompanied El Niño Modokis are that of 1991, 2012, and 2017 (Fig. 5
4). Considering those cooccurrences, the interaction between them may not be trivial. A recent study (Doi et al., 2020) discussed a possible role of El Niño Modoki in the long-lead forecast of the IOD as discussed in Section 5.6. Future studies will help us to understand the real nature of those interactions and their influences on each other.

5.5 Other teleconnections The IOD influences various parts of the globe through atmospheric teleconnections (Saji and Yamagata, 2003). As shown in the schematic diagram (Fig. 5
10), it affects the Sri Lankan Maha rainfall (Zubair et al., 2003), the Australian rainfall (Ashok et al., 2003; Ummenhofer et al., 2013, 2008), and the South American rainfall (Chan et al., 2008) through various teleconnections besides directly affecting the East African rainfall (Black et al., 2003; Clark et al., 2003; Behera et al., 2005; Marchant et al., 2006; Manatsa et al., 2008;

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FIGURE 5–10 Schematic of the positive IOD
related surface temperature and rainfall anomalies together with some of the teleconnection pathways during boreal summer. Blue (brown) color means colder (warmer) than normal temperature and clouds (hashed lines) indicate wetter (drier) than normal condition. Adapted from Yamagata T., Morioka Y., Behera S.K., Longstanding and new modes of Indo-Pacific climate variations. In: Behera S., Yamagata T., (Eds), World Scientific Series on Asia-Pacific Weather and Climate, Ch. 1, Vol. 7, 2015.

Manatsa and Behera, 2013; Gebregiorgis et al., 2019). In Indonesia, it is found that the low streamflows of Citarum River were associated with pIODs (Sahu et al., 2012). The summer conditions of Eastern Australia are so much affected in some of the super IOD years so that the IOD-induced dryness is said to cause severe forest fires in several parts of the region (Cai et al., 2009; Harris and Lucas, 2019). Recently discovered Ningaloo Niño (Kataoka et al., 2014; Tozuka et al., 2020, Chapter 8: The Ningaloo Niño/Niña: Mechanisms, Relation with Other Climate Modes and Impacts, of this book) is also linked to IOD variability. It is noted that about 30% of Ningaloo Niño events were associated with the pIODs (Zhang et al., 2018). The role of pIODs favoring northerly wind anomalies off the west coast of Australia was confirmed by their Atmospheric General Circulation Model experiments. The IOD also affects the summer conditions over East Asia (Guan and Yamagata, 2003; Guan et al., 2003). The number of heatstroke related deaths in Kanto region of Japan increased when the daily maximum temperature exceeded 35 C in some of the pIOD years (Akihiko et al., 2014). Especially, pIODs and/or La Niñas like that of 1994, 2007, and 2010 (even witnessed in some of the recent pIOD years like 2018) caused stronger heat weaves and a greater number of heatstrokes. In a warmer climate, as expected in future, even a small temperature perturbation related to the tropical climate variability may lead to more extremely hot days and associated fatalities. The teleconnection to East Asia including Japan is seen through the Silk Road processes in which signals from Europe propagates to the region. IOD-induced diabatic heating around

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India, similar to the monsoon-desert mechanism that connects the circulation changes over the Mediterranean Sea/Sahara region with the heating over India (Rodwell and Hoskins, 1996), excites a long atmospheric Rossby wave to the west of the heating and carry the signal to Europe. The westerly Asian jet acts as a waveguide for the eastward-propagating tropospheric disturbances to connect the circulation change around the Mediterranean Sea with the anomalous circulation changes over East Asia (Fig. 5
10). This mechanism seemed to have operated in 1994 to strengthen the equivalent-barotropic Bonin High in Far East (Guan and Yamagata, 2003; Yamagata et al., 2004). It may be related closely to the “Silk Road process” discussed by Enomoto et al. (2003). In addition, a Pacific-Japan like atmospheric pattern (Nitta, 1987) operates from the eastern Indian Ocean to cause enhanced rainfall over Philippines and subsequent subsidence over East Asia including Japan. There is yet another pathway of the IOD teleconnection explaining some of the hottest summers over Europe (light blue arrows in Fig. 5
10). Behera et al. (2012) found that the enhanced rainfall over Indian subcontinent and Western Pacific (Fig. 5
11C and D) associated with IOD (Fig. 5
11A) during the July-August season develops a local baroclinic response to diabatic heating, which then disperses a circumglobal wavetrain (as seen from the contours of meridional wind anomalies in Fig. 5
11C) of equivalent-barotropic nature. The projected signal on the mid-latitude wave-guide is then transmitted to the Western

FIGURE 5–11 (A) July-August composite anomalies of surface temperature ( C) (shaded), 850 hPa wind (m s21) and geopotential height (m) (contour) for the four extreme summers of Western Europe as given in Behera et al. (2012). (C) The corresponding composites of rainfall (mm day21) (shaded) and 300 hPa meridional wind anomalies (m s21). (B) and (D) are same as (A) and (C) but for anomalies related to four extreme events of Eastern Europe. Values shown are above 85% level of statistical confidence from a two-tailed t-test. Adapted from Behera S.K., Ratnam, J.V., Masumoto, Y., Yamagata, T., 2012. Origin of extreme summers in Europe
the Indo-Pacific connection. Clim. Dyn. doi: 10.1007/s00382-012-1524-8.

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Europe like what happened during 2003 as well as 2007 summer heatwaves. A double-jet structure noticed in such events in the zonal winds of the upper troposphere indicates the development of the atmospheric blocking that stops the eastward propagation of northern Atlantic cyclones to Western Europe and thereby supports the long-lasting heatwaves. On the other hand, Eastern Europe hot summers are found to be originated from the diabatic heating caused by rainfall anomalies in northwestern Pacific (Fig. 5
11D). SST anomalies related to evolving La Niñas are seen to be causing those rainfall anomalies (Fig. 5
11B). Warm SST anomalies develop on the northwest Pacific at this time and, associated with these warm anomalies, positive rainfall anomalies are seen in the adjacent landmasses of the Asian Continent. The signals associated with the diabatic heating of these above normal rainfall anomalies disperse to the mid-latitude waveguide and a wavetrain emanates from East Asia to travel around the globe (as seen from the contours of meridional wind anomalies in Fig. 5
11D). Nevertheless, this wavetrain takes a different path to cause a blocking further east (over Eastern Europe) compared to that of the Western Europe case. The snow cover over the Tibetan plateau is also greatly affected by IODs (Yuan et al., 2009) through atmospheric teleconnections. For example, abundant moisture supplies from the Bay of Bengal during pIOD years cause positive precipitation anomalies over the plateau leading to higher than normal accumulation of snow in the region. Yuan et al. (2009) also found that ENSO is irrelevant to the spring/early-summer Tibetan snow cover, whereas the IOD-induced snow cover anomalies can persist long from the early winter to the subsequent early-summer. On the western side of the Indian Ocean, the southeasterly moisture flux anomaly over the Arabian Sea turns anticyclonically during the pIODs and transports more moisture to the southern part of Iran giving rise to excess rainfall during the first part (October-November) of the local rainy season (Pourasghar et al., 2012). Through changes in moisture supplies and atmospheric circulations, IOD is also found to influence the tropical cyclone variability in the northwestern Pacific. The time series of number of tropical cyclones making landfall in China is negatively correlated with the IOD index. This is attributed to IOD-induced shifts in the genesis region of TCs in addition to changes in steering flow at 500 hPa (Qun and Runyu, 2019).

5.6 Indian Ocean Dipole predictions Before the arrival of the coupled GCMs, simple statistical models were used to predict climate variations. Though the statistical models were useful in seasonal predictions, those cannot be reliable sometimes since the assumed relationship between predictor and predictant is not linear and it might change over time. Some of the new generation statistical models overcome the linearity by employing sophisticated artificial intelligence/machine learning techniques. For example, Ratnam et al. (2020) showed high skills in the prediction of the IOD at one to two seasons ahead. On the other hand, the coupled GCMs that mimic the real world (the physics and the dynamics of the system) to a great extent could be the future of

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the prediction system especially for the tropical regions where the coupling of ocean and atmosphere provides a basis for the long-memory of the prediction system. Several coupled GCMs have successfully predicted the IOD on seasonal scales (e.g., Wajsowicz, 2005; Luo et al., 2007, 2008; Wang et al., 2009; Feng et al., 2014; Doi et al., 2017, 2019; Zhao and Hendon, 2009). In spite of those early successes, the coupled GCM-based prediction of IOD events remains a challenge, especially on long-lead times. While the SST predictions in the western and eastern Indian Ocean show some useful skills, prediction skill of the DMI is quite low. The forecast skill drops at 3-month lead for February and May initial conditions and it drops further at 6-month lead for August and November initial conditions. Wang et al. (2009) indicated a July prediction barrier and a severe unrecoverable January prediction barrier, the so-called “winter predictability barrier” (Wajsowicz, 2005, 2007; Luo et al., 2007; Feng et al., 2017; Mu et al., 2017). Luo et al. (2007) found that the unique winter barrier for prediction of IOD and the eastern Indian Ocean SST is related to IOD’s strong phase-locking to the annual reversal of the monsoon. For the predictions initiated in early May, there is a robust bounce-back after July, which suggests that the mature phase of IOD in September-November is more predictable from May initial state (Fig. 5
12). This is probably due to better predictability of the eastern Indian

FIGURE 5–12 Anomaly correlation coefficients between the SINTEX-F1 predicted and the observed DMI as a function of forecast lead time, initiated from (A) February 1, (B) May 1, (C) August 1, and (D) November 1 for the period of 1981
2001 derived from 14 coupled models that participated in CliPAS and DEMETER projects. The lightgray lines indicate the average skills of the individual models controlled by the range between the best and worst coupled model skills shown in vertical lines. The solid dark lines denote the 14 coupled models’ mean prediction skill. Adapted from Luo, J.-J., Lee, J.-Y., Yuan, C., Sasaki, W., Masson, S., Behera, S., et al., 2015. Current status of intraseasonal-seasonal-to-interannual prediction of the Indo-Pacific climate (Ch. 3). In: Behera, S., Yamagata, T. (Eds.), World Scientific Series on Asia-Pacific Weather and Climate, vol. 7.

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Ocean where the SST dominates the mature phase of the IOD. Tanizaki et al. (2017) found that IOD events in which the vertical diabatic term plays an important role in the development of the eastern pole are better predicted than those without such an important contribution. Furthermore, a large asymmetry is reported in the surface warming and cooling intensity in the eastern Indian Ocean between nIOD and pIOD events (Hong et al., 2008): Negative IOD events do not appear to evolve into strong air-sea coupled processes in the Indian Ocean, and therefore their peak magnitudes are weak with low predictability in general (Luo et al., 2007). In spite of the predictability barriers discussed earlier a few of the IOD events are predicted several seasons ahead. Especially, the 2006 and 2019 events were predicted almost a year ahead (Luo et al., 2007, 2008; Doi et al., 2020). The possible source of the 2019 IOD predictability was investigated by Doi et al. (2020) recently. They found that the relatively long-lead time predictability of the 2019 IOD event, overcoming the “winter predictability barrier,” was related to the success in predicting the El Niño Modoki and its atmospheric connection to the eastern Indian Ocean.

5.7 Indian Ocean Dipole in future climate The IOD variability has considerably changed over the past century (Abram et al., 2008; Nakamura et al., 2009) as discussed earlier in Section 5.3. Based on the IOD index derived from coral it is suggested that there has been an exceptional increase in the frequency and strength of IOD events though the 20th century, especially more pIODs are observed in the last couple of decades. As we go to the publication of this article, we have observed three consecutive pIODs during 2017
19. This skewness toward the pIOD is also seen in future projections of Indian Ocean variability (e.g., Cai et al., 2013). Under the global warming projections, the mean climate of the tropical Indian Ocean is expected to change considerably. For example, higher warming in the western Indian Ocean will result in a pIOD like mean state (Cai et al., 2013; Zheng, 2019). This would certainly favor frequent pIODs. However, model uncertainties exist. Cai et al. (2013) reported that the future IOD amplitude and frequency above the timevarying mean state do not show a significant change. Some coupled GCMs projects an increase in the amplitude and/or frequency of IOD variability in the 21st century, but some others show a decrease, and most importantly statistically significant results could not be obtained for most of the changes. One possible physical interpretation of the projected results is that an increase in atmospheric stability could weaken the sensitivity of zonal wind to IOD-induced SST anomalies, offsetting the effect of increased thermocline feedback (Zheng, 2019; Huang et al., 2019). This explains the lack of a statistically significant result for the future IOD variability in the projected results. One of the issues in the projected IOD response to global warming is the intermodel uncertainties in capturing the internal variability in the models besides the model biases. So, it is important to understand the link between mean thermocline in the basin and the IOD variability. Ng et al. (2018) examined the variability in 35 model simulations of present and

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future climate to determine how internal variability influences properties of the IOD and their response to a warmer climate. The climatological thermocline slope in 35 model integrations found to be opposite to what is observed. In the observation the western Indian Ocean thermocline is slightly shallower than the eastern Indian Ocean whereas in CESM-LE results the eastern equatorial Indian Ocean thermocline depth is considerably shallower than that of the western equatorial Indian Ocean. As a result, the mean easterly winds and the zonal SST gradient are stronger in CESM-LE with a strong Bjerknes feedback. This results considerably stronger IODs in the model simulations compared to the observations. Model biases and intermodel differences pose a major issue for reducing uncertainty in projecting such changes in IOD in a warming climate. Nevertheless, those 35 model integrations only differ in their initial conditions by a small round-off error. Therefore, any difference among ensemble simulations could be a result of internal variability. Ng et al. (2018) suggested that the variations in mean thermocline depth are enough to generate significant correlations with IOD amplitude, skewness, and the mean zonal SST gradient.

5.8 Summary This chapter reviews the past and present studies on the IOD, especially to discuss its unique features and associated intrinsic variations in the tropical Indian Ocean. A few past studies have failed to recognize these unique characteristics in spite of the independent mechanisms discussed in the original research article of Saji et al. (1999). The 2019 pIOD event, which developed in absence of an El Niño event, provided an excellent opportunity to revisit the IOD discussion from the perspective of this event in the present time. The results analyzed here clearly indicated the existence of the independent IOD in the Indian Ocean and the unique SST and rainfall anomalies associated with the event. Interestingly, it appears the triggering of this IOD in SINTEX-F coupled GCM prediction was aided by the presence of an El Niño Modoki in 2018 (Doi et al., 2020). This helps in the long-lead predictability of the IOD though its formation and developments remain intrinsic to the Indian Ocean. The IOD influences the regional climate variations in several parts of the world. The 2019 pIOD event is seen to cause droughts and forest fires in many parts of Indonesia and Australia. On the western Indian Ocean side, Kenya, Somalia, and some other parts of East Africa were flooded. India also suffered from several flooding events late in the summer monsoon season. The infestation of exceptionally huge number of locusts swarming in East Africa and South Asia might be associated with those abundant rainfalls caused by the pIOD. Since the event was predicted well in advance (Doi et al., 2020), we hope the success in the prediction will stimulate local government agencies to develop mitigation measures in the future.

Acknowledgment We thank the reviewer for constructive comments. We thank Prof. Toshio Yamagata for his insightful guidance during many of the IOD studies reviewed in this chapter.

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211, Geophysical Monograph Series. Yamagata, T., Behera, S.K., Rao, S.A., Guan, Z., Ashok, K., Saji, H.N., 2003. Comments on “Dipoles, temperature gradient, and tropical climate anomalies. Bull. Am. Meteorol. Soc. 84, 1418
1422. Yamagata T., Morioka Y., Behera S.K., Longstanding and new modes of Indo-Pacific climate variations. In: Behera S., Yamagata T., (Eds), World Scientific Series on Asia-Pacific Weather and Climate, Ch. 1, Vol. 7, 2015. Yu, J.-Y., Mechoso, C., McWilliams, J., Arakawa, A., 2002. Impacts of the Indian Ocean on the ENSO cycle. Geophys. Res. Lett. 29, 1204. Available from: https://doi.org/10.1029/2001GL014098. Yuan, C., Tozuka, T., Miyasaka, T., Yamagata, T., 2009. Respective influences of IOD and ENSO on the Tibetan snow cover in early winter. Clim. Dyn. 33, 509
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9259. Available from: https://doi.org/10.1175/JCLI-D-18-0175.1. Zhao, M., Hendon, H.H., 2009. Representation and prediction of the Indian Ocean Dipole in the POAMA seasonal forecast model. Quart. J. Roy. Meteor. Soc. 135, 337
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321. Available from: https://doi.org/10.1007/s40641-01900152-9. Zubair, L., Rao, S.A., Yamagata, T., 2003. Modulation of Sri Lankan Maha rainfall by the Indian Ocean Dipole. Geophys. Res. Lett. 30 (2).

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6 The Indo-western Pacific Ocean capacitor effect Yu Kosaka1, Yuhei Takaya2, Youichi Kamae3 1

RESEARCH CENTER FOR ADVAN CED SCIENCE AND TECHNOLOGY, T HE UNIVERSITY OF TOKYO, TOKYO, JAPAN 2

METEOROLOGICAL RESEARCH INSTITUTE, JAPAN MET EOROLO GICAL AGENCY, TS UK UB A, JAPAN

3

FACULTY OF LIFE AND ENVI RONMENTAL SCIENCES, UNIVERSITY OF TSUKUBA, TS UK UB A, JAPAN

6.1 Introduction El Niño-Southern Oscillation (ENSO) changes atmospheric circulation globally throughout its lifetime. Atmospheric teleconnections associated with ENSO induce remote climate anomalies worldwide. ENSO’s lifecycle is anchored to the annual cycle with maturing in winter (gray curve in Fig. 6
1; a season refers to that of the Northern Hemisphere in this chapter). Due to the coupling of the ENSO’s seasonality and climatological seasonal cycle as the background, the ENSO teleconnections vary from a season to another. It is natural that early studies focused on winter when ENSO peaks. After the peak in winter, ENSO’s equatorial Pacific sea surface temperature (SST) anomalies dissipate by subsequent summer (gray curve in Fig. 6
1). Since tropospheric circulation anomalies are unlikely to persist beyond a month without external forcing, not much attention has been paid to climate anomalies in summer and fall in the year of ENSO decay. However, studies in the early 21st century identified anomalous anticyclone (AAC) anchored to the tropical western North Pacific (WNP), which persists from winter of El Niño peak through subsequent summer (black curve in Figs. 6
1 and 6
2A and C). This AAC is also called as the Philippine Sea anticyclone, but in summer it extends to the northern Indian Ocean (IO). Efforts have been made to identify the memory of ENSO—likely in the ocean—and key mechanism that enables the AAC to persist after the dissipation of ENSO’s main SST signal. The AAC is coupled to weakening of convective activity in the tropical WNP (Fig. 6
2B and D), suggesting that the former is a Matsuno
Gill-type Rossby-wave response to anomalous diabatic cooling (Gill, 1980; Matsuno, 1966). A notable difficulty in linking this AAC forcing with the potential ocean memory of ENSO is that in summer, the interannual correlation between precipitation and local SST is weakly negative in broad regions of the South China Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00012-5 © 2021 Elsevier Inc. All rights reserved.

141

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1 0.8 0.6 0.4 0.2 0 –0.2 –0.4 –0.6 Niño 3.4 SST

Northern IO SST

–0.8 Tropical WNP SST SCS-tropical WNP SLP

–1 DJF MAM JJA SON DJF MAM JJA SON Developing year Decay year FIGURE 6–1 Lag correlations against Niño 3.4 SST in November-December-January (NDJ) for 1978/79
2017/18, Niño 3.4 SST (gray), Northern IO SST (brown, the domain indicated by the brown box in Fig. 6
2A and C), tropical WNP SST (blue, the domain indicated by the blue box in Fig. 6
2A and C), and SLP over the South China Sea and tropical WNP (black, the domain indicated by the black box in Fig. 6
2A and C). Three-month running averaging has been applied beforehand. Thickened lines and gray shading indicate confidence level .95% in t-test. Based on Hadley Centre Sea Ice and SST (HadISST) version 1.1 (Rayner et al., 2003) and Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) after linear detrending. Vertical lines indicate NDJ as a reference season and June-July-August (JJA) of ENSO decay year.

and Philippine Seas (Fig. 6
3; Lu and Lu, 2014; Wang et al., 2005; Wu et al., 2009). Two schools of studies identified the ocean memory in the tropical WNP and northern IO, respectively. A recent study synthesizes them and proposes an ocean-atmospheric coupled mode called the Indo-western Pacific Ocean capacitor (IPOC) mode, with the AAC as a key component in the atmosphere (Xie et al., 2016). The IPOC mode is associated with a variety of climate impacts in South, Southeast, and East Asia, where more than 3 billion people live. Summer monsoon rainfall provides a major water resource to these regions. Tropical cyclone (TC) activity in the WNP is also related to the IPOC mode. Accurate prediction of the summer monsoon is thus greatly beneficial. The relay of ENSO and the IPOC mode is the most important origin of seasonal predictability, as discussed in the present chapter. This chapter reviews current understanding of the capacitor effect of the Indo-western Pacific Oceans. Besides the mechanism that is briefly introduced in Section 6.2, our emphasis is placed on its regional impacts (Section 6.3) and long-term modulations (Section 6.4). Section 6.5 gives a summary.

6.2 Mechanism and predictability The present section focuses on post-El Niño spring and summer. La Niña is expected to induce anomalies in the opposite polarity. Asymmetry in IO SST-WNP monsoon relationship has been suggested (Lu and Lu, 2015; Song and Li, 2014), which may be applicable to El Niño-La Niña asymmetry (refer to Behera et al., 2020a, Chapter 3, Air
Sea Interaction in

Chapter 6 • The Indo-western Pacific Ocean capacitor effect

(A)

(B)

(C)

(D)

–0.9 –0.8 –0.7 –0.6 –0.5 –0.4 –0.3 0.3 0.4 0.5 0.6 0.7 0.8 0.9

–2.1 –1.7 –1.3 –0.9 –0.5 –0.1 0.1 0.5 (mm day−1)

0.9

1.3

1.7

143

2.1

FIGURE 6–2 Correlation of SST and land surface air temperature (left, shading) and 850
250 hPa mean temperature (right, contours for 6 0.3, 6 0.4, . . . with thick contours for 6 0.4 and 6 0.7) and regressed anomalies of SLP (left, contours for every 0.1 hPa with zero contours thickened) and precipitation (right, shading, mm day21) in March-AprilMay (MAM; A and B) and JJA (C and D) with standardized Niño 3.4 SST anomalies in preceding NDJ for 1978/79
2017/ 18. Stippling indicates confidence level .95% in t-test for SLP (left) and precipitation (right) anomalies. Based on HadISST, JRA-55, and Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997) after linear detrending. Boxes in left panels indicate key regions for Fig. 6
1.

Tropical Pacific: The Dynamics of El Niño-Southern Oscillation, of this book for a review), but it remains to be examined in more detail.

6.2.1 The wind-evaporation-sea surface temperature feedback in the tropical western North Pacific From summer to fall of an El Niño-developing year, an anomalous surface cyclone forms in the off-equatorial WNP as a Rossby-wave response to anomalous diabatic heating in the equatorial Pacific near the dateline (Lau and Nath, 2006; Wang et al., 2013). The reduction of insolation and intensification of surface summer monsoon westerlies east of 140 E cool SST and precondition the AAC formation in the following seasons. Note that SST is predominantly passive against the overlying atmosphere in this process, contributing to their negative local correlation in summer (Fig. 6
3). In fall of El Niño-developing year, the anomalous cyclone transitions to the AAC with negative precipitation anomalies. This takes place in a few weeks, accentuated by intraseasonal variability (Lau and Nath, 2006; Wang and Zhang, 2002). The local cool SST anomalies

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and (A)

(B)

–0.9

–0.7

–0.5

–0.3

–0.1

0.1

0.3

0.5

0.7

0.9

FIGURE 6–3 Local correlation between precipitation and SST for 1979
2018 for MAM (A) and JJA (B) (shading). Calculated after seasonal averaging and linear detrending. Stippling indicates confidence level .95% based on t-test. Contours show climatological mean SST for every 1 C with 20 C and 30 C contours thickened. Dashed contours indicate 29.5 C. Based on HadISST and CMAP after linear detrending.

formed in the preceding seasons are enhanced through the wind-evaporation-SST (WES) feedback (Wang et al., 2000). Namely, the cooler SST suppresses local atmospheric convection, which in turn reinforces the surface AAC that extends northwestward. Northeasterly anomalies along its southeastern periphery intensify the background easterly trade winds, feeding back to the SST cooling. This mechanism sustains the cool SST anomalies and precipitation reduction east of the Philippines and the AAC for at least two seasons (Fig. 6
2A and B and Fig. 6
4A and B; Wang et al., 2003). Obviously, the WES feedback requires background surface easterlies. From spring to summer, the Pacific trade wind region retracts eastward as the climatological monsoon westerlies expand from the northern IO to the tropical WNP (Fig. 6
4). The boundary between mean easterlies and westerlies reaches 140 E, at 15 N, in August. Consistently, negative SST anomalies in post-El Niño

Chapter 6 • The Indo-western Pacific Ocean capacitor effect

145

Surface wind and its speed anom and zonal wind clim (A) D(0)JF(1)

2 m s–1

20ºN 10ºN Eq 10ºS 60ºE

90ºE

120ºE

150ºE

180º

(B) MAM(1)

1.5 m s–1

20ºN 10ºN Eq 10ºS 60ºE

90ºE

120ºE

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180º

(C) JJA(1)

1 m s–1

20ºN 10ºN Eq 10ºS 60ºE

90ºE

–0.5 –0.4 –0.3 –0.2 –0.1

120ºE

0

(m s–1)

0.1

150ºE

0.2

0.3

180º

0.4

0.5

FIGURE 6–4 Anomalies of wind velocity (arrows; note difference scales among panels) and scalar speed (shading) at 10 m level regressed onto standardized Niño 3.4 SST in NDJ for 1978/79
2016/17. December-January-February (DJF) that overlaps reference NDJ (A) and subsequent MAM (B) and JJA (C). Wind speed has been calculated from 6-hourly data. Stippling indicates confidence level .95% for the wind speed anomalies, based on t-test. Contours show climatological zonal wind velocity for 6 1, 6 3, 6 5, . . . m s21. Based on JRA-55 and HadISST after linear detrending.

summer are limited to a narrow domain around 160 E (Fig. 6
2C), questioning the effectiveness of the WES feedback in maintaining the AAC until summer.

6.2.2 The Indian Ocean capacitor 6.2.2.1 Persistent Indian Ocean warming In the interannual timescales, the tropical IO is subject to strong influence from ENSO. In an El Niño-developing year, the IO dipole (IOD) mode often emerges in summer to fall with warming and cooling in the western and eastern equatorial IO, energized through the Bjerknes feedback (Fig. 6
5A
C; Saji et al., 1999). In late fall, the IOD’s eastern lobe dissipates as climatological thermocline deepens in the equatorial southeastern IO and the

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Bjerknes feedback becomes ineffective (refer to Behera et al., 2020b, Chapter 5, Air
Sea Interactions in Tropical Indian Ocean: The Indian Ocean Dipole, of this book for a review). Subsequently, the IO transitions to basin-wide warming in response to El Niño (Fig. 6
5D
F; Tokinaga and Tanimoto, 2004). Induced by El Niño forcing, this IO basinwide warming peaks in spring, a season after the peak of El Niño (Klein et al., 1999). El Niño’s equatorial Pacific SST anomalies dissipate before the subsequent summer due to its internal dynamics (gray curve in Fig. 6
1). By contrast, the IO basin-wide warming often persists until summer. In particular, the northern IO warming peaks in early summer Surface downward shortwave+longwave radiation anom

Atm-forced latent heat flux anom +surface downward radiation anom

(A)

(B)

(C)

(D)

(E)

(F)

(G)

(H)

(I)

(J)

(K)

(L)

JJA(1)

MAM(1)

D(0)JF(1)

SON(0)

Atm-forced latent heat flux anom

–0.5 –0.4 –0.3 –0.2 –0.1

0.1

0.2

0.3

0.4

0.5 (ºC)

FIGURE 6–5 Anomalies of latent heat flux (left), surface downward shortwave plus longwave radiation (middle), and their sum (right) regressed onto standardized Niño 3.4 SST in NDJ for 1978/79
2017/18 (contours for every 3 W m22; positive if downward; zero contour thickened). (A
C) September-October-November (SON) and (D
F) DJF overlapping reference NDJ, and (G
I) MAM and (J
L) JJA subsequent to reference NDJ. In order to estimate anomalous atmospheric forcing to the ocean, contributions of SST anomalies on latent heat flux anomalies have been subtracted by using the bulk formula following Du and Xie (2008). Since sensible heat flux anomalies are weak, those plotted in the right column approximates anomalous atmospheric thermodynamical forcing to the ocean. Stippling indicates confidence level .95% based on t-test. Shading shows SST anomalies ( C) in each season. Based on JRA-55 and HadISST after linear detrending.

Chapter 6 • The Indo-western Pacific Ocean capacitor effect

147

(brown curve in Fig. 6
1), implying the importance of internal feedbacks (Du et al., 2009). Indeed, surface easterly anomalies persist in the northern IO from winter to summer (Fig. 6
4). From spring to summer, these anomalies counteract background winds and weaken evaporation in the eastern tropical IO in spring (Figs. 6
4B and 6
5G) and the northern IO in summer (Figs. 6
4C and 6
5J). Besides, increase in surface downward radiation persists in the northern IO from winter to summer (Fig. 6
5E, H, and K), mainly through cloud reduction and increase in shortwave radiation. These surface heat flux and radiation anomalies sustain warmer SST in the northern IO. The easterly anomalies in the northern IO are part of an equatorially asymmetric circulation pattern (Fig. 6
2A and C) anchored by persistent warm anomalies in the southwestern equatorial IO (Fig. 6
5). In the Bay of Bengal and over the Indian subcontinent, the anomalous easterlies are viewed as part of a westward extension of the AAC, which will be revisited in Section 6.2.3. In the pan-IO scale, the anomalous easterlies turn to northeasterlies in the northwestern tropical IO and then northwesterlies across the equator. An important role of oceanic Rossby waves has been suggested for the formation of this anomalous crossequatorial circulation pattern (Du et al., 2009). El Niño in the mature winter induces a downwelling-ocean Rossby wave in the equatorial southeastern IO through anomalous wind stress curl (Fig. 6
4A; Masumoto and Meyers, 1998). This wave propagates westward across the basin and reaches the southwestern equatorial IO in a season or two (Fig. 6
6), where

Developing year

SSH (every 1 cm) and SST (ºC) anom over 15ºS–5ºS Jan(0) Apr(0) Jul(0) Oct(0) Jan(1)

Decay year

Apr(1) Jul(1) Oct(1) 40ºE

60ºE –0.4

–0.2

80ºE 0

100ºE 0.2

0.4

(ºC)

FIGURE 6–6 Lagged anomalies of monthly sea surface height (contours for every 1 cm; zero contour thickened) and SST (shading,  C) averaged over 15 S-5 S regressed onto standardized Niño 3.4 SST in NDJ for 1979/80
2016/17. Stippling indicates confidence level .95% for sea surface height anomalies based on t-test. Based on HadISST and Ocean Reanalysis System 5 (ORAS5; Zuo et al., 2019) after linear detrending.

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the climatological thermocline is shallow enough so that its perturbation can affect SST (Xie et al., 2002). Warm SST anomalies thus emerge in the equatorial southwestern IO from spring to summer (Fig. 6
2A and C). This warm SST reinforces atmospheric convection above it (Fig. 6
2B and D). The latent heating forces an equatorially antisymmetric surface wind pattern with northeasterly and northwesterly anomalies in the northern and southern IO, respectively. These wind anomalies counteract the background winds, contributing to sustain the warm SST anomalies through the WES feedback (Xie and Philander, 1994). Wu and Yeh (2010) show that the surface heat flux anomalies dominate the oceanic dynamical contribution to the boreal spring peak of the southwestern equatorial IO warming.

6.2.2.2 The Kelvin wave
induced surface Ekman divergence After the dissipation of ENSO, the IO warming takes a major role in exerting climate anomalies in Asia and the Indo-western Pacific. This is supported by a set of coupled general circulation model (CGCM) experiments initialized with basin-wide warm SST anomalies in the tropical IO (Yang et al., 2007). The question is its mechanism. A leading hypothesis involves the atmospheric equatorial Kelvin wave (Xie et al., 2009). The warmer IO is accompanied by a warm atmospheric Kelvin wave as evident in the tropospheric temperature anomalies (Fig. 6
2D). Coupling of equatorial asymmetries in the SST anomalies and background circulation presumably leads to the equatorially symmetric Kelvin wave (Hu et al., 2019). The Kelvin-wave wedge penetrates into the equatorial western Pacific. The surface divergence acting on the Kelvin wave wedge induces anomalous Ekman convergence on, and divergence off, the equator. The latter suppresses atmospheric convection in the off-equatorial western Pacific (Hamouda and Kucharski, 2019), exciting the AAC. The Kelvin wave
induced anomalous Ekman divergence would act to induce equatorially symmetric convection anomalies. However, the convective feedback is operative more efficiently in the Northern Hemisphere where the climatological Intertropical Convergence Zone resides and thus convection is active, leading to stronger convection anomalies and the AAC (Xie et al., 2009). Furthermore, the zonally elongated AAC gains kinetic energy through barotropic energy conversion from the confluent background flow in the tropical WNP associated with monsoon westerlies and trade winds (Hu et al., 2019). This background circulation effect also contributes to the preference of the response on the Northern Hemisphere.

6.2.3 The Indo-western Pacific Ocean capacitor mode The above arguments highlight internal feedback processes within the Indo-western Pacific that sustain the AAC throughout post-El Niño spring and summer. As mentioned earlier, the westward extension of the AAC induces anomalous surface easterlies in the northern IO, which can also contribute to the northern IO warming by counteracting the mean monsoon westerlies in summer (Fig. 6
5J). Additionally, there is shortwave radiation increase due to cloud reduction in the westward extension of the AAC (Fig. 6
5K). In turn, the warm

Chapter 6 • The Indo-western Pacific Ocean capacitor effect

149

northern IO excites the warm Kelvin wave, which suppresses convection over the tropical WNP and reinforces the AAC. This interbasin feedback suggests an internal mode of oceanatmospheric variability in the Indo-western Pacific warm pool region (Kosaka et al., 2013). This view has been further generalized into a seasonally evolving coupled mode in the Indo-western Pacific oceans—the IPOC mode—which encompasses the WES feedback in the tropical WNP and the interbasin feedback (Xie et al., 2016). The IPOC mode is an internal mode that can exist without ENSO forcing (Kosaka et al., 2013; Wang et al., 2018). Here, we examine a “NoENSO” simulation based on Geophysical Fluid Dynamics Laboratory Coupled Model 2.1 (CM2.1; Delworth et al., 2006), where momentum flux to the ocean is overridden with daily climatology of a 1000-year preindustrial control simulation of the same model over the equatorial Pacific, thereby suppressing ENSO. The leading mode of seasonal empirical orthogonal function (EOF) analysis applied to sea-level pressure (SLP) over the northeastern IO-tropical WNP domain for MAM and June-July-August (JJA) features the AAC with tropical WNP SST cooling and suppressed atmospheric convection that persist from spring to summer, and a northern IO-South China Sea warming in summer (Fig. 6
7). Despite the SST anom and

MAM

(A)

(B) Atm-forced latent heat flux + sfc downward rad anom

Precipitation anom and SLP anom

30ºN

30ºN

EQ

EQ

30ºS

30ºS

JJA

(C)

(D)

30ºN

30ºN

EQ

EQ

30ºS

30ºS 60ºE

–1.3

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180º

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1.3

60ºE

90ºE

120ºE

150ºE

180º

–0.25 –0.2 –0.15 –0.1 –0.05 0.05 0.1 0.15 0.2 0.25 (ºC)

FIGURE 6–7 The IPOC mode in a 300-year “NoENSO” simulation with CM2.1. The momentum flux into the model ocean is overridden with daily climatology of a 1000-year preindustrial control simulation of the same model over the equatorial Pacific between the west and east coasts [purple box in (B) and (D), where the flux is fully overridden within the inner box, and blended with model-simulated flux with a weight that linearly tapers to zero at the outer box]. Regressed anomalies onto the leading seasonal principal component of SLP within the blue box in (A) and (C) for MAM and JJA. (A and C) Precipitation (shading), SLP (contours for every 0.1 hPa, with zero contours thickened), (B and D) SST (shading), and atmospherically forced latent heat flux plus surface downward radiation (contours for every 1 W m22 with zero contours thickened) evaluated in the same manner as in Fig. 6
5. Stippling indicates confidence level .95% for anomalies shown by shading, based on t-test.

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lack of ENSO forcing, these anomalies project onto those in post-El Niño spring and summer, confirming internal feedbacks. Similar modes are identified in the ensemble spread of coupled seasonal predictions where ensemble spread of ENSO evolution is weak or moderate (Li et al., 2012; Ma et al., 2017). In spring, the WES feedback is the leading feedback that sustains the AAC, with negative SST anomalies collocated in the tropical WNP. As the monsoon westerly region penetrates into the WNP and the trade wind region shrinks, the cool anomalies weaken and warm anomalies emerge in the northern IO and the South China Sea, suggesting the interbasin feedback. The boundary of these cool and warm anomalies migrates eastward together with the seasonal changes in the climatological wind direction (Xie et al., 2016). Interestingly, the negative anomalies of convection lie also around the boundary of the mean easterlies and westerlies and migrate eastward from spring to summer. The above modeling studies substantiate that the IPOC mode is an internal mode. Nevertheless, ENSO can trigger the IPOC mode. El Niño heats the tropical IO through surface heat flux anomalies and the ocean Rossby waves and thereby sets the IPOC mode in motion while ENSO itself is decaying. This relay forms a major basis of seasonal predictability in the WNP in summer. Ohba and Ueda (2006) and Wu et al. (2010) examine relative contributions of SST anomalies in the IO and tropical WNP for the AAC formation and convection suppression over the tropical WNP. Their atmospheric general circulation model (AGCM) experiments find comparable contributions from SST anomalies in the two basins. In this regard, the interbasin gradient of SST anomalies, rather than in individual basins, can be more essential to the IPOC mode (Terao and Kubota, 2005). It should be noted, however, that convection in AGCMs tend to be overly sensitive to local SST (Wang et al., 2005; Wu and Kirtman, 2007) and that AGCM biases in mean surface winds, if any, can cause evaporation anomalies that are inconsistent with prescribed SST anomalies. Indeed, because it is situated in the mean confluence zone of the monsoon westerlies and the trade winds, a small bias can cause a reversal of the background wind direction.

6.2.4 Seasonal predictions Predicting atmosphere-ocean coupled variability is intrinsically an initial value problem in the coupled system, as opposed to a boundary condition problem of the atmosphere. The coupled IPOC idea matches this view and provides a rationale for seasonal prediction. Studies envisaged the high predictability of WNP summer climate through the IPOC mode (Kosaka et al., 2013; Wang et al., 2013). Until the early 2000s, many modeling centers employed AGCMs for seasonal forecasts, driven by SST from CGCM predictions. Such two-tiered predictions had a certain degree of the predictive capability. However, it had difficulty to represent the key atmosphere-ocean coupled variability in the WNP and IO (Kosaka et al., 2013; Takaya et al., 2017b; Wang et al., 2013, 2005; Xie et al., 2009). The most obvious deficiency is a negative local correlation of SST and precipitation in the tropical WNP in summer (Fig. 6
3; Kobayashi et al., 2005;

Chapter 6 • The Indo-western Pacific Ocean capacitor effect

151

Wang et al., 2005), which many AGCMs could not reproduce. CGCMs are able to reproduce this feature successfully, providing a seasonal prediction skill of the IPOC mode. The latest coupled prediction systems, even if not all, outperform uncoupled forecast systems of a former age (Takaya et al., 2017b; Zhu and Shukla, 2013). One of the foremost improvements by the introduction of CGCMs is the seasonal prediction of the Asian summer monsoon (Kim et al., 2012; Takaya et al., 2017b, among others) by virtue of successful IPOC predictions. Advances of climate modeling and initialization techniques provide the capabilities of representing atmosphere-ocean interaction processes embedded in the IPOC mode, enabling prediction of the IPOC mode a few seasons in advance (Chowdary et al., 2010; Li et al., 2012, 2016b; Takaya et al., 2017b). Fig. 6
8 presents correlation skills of the JJA WNP monsoon index (Wang and Fan, 1999; defined as the difference of 850 hPa zonal wind between 5
15 N, 90
130 E and 22.5
32.5 N, 110
140 E), which measures the AAC, by the latest version of Japan Meteorological Agency (JMA) seasonal prediction system (JMA/MRI-CPS2). The correlation skill was evaluated based on its 10-member hindcasts initialized at each month from January to June during the period of 1980
2010. Zero-month lead predictions (i.e., initialized at June) reach the correlation skill of 0.8. More importantly, the correlation is quite stable when initialized in or earlier than May, and the correlation exceeds 0.6 even when initialized in January. Shin et al. (2019) also find high prediction skill of the JJA AAC in another prediction model CFSv2 initialized in January. The skill is most obvious in ENSO-decay years, illustrating the IPOC predictability. The improvements of the seasonal predictive skill are considered to arise from better predictive skills of remote processes influencing the IPOC variability (IO SST and ENSO), especially for a longer-lead prediction, in addition to the local WES feedback. Chowdary et al. (2017) pointed out that post-El Niño climate anomalies in summer are distinct

1

Correlation score of the JJA WNP monsoon index

0.8

0.6

0.4

0.2

0

January February

March

April

May

June

Initialization month FIGURE 6–8 Correlation of the JJA WNP monsoon index between JRA-55 and the ensemble mean of 10-member hindcasts by JMA/MRI-CPS2 initialized at each month from preceding January to June for 1980
2010.

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depending on decay timing (early decay, decay by mid-summer, or persistence to autumn). This result implies that accurate prediction of the timing of ENSO decay is crucial to better predict the Asian summer monsoon.

6.3 Climate impacts 6.3.1 The Pacific-Japan pattern Climate influence of the IPOC mode extends to East Asian mid-latitudes through the PacificJapan (PJ) teleconnection pattern (Kosaka and Nakamura, 2010; Nitta, 1987). This pattern features dipolar anomalies of precipitation (Fig. 6
2D) and lower-tropospheric circulation (Fig. 6
2C) over the WNP, whose tropical lobe projects onto the AAC. The anomalous diabatic cooling due to suppressed convection over the tropical WNP excites the PJ pattern in post-El Niño summer (Xie et al., 2009). This brings wetter and cooler summer to midlatitude East Asia (Kubota et al., 2016). Climatologically, East Asia and the WNP are under the influence of the surface North Pacific subtropical high, which extends westward with warm and moist tropical air mass. The Meiyu
Baiu rainband forms along its northwestern periphery due to moisture transport from the tropics and upwelling induced by warm mid-tropospheric advection (Sampe and Xie, 2010) or as secondary circulation associated with the upper-tropospheric jet (Horinouchi, 2014). The WNP subtropical high, mid-, to upper-tropospheric jet stream and Meiyu-Baiu rainband gradually shift northward with season. Around mid-July, this combined circulation-convection system abruptly jumps northward and rainfall along the Meiyu-Baiu front weakens, marking the end of the rainy season in central China, Korea, and Japan (Ueda et al., 1995). After this jump, these regions are subject to the hot tropical air of the subtropical high. The dipolar circulation and precipitation anomalies associated with the PJ pattern correspond to a meridional displacement of the above circulation-convection system in midlatitudes relative to its climatological seasonal migration. The AAC or the PJ dipole in post-El Niño summer correspond to anomalous southwestward extension of the WNP subtropical high together with a southward shift of the Meiyu
Baiu rainband. In midlatitude East Asia, this southward shift of the rainband corresponds to a prolonged rainy season with less chance of exposure to the hot tropical air mass, resulting in wet and cool anomalies when seasonally averaged (Fig. 6
9A, C, and E). Meanwhile, the southwestward extension of the subtropical high brings warmer condition in southern China, Taiwan, Indochina Peninsula, and the northern Philippines (Fig. 6
9A, C, and E). South of 20 N, anomalous surface easterlies weaken monsoon westerlies, reducing precipitation in Myanmar and the northern Philippines where climatological westerlies bring moisture (Fig. 6
9B, D, and F). The PJ pattern is an atmospheric internal mode of variability energized through barotropic and baroclinic energy conversions from the background state as well as anomalous diabatic heating. The canonical PJ pattern often features a tripolar circulation anomaly in the

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FIGURE 6–9 Anomalies of land surface air temperature (left, shading), SLP (left, contours for 0.2 hPa with zero contours thickened), precipitation (right, shading) and 850 hPa wind velocity (right, arrows) in June (A and B), July (C and D), and August (E and F) regressed onto standardized SST difference between the northern IO (5
25 N, 40
100 E) and the tropical WNP (10
20 N, 150
170 E) in JJA for 1979
2015. Stippling indicates confidence level .95% for anomalies shown by shading, based on t-test. Based on HadISST, JRA-55 SLP and wind fields, and Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Extreme Events (APHRODITE) precipitation (version 1101EX; Yatagai et al., 2012) and surface temperature (version 1808; Yasutomi et al., 2011), after linear detrending.

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lower troposphere with the northernmost lobe corresponding to the anomalous Okhotsk high development (Hirota and Takahashi, 2012), which is less conspicuous in the IPOC mode due probably to its tropical origin. A modeling study estimates that about 40% of monthly PJ variance, represented by 850 hPa vorticity anomalies in the WNP, is forced by ENSO through the capacitor mechanism (Kosaka et al., 2013). The ENSO-forced PJ pattern is a major origin of seasonal predictability to mid-latitude East Asia.

6.3.2 Extremes in Southeast and East Asia 6.3.2.1 Heat waves Through the meridional dipole of temperature anomalies associated with the PJ pattern, the IPOC mode affects occurrence of extreme temperature events in the WNP and East Asia. Heat waves have broad impacts on society such as energy, economics, and human health, while anomalous coldness can affect agriculture. The positive IPOC mode (i.e., associated with the warmer northern IO) increases frequency of heat waves in southern China especially in late summer (Hu et al., 2012). The AAC reaches southern China and induces anomalous warmth through warm southerly advections, adiabatic warming by descent, higher insolation because of cloud reduction, and weaker evaporative cooling due to precipitation shortage (Fig. 6
9E and F). Indeed, in Deng et al. (2019), the IPOC mode is associated with the third EOF mode of heat wave occurrence in China. The opposite polarity of the IPOC mode (with the cooler northern IO) can increase the probability of heat waves in mid-latitudes. Heat waves in Japan and Korea occur with a northward shift of the North Pacific subtropical high, which is often associated with the PJ pattern. The 2018 heat waves in Japan is an example. It was induced by a combination of the Silk Road teleconnection pattern (a Rossby wavetrain pattern along the Asian jet) and the PJ pattern (Imada et al., 2019), to which the cooler IO driven by decaying La Niña likely contributed.

6.3.2.2 Heavy rains The 1997/98 major El Niño event was followed by the 1998 great Yangtze-river flood (Jiang et al., 2008; Ye and Glantz, 2005), which motivated studies on the capacitor effect. The Meiyu rainband stalled on the Yangtze-river delta caused torrential and lingering rainfall locally. In most of Japan, the end of the Meiyu season was anomalously late or unidentified in 1998. Yangtze-river valley rainfall was also strong in 2016 summer after another massive El Niño in 2015/16 (Sun and Miao, 2018; Yuan et al., 2018), although less devastating compared to 1998 due to counteracting influence from the mid-latitude Silk Road pattern (Li et al., 2017a). The Meiyu
Baiu rainband is coupled to the mid- to upper-tropospheric westerly jet with poleward tilt with height (Chen and Chang, 1980; Matsumoto et al., 1971; Yamazaki and Chen, 1993). The jet is accompanied by ascent due to mid-tropospheric warm-air advection (Sampe and Xie, 2010) or upper-tropospheric potential vorticity advection (Horinouchi,

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2014), which uplifts moisture and sets a favorable environment for convection. Thus migration of the jet can induce shift of the Meiyu
Baiu rainband (Kosaka et al., 2011). The AAC in the tropical WNP (or the PJ dipolar circulation anomalies) in post-El Niño summer corresponds to a southward shift of the westerly jet and thereby the Meiyu
Baiu rainband. This counteracts seasonal poleward migration and brings lingering rainy season in midlatitude East Asia. Besides, the anomalous southerlies in the western flank of the AAC bring a lot of moisture from the tropical Pacific Ocean and fuels the rainfall. In particular, the moist anomalous southerlies hit the eastern flank of the Tibetan Plateau and bring orographically anchored increase of rainfall in post-El Niño summer (Hu et al., 2017) in contrast to seasonally migrating rainfall anomalies in eastern China and to its east (Fig. 6
9B, D, and F). Anomalous diabatic heating, associated with the Meiyu
Baiu rainfall increase, feeds back to the PJ circulation anomalies (Lu and Lin, 2009). The key role of the AAC indicates that the IPOC feedback amplifies precipitation anomalies in the Meiyu
Baiu region, contributing to strong interannual variability of the East Asian summer monsoon. The IPOC mode also provides seasonal predictability of seasonal rainfall in East Asian summer. In fact, some studies report skillful predictions of summer precipitation in the Yangtze river basin initialized at late April to early May (e.g., Li et al., 2016b). Individual events of intense rainfall are often induced by narrow corridors of water vapor transport called atmospheric rivers (Ralph and Dettinger, 2011). Kamae et al. (2017a) identified that atmospheric rivers explain a large fraction of East Asian heavy rainfall events occurred during spring, summer, and autumn especially over the Korean Peninsula and Japan. They further revealed that seasonal frequency of atmospheric rivers and resultant heavy rainfall events increase during post-El Niño summer through the IPOC effect (Fig. 6
10). The AAC in the tropical WNP intensifies moisture transport on its northwestern flank and increases occurrence of atmospheric rivers over East Asia (Kamae et al., 2017b). This favors heavy rainfalls more frequently over the Pacific coast of western Japan during April-to-June and the Korean Peninsula and the western-to-central Japan during July-to-September. The relationship between El Niño, IPOC, and atmospheric rivers sheds light on the predictability of natural disaster risk over East Asia.

6.3.2.3 Tropical cyclones Strong El Niño tends to be followed by reduction of TC genesis in the WNP in summer despite weak and insignificant local SST anomalies (Du et al., 2011; Kubota et al., 2016; Takaya et al., 2017a), leading to a late onset of the WNP typhoon season (Zhao et al., 2019). This tendency contrasts with an El Niño-developing year when TC genesis anomalies feature a dipolar pattern but without robust changes in the total counts. While the observed correlation is moderate due to other factors (e.g., transition of ENSO phases and the Pacific meridional mode; Wang et al., 2019; Zhan et al., 2017), a large ensemble of high-resolution AGCM simulations confirms the relationship robustly (Ueda et al., 2018). Du et al. (2011) attributed the post-El Niño TC reduction to the IO basin-wide warming and the resultant AAC in the tropical WNP, suggesting that it is a manifestation of the IPOC mode. This is consistent

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FIGURE 6–10 Occurrence anomalies of atmospheric rivers (shading) in April-May-June (A) and July-AugustSeptember (B) regressed onto Niño 3.4 SST in preceding NDJ for 1978/79
2006/07. Stippling indicates confidence level .95% based on t-test. Superposed are corresponding anomalies of SLP (contours for 6 0.2, 6 0.4, 6 0.6 hPa K21) and 850 hPa wind velocity (arrows). Based on HadISST and JRA-55. Adapted from Kamae, Y., Mei, W., Xie, S.-P., 2017a. Climatological relationship between warm season atmospheric rivers and heavy rainfall over East Asia. J. Meteorol. Soc. Japan. Ser. II 95, 411
431. https://doi.org/10.2151/jmsj.2017-027.

with the pioneering work on the PJ pattern by Nitta (1987) who traced its energy source to modulations of TC activity in the tropical WNP. However, the causality may not be as simple as Nitta (1987) hypothesized. While anomalous diabatic cooling can excite the AAC, the AAC weakens the monsoon trough over the tropical WNP and suppresses TC genesis, implying

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their coupling. The remote induction of the AAC by the IO basin warming can drive this feedback. The IO effect is supported by a numerical experiment (Zhan et al., 2011). Through an analysis of interannual TC occurrence variability over the WNP in an ensemble AGCM simulation, Mei et al. (2015) find that the leading EOF mode, characterized by an overall increase of the occurrence, is associated with the IPOC mode associated with negative IO SST anomalies, together with Central Pacific El Niño (please refer to the review by Marathe and Ashok (2020), Chapter 4, The El Niño Modoki, of this book). Whereas the summertime TC genesis reduces in a roughly uniform fraction to its climatology, TC occurrence (passage frequency) anomalies are distinct in structure from the corresponding climatology (Kosaka et al., 2013; Xie et al., 2016). The PJ pattern associated with the positive IPOC mode induces westerly anomalies in the subtropical WNP, which changes the steering flow for the TCs and prevents their northwestward travel (Choi et al., 2010). Combination of this track change and the TC genesis anomalies determines the spatial distribution of the TC occurrence anomalies. The seasonal predictability of the IPOC mode and successful simulation of TC activity modulations by high-resolution AGCM simulations motivate seasonal prediction of WNP TC activity (Li et al., 2017b). This is demonstrated by Takaya et al. (2017a) for summer following 2015/16 major El Niño. Their seasonal prediction initialized in April 2016 successfully reproduced the TC-inactive 2016 summer until July. The hindcast skill is confirmed for all the post-El Niño early summers in the satellite era (Fig. 6
11). By contrast, the hindcast fails when IO SST is restored to climatology, supporting the crucial role of the IO.

6.3.3 South Asia The IPOC mode affects South Asian summer monsoon. In post-El Niño summer, a tripolar pattern of precipitation anomalies is observed in South Asia with increased rainfall in Bangladesh and Assam and along the Western Ghats of India, and decreased rainfall west of the Ganges Delta (Fig. 6
2D; Chowdary et al., 2019, 2013). The northern IO warming increases moisture and its transport into Indian subcontinent, especially along the Western Ghats that face mean monsoon westerlies (Chowdary et al., 2015, 2019; Park et al., 2010; Srinivas et al., 2018). In addition, the westward extension of the AAC from the WNP associated with the IPOC mode contributes to the rainfall anomalies. The AAC is coupled with precipitation decrease, and their westward extension brings dry anomalies to the Gangetic Plain (Chowdary et al., 2019). These dry anomalies are interrupted by positive precipitation anomalies around Bangladesh induced by orographic uplift acting on southwesterlies in the northwestern flank of the AAC (Chowdary et al., 2019; Srinivas et al., 2018; Fig. 6
2C and D). It is noteworthy, however, that these seasonal anomalies of precipitation are a residual of strong compensation of intraseasonal anomalies (Fig. 6
9). In post-El Niño summer, South Asian precipitation anomalies are sensitive to whether El Niño persists or decays with subsequent La Niña development, and the pace of decay in the latter case (Chowdary et al., 2017). By contrast, warm anomalies persist throughout post-El Niño summer in most of South Asia, presumably due to the warm northern IO condition (Fig. 6
9).

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(A)

Analysis composite for post-El Niño early summer

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50ºN 40ºN 30ºN 20ºN 10ºN EQ 90ºE 100ºE 110ºE 120ºE 130ºE 140ºE 150ºE 160ºE 170ºE 180º

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FIGURE 6–11 TC occurrence anomalies in May-June-July composited for post-El Niño summers (1983, 1988, 1992, 1998, 2003, and 2010) based on Regional Specialized Meteorological Center (RSMC) Tokyo TC best track data (A) and 10member hindcasts by the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2, initialized in April of each year (B). The anomalies are shown relative to climatology of each dataset for 1981
2010. Adopted from Takaya, Y., Kubo, Y., Maeda, S., Hirahara, S., 2017a. Prediction and attribution of quiescent tropical cyclone activity in the early summer of 2016: case study of lingering effects by preceding strong El Niño events. Atmos. Sci. Lett. 18, 330
335. https://doi.org/10.1002/asl.760. Copyright (c) 2017, Y. Takaya.

6.4 Long-term modulations 6.4.1 Historical changes Studies on interdecadal modulations of the IPOC mode have mostly focused on its relationship with ENSO. While the lagged association of the IPOC mode with ENSO is robust in the satellite era since the late 1970s, the correlation vanishes from the 1950s to 1970s (Xie et al., 2010).

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This change is found in rain-gauge measurements and ship observations (Xie et al., 2010) in addition to atmospheric reanalyses (Wang et al., 2008). Consistently, rainfall anomalies in China are stronger and extend into late summer of post-El Niño years after the 1970s than before (Ye and Lu, 2011). Significant anomalies in heat wave occurrence in post-ENSO summer also emerged after the 1980s (Hu et al., 2013; Wang et al., 2014). This change also has led to an enhancement of seasonal predictability of the IPOC mode (Li et al., 2016a). Longer analyses with instrumental records have consistently revealed interdecadal modulations of the ENSO-IPOC correlation. Chowdary et al. (2012) use shipboard observations along a ship track from the Suez Canal through the northern IO and South China Sea to East Asia since 1870. Kubota et al. (2016) constructed a PJ index based on station SLP since 1897. Correlations of the northern IO SST and the PJ index with ENSO in preceding winter have varied in concert, with an additional era of significant correlations in the early 20th century (Fig. 6
12). Magnitude

(A) Northern IO SST anom regressed onto ND(0)J(1) Niño 3.4 SST JJA(0) SON(0) D(0)JF(1) MAM(1) JJA(1) SON(1) 1900

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FIGURE 6–12 (A) Twenty-one-year sliding regression anomalies of northern IO SST onto Niño 3.4 SST in NDJ. The vertical axis indicates seasonal lag from preceding JJA to subsequent NDJ. Stippling indicates confidence level .90% for sea surface height anomalies based on t-test. (B) Twenty-one-year sliding correlation of the PJ index (black) and the northern IO (brown) in JJA with Niño 3.4 SST in preceding NDJ. Thick black lines and shading indicate confidence level .90% in t-test. Both plotted at year 11 of the 21-year sliding window. Linear detrending has been applied within the 21-year window. The northern IO domain is indicated by the brown box in Fig. 6
2A and C. The PJ index is from Kubota et al. (2016) with the sign flipped so that its positive polarity accompanies the AAC. SST is based on Extended Reconstructed SST version 5 (Huang et al., 2017).

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of the IPOC mode has varied in accordance with the modulations of ENSO forcing (Wang et al., 2018). ENSO magnitude modulations appear key to these interdecadal variations. Higher ENSO-IPOC correlation is accompanied by stronger ENSO and resultant IO warming, which can persist longer against noise. Changes in the association of the IOD and ENSO may play a role (Annamalai et al., 2005). From the 1950s to 1970s, the IOD was unassociated with developing ENSO and instead the IO basin-wide warming emerged already in fall of El Niño-developing year. Annamalai et al. (2005) argue that since the IO basin-wide warming acts to damp El Niño, the earlier development of the basin-wide warming led to earlier decay of El Niño and the IO warming in the 1950s through 1970s. Besides, interdecadal shoaling of the thermocline in the southwestern equatorial IO from the mid- to late-20th century likely contributed to the correlation enhancement by making local SST more sensitive to ocean Rossby waves (Xie et al., 2010). A multi-CGCM study by Tao et al. (2015) concludes that interdecadal changes in the thermocline dome and ENSO intensity and persistence are all responsible for the ENSO-IPOC modulations. These modulations offer a few implications. First, the notable variation instead of a monotonic change revealed by long observational records suggests a dominant role of natural variability for the modulations. Second, this supports the essential role of the IO warming in forming the AAC in post-El Niño summer. Third, these strong modulations pose difficulty in evaluating CGCMs with only a few decades of simulations.

6.4.2 Future changes The dominance of natural variability, however, does not preclude possibility of humaninduced changes embedded in the historical and future modulations. Attribution studies have been performed with the aid of CGCM simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). They are inevitably limited by models’ skill in reproducing the IPOC mode and its association with ENSO. Hu et al. (2014) and Jiang et al. (2017, 2018) focus on the AAC and the SLP anomalies in the WNP in post-El Niño summer and show that in 20%
30% of CMIP5 models the ENSO-AAC relationship is opposite in sign from observations. Tao et al. (2016) examine biases in the IO basin-wide warming/cooling and its capacitor effect to the summer WNP. They find that the tropospheric warming in response to the IO basin warming is shifted westward from the observational counterpart, which contributes to the too weak AAC in summer in multimodel ensemble mean. For model selection to examine future changes, Hu et al. (2014) and Tao et al. (2015) take into account the level of interdecadal modulations in the correlations of the summer AAC and spring IO basin warming, respectively, with preceding ENSO, and find that the correlations are too stable in some models. Gong et al. (2018) evaluate model performance in reproducing the PJ pattern and link the model skill to biases in the background state. Unsurprisingly, a different metric results in different model rating. Studies disagree on future modulations of the lagged ENSO-IPOC relationship despite that they are commonly based on the CMIP5 multimodel archive. Based on selected CGCMs that simulate a realistic level of interdecadal ENSO-IPOC modulations in historical simulations, Hu

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et al. (2014) and Tao et al. (2015) find that the IO warming, tropospheric warming, and the AAC in early post-El Niño summer intensify under warmer climate. Chen et al. (2016) focus on distinct influence of rapidly and slowly decaying ENSO (whose equatorial Pacific SST signal dissipates by summer and persists throughout the decay year, respectively; Srinivas et al., 2019) and find an enhancement of the post-El Niño summer AAC in association with rapidly decaying El Niño in future simulations of selected models. In Jiang et al. (2018) and He et al. (2019), the post-El Niño summer AAC weakens in future simulations. Jiang et al. (2018) attribute it to weaker anomalies in tropical WNP SST and associated interbasin SST contrast, while He et al. (2019) invoke enhancement of dry static stability that suppresses the Kelvin wave excitation in response to the IO warming. The apparently inconsistent results are presumably attributable to different selections of models, which imply lack of robustness in the results. Yet, there are rooms to pursue a unified view. First, while most studies care about ENSO amplitude changes in its peak season, less attention is paid on ENSO’s seasonality and persistence. Second, details in analysis methods can be important. For instance, a higher correlation does not necessarily indicate an enhancement, but only means a tighter association. Second, mixed comparison of changes from early to late historical simulations and from late historical to future simulations complicates interpretation, since anthropogenic and volcanic aerosols can play a role in the historical changes while the future changes will be dominated by greenhouse gas influence.

6.5 Summary Recent studies have identified the IPOC mode that is an internal ocean-atmosphere coupled mode sustained by the WES and interbasin feedbacks in the Indo-western Pacific from spring to summer. This achievement was aided by CGCM simulations where ENSO is artificially controlled, as opposed to observations in which ENSO influence dominates and obscures internal feedbacks. Coupled ensemble hindcasts are also helpful, where ensemble spread is dominated by internal variability. The need of a coupled rather than atmospheric model arises due to the negative local correlation of SST and atmospheric convection over the tropical WNP in summer. The leading modes of IO SST variability are the IO basin mode and IOD. While internal feedbacks in the IOD have been well established, the basin mode was considered as a response to ENSO and thus the naming of “mode” merely meant a statistical one. However, the IPOC feedback indicates that the northern hemispheric portion of the summertime basin mode is indeed part of a coupled mode. The IOD and IPOC modes illustrate dynamic nature of the tropical IO. ENSO is a major driver of the IPOC mode. ENSO triggers the mode through thermodynamical (by anomalous surface heat flux and radiation) and dynamical (by ocean Rossby wave) processes, which involve interbasin influence from the tropical Pacific to the IO via atmospheric bridge. The IPOC mode itself also includes the interbasin coupling as key

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feedback. While recent studies highlight Atlantic-Pacific interbasin coupling, particularly in relation to the so-called “hiatus” in surface global warming (Chikamoto et al., 2016; Kucharski et al., 2016; Li et al., 2015; McGregor et al., 2014), the ENSO-IPOC relay offers another case of interbasin coupling. Like the Atlantic-Pacific coupling, the sequence of seasonally evolving ENSO and the IPOC mode is expected to bring extended predictability, offering hope for predictions beyond a few seasons. However, such IPOC prediction likely requires precise prediction of the timing of ENSO decay as well as its development.

Acknowledgment Yu Kosaka is supported by Japan Society for the Promotion of Science (JSPS) through Grant-in-Aid for Scientific Research (B) JP18H01278 and JP18H01281 and Scientific Research on Innovative Areas 19H05703, and by Japan Science and Technology Agency through Belmont Forum CRA “InterDec.” Yuhei Takaya is supported by JSPS through Grant-in-Aid for Science Research (C) JP17K01223 and Young Scientist (B) JP17K14395. Youichi Kamae is supported by JSPS through Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area) 19H05704 and Young Scientist (B) 17K14388. All authors are supported by Japanese Ministry of Education, Culture, Sports, Science and Technology through Integrated Research Program for Advancing Climate Models.

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7 The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections Ingo Richter1, Hiroki Tokinaga2 1

APPLICAT ION LABORATORY, RE SEARCH INSTITUTE FOR VALUE-ADDED-INFORM ATION GENE RATION, JAP AN AGENCY FOR MARINE-EARTH SCIENCE AND TECHNOLOGY,

YOKOHAMA, JAPAN 2 RE SEARCH INSTITUTE FOR APPLIED M ECHANICS, KYUSHU UNIVERSITY, KASUGA, JAPAN

7.1 Introduction The equatorial Atlantic is subject to sea surface temperature (SST) variations that are most pronounced in the eastern and central part of the basin and occur on interannual timescales. These variations are tightly locked to the annual cycle and have their peak in boreal summer, with a secondary peak in boreal fall (e.g., Lübbecke et al., 2018; Fig. 7
15). Historically, this pattern of variability was discovered after El Niño-Southern Oscillation (ENSO) came to prominence, with first observational evidence emerging in the 1970s and 1980s (Hastentrath and Heller, 1977; Katz et al., 1977; Merle, 1980; Hisard, 1980). Those observations suggested a certain similarity to the ENSO phenomenon in the Pacific, including (for the positive phase) a weakening of the equatorial trades, weakening of the south-equatorial current, strengthening of the north-equatorial counter current and equatorial undercurrent, and deepening of the equatorial thermocline. Due to these similarities, Hisard (1980) described the phenomenon as the Atlantic counterpart to El Niño, and the term “Atlantic Niño” gradually gained currency in the literature. Subsequent studies identified differences between equatorial variability in the Atlantic and Pacific (Zebiak, 1993; Keenlyside and Latif, 2007; Foltz and McPhaden, 2010a; Burls et al., 2012; Richter et al., 2013; Nnamchi et al., 2015). In recognition of these differences, the term “Atlantic zonal mode” (AZM) is now increasingly being used in the literature, and we will follow this terminology. Compared to ENSO, the AZM has a relatively small SST amplitude of about 1K. Nevertheless, AZM events have been associated with far reaching impacts, including European winter weather (Venzke et al., 1999; Rodwell et al., 1999; Scaife et al., 2017) the tropical Pacific (RodriguezFonseca et al., 2009), and the Indian summer monsoon (Pottapinjara et al., 2014, 2016; Kucharski et al., 2016). In this chapter, we will give an overview of the current understanding of equatorial Atlantic variability. Recently, several reviews on the topic have been published, with one Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00008-3 © 2021 Elsevier Inc. All rights reserved.

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focusing on equatorial Atlantic variability alone (Lübbecke et al., 2018) and two discussing it as part of their description of the tropical Atlantic observation network (Bourlès et al., 2019; Foltz et al., 2019). In difference to those reviews, here we will focus more narrowly on the mechanisms of equatorial Atlantic variability, which allows us to go into some more detail. Based on recently published data sets, we will also present some new analysis that hopefully may serve as a reference for future research. Being the work of only two authors, this chapter will certainly be biased toward the authors’ own research findings, though we will strive to give a relatively comprehensive overview, present a spectrum of opinions, and point to controversies and unsolved questions. The data sets and some definitions are briefly described in Section 7.2. In Section 7.3, we present the climatological annual cycle of the equatorial Atlantic, which is needed for a deeper understanding of its interannual variability. Section 7.4 explains the dynamical and thermodynamical mechanisms underlying equatorial Atlantic variability. The linkage of the AZM to other tropical Atlantic patterns of variability is described in Section 7.5. The influence of the AZM on the surrounding continents, and its two-way interactions with remote basins are the topic of Section 7.6. The extent to which numerical models can simulate and predict equatorial Atlantic variability is examined in Sections 7.7 and 7.8, respectively. In Section 7.9, we take a brief look at low-frequency modulation and long-term trends in the equatorial Atlantic. The final Section 7.10 presents a summary of this chapter and outlines some outstanding research questions and conundrums.

7.2 Data description and definition Most of the analysis presented here is based on the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis 5 (ERA5; Hersbach et al., 2018), and on the ECMWF Ocean Reanalysis 4 (ORAS4; Balmaseda et al., 2013), both for the period 1979
2018. Precipitation data are from the Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2018). National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (Kalnay et al., 1996) is used to present a longer time series of equatorial Atlantic SST variability (1948
2018). To illustrate the performance of global climate models (GCMs), we use an ensemble of simulations from the preindustrial control experiment (piControl) of the Coupled Model Intercomparison Project phase 5 (CMIP5). These simulations were performed as part of the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5). The ensemble consists of the following members: ACCESS1-0, ACCESS1
3, bcc-csm1-1, BNU-ESM, CanESM2, CCSM4, CSIRO-Mk3
6-0, EC-EARTH, FGOALS-g2, FGOALS-s2, FIO-ESM, GFDL-CM3, GFDLESM2G, GFDL-ESM2M, GISS-E2-H, GISS-E2-R, HadGEM2-ES, inmcm4, MIROC4h, MIROC5, MIROC-ESM, MPI-ESM-LR, and MPI-ESM-MR. Several averaging areas are used to describe the climate and variability of the tropics. These are summarized in Table 7
1. The following abbreviations are used to denote seasonal averages: MAM (March
May), JJA (June
August), SON (September
October), and DJF (December
February).

Chapter 7 • The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections 173

Table 7–1

Definition of averaging areas used in this study.

Area name Definition ATL3 ATL4

20 W-0, 3 S-3 N 45 -20 W, 3 S-3 N

Niño 3 Niño 4

150-90 W, 5 S-5 N 160 E-150 W, 5 S-5 N

Use Area of maximum SST variability; primary index of AZM Area of maximum surface zonal wind variability; equatorial Atlantic SST gradient (by taking the difference ATL4 minus ATL3) Measure of ENSO SST variability Measure of ENSO surface zonal wind variability

7.3 Climatological annual cycle of the equatorial Atlantic The equatorial Atlantic is subject to a pronounced annual cycle. In March and April, SSTs tend to be uniformly warm across the equatorial basin while the intertropical convergence zone (ITCZ) attains its southernmost position, right over the equator, and the equatorial trades are at their weakest (Fig. 7
1). From April through August, SST in the eastern equatorial Atlantic (measured here by the ATL3 index, Table 7
1) drop from 28.7 C to 24.4 C (Fig. 7
2). In the western and central equatorial Atlantic (as measured by the ATL4 index, Table 7
1), SSTs decrease as well over this period but at a slower rate, resulting in a noticeable zonal SST gradient along the equator in boreal summer (Fig. 7
1). The relatively cool summer SSTs that extend from the African coast to about 20 W are usually referred to as the Atlantic cold tongue. Its development is closely linked to the strengthening of the equatorial trades (Fig. 7
1), which drive cooling both through local Ekman divergence (Busalacchi and Picaut, 1983; Foltz et al., 2003) and through shoaling of the thermocline induced by Kelvin waves forced in the western equatorial Atlantic (Moore et al., 1978; Adamec and O’Brien, 1978; Busalacchi and Picaut, 1983; McCreary et al., 1984). A further contribution comes from the annual cycle of surface net shortwave radiation (Foltz et al., 2003). The strength of the equatorial trades is linked to the latitudinal position of the ITCZ (Fig. 7
3; Richter et al., 2014a), which moves from about 0.5 N in March to 9 N in August. The ITCZ represents the convergence of the southeast and northeast trade wind systems and thus a shift in its latitude is expected to modulate the strength of the trades at any given location. This relation, however, can be complicated by other factors, such as horizontal and vertical momentum transport, and this appears to be the case over the equatorial Atlantic, as will be discussed in Section 7.4. It is of interest that, during boreal spring, the equatorial Atlantic zonal sea level pressure (SLP) gradient is directed eastward from about 35 W to the coast (Fig. 7
4). If the pressure gradient force were the sole driver of surface zonal winds, westerlies should prevail. Observations, however, show easterlies almost all the way to the eastern boundary (Fig. 7
4). Thus, other processes must maintain the easterly surface winds. Based on a simple momentum budget analysis, Richter et al. (2014b) conclude that horizontal momentum transport is insufficient to provide the missing source of easterly momentum, leaving vertical momentum transport as the most likely candidate. This is supported by the fact that, in spring, deep convection is present over the equator, and that horizontal momentum is well mixed in the lower troposphere (Richter et al., 2014b).

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FIGURE 7–1 Climatological SST (color shading;  C), 10-m wind vectors (m s21) and precipitation (blue shading; mm d21) for 1979
2017. (A) MAM and (B) JJA seasons. SST and near-surface winds are from ERA5, precipitation from GPCP.

The vertical sections along the equator of atmospheric and oceanic fields (Fig. 7
5) give an impression of the large-scale circulation changes that occur from MAM to JJA. The trade wind strengthening from April through August has a clear effect on the equatorial thermocline (and, more evidently, on the 23 C isotherm), whose zonal slope steepens. The atmospheric Walker circulation also undergoes profound changes between MAM and JJA, with subsidence strengthening over the eastern equatorial Atlantic and ascent weakening over the western equatorial Atlantic and South America. This, again, is linked to the northward shift of the ITCZ (Fig. 7
1), which is associated with the end of the rainy season over the Nordeste region on one side and the beginning of the West African monsoon on the other. An interesting detail to note in Fig. 7
1 is the lack of collocation between maximum SST and precipitation. In MAM, for example, maximum precipitation is located right over the equator while the maximum SST is centered on 5 S. The reason for this behavior has not been fully explained but it may be related to the strong precipitation maximum over equatorial South America.

Chapter 7 • The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections 175

FIGURE 7–2 Climatological ERA5 SST (blue) and 10-m zonal wind (red) averaged over the ATL3 (solid line) and ATL4 (dashed line) regions, as a function of calendar month.

FIGURE 7–3 ITCZ latitude (degrees north; green line; calculated as the latitude of maximum GPCP precipitation averaged between 45 and 20 W) and ERA5 surface zonal wind averaged over the ATL4 region (blue line; m s21). The winds have been multiplied by a factor 21 to facilitate comparison.

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FIGURE 7–4 ERA5 climatological SLP (black line) and surface zonal wind (green dashed line) along the equator, averaged over March
April
May (MAM) and from 3 S to 3 N.

Fig. 7
2 shows that, from July to September, the surface zonal winds are weakening, before strengthening again until November. This behavior is particularly pronounced in the eastern equatorial Atlantic, where it is accompanied by a secondary shoaling of the thermocline in November (Okumura and Xie, 2006). The timing of the climatological thermocline shoaling is closely related to the phasing of equatorial Atlantic variability patterns as will be explained in the following section.

7.4 Dynamical and thermodynamical elements of equatorial Atlantic variability 7.4.1 Introduction Interannual variability in the equatorial Atlantic displays some similarity to that in the equatorial Pacific. This includes SST anomalies in the central equatorial basin that are preceded by a weakening of the equatorial trades and a deepening of the equatorial thermocline. It is also thought that the Bjerknes feedback1 plays a crucial role in both basins (Zebiak, 1993; Keenlyside and Latif, 2007; Dippe et al, 2017; Lübbecke and McPhaden, 2013, 2017). In difference to ENSO, however, the AZM preferentially occurs in boreal summer is shorter lived and of weaker amplitude. Fig. 7
6 shows the ATL3 time series from 1949 to 2018. Interannual variability is evident in Fig. 7
6 but also extended warm and cold periods during which multiple peaks of the same sign occur. Previous studies have shown that the ATL3 spectrum is generally red, with weak peaks in the 1.5-to-4-year band (Zebiak, 1993; Latif and Grötzner, 2000; Ruiz-Barradas 1 An air-sea coupled feedback on the equator, first hypothesized by Bjerknes (1969), in which initial SST anomalies induce surface wind anomalies that change the thermocline depth and reinforce the initial SST anomalies. See Chapter 3 on ENSO, or Keenlyside and Latif (2007).

(A)

(b)

FIGURE 7–5 Equatorial vertical sections of climatological mass stream function (top; contours in 3 3 109 kg s21 intervals; zero contour thickened), 10-m zonal wind (middle; m s21 with westerly vectors in red and easterly in blue), and ocean subsurface temperature (bottom; color in  C). (A) MAM and (B) JJA. Black lines on the bottom panels indicate the climatological thermocline depth, defined by the maximum of vertical temperature gradient. All fields are derived from ERA5.

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FIGURE 7–6 Time series of National Centers for Environmental Prediction Reanalysis SST anomalies (K) averaged over the ATL3 region for the period 1949
2018. Linear detrending and 3-month running mean have been applied.

et al., 2000; Tseng and Mechoso, 2001). The most pronounced warm event in the 70-year record appears to have occurred in 1963, followed by the most pronounced cold event in late 1964. There is a general impression that variability has weakened in recent decades, which will be briefly discussed in Section 7.9.

7.4.2 Composite evolution of the Atlantic zonal mode The composite evolution of a positive AZM event (Atlantic Niño) is shown in Fig. 7
7. As early as February, there are weak warm SST anomalies on the equator and along the southwest

Chapter 7 • The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections 179

Feb

Mar

Apr

May

Jun

Jul

FIGURE 7–7 February to July composite anomalies of SST (shading;  C), 10-m wind (vector; m s21), and precipitation (green contours at 1, 2, 3 mm day21, and red contours at 21, 22, 23 mm day21) for positive AZM events (Atlantic Niños). Data are from ERA5 (SST and 10-m wind) and GPCP (precipitation). The compositing criterion is based on the JJA mean ATL3 SST exceeding 0.75 standard deviations. The years 1984, 1988, 1991, 1995, 1996, 1999, 2007, and 2008 are selected. The years 1987, 1998, 2006, and 2010 also meet the criterion but are rejected due to being noncanonical (see Section 7.4.6).

African coast, the latter being indicative of a Benguela Niño (see Chapter 10: The Other Coastal Niño/Niña—The Benguela, California, and Dakar Niños/Niñas of this volume). At the same time, northwesterly surface wind anomalies can be seen over the western equatorial Atlantic and, indeed, over the entire southern tropical Atlantic. In March, a more coherent pattern emerges, with surface wind and SST anomalies intensifying on the equator, accompanied by positive rainfall anomalies. The wind and precipitation anomalies continue to grow in April and peak in May. In terms of horizontal distribution, there is a clear east
west asymmetry with wind and precipitation anomalies strongest in the west, and SST anomalies strongest in the east. The SST anomalies reach their peak one month later, in June, when wind and precipitation anomalies are already subsiding. In July, SST anomalies on the equator are decaying but remain quite strong in the southeastern tropical Atlantic. Throughout the evolution, precipitation anomalies are most pronounced over the ocean but there are wet anomalies over equatorial South America from March through July and along the Guinea coast in June and July. The vertical sections of ocean temperature (Fig. 7
8) show how warm anomalies at the depth of the thermocline are already present in February. As cool anomalies develop in the west during the following months, the warm anomalies become confined to the east. The accompanying thermocline anomalies, while relatively subtle, indicate a shoaling of the thermocline. Both the surface and subsurface warm anomalies peak in June. One month later, the subsurface anomalies are much weaker, while the surface anomalies are only slightly weakened. The sequence of events suggests that the climatological upwelling that picks up in May brings the subsurface anomalies to the surface but also gradually erodes the surface warm anomalies once the event runs out of fuel (i.e., wind stress forcing and downwelling Kelvin waves). The vertical atmospheric sections (Fig. 7
8) show that convective anomalies in May and June are strongest at 30 W and to the west, which is consistent with the precipitation anomalies

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Feb

Mar

Apr

May

Jun

Jul

FIGURE 7–8 Equatorial vertical sections of positive AZM composite anomalies from February to July. Zonal wind (top; contours in 0.5 m s21 intervals; zero contour thickened), pressure velocity (top; color in 0.01 Pa s21), 10-m zonal wind (middle; m s21 with westerly vectors in red and easterly in blue), and ocean subsurface temperature (bottom; color in  C). Green and red lines on the bottom panels indicate the climatological and composite thermocline depth, respectively. Atmospheric data are from ERA5, oceanic data from ORAS4.

(Fig. 7
7). This may first seem surprising, as the SST anomalies are stronger to the east, but can be explained by the evolving background state (Fig. 7
1). Closer inspection of climatological SSTs in May and June (not shown though inferable from Fig. 7
2) indicates that, due to the incipient cold tongue formation, SSTs in the eastern equatorial Atlantic drop well below 27 C while at 30 W they are around 27.5 C, close to the deep convective threshold. This is likely one of the reasons why deep convection occurs there, though other factors may contribute.

Chapter 7 • The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections 181

7.4.3 Phase locking An interesting detail in Fig. 7
8 concerns the zonal wind anomalies in the lower troposphere. While the anomalies at the surface peak in May, those at 700 hPa are actually stronger in June and July, consistent with the maximum anomalous SST and SLP gradients during these months. Richter et al. (2017) argue that the northward shift of the ITCZ in June and July is responsible for this behavior. They hypothesize that, as the ITCZ moves northward, vertical transport of horizontal momentum decreases on the equator due to the decline of convective activity, allowing for momentum anomalies to grow in the lower troposphere, above the surface. Irrespective of the mechanism, it is evident that the northward migration of the ITCZ is closely linked to the decline of equatorial surface wind anomalies (Figs. 3 and 11 in Richter et al., 2017; also Fig. 7
7), which in turn leads to the demise of AZM events. Thus, evidence seems to suggest that the seasonal migration of the ITCZ is a key element to the phase locking of AZM events to boreal summer. Other studies have argued for the seasonal shoaling of the thermocline to be an important factor for invigorating the Bjerknes feedback and thus allowing for the growth of SST anomalies (Okumura and Xie, 2006; Keenlyside and Latif, 2007; for the Pacific: Battisti and Hirst, 1989; Philander et al., 1996; Jin, 1997). This argument certainly has merit too but cannot explain why events often decay in July and August, when the thermocline is still shallow. Martin-Rey, Lazar (2019) note that positive (negative) AZM events are often accompanied by wind stress curl anomalies just north of the equator that excite upwelling (downwelling) Rossby waves. These are subsequently reflected into equatorial Kelvin waves at the western boundary and can thus influence the equator. A similar mechanism has been described to explain other aspects of equatorial Atlantic variability (see Section 7.4.6). Martin-Rey, Lazar (2019) argue that these Kelvin waves are crucial to the decay of AZM events as they counteract the original SST anomalies. Their analysis, however, seems to suggest that the reflected Kelvin waves do not reach the ATL3 region until August or September, when the decay is already well underway. More detailed case studies will be necessary to quantify the importance of this mechanism. It should also be noted that the mechanism proposed by Richter et al. (2017) struggles to explain the seasonality of the Atlantic Niño II (Section 7.4.5), as the ITCZ is off the equator during both development and decay of this variability pattern.

7.4.4 Negative Atlantic zonal mode events—symmetry The composites of negative AZM events (Figs. 7
9 and 7
10) suggest they evolve analogously to positive ones, though the former appear to decline more rapidly in July. Lübbecke and McPhaden (2017) performed a detailed observational analysis of negative and positive AZM events and found that they are essential mirror images of each other with respect to amplitude, location, and temporal evolution, consistent with the impression from our composites. Analyzing an oceanic GCM (OGCM) forced with CORE2 surface fluxes, Jouanno et al. (2017) obtained similar results but found that the damping effect of horizontal advection is somewhat more pronounced during warm events.

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Feb

Mar

Apr

May

Jun

Jul

FIGURE 7–9 As in Fig. 7
7 but for negative AZM events. The years 1982, 1983, 1992, 1997, 2004, 2005, and 2015 are selected for the composite.

7.4.5 Atlantic Niño II As noted in Section 7.3, in boreal fall, there is a secondary strengthening of the equatorial trades (Fig. 7
2) and shoaling of the thermocline. Okumura and Xie (2006) show that this secondary shoaling is associated with equatorial Atlantic SST variability that peaks in November and December and name it Atlantic Niño II (AN2). AN2 shows no significant correlations to either the AZM or ENSO (Okumura and Xie, 2006). Like the AZM, it appears to rely on the Bjerknes feedback. Its amplitude is only about half as strong as that of the AZM but may contribute to rainfall anomalies over Africa and also contribute to the development of the tropical Atlantic meridional mode, to be described in Section 7.5.

7.4.6 Noncanonical Atlantic zonal mode events The analogy between the AZM and ENSO has often been emphasized (e.g., Lübbecke and McPhaden, 2017). The evolution of ENSO, in its developing phase, crucially depends on wind stress anomalies over the western and central equatorial Pacific, the so-called westerly wind bursts and easterly wind surges (e.g., Harrison and Vecchi, 1997; Eisenman et al., 2005). These wind events force equatorial Kelvin waves that influence thermocline depth and SSTs to the east. Thus, there is a strong link between anomalous wind stress and SST. This is clearly seen in a scatter plot of JJA wind versus DJF SST anomalies (Fig. 7
11A; also, Richter et al., 2013). Richter et al. (2013) found that such a clear relation is lacking in the equatorial Atlantic. In fact, westerly wind events can be followed by cool SST anomalies and vice versa (Fig. 7
11B). Richter et al. (2013) termed these events noncanonical, to distinguish them from the regular (or canonical) AZM. They suggested, based on an OGCM forced with National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis surface forcing, that noncanonical events develop due to meridional temperature advection in the subsurface ocean. They show that noncanonical warm events are typically preceded by trade wind weakening and SST warming in the northern tropical Atlantic (NTA), accompanied by easterly surface wind anomalies on

Chapter 7 • The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections 183

Feb

Mar

Apr

May

Jun

Jul

FIGURE 7–10 As in Fig. 7
8 but for negative AZM events.

the equator (Fig. 7
11B). This configuration leads to wind stress curl anomalies just north of the equator that induce downward Ekman pumping and subsurface warming. As a result, warm ocean anomalies form just north of the equator, which can be advected into the equatorial region by the mean circulation. These subsurface anomalies then warm the SST through upwelling and vertical mixing. Once an initial SST warming has occurred, it can be amplified by the Bjerknes feedback. Several other studies have analyzed this phenomenon but have arrived at a different conclusion regarding the mechanism underlying noncanonical AZM events (Foltz and McPhaden, 2010a; Lübbecke and McPhaden, 2012; Burmeister et al., 2016). While those authors agree on the importance of wind stress curl anomalies, they suggest that these excite

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FIGURE 7–11 Scatter plot of SST versus surface zonal wind in the equatorial Pacific and Atlantic. The indices plotted are (A) JJA surface zonal wind anomalies averaged over the Niño 4 region versus DJF SST anomalies averaged over the Niño 3 region, and (B) MAM surface zonal wind anomalies averaged over the ATL4 region versus JJA SST anomalies averaged over the ATL3 region. The correlation coefficient between wind and SST is noted in the lower right.

off-equatorial downwelling Rossby waves, which are subsequently reflected into downwelling equatorial Kelvin waves at the western boundary. A heat budget analysis by Burmeister et al. (2016) suggests that most noncanonical events are due to the Rossby wave mechanism, though there may be an additional role for meridional advection in a few cases. Richter et al. (2013), on the other hand, point out that wave reflection at the western boundary is difficult to detect in observations and that it may occur too late to have a significant influence. More detailed analysis will be required to arrive at a consensus. See also the discussion by Lübbecke (2013).

7.4.7 Thermodynamic Atlantic zonal mode While ocean dynamics have long been regarded as central to the understanding of ENSO (e.g., Jin, 1997; Neelin et al., 1998), a few recent studies have challenged this notion (Clement et al., 2011; Zhang et al., 2014) by suggesting that thermodynamic processes can explain some portion of ENSO variability. In a similar vein, Nnamchi et al. (2015, 2016) suggested that the Atlantic Niño can be largely explained by thermodynamic processes. This was mainly based on an analysis of atmospheric GCMs coupled to a slab ocean (slab-ocean control experiment in the CMIP3 archive). Despite the absence of ocean dynamics, these models produce similar patterns as their fully coupled GCM (CGCM) counterparts at about two-third of the amplitude. Nnamchi et al. (2015) suggest that solar radiation and latent heat flux are the crucial components in the development of slab-ocean AZM events. Inspection of the early stages of positive AZM events (Fig. 7
7) does indeed show that weakening of the trade winds is not confined to the equator but covers most of the southern tropical Atlantic. This is indicative of a weakening of the St. Helena high, as noted by several authors (Lübbecke et al., 2010; Richter et al., 2010; Nnamchi et al., 2015; Richter and Doi, 2019). Such a weakening of the southeast trades leads to reduced latent heat flux and anomalous

Chapter 7 • The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections 185

SST warming. Two subsequent studies (Dippe et al., 2017; Jouanno et al., 2017) have questioned the dominance of thermodynamical processes in AZM events on the grounds that these results were mainly obtained from model simulations, which are subject to severe biases (e.g., Richter et al., 2014a). These studies argue that the unrealistically deep equatorial thermocline in biased model simulations render the Bjerknes feedback ineffective, leading to equatorial variability that is dominated by thermodynamic processes. The notion that thermodynamic processes dominate equatorial Atlantic variability is also at odds with several observation-based studies of the ocean heat budget during AZM events (Ding et al., 2010; Planton et al., 2018). On the other hand, turbulent heat fluxes are difficult to measure, as are terms in the ocean heat budget. Thus, while ocean dynamics should not be discounted on account of the work of Nnamchi et al. (2015, 2016), these results do point to a need for a better understanding of the role of thermodynamic processes in AZM events.

7.4.8 Initiation of Atlantic zonal mode events The temporal evolution of AZM events suggests that equatorial wind stress forcing in early spring is key to their development. During this time, SST anomalies tend to be weak (Fig. 7
7), raising the question of what drives the initial wind anomalies. Richter et al. (2014a) show that there is a high correlation between the strength of equatorial surface wind anomalies and the latitude of the Atlantic ITCZ: the further south the ITCZ moves, the weaker the equatorial winds become (Fig. 7
12). This relation, which also holds for the climatological annual cycle (Fig. 7
2), is not only remarkable for its strength (correlation coefficientB0.8) but also for its asymmetry with respect to the equator. While the reason for this asymmetry remains largely unexplored, it is known that the ITCZ position is subject to off-equatorial influences (Kang et al., 2008; Frierson and Hwang, 2012), and that it may be influenced from remote basins (Giannini et al., 2001; Sasaki et al., 2015). This allows other variability patterns to link to the AZM.

FIGURE 7–12 ERA5 surface zonal wind stress (N m22; averaged 3 S-3 N) plotted as a function of Atlantic ITCZ latitude, here defined as the latitude where the zonal average of precipitation, averaged from 40 W to 20 W, attains its maximum. Surface zonal wind stress and precipitation are from ERA5 and GPCP, respectively (black line), and from the CMIP5 ensemble (blue line). The thin blue lines indicate the standard deviation of the interensemble spread.

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While coupled air
sea processes undoubtedly are important to the AZM, Richter et al. (2014a) and Richter and Doi (2019) argue that internal atmospheric variability also plays a significant role. Using an atmospheric GCM (AGCM) forced with the observed climatological annual cycle of SST (i.e., no SST anomalies anywhere), Richter and Doi (2019) show that anomalous wind events still preferentially occur in MAM though the amplitude is about onefourth of that of the control experiment with observed SST. The equatorial wind anomalies are part of a much larger pattern that, for westerly wind events, includes a southward shift of the Atlantic ITCZ, weakening of the St. Helena high, strengthening of the Azores high, and high-pressure anomalies over the eastern equatorial Pacific. While such anomalies can be the seed for AZM events, it is clear that coupled feedbacks, in particular the Bjerknes feedback, will be needed to develop the observed strength. Brandt et al. (2011) argue for the importance of intrinsic variability in the ocean. The deep equatorial Atlantic features vertically alternating zonal jets that vary at a period of 4.5 years (Lebedev et al., 2007) and display upward energy propagation (Bunge et al., 2008). Brandt et al. (2011) suggest that this variability pattern in the deep ocean, associated with the basin mode, may be able to imprint on the surface circulation and thus promote interannual variability in the 4.5-year range. Thus, there is a suggestion that intrinsic oceanic variability may be able to influence the AZM.

7.5 Linkage to tropical Atlantic variability As suggested in Section 7.4.8, the AZM may be closely linked to other modes of tropical Atlantic variability. Here we examine these links in more detail.

7.5.1 Link to the meridional mode The Atlantic meridional mode (AMM) is a pattern of variability characterized by subtropical SST anomalies of opposite sign straddling the equator (Fig. 7
13; Chang et al., 1997; RuizBarradas et al., 2000). It is thought to arise from coupled air
sea interaction in the form of the wind stress-evaporation-SST (WES) feedback (Xie and Philander, 1994). In the WES feedback, an initial warm (cold) anomaly in one hemisphere is accompanied by low (high) SLP anomalies. This leads to a cross-equatorial flow whose zonal component, due to the coriolis force, has opposite signs north and south of the equator. Thus the wind anomalies reinforce the trade winds in one hemisphere and weaken them in the other, leading to latent heat flux anomalies that amplify the preexisting SST anomalies. Servain et al. (1999) noted a correlation between indices of the AMM, ATL3 thermocline depth, and ITCZ latitude in observations. They suggested that anomalous shifts in ITCZ latitude lead to shifts in the entire trade wind system, which induces zonal wind anomalies on the equator. Foltz and McPhaden (2010b) argued that the AMM exerts two competing influences on the AZM. During positive AMM events, characterized by warming in the NTA, the immediate impact on the equator is a strengthening of the zonal winds and Kelvin waveinduced cooling in the east. North of the equator, however, the AMM is often accompanied

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FIGURE 7–13 Anomalies of SST (shading; K) and near-surface winds (vectors; reference 0.5 m s21) from the first mode of a maximum covariance analysis (MCA) for MAM. The MCA was calculated from linearly detrended ERA5 data. The pattern depicts the positive phase of the AMM.

by wind stress curl anomalies that induce downwelling Rossby waves, which upon reflection on the western boundary, lead to warming on the equator. This mechanism has also been discussed in the context of noncanonical events (see Section 7.4.6) and the termination of AZM events (Martin-Rey, Lazar, 2019). Murtugudde et al. (2001) suggested that the link between AMM and AZM described by Servain et al. (1999) is only strong during certain periods, implying that, on average, the link between them is weaker than suggested by Servain et al. (1999). The relation between equatorial trades and ITCZ latitude, however, seems to be very close even for periods suggested to have a weak link by Murtugudde et al. (2001), as suggested by Richter et al. (2014a) and Fig. 7
12. It should be noted, on the other hand, that these results concern only the link between ITCZ latitude and equatorial surface winds, not the relation between AMM and AZM per se. As discussed in Section 7.4.3, wind anomalies are not a very good predictor of AZM events due to the existence of noncanonical events, which, in turn, may also be related to the wave-reflection mechanism. Due to the delayed wave feedback, the exact temporal evolution of the AMM may be crucial for the way it influences the AZM. An early onset of wind stress curl anomalies may have a significant impact on the developing phase of AZM events and even change their sign, while a later onset may just contribute to the decay of events.

7.5.2 Link to the Benguela Niño The Benguela Niño (Oettli et al., 2020 review it in Chapter 10: The Other Coastal Niño/Niña—The Benguela, California, and Dakar Niños/Niñas, of this book) is a variability pattern characterized by SST anomalies along the southwestern coast of Africa that occurs on interannual timescales. While the maximum variability occurs along the coast, SST

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anomalies extend into the ocean interior (the March and April panels of Figs. 7
7 and 7
9 convey a good sense of this mode). Variability in the region is due to upwelling anomalies, which, in turn, can be generated by equatorially forced Kelvin waves that are transmitted into coastally trapped waves at the eastern boundary (e.g., Shannon et al., 1986). This suggests an obvious pathway for a link between the AZM and Benguela Niños because Kelvin waves that are generated predominantly in the western and central equatorial Atlantic should influence, in sequence, both the eastern equatorial Atlantic and the coast of southwest Africa. While the AZM and Benguela Niño SST time series are indeed well correlated (Lübbecke et al., 2010), the Benguela Niño typically leads the AZM, leading to an apparent conundrum that has not been fully resolved. More discussion on this can be found in Chapter 10, The Other Coastal Niño/Niña—The Benguela, California, and Dakar Niños/ Niñas. The signature of the Benguela Niño is clearly visible in the structure of the AMM (Fig. 7
13). As the AZM and Benguela Niño tend to be of the same sign during a given year, they form part of the southern pole of the AMM. As discussed in Section 5.1, zonal wind anomalies typically display a sign change north of the equator. Thus, the trade wind anomalies north of the equator will help to establish SST anomalies that are of opposite sign to those on and south of the equator. This relation, however, is not consistently observed, and some studies have questioned the existence of a “dipole mode” (Dommenget and Latif, 2000). Thus, while there is a potential mechanism for a dipole, complicating factors may obscure this relation.

7.6 Relations of equatorial Atlantic variability to terrestrial precipitation and remote basins While the AZM amplitude is weaker than that of ENSO, it has been shown to influence precipitation over the surrounding continents. More recently, some studies suggest that the AZM can even influence ENSO. Conversely, the influence of ENSO on the NTA has long been established but its influence on the equatorial Atlantic and the AZM is inconsistent. In the following, we take a closer look at these teleconnections.

7.6.1 Impact on tropical precipitation Warm AZM events are associated with increased precipitation over the equatorial Atlantic and the Guinea coast just to the north. On interannual timescales, this is the most robust impact on African rainfall of any of the tropical Atlantic SST variability patterns (Rowell, 2013). This impact can be seen in our AZM composites (Figs. 7
7 and 7
9) and has been well documented in the literature (Hastenrath and Lamb, 1977; Hastenrath, 1984; Horel et al., 1986; Wagner and da Silva, 1994; Okumura and Xie, 2004; Rowell, 2013; Lutz et al., 2015; see also review by Rodriguez-Fonseca et al., 2015). These rainfall anomalies can be explained by the destabilizing impact of the warm SST anomalies on the overlying atmosphere. Particularly in late spring and early summer, when the West African monsoon develops, the prevailing southerly surface winds can carry warm moist air toward the Guinea coast and fuel increased precipitation there. As the moisture rains out over the

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coast, less moisture is available downstream for the Sahel region and, consistently, warm AZM events are associated with reduced rainfall there (Rowell et al., 1995; Janicot et al., 1998), though the link is somewhat weaker than that with the Guinea coast and is not evident in our composites. The lack of a robust response in Sahel precipitation may be due to the competing influence from the tropical Pacific, which has gained prominence in recent decades (Losada et al., 2012). The AZM is also associated with rainfall anomalies over northeast South America (Figs. 7
7 and 7
9). In the spring of positive AZM events, there is an anomalous southward shift and strengthening of the ITCZ precipitation over South America (Fig. 7
7), which leads to a north
south dipole in precipitation anomalies. While these anomalies are statistically related to the AZM, it is not clear to what extent they are driven by it. The southward shift of the ITCZ in early spring, for example, occurs during a time when the SST anomalies are still weak and may thus be seen as a driver of westerly wind events and the AZM. At the same time, the early stages of the AZM are typically associated with an AMM-like pattern that may have a larger impact on ITCZ position than the local SST anomalies associated with the AZM. Some studies have suggested that the AZM may even influence the Indian summer monsoon (Kucharski et al., 2007, 2008; Pottapinjara et al., 2014). According to these studies, a warm AZM event warms the troposphere over the tropical Indian Ocean through the Gill (1980) mechanism. This leads to stabilization of the atmospheric column, reduction in the number of monsoon depressions, and less rain over land. Pottapinjara et al. (2014) argue that this mechanism should be particularly important during neutral ENSO years.

7.6.2 Impact of the Atlantic zonal mode on El Niño-Southern Oscillation A study by Rodríguez-Fonseca et al. (2009) brought attention to the possibility that the AZM may enhance or even trigger ENSO events, and that the strength of this influence may be subject to decadal modulation. While, compared to ENSO, the SST anomalies of the AZM are of weak amplitude and small spatial extent, they occur during late spring and early summer, when SST anomalies are still weak in the equatorial Pacific. The proposed mechanism for the equatorial Atlantic influence on ENSO is through modulation of the Walker circulation (Rodri'guez-Fonseca et al., 2009; Losada et al., 2010; Ding et al., 2012; Kucharski et al., 2016). During positive AZM events, convection and upper level divergence are strengthened over the central equatorial Atlantic. This is compensated, in part, by descending motion and easterly surface wind anomalies over the central and western equatorial Pacific. Such wind anomalies trigger upwelling Kelvin waves that drive SST cooling toward the east, enhancing the chance of La Niña development. Thus, positive AZM events may assist in the development of opposite-signed ENSO events. A study by Jia et al. (2019) suggests that this influence may be weakening under global warming due to the stabilization of the troposphere. Several studies suggest that ENSO prediction can be enhanced when SST anomalies are prescribed in the tropical Atlantic (Frauen and Dommenget, 2012; Dayan et al., 2014) or even in the equatorial Atlantic only (Keenlyside et al., 2013), supporting the idea that remote impacts from the Atlantic have the potential to alter ENSO development.

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Notwithstanding the supportive evidence above, there remains considerable uncertainty regarding the equatorial Atlantic influence on ENSO. While the AZM tends to peak about half a year before ENSO, both events usually start developing in boreal spring. Thus, it is difficult to establish the directionality of interbasin influences based on observational studies alone. Numerical experiments can be helpful in this regard but are not unambiguous either; restoring SSTs to observations in the tropical Atlantic, for example, may implicitly introduce information from the Pacific, as the Pacific remotely influences Atlantic SST. In the context of prediction experiments, this may lead to an overestimate of the skill that can be gained from tropical Atlantic variability, as noted by Keenlyside et al. (2013).

7.6.3 Impact of El Niño-Southern Oscillation on the Atlantic zonal mode Despite ENSO’s far reaching impacts around the globe, its impacts on the equatorial Atlantic are surprisingly inconsistent (Chang et al., 2006; Lübbecke and McPhaden, 2012), with the instantaneous correlation between ENSO and AZM indices close to zero (e.g., Tokinaga et al., 2019). Perhaps most puzzling is the fact that two of the strongest El Niño events on record (1982 and 1997) were followed by AZM events of opposite sign (negative and positive, respectively). This inconsistent relation can partly be explained by the different seasonality of the two phenomena: ENSO is still in its early development when the AZM develops and therefore cannot exert its full strength on the equatorial Atlantic. This, however, may only be part of the explanation. Chang et al. (2006) suggest that El Niño has two competing impacts on the equatorial Atlantic: a thermodynamic and a dynamical one. The thermodynamic influence consists of tropospheric warming over the tropical Atlantic, which leads to SST warming. The dynamic influence is a change in the Walker circulation that leads to easterly surface wind anomalies and thus cools the equatorial Atlantic. Chang et al. (2006) argue that, due to these competing effects, the net response of the equatorial Atlantic is often weak. Lübbecke and McPhaden (2012) point to the opposing surface wind anomalies on and north of the equator that typically occur during ENSO. When positive SST anomalies are present in the tropical Pacific during boreal spring, the Atlantic northeast trade winds weaken and NTA SSTs warm, a well-established and robust impact of ENSO (e.g., Enfield and Mayer, 1997). Combined with the strengthening of the equatorial trades, this leads to negative wind stress curl anomalies and downwelling just north of the equator. As described in Section 7.4.6, subsequent Rossby wave reflection (Lübbecke and McPhaden, 2012) or meridional advection (Richter et al., 2013) can eventually lead to warming on the equator, which opposes the SST anomalies that were generated by local dynamical processes. Tokinaga et al. (2019) show that, for multiyear ENSO events, there is a consistent influence on the equatorial Atlantic. Their results indicate that, during multiyear events, the zonal SST gradient across the western and central equatorial Pacific persists well into the spring following the ENSO peak. This, they argue, allows the tropical Pacific to have a strong influence on the equatorial Atlantic, whereas single-year events decay too quickly in spring. Inconsistency of the Pacific
Atlantic relation may also arise through the modulation of teleconnections by the background state. Based on observational analysis, Martin-Rey et al.

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(2014) suggest that the Atlantic multidecadal oscillation (AMO; also referred to as Atlantic multidecadal variability or AMV) modulates the link between ENSO and the AZM. According to their study, the interbasin link is strong during negative phases of the AMV, when SSTs are anomalously warm in the southern hemisphere, which is accompanied by a southward shift of the Atlantic ITCZ. There is substantial uncertainty, however, because the observational record is too short to reliably assess multidecadal variability, and model simulations suffer from severe biases. The latter will be the topic of the next section.

7.7 Representation of equatorial Atlantic variability in global climate models 7.7.1 Mean state biases While the observed equatorial Atlantic features a pronounced zonal SST gradient in the annual mean, with warm SST in the west and cool SST in the east, GCMs struggle to reproduce this gradient and, in some cases, even reverse it. An intercomparison study by Davey et al. (2002) was perhaps the first to point out the pervasiveness of this problem across models. Subsequent multimodel studies not only confirmed that this bias is near-universal but also showed that it has improved little despite decades of model development (Richter and Xie, 2008; Richter et al., 2014a), though a recent study suggests that a few CMIP6 models have relatively small equatorial Atlantic biases (Richter and Tokinaga, 2020). Equatorial SST biases are related to the underrepresentation of the equatorial Atlantic cold tongue, which is most developed in boreal summer. As a consequence, the SST biases also display a clear seasonality, with the weakest biases in spring and the most severe biases in summer (Fig. 7
14; Chang et al., 2007; Richter and Xie, 2008). This is in marked contrast to the equatorial surface westerly wind bias, which is most pronounced in spring but weak in summer and other seasons (Fig. 7
14). In addition, AGCMs forced with observed SSTs do still produce a pronounced westerly bias in spring (Richter and Xie, 2008; Richter et al., 2014a; Richter and Tokinaga, 2020). Based on these facts, Chang et al. (2007) and Richter and Xie (2008) suggest that a significant portion of the equatorial Atlantic SST biases is due to deficiencies in the atmospheric model component, which produces a westerly wind bias even in the absence of SST biases. In coupled ocean-atmosphere models, such a westerly bias deepens the thermocline in the east (approximated by the depth of the 20 C isotherm in Fig. 7
14), which renders upwelling-related cooling less effective in summer, when the cold tongue is observed to form. As a result, the most severe SST bias is seen in July. Several subsequent studies have confirmed the important role of equatorial surface wind biases (Wahl et al., 2011; Richter et al., 2012; Zermeño-Diaz and Zhang, 2013; Richter et al., 2014a; Voldoire et al., 2019). Errors in the oceanic model components, however, also likely play an important role, as shown by several other studies (Hazeleger and Haarsma, 2005; Jochum et al., 2013; Xu et al., 2014a; Song et al., 2015). In particular, the representation of the sharp equatorial thermocline and vertical mixing in the upper ocean poses a challenge to models.

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In addition to equatorial SST and wind, several other aspects are subject to biases. This includes underrepresentation of stratocumulus clouds and warm SST biases in the southeastern tropical Atlantic (see Richter, 2015; Zuidema et al., 2016 for reviews), an Atlantic ITCZ that is erroneously placed south of the equator in spring, deficient precipitation over northeast South America, and excessive precipitation over Africa (e.g., Richter et al., 2016). The southeast Atlantic SST biases may be related to the equatorial ones through the equatorial-coastal wave guide (Xu et al., 2014a). In turn, the southeast Atlantic warm bias may contribute to the southward shift of the Atlantic ITCZ (Xu et al., 2014b), though there might be some model dependence regarding this impact (Small et al., 2015).

7.7.2 Errors in the simulated variability In spite of their severe mean state errors, many models produce a mode of variability that has similarities with the observed AZM in terms of spatial structure and temporal evolution, including phase locking to summer (Richter et al., 2014a; Richter and Tokinaga, 2020). The simulated AZM, however, is typically too weak and peaks one month later than observed (Fig. 7
15; Richter et al., 2014a). This is consistent with a generally slower cold tongue

FIGURE 7–14 Latitude-time sections of CMIP5 ensemble mean biases of SST (shading; K), surface winds (vectors; reference 5 2 m s21), and depth of the 20 C isotherm (D20; purple contour lines; interval 5 4 m), averaged between 3S and 3N. The reference fields are ERA5 (SST and surface winds), and ORAS4 (D20).

Chapter 7 • The Atlantic zonal mode: Dynamics, thermodynamics, and teleconnections 193

formation and equatorial trade wind strengthening (Fig. 7
16). The origin of this delay in the seasonal cycle is unclear but it may be related to the unrealistic southward excursion of the ITCZ in spring, which has a strong impact on surface winds and, subsequently, SST. The southward excursion and delayed northward migration of the ITCZ are associated with the late onset of the West Africa monsoon in simulations (Steinig et al., 2018). The relation between the year-to-year variability of ITCZ latitude and strength of the equatorial surface zonal wind stress is reproduced by GCMs in a general sense (Fig. 7
12), though there is one qualitative difference: the simulated ITCZ can shift farther south than the observed one. This has important implications for the mean state biases. Due to the close association of ITCZ latitude and equatorial surface wind stress, the southward position of the ITCZ translates into weak equatorial trades in simulations. This hints that misrepresentation of deep convection lies at the heart of the equatorial Atlantic bias problem. Several studies suggest that the excessively deep thermocline in many models renders upwelling-related cooling less effective (Deppenmeier et al., 2016; Dippe and Greatbatch, 2017; Jouanno et al., 2017). This weakens the Bjerknes feedback and thus reduces the role of ocean dynamics. Instead, thermodynamic processes, especially surface latent heat flux, may exert a stronger control on SST variability in such models (Ding et al., 2015; Dippe and Greatbatch, 2017; Jouanno et al., 2017). In addition to an excessively deep mean thermocline, errors in the seasonal cycle of both SST and thermocline may also affect the simulated interannual variability (Ding et al., 2015; Prodhomme et al., 2019). The spatial structure of the AZM is captured relatively well by several GCMs (Richter et al., 2014a), with SST warming in the central and eastern equatorial Atlantic and along the southwest African coast. In some of the models, the amplitude of both SST and surface wind anomalies is greater than observed, and in most of them the SST signature is too narrowly confined along the equatorial-coastal wave guide. This gives the impression that upwelling plays too prominent a role in the development of the SST anomalies but could also mean that other processes that spread

FIGURE 7–15 Climatological annual cycle of the standard deviation of ATL3 SST (K) in ERA5 (black line) and the CMIP5 ensemble (green line).

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the SST anomalies horizontally are poorly represented. The former would be in contradiction to the above-mentioned studies that suggest underestimation of dynamical processes in GCMs.

7.8 Prediction of equatorial Atlantic variability Prediction of equatorial Atlantic interannual variability is a longstanding challenge for GCM prediction systems (Stockdale et al., 2006; Richter et al., 2018), with the skill of dynamical forecasts often matched or even outperformed by persistence forecasts and simple statistical models. This is in stark contrast to the equatorial Pacific, where dynamical forecasts clearly outperform persistence. There are two possible explanations for the Atlantic predictability hurdle: (1) Current

FIGURE 7–16 Climatological annual cycle of (A) ATL3 SST ( C) and (B) ATL4 surface zonal wind. The black and green lines denote ERA5 and the CMIP5 ensemble, respectively.

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prediction models are inadequate. This could be due to systematic errors in the model formulation, insufficient observations to initialize the models, or shortcomings in the initialization procedure (data assimilation etc.). (2) The theoretical predictability of the equatorial Atlantic is inherently low, due to, for example, weak coupled feedbacks or internal variability. The relative roles of (1) and (2) in the predictability hurdle are difficult to estimate. The few studies that have systematically investigated the link between systematic model errors and prediction skill typically obtained ambiguous results (see Richter et al., 2018 for a discussion), though they do point to a link. Two recent studies suggest that there is a strong link between skill and variability errors, if the latter are severe (Dippe et al., 2019; Noel Keenlyside, personal communication). While alleviating biases in these models through flux correction showed significant skill improvement, the flux-corrected models could only rise to about the skill of the persistence forecast. Thus it remains an open question, whether the current low skill in hindcast experiments (Richter et al., 2018) can be significantly enhanced by fixing model errors. Several studies suggest that the Bjerknes feedback in the equatorial Atlantic is much weaker than in the Pacific (Zebiak, 1993; Keenlyside and Latif, 2007; Richter et al., 2014b; Deppenmeier et al., 2016; Lübbecke and McPhaden, 2013). Richter et al. (2017) point out that coupled feedbacks are only strong during a relatively short period (approximately April and May), leaving little time for AZM growth through dynamical processes. The results of Nnamchi et al. (2015, 2016), by pointing to a significant role of thermodynamic processes, also imply a weaker role of dynamic processes. Regarding the influence of SST biases on model performance, Richter et al. (2018) showed that when an AGCM is forced with observed SST anomalies added to a severely biased SST climatology, surface wind anomalies are quite realistic. This suggests that AGCMs can produce a realistic response to SST anomalies even in the presence of mean state biases and, by extension, that SST biases are not a major reason for the poor prediction skill (though oceanic subsurface temperature biases might be). This could mean that only moderate gains in prediction skill can be expected from fixing model errors. Additionally, the relatively strong role of atmospheric internal variability in the equatorial Atlantic (Richter and Doi, 2019) suggests that inherent predictability may be relatively low in the equatorial Atlantic. On the other hand, we do not know exactly how close to the limit current prediction systems are. Thus, there is the possibility that skill improvement can be obtained through a denser observational network (Tompkins and Feudale, 2010) or refined initialization procedures. In any event, more work will be needed to quantify the limits of equatorial Atlantic predictability and the role of model errors.

7.9 Low-frequency modulation of equatorial Atlantic variability and the impact of climate change The observational record of Atlantic SST roughly extends from the 1870s to present, but spatial coverage is mostly limited to commercial shipping routes until the advent of the satellite observation era in the late 1970s. Estimating decadal and longer variability from these data

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poses a challenge, as is projecting future changes based on biased GCMs. We therefore keep brief the discussion of these aspects. Decadal-to-interdecadal variability in the Atlantic basin is dominated by the AMV. During the negative phase of the AMV, SSTs are warmer than normal on and south of the equator (Kerr, 2000; Knight et al., 2006), which is associated with a southward shift of the ITCZ and weakening of the equatorial trades. This should lead to deepening of the thermocline and reduce SST variability in the eastern equatorial Atlantic (Haarsma et al., 2008; Polo et al., 2013). Martin-Rey et al. (2018), on the other hand, find that negative AMV phases are associated with a shoaling of the thermocline and a higher amplitude of the AZM. This is partially supported by Svendsen et al. (2014), who find a strengthening of the equatorial Atlantic zonal SST gradient during negative AMV events. Strengthening and further westward extent of AZM events may also alter the teleconnections of the AZM, since convection is most active in the central and western part of the basin. In combination with the warming of the equatorial background state, this leads to stronger remote influences, particularly on the equatorial Pacific (Svendsen et al., 2014; Losada and Rodri'guez-Fonseca, 2016). There is evidence that the equatorial cold tongue and trades have weakened and that the thermocline has deepened during the period 1950
2009 (Tokinaga and Xie, 2011). According to the authors, this is associated with reduced SST variability in the eastern equatorial Atlantic. These results partially conflict with those of Martin-Rey et al. (2018), who suggest strengthening of AZM variability during the recent negative phase of the AMV. The weakening of the trades also needs to be reconciled with the observations of Servain et al. (2014), who find trade wind strengthening over the entire tropical Atlantic. The ATL3 time series (Fig. 7
6) is suggestive of a reduction in variability during the last seven decades, consistent with the result of Tokinaga and Xie (2011). Much more analysis will be needed, however, to assess the relative roles of decadal modulation, greenhouse gas forcing, and intrinsic variability [for the last, see Wittenberg (2009) who discuss this problem in the context of ENSO]. While the tropical Atlantic and most of the other tropical areas have warmed in recent decades, the eastern tropical Pacific has been subject to cooling (Kosaka and Xie, 2013), with implications on global climate. Li et al. (2016) argue that the tropical Atlantic warming has played an important role in this by inducing easterly anomalies over the equatorial Pacific that strengthened upwelling-related cooling in the region. Historical simulations in the CMIP5 archive are unable to replicate cooling periods of similar length in the eastern tropical Pacific. McGregor et al. (2018) attribute this underestimation of decadal variability to the remote impacts of tropical Atlantic SST biases. The above discussion indicates that much work remains to verify historical trends and to build confidence in global warming projections for the tropical Atlantic region and beyond.

7.10 Summary and open questions In this chapter, we have reviewed variability in the equatorial Atlantic with a focus on the AZM, a pattern of interannual variability that resembles ENSO in the Pacific.

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7.10.1 Summary The AZM is characterized by SST warming in the eastern and central equatorial Atlantic that typically starts in early boreal spring, peaks in early summer, and decays in late summer and early fall. SST variability has an amplitude of 1K and shows spectral peaks at periods of about 1.5
4.5 years, though these are not distinct from red noise. SST warming is often preceded by westerly wind events over the western equatorial Atlantic, suggesting a dynamic generation mechanism similar to ENSO, in which downwelling Kelvin waves deepen the eastern thermocline and weaken cold tongue formation in the following months. There are, however, also warm events that are preceded by easterly wind anomalies. During these events, off-equatorial processes, such as oceanic Rossby waves and meridional temperature advection, seem to play an important role. Coupled feedbacks, in particular the Bjerknes feedback, appear to be active but are mostly limited to April and May. Before April, SST anomalies tend to be too weak to initiate coupling, while after May the ITCZ migrates away from the equator (regardless of any existing SST anomalies), which drastically weakens coupling strength. Together with the significant influence of thermodynamics processes, and the relatively large role of internal atmospheric variability, this suggests relatively low predictability of the AZM, which is consistent with current prediction systems struggling to beat persistence. The AZM appears to be linked to the AMM, a pattern of opposite-signed SST anomalies north and south of equator. This link can be explained through the wider circulation changes that accompany AZM events. In the early phase of positive AZM events, the ITCZ shifts southward and trade winds strengthen north of the equator but weaken on the equator and to the south of it. The resultant latent heat flux anomalies lead to SST cooling to the north and warming to the south, which may be further amplified through the WES feedback. The Benguela Niño in the southeastern tropical Atlantic, which is located within the southern lobe of the AMM, is also closely linked to the AZM. This link can be explained through Kelvin waves along the equatorial-coastal waveguide, but also, to some extent, through the basin-wide wind anomalies. ENSO has a strong and robust influence on the NTA but the influence of ENSO on the AZM is weak and inconsistent. Explanations for this include the competition of dynamical and thermodynamical influences, the north-equatorial wind stress curl anomalies that often accompany ENSO events, and the sensitivity to the timing of ENSO decay. The AZM itself may be able to contribute to the development of ENSO events and may also have an influence on the Indian summer monsoon. It appears though that these relations are subject to decadal-scale modulation. Finally, the AZM and its remote influences may be weakening under the influence of greenhouse gas forcing, but much work remains to be done to confirm this.

7.10.2 Open questions While much progress has been achieved in understanding the equatorial Atlantic mean state and variability, many interesting questions and puzzles remain. Here we list some questions we regard as central. We stress that this selection is very subjective.

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7.10.2.1 What maintains the equatorial surface easterlies in boreal spring? The pressure gradient force by itself would mandate westerlies over much of the basin width but easterlies are observed. It has been hypothesized that vertical momentum transport plays a crucial role but, to the authors’ knowledge, no study has systematically analyzed this (cf. Section 7.3.1).

7.10.2.2 What is the role of atmospheric vertical momentum transport in interannual variability? Previous studies have hinted that vertical momentum transport also plays a crucial role in interannual variability, and that the decay of events is triggered by the decrease in vertical momentum transport that occurs when the ITCZ migrates away from the equator. Much more work remains to be done to understand the role of vertical momentum transport (cf. Section 7.4.3).

7.10.2.3 What is the cause of the asymmetric relation between equatorial surface zonal winds and Atlantic intertropical convergence zone latitude? There is a close relation between ITCZ latitude and the strength of the equatorial easterlies in the western part of the basin. This relation is not symmetric about the equator because winds are strong when the ITCZ is north of the equator but weak when it is on or south of the equator. The cause of this asymmetry remains unknown. It may be related to the geometries of South America and Africa or to the nature of the convective systems that constitute the ITCZ (cf. Section 7.4.8).

7.10.2.4 What causes the inconsistent influence of El Niño-Southern Oscillation on the Atlantic zonal mode? Several hypotheses have been put forward but their relative merits await further exploration. None of them can satisfactorily explain the opposite outcomes of the 1982 and 1997 El Niños (cf. Section 7.6.3).

7.10.2.5 To what extent does equatorial Atlantic variability contribute to the development of El Niño-Southern Oscillation events? It is clear that the equatorial Atlantic must have some influence on ENSO but, given the small amplitude and geographical extent of the AZM, the question is how important this influence can be. Observations may not be sufficient to solve this problem because the record is relatively short and because both events tend to develop in boreal spring (cf. Section 7.6.2).

7.10.2.6 What are the theoretical limits of Atlantic zonal mode predictability? Recent studies suggest that predictability of the AZM is inherently low but much more work has to be done to quantify this (cf. Section 7.8).

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7.10.2.7 What is the role of global climate model mean state biases in the tropical Atlantic on basin interaction and global warming projections? GCM biases in the tropical Atlantic have proven to be hard to fix and may continue to pose a challenge in the foreseeable future. Thus, it is important to understand how these biases influence other basins, and how they might affect global warming projections. While a few studies have started to address this, much work remains to be done (cf. Section 7.9).

7.10.3 Ways forward Finally, we comment on how the questions raised in Section 7.10.2 may be addressed. Convective momentum transport (Sections 7.10.2.1 and 7.10.2.2) is difficult to observe as it requires precise measurements of horizontal and vertical velocity at high temporal resolution in adverse weather conditions. Nevertheless, there are measurements that can be leveraged, including field campaigns that are ongoing (Bony et al., 2017) or being planned (Bjorn Stevens, personal communication). Explicitly simulating convective momentum transport requires models with very high resolution (Ba few kilometers horizontally) in the tropical Atlantic. Such simulations are becoming increasingly feasible, and it will be exciting to see what can be learned from them. Both the aforementioned observations and simulations may also shed light on the asymmetric relation between ITCZ latitude and equatorial surface winds (Section 7.10.2.3). In addition, GCM sensitivity experiments with idealized changes in the continental geometry may help to solve this puzzle. GCM experimentation may also help in disentangling the two-way interaction between the tropical Pacific and tropical Atlantic (Sections 7.10.2.5 and 7.10.2.6). Coordinated multimodel experiments could be a valuable tool for this, and the authors are part of an effort to set up such a model intercomparison. In addition, long-term climate records, such as those obtained from coral proxies, could provide a means to corroborate results obtained from the observational record (e.g., Tierney et al., 2015). Quantifying the limits of AZM predictability (Section 7.10.2.6) is a tough problem and any results may eventually be rendered obsolete by the actual skill improvements of prediction systems. To wit, a recent multimodel skill assessment suggests substantial improvements in the equatorial Atlantic (Chloe Prodhomme, personal communication). Nevertheless, attempting to quantify skill limits should remain an important endeavor that can guide improvement efforts. This will require quantifying the stochastic component of the system as well as clarifying the impact of model biases on prediction skill. Neither of these two topics has received much attention so far. Exploring the limits of predictability will also require assessing the benefit of observational networks (e.g., data denial experiments) and initialization procedures (e.g., coupled data assimilation). The impact of mean state biases on interbasin interaction and global warming projections (Section 7.10.2.7) is another difficult problem. Numerical experiments with prescribed SST can partially address this issue but suffer from all the potential inconsistencies of fixing one component of a coupled system. A recent intercomparison of CMIP6 models suggests that a few models now have relatively small biases in the tropical Atlantic, while at the same time producing relatively realistic interannual variability (Richter and Tokinaga, 2020). Thus, there is now a wide

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range of model behavior (from light to severe model errors) that may allow systematic study of the impacts of tropical Atlantic biases on basin interaction and climate change projections.

Acknowledgment The authors thank Prof. Noel Keenlyside for his insightful comments on the manuscript. The authors were partially supported by the Japan Society for the Promotion of Science, KAKENHI Grant 18H01281.

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8 The Ningaloo Niño/Niña: Mechanisms, relation with other climate modes and impacts Tomoki Tozuka1, Ming Feng2, Weiqing Han3, Shoichiro Kido1, Lei Zhang3 1

DEPART ME NT OF EARTH AND PLANETARY SCIENCE, GRADUATE SCHOOL OF SCIENCE, T H E UNIV ER S I T Y OF T O KY O , TO K YO, JA PA N 2

CSIRO OCEANS AND ATMOSPHE RE, INDI AN OCEAN MARINE RE SEARCH CENT RE, CRAWLEY, W A, AUSTRALIA

3

DEPARTMENT OF ATMOSPHE RIC AND OCEANIC SCIENCES, UNIVE RSITY O F C OLORADO, BOULDER, CO, UNITED STATES

8.1 Introduction Ningaloo Niño (Niña) is the most recently identified interannual climate mode in the Indian Ocean associated with the positive (negative) sea surface temperature (SST) anomalies off the west coast of Australia and phase-locked to austral summer (Fig. 8
1) (Feng et al., 2013; Kataoka et al., 2014). An unprecedented warming in the 2010
11 austral summer along the West Australian coast led to the identification of this coastal niño (Feng et al., 2013). Generally, Ningaloo Niño SST anomalies start to develop off the northwest coast in September, evolve to the peak off the west coast during its mature phase in December-February, and then start to demise from March. Since 6 years have passed after its identification, and significant advances have been made after the first review article on this climate mode in 2015 (Feng et al., 2015b), this chapter provides an updated review and identifies outstanding issues related to this particular phenomenon. This chapter is organized as follows. In Section 8.2, we review the generation mechanisms of the Ningaloo Niño/Niña. Then in Section 8.3, we discuss its relationships with other climate modes including the El Niño/Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Indian Ocean Subtropical Dipole (IOSD), the Interdecadal Pacific Oscillation (IPO), and the Madden
Julian Oscillation (MJO). In Section 8.4, we review impacts of the Ningaloo Niño/Niña on the local marine ecosystem and climate. The final section summarizes what have already been understood and suggests some future directions. Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00006-X © 2021 Elsevier Inc. All rights reserved.

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FIGURE 8–1 Sea surface temperature (SST) anomalies associated with the Ningaloo Niño revealed by a lead-lag linear regression analysis for (A) September (
1), (B) November (
1), (C) January (0), (D) March (0), (E) May (0), and (F) July (0). High-pass filtered (73-month running mean removed) SST anomalies (in  C) are regressed against the normalized Ningaloo Niño Index (NNI) of December (21)
February (0), which is defined as area-averaged SST anomalies along the west coast of Australia (108 E
coast, 22 S
28 S). Regression coefficients significant at the 90% confidence level by a two-tailed t-test are shaded, and contour intervals are 0.3. Here, Year 0 represents the year that a Ningaloo Niño/Niña event peaks, and Year 21 represents the previous year. The SST data from 1958 to 2017 used to plot this figure is Centennial Observation-Based Estimates of SSTs (COBESST) (Japan Meteorological Agency, 2006).

8.2 Mechanisms Three-generation mechanisms have been proposed to contribute to the development of the Ningaloo Niño/Niña. This section reviews each of the mechanisms and insights obtained from mixed-layer heat budget analyses. Although some asymmetries exist between Ningaloo Niño and Niña (e.g., Tozuka and Oettli, 2018), the Ningaloo Niña is close to a mirror image of the Ningaloo Niño and thus the description in this section focuses on the Ningaloo Niño.

8.2.1 Remote oceanic forcing The first mechanism involves an oceanic teleconnection from the Pacific. Fig. 8
2 shows sea surface height (SSH) anomalies associated with the Ningaloo Niño. Easterly wind stress anomalies associated with La Niña induce downwelling equatorial Rossby waves (Suarez and Schopf, 1988; Battisti and Hirst, 1989). These signals from the western tropical Pacific (Fig. 8
2A) penetrate through the Indonesian Seas and propagate poleward along the west Australian coast (Fig. 8
2B)

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FIGURE 8–2 Sea surface height (SSH) anomalies associated with the Ningaloo Niño revealed by a lead-lag linear regression analysis for (A) September (
1), (B) November (
1), (C) January (0), (D) March (0), (E) May (0), and (F) July (0). Contour intervals are 0.03 m  C21. The SSH data from 1958 to 2017 used to plot this figure are the Ocean Reanalysis System version 4 (ORA-S4) (Balmaseda et al., 2013) product.

(Clarke, 1991; Clarke and Liu, 1994; Meyers, 1996); such propagation mechanism is known as the Clarke
Meyers effect (Yamagata et al., 2004). These downwelling coastal waves lead to anomalous strengthening of the Leeuwin Current, a poleward-flowing warm current, (Feng et al., 2003, 2008; Clarke and Li, 2004) and thus anomalous warming in the region due to reduced upwelling cooling and warm water advection. The anomalously low-salinity water from the upstream may further enhance the warm advection by the Leeuwin Current. During the 2010
11 Ningaloo Niño event, the salinity effect contributed about 30% of the increase in the Leeuwin Current transport and contributed to the unprecedented warming (Feng et al., 2015c). The SSH anomalies along the coast may further be strengthened (Fig. 8
2C and D) by local wind stress anomalies, reviewed in more detail in Sections 8.2.2 and 8.2.3. The SSH anomalies eventually attenuate and partly radiate into the ocean interior by Rossby waves (Fig. 8
2E and F), possibly with some modifications by the local wind forcing.

8.2.2 Remote atmospheric forcing Feng et al. (2013) suggested that a Matsuno
Gill-type response (Matsuno, 1966; Gill, 1980) to diabatic heating anomalies associated with La Niña induced negative sea level pressure (SLP) anomalies displaced westward from the peak SST anomalies off the Australian coast during the 2010
2011 Ningaloo Niño event. The source of atmospheric Rossby waves is

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FIGURE 8–3 Outgoing longwave radiation (OLR) anomalies in November ( 2 1), which is at the peak phase of El Niño/La Niña and corresponds to the developing phase of the Ningaloo Niño/Niña revealed by a lead-lag linear regression analysis. Contour intervals are 3 W m22  C21. The OLR data from 1958 to 2017 used to plot this figure are JRA-55 (Kobayashi et al., 2015) reanalysis data.

nicely captured in Fig. 8
3, which reveals that equatorial deep convection centered around 170 E is suppressed prior to the peak of the Ningaloo Niño and at the peak of La Niña. They further suggested that the northerly alongshore wind anomalies associated with the low SLP contributed to the unprecedented warming. This ENSO effect on SLP via atmospheric bridge was confirmed by a series of experiments with an atmospheric general circulation model (AGCM), which can realistically simulate SLP anomalies associated with the Ningaloo Niño (Fig. 8
4B). When an AGCM is forced by SST anomalies outside of the eastern South Indian Ocean, negative SLP anomalies are also generated in Ningaloo Niño years owing to a Matsuno
Gill-type response to atmospheric convection anomalies in the tropical Pacific (Fig. 8
4C; Tozuka et al., 2014). Thus the ENSO may contribute to the development of the Ningaloo Niño through both oceanic and atmospheric teleconnections.

8.2.3 Local ocean-atmosphere coupled feedback The first two mechanisms emphasize the importance of remote forcing, but the local oceanatmosphere coupled feedback may also play an important role. Kataoka et al. (2014) suggested that SST anomalies off the west coast of Australia initiate a positive feedback called “coastal Bjerknes feedback.” Positive SST anomalies associated with the Ningaloo Niño induce negative SLP anomalies in the overlying atmosphere. As a result, anomalous positive zonal SLP gradient is formed in the coastal area and induces northerly wind anomalies along the coast. Since these wind anomalies reduce both surface wind speed and climatological transport of cool and dry air from the Southern Ocean, latent and sensible heat loss is suppressed (Zhang et al., 2018). The northerly wind anomalies, at the same time, strengthen the warm advection by the poleward-flowing Leeuwin Current and reduce the coastal upwelling, both contributing to the anomalous warming. The generation of the anomalous atmospheric cyclonic circulation by local positive SST anomalies is confirmed by Tozuka et al. (2014),

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FIGURE 8–4 Sea level pressure (SLP) anomalies during the peak of the Ningaloo Niño revealed by a lead-lag linear regression analysis: (A) observation, and those simulated with an atmospheric general circulation model forced by SST anomalies (B) over the global ocean (referred to the control (CTRL) run), (C) outside of the eastern South Indian Ocean (80 E
120 E, 15 S
40 S) (referred to the "No Eastern South Indian Ocean" (NoESIO) experiment), and (D) within the eastern South Indian Ocean (referred to the "Eastern South Indian Ocean" (ESIO) experiment). Contour intervals are 0.2 hPa  C21. For the observation, the Japanese 55-year Reanalysis (JRA-55) (Kobayashi et al., 2015) reanalysis data are used. For more details about the model experiments, see Tozuka et al. (2014).

who reproduced the atmospheric anomalies with their AGCM even when SST anomalies are imposed only in the eastern South Indian Ocean (Fig. 8
4D). Kataoka et al. (2014) suggested that SLP anomalies over the Australian continent are the key to the difference between locally and nonlocally amplified events of the Ningaloo Niño/ Niña. When SLP anomalies over the continent are positive, the anomalous low over the ocean forms a cell-like pattern and the associated strong positive zonal SLP gradient anomalies result in alongshore northerly wind anomalies. On the other hand, when SLP is anomalously low over the continent, zonally elongated negative SLP anomalies with very weak zonal SLP gradient near the coast are formed and no significant alongshore wind anomalies are generated. They suggested that interannual variations in the Australian summer monsoon (Kullgren and Kim, 2006) and/or the southern annular mode (Hendon et al., 2007; Visbeck, 2009) may influence the continental SLP anomalies, but further studies are required to clarify the ocean
atmosphere
land interaction processes. Although both oceanic and atmospheric remote influences are shown to play an important role in the development of the Ningaloo Niño, it may develop solely through the local air
sea

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interaction. Marshall et al. (2015) pointed out that the wind-evaporation-SST feedback may initiate the Ningaloo Niño event off the northwest coast and a Ningaloo Niño event developed together with the 1982
83 El Niño event. More recently, Kataoka et al. (2018) showed that Ningaloo Niño/Niña events with similar magnitude occur in their coupled general circulation model (CGCM) even when the tropical Pacific is decoupled and no ENSO events occur.

8.2.4 Thermodynamics The above qualitative description of the generation mechanisms was examined based on mixedlayer heat budget analyses (Benthuysen et al., 2014; Marshall et al., 2015; Kataoka et al., 2017). First, Benthuysen et al. (2014) conducted a mixed-layer heat budget analysis using a highresolution regional ocean model. They showed that both positive meridional advection anomalies associated with the stronger Leeuwin Current and surface heat flux anomalies contributed to the development of strong positive SST anomalies in the 2010
11 Ningaloo Niño event. Since the above study was a case study, Marshall et al. (2015) constructed composites of each term in the mixed-layer heat budget equation for all Ningaloo Niño events that occurred during 1960
2011 using a longer reanalysis data. They confirmed the importance of latent heat flux and meridional advection anomalies. However, the mixed-layer heat budget in these two studies was calculated for the upper 50 m. Since mixed layer depth (MLD) undergoes large seasonal and interannual variations in the region, such mixed-layer heat budget may not necessarily explain SST anomalies. For this reason, Kataoka et al. (2017) conducted an on-line mixed-layer heat budget analysis taking account of variable MLD. They showed that an anomalously shallow mixed layer with anomalously small heat capacity during the Ningaloo Niño leads to enhanced warming by the climatological shortwave radiation and this effect contributes dominantly to positive SST anomalies, especially in the offshore region. The negative MLD anomalies are shown to be mainly generated by positive latent heat flux anomalies (i.e., reduced evaporation), which contribute to stabilization of the upper ocean. Thus latent heat flux anomalies play an important role in the generation of the Ningaloo Niño/Niña, through their effect on MLD.

8.3 Relations with other climate modes The Ningaloo Niño is influenced by La Niña, as reviewed in Sections 8.2.1 and 8.2.2, but La Niña is not a necessary condition and the location of maximum SST anomalies seems to be the key for the remote influences from the ENSO (Marshall et al., 2015). They found a much higher correlation coefficient between Ningaloo Niño index (NNI; SST anomalies averaged over 108 E
coast, 22 S
28 S) and Niño-4 index (SST anomalies averaged over 160 E
175 W, 5 S
5 N) than NNI and Niño-3 index (SST anomalies averaged over 90 W
150 W, 5 S
5 N). This may partly explain why a Ningaloo Niño event occurred along with the extreme El Niño event of 1982
83, whose maximum positive SST anomalies were located in the far eastern equatorial Pacific. As stated earlier, this is also supported by a CGCM experiment of Kataoka et al. (2018) with the decoupled tropical Pacific. The above relation between ENSO and Ningaloo Niño/Niña may not be stationary over the past 215 years according to coral records (Zinke et al., 2014).

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Although most past studies discussed remote influences from the ENSO, Zhang and Han (2018) suggested a two-way interaction between the southeast Indian Ocean and western tropical Pacific. When their AGCM was forced by SST anomalies associated with the Ningaloo Niño, negative SLP anomalies extending to the Maritime Continent are induced. The resulting anomalous positive zonal SLP gradient in the western equatorial Pacific leads to strengthening of trade winds and induces upwelling equatorial Kelvin waves, which are favorable for the development of La Niña. This, in turn, may strengthen the Ningaloo Niño through the above mentioned remote forcing mechanisms. Approximately a half of Ningaloo Niño that does not cooccur with La Niña cooccurred with the positive IOD (Zhang et al., 2018), a climate mode associated with negative SST anomalies in the southeastern tropical Indian Ocean and positive SST anomalies in the western tropical Indian Ocean (Saji et al., 1999; Behera et al., 2020b, reviewed it in Chapter 5, Air
Sea Interactions in Tropical Indian Ocean: The Indian Ocean Dipole, of this book). According to their AGCM experiments, negative SST anomalies in the eastern pole of the IOD induce anomalous northerly winds off the west coast of Australia. However, not all positive IOD events are followed by a Ningaloo Niño event and they suggested that this may be due to diversity in SST anomalies associated with the IOD (Endo and Tozuka, 2016; Tozuka et al., 2016). Also, this relation may be period or data-dependent as Marshall et al. (2015) found a correlation coefficient of only 20.05 between NNI and Dipole Mode Index (SST anomalies averaged over 50 E
70 E, 10 S
10 N minus those averaged over 90 E
110 E, 10 S
Equator) from 1960 to 2011. There exists another climate phenomenon in the southern Indian Ocean, known as the IOSD (Morioka et al., 2020, reviewed it in Chapter 9, Interannual-to-Decadal Variability and Predictability in South Atlantic and Southern Indian Oceans, of this book). Although the IOSD is weakly correlated with the ENSO, it has an independent component (Zhang et al., 2019). Since both IOSD and Ningaloo Niño/Niña are linked with interannual variations of the Mascarene High (see Behera and Yamagata, 2001 and Morioka et al., 2010 for the role of the subtropical high in the IOSD), how these two climate modes may interact with each other is an interesting issue. On the interdecadal timescale, Feng et al. (2015a) investigated a possible link between Ningaloo Niño/Niña and IPO based on CGCM experiments and instrumental observational data analysis. They showed that the decadal increase in the Ningaloo Niño after the late 1990s is due to the recent shift to the negative phase of the IPO; enhanced poleward warm advection by the Leeuwin Current related to anomalous strengthening of the Indonesian Throughflow and cyclonic wind anomalies off the west coast of Australia associated with the negative IPO are suggested to provide a more favorable condition for the development of Ningaloo Niño events. The Ningaloo Niño/Niña may also be influenced by intraseasonal variations. For instance, Marshall and Hendon (2014) showed how the MJO may influence the Western Australian coast. Also, the MJO contributed to the development of the 2011 Ningaloo Niño event by an intraseasonal strengthening of the warm advection by the Leeuwin Current (Feng et al., 2013). More recently, Zhang et al. (2017) showed that phases 3
6 of the MJO provide a more favorable condition for the marine heatwaves by exciting downwelling Kelvin waves in the northwestern Australian shelf that lead to the enhanced warm advection by the Leeuwin Current.

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8.4 Impacts Since the Ningaloo Niño is the major cause of marine heatwaves (Hobday et al., 2016) along the western coast of Australia, which is known as one of the biodiversity hotspots (Tittensor et al., 2010), it has major impacts on the marine ecosystem there. Especially during the unprecedented warming associated with the 2010
11 Ningaloo Niño, widespread coral bleaching in the Ningaloo Reef (Depczynski et al., 2013; Pearce and Feng, 2013; Falter et al., 2014; Foster et al., 2014; Zhang et al., 2017) and tropicalization of marine species including extensive loss of kelp forests (Wernberg et al., 2013, 2016; Caputi et al., 2016; Pearce et al., 2016) occurred. Impacts on the biological productivity have also been examined using satellite observations (Huang and Feng, 2015) and an ocean model with a biogeochemical module (Narayanasetti et al., 2016). Tozuka et al. (2014) showed through observational data analyses and simulations with an AGCM that precipitation over northwestern Australia, which is vulnerable to rainfall variability (Turner and Asseng, 2005), tends to increase during Ningaloo Niño and decrease during Ningaloo Niña. Doi et al. (2015) suggested that the recent warming in the coastal region off Western Australia associated with global warming (Zinke et al., 2014) and the negative phase of the IPO (Feng et al., 2015a) may have strengthened the link between rainfall anomalies over northwestern Australia and Ningaloo Niño/Niña. Because of the large impacts, it is useful to predict the occurrence of Ningaloo Niño/Niña, and the first such attempt was made by Doi et al. (2013) using their dynamical seasonal prediction system based on a CGCM. They showed that Ningaloo Niño (Niña) events that cooccur with La Niña (El Niño) events may be predicted a few seasons ahead, because their model has very high skills in predicting ENSO events (Behera et al., 2020a, reviewed it in Chapter 3, Air
Sea Interaction in Tropical Pacific: The Dynamics of El Niño/Southern Oscillation, of this book) and simulates remote influences from the ENSO on the west coast of Australia relatively well. However, their model has almost no skill in predicting other Ningaloo Niño/Niña events. Thus more effort is required for further improvements in the seasonal prediction, although a recent work by Doi et al. (2016) presented promising results in this direction.

8.5 Conclusion The mechanisms of the Ningaloo Niño reviewed in this chapter are summarized in Fig. 8
5. A series of positive air-sea feedback may operate once positive SST anomalies are generated. An anomalous cyclone induced by positive SST anomalies weakens the climatological anticyclonic wind (i.e., northerly wind anomalies along the coast) and enhances warm advection by the Leeuwin Current. At the same time, the reduced latent heat loss contributes both directly and indirectly to positive SST anomalies. For the latter, it is due to enhanced warming by the climatological shortwave radiation owing to an anomalously shallow mixed layer induced by the latent heat flux anomalies. La Niña may also contribute to the development of a Ningaloo Niño event both through coastal downwelling waves that strengthen the Leeuwin Current and an atmospheric teleconnection that enhances anomalous cyclone off

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FIGURE 8–5 Schematic diagram summarizing the proposed generation mechanisms of the Ningaloo Niño. Light green boxes represent atmospheric processes, while light blue boxes represent oceanic processes.

the west coast of Australia. Although generation mechanisms of the Ningaloo Niño/Niña have been discussed quantitatively based on mixed-layer heat budget analyses, relative importance of the three mechanisms has not been fully quantified. Dedicated experiments controlling atmospheric forcing and oceanic lateral boundary conditions may be useful in this respect, as was the case for other climate modes such as the IOD (cf. Chen et al., 2016). Marine heatwaves that have detrimental effects on marine ecosystems are prevalent in the global ocean (Hobday and Coauthors, 2016; Smale and Coauthors, 2019) and the Ningaloo Niño is their major cause along the west coast of Australia. Such marine heatwaves in eastern boundary coastal regions are caused by similar coastal niños, that is, Benguela Niño off the southwest coast of Africa, California Niño off the west coast of North America, and Dakar Niño off the northwestern Africa (Oettli et al., 2020, reviewed those in Chapter 10, The Other Coastal Niño/Niña—The Benguela, California and Dakar Niños/Niñas, of this book). Although there are similarities among the four coastal niños, the Ningaloo Niño is unique in that it occurs in a region where SSTs are higher than other eastern boundary regions owing to the poleward-flowing Leeuwin Current. As a result, marine heatwaves in the Ningaloo Niño region may have more devastating impacts, and the situation may exacerbate under global warming (Frölicher et al., 2018; Oliver and Coauthors, 2018). We note that figures presented in this chapter are based on linear regression analyses and the method does not allow us to discuss nonlinearity in this climate mode. The Ningaloo Niño/Niña is known to be positively skewed with stronger Ningaloo Niño (Feng et al., 2015a). Given that the ENSO is positively skewed and negatively correlated with the Ningaloo Niño/Niña, the positive

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skewness of the Ningaloo Niño/Niña is counterintuitive and may not be solely explained by that of the remote effects. Although this aspect of the Ningaloo Niño/Niña has been partially discussed (e.g., Tozuka and Oettli, 2018), further studies are necessary. Another interesting topic is to examine the role of salinity. In the Indian Ocean, several studies have revealed that salinity anomalies affect upper ocean temperature anomalies associated with the IOD through their influences on stratification and circulation (e.g., Masson et al., 2004; Kido and Tozuka, 2017). In this regard, Feng et al. (2015c) is the only study that investigated the salinity effect on the Ningaloo Niño, but they did not quantify how salinity anomalies affect the evolution of temperature anomalies through their impacts on upper ocean stratification. Also, there is some room for further improvements in the prediction, which may help us to better prepare for detrimental influences of the Ningaloo Niño/Niña. Since the state-ofthe-art CGCMs have various biases in simulating the amplitude of the Ningaloo Niño/Niña (Kido et al., 2016), further model developments are necessary. It is also important to search for possible sources of predictability other than the ENSO. Only 6 years have passed since its identification. Although significant advancements have been made, there is at least as much work to be done in the coming decade with both observations and modeling to obtain more comprehensive understanding of the Ningaloo Niño/ Niña. In the meantime, maintaining long-term monitoring of the variability and heat transport of the Leeuwin Current, through Integrated Marine Observing System mooring network and event-based glider sampling, and regional air-sea flux measurement are important to better understand the dynamics and predictability of the future occurrences of the Ningaloo Niño/Niña (Hermes and Coauthors, 2019).

Acknowledgment We thank the reviewer for constructive comments. T.T. wishes to thank Prof. Toshio Yamagata for the development of the Ningaloo Niño/Niña research. T.T. is supported by the Japan Society for the Promotion of Science (JSPS) through Grant-in-Aid for Scientific Research (B) JP16H04047. S.K. is financially supported by the Research Fellowship of the JSPS and Leading Graduate Course for Frontiers of Mathematical Sciences and Physics, MEXT, Japan. M.F. is supported by Centre for Southern Hemisphere Ocean Research (CSHOR) and Integrated Marine Observing System. CSHOR is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales and University of Tasmania. W.H. and L.Z. are supported by NSF OCE 1658132 and NASA Ocean Surface Topography Science Team award NNX17AI63G. T.T., M.F., and W.H. served as panel members for the CLIVAR/IOC-GOOS Indian Ocean Region Panel and discussed about the Ningaloo Niño/Niña during their terms.

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9 Interannual-to-decadal variability and predictability in South Atlantic and Southern Indian Oceans Yushi Morioka1, Francois Engelbrecht2, Swadhin Kumar Behera1 1

APPLICAT ION LABORATORY, RE SEARCH INSTITUTE FOR VALUE-ADDED-INFORM ATION GENE RATION, JAP AN AGENCY FOR MARINE-EARTH SCIENCE AND TECHNOLOGY, YOKOHAMA, JAPAN 2 GLOBAL CHANGE INS TITUTE, UNIVERS ITY OF T HE WITWATERSRAND, JOHANNESBUR G, SOUTH AFRICA

9.1 South Atlantic and Indian Ocean subtropical dipoles South Atlantic and southern Indian Oceans are characterized by western boundary current systems under the strong influences of subtropical highs, that is, the St. Helena High and the Mascarene High, respectively. The Agulhas Current flowing westward along the southern coastal areas of South Africa (e.g., Lutjeharms, 2006) and the Antarctic Circumpolar Current (ACC) flowing eastward in the Southern Ocean (e.g., Rintoul et al., 2001) exchange seawater zonally between the two oceans. Accumulation of warm water associated with the Agulhas Current and the presence of Madagascar orography (Barimalala et al., 2018) anchors the South Indian convergence zone (SICZ; Cook, 2000) over the Mozambique Channel during austral summer (December
February). The tropical-temperate cloud bands that manifest within the SICZ bring most of annual rainfall over the summer rainfall regions of southern Africa (e.g., Ratna et al., 2013). On the other hand, formation of cold sea surface temperature (SST) in the eastern part of the South Atlantic due to coastal ocean upwelling (please refer to Oettli et al., 2020, Chapter 10 of this book for a review on Benguela Niño) induced by offshore southeasterly contributes to the arid climate over the southwestern part of southern Africa (Engelbrecht and Engelbrecht, 2016). The development of continental heat low over Namibia and South Africa during austral summer (e.g., Taljaard, 1986; Engelbrecht et al., 2009), which occasionally links to the dynamically induced Angola Low (Mulenga, 1999), plays an important role in advecting moisture from the tropical Africa southward (D’abreton and Lindesay, 1993). This moisture flux converges with the moisture transported from the southern Indian Ocean to define the origin of the SICZ and support the formation of tropicaltemperature cloud bands (Cook, 2000). The background SSTs over the South Atlantic and the

Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00004-6 © 2021 Elsevier Inc. All rights reserved.

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southern Indian Oceans regulate regional climate over southern Africa through modulation of atmospheric circulation and moisture transport (Reason et al., 2006). Interannual SST variations over the South Atlantic and southern Indian Oceans have been extensively studied in the context of significant links to the southern African rainfall variability (e.g., Reason, 1998; Reason and Mulenga, 1999; Behera and Yamagata, 2001; Fauchereau et al., 2003; Reason et al., 2006). For example, southern Africa experienced consecutive flood events associated with extreme rainfall during December 2010 and February 2011, causing significant damages on regional economy and human lives [OCHA (United Nations Office for the Coordination of Humanitarian Aids), 2011]. The associated December to February SST anomalies represented distinct meridional dipole patterns over the South Atlantic (Venegas et al., 1997) and the southern Indian Ocean (Fig. 9
1A), so-called “subtropical dipole” (Behera and Yamagata, 2001). Here, the anomalies shown in Fig. 9
1A with respect to the base period of 1982
2018 were calculated from the OISST v2 data (Reynolds et al., 2002). Associated with the dipole SST anomalies, subtropical highs over both the basins shifted southward and strengthened, which facilitated more moisture advection from the southern Indian Ocean to southern Africa (Behera and Yamagata, 2001; Fauchereau et al., 2003). A strong La Niña event occurred in 2010 in the tropical Pacific, and an atmospheric teleconnection from the tropical Pacific

FIGURE 9–1 (A) Observed SST anomalies (in  C) during December 2010 and February 2011. The SST anomalies associated with the South Atlantic subtropical dipole (SASD) and the Indian Ocean subtropical dipole (IOSD) are indicated by black arrows, respectively. Anomalies are calculated with respect to the base period of 1982
2018. (B) Power spectra of the SASD (red line) and IOSD (green line) indices. Here we define the SASD (IOSD) index as the SST anomaly difference between 30
40 S, 10
30 W and 15
25 S, 0
20 W (30
40 S, 50
70 E and 15
25 S, 85
105 E), following Morioka et al. (2012).

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may thus have played an important role in triggering the subtropical high variations (please refer to Tozuka et al., 2020 in Chapter 8 of this book for a review and its importance for Ningaloo Niño) and generating the dipole SST anomalies over the South Atlantic and southern Indian Oceans (Morioka et al., 2014). However, a recent study suggests that the development of dipole SST anomalies may also anchor the subtropical high variations in the southern Indian Ocean over one season through local air
sea interaction process involving storm tracks variability (Morioka et al., 2015a). Physical mechanisms on development and decay of the South Atlantic subtropical dipole (SASD) and the Indian Ocean subtropical dipole (IOSD) are well established in the literature (Fauchereau et al., 2003; Sterl and Hazeleger, 2003; Suzuki et al., 2004; Hermes and Reason 2005; Haarsma et al., 2005; Morioka et al., 2010, 2011). A statistical analysis of the SASD and IOSD indices defined by Morioka et al. (2012) shows that both the SASD and IOSD events tend to develop during austral summer with an interannual frequency, as shown in Fig. 9
1B. During the positive phase of the SASD and IOSD events, strengthening and southward shift of the subtropical high induce wet northwesterly wind anomalies over the southwestern part of each basin, which acts to reduce evaporation due to a decrease in humidity difference between the ocean and the near-surface atmosphere (Morioka et al., 2012). This results in causing ocean mixed-layer thickness thinner than normal condition and enhances warming of mixed layer by incoming shortwave radiation, leading to the development of warm SST anomaly pole (Morioka et al., 2012). The aforementioned processes are clearly seen in the analysis of mixedlayer heat balance (Fig. 9
2A and C). On the other hand, during decay phase of the SASD and IOSD events, cooling of mixed layer by entrainment over the positive SST anomaly pole enhances due to the thinner-than-normal mixed-layer thickness. Along with the entrainment process, anomalous increase in evaporation due to the warmer SST also contributes to decaying the warmer SST itself (Morioka et al., 2012). Similar but opposite processes tend to operate for the cold SST anomaly pole associated with the SASD and IOSD events (Fig. 9
2B and D). Therefore ocean mixed-layer thickness variability associated with the subtropical high variations plays a critical role in the development and decay of the SASD and IOSD. There are multiple factors that excite subtropical high variations over the South Atlantic and southern Indian Oceans, which are crucial for the development of the SASD and IOSD (Fig. 9
3). Earlier studies have indicated atmospheric teleconnections from El NiñoSouthern Oscillation (ENSO; Behera et al., 2020 reviewed it in Chapter 3 of this book), so-called the Pacific-South American (PSA; Mo and Paegle, 2001) pattern, and from the mid-high-latitude intrinsic atmospheric variability, known as the Southern Annular Mode (SAM; Thompson and Wallace, 2000). For example, strengthening and southward shift of subtropical highs during the positive phase of the SASD and IOSD events tend to cooccur with La Niña phenomenon via the PSA teleconnection pattern (Rodrigues et al., 2015) and the positive phase of the SAM (Malherbe et al., 2014). Furthermore, atmospheric variability over the South Atlantic has been recently found to receive influence from sea-ice variability over the Weddell Sea (Morioka et al., 2017b). An anomalous decrease in the sea-ice concentration (SIC) acts to increase surface air temperature through a decrease in surface albedo and hence weaken near-surface baroclinicity to the north of the Weddell Sea through

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FIGURE 9–2 (A) Composite time series of mixed-layer temperature tendency anomalies (MLT tend.) and relative contribution from net surface heat flux (NSHF), horizontal advection (Hor. Adv.), entrainment (Ent), and residual (Res) over the warm SST anomaly pole of the positive IOSD obtained from 500-year simulation of the SINTEX-F1 CGCM. (B) Same as in (A), but over the cold SST anomaly pole of the positive IOSD. (C) Same as in (A), but for anomalous contribution from the NSHF, shortwave radiation (SW), longwave radiation (LW), latent heat flux (LH), and sensible heat flux (SH) to the mixed-layer temperature tendency anomalies. (D) Same as in (C), but over the cold SST anomaly pole of the positive IOSD. Closed (open) circles indicate anomalies exceeding 99% (95%) confidence level using a twotailed t-test. Courtesy Morioka, Y., Tozuka, T., Masson, S., Terray, P., Luo, J.J., Yamagata, T., 2012. Subtropical dipole modes simulated in a coupled general circulation model. J. Clim. 25, 4029
4047.

reducing the meridional gradient of surface air temperature (Morioka et al., 2017b). This provides a favorable condition for maintaining anomalously high pressure over the South Atlantic during the positive phase of the SASD.

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FIGURE 9–3 Schematic diagram on generation of the SASD and the IOSD. Respective roles of remote forcings (ENSO, Southern Annular Mode, and sea-ice variability over the Weddell Sea) and local air
sea interaction are described. Red color indicates warmer-than-normal SST, while blue color represents colder-than-normal SST.

Linkages between the Antarctic sea-ice variability and the IOSD remain to be demonstrated. However, anomalously high pressure in the southwest Indian Ocean has been shown to correlate significantly to the SAM (Malherbe et al., 2014). Local air
sea interaction involving dipole SST anomalies has been suggested to play a secondary role in sustaining the Mascarene High variations through changes in near-surface baroclinicity (Morioka et al., 2015a). During the positive (negative) phase of the IOSD, the warm SST anomalies act to enhance (reduce) the meridional temperature gradient to the south (north), leading to an increase (decrease) in near-surface baroclinicity. Associated with an increase in baroclinicity south of an anomalously strong Mascarene High, storm tracks shift anomalously southward in the southern Indian Ocean, which leads to southward shift of the westerly jet in the upper troposphere (Morioka et al., 2015a). This condition helps sustain the southward shift and strengthening of the Mascarene High over the southern Indian Ocean. Since strong SST fronts exist in the Agulhas Return Current region, the warm SST anomalies associated with the positive phases of the IOSD would additionally affect the storm tracks activity and hence large-scale atmospheric variability through eddy
mean interaction process (Nakamura, 2012). This ocean-atmosphere feedback processes involving the SST anomalies are considered relatively weak over the South Atlantic, probably due to lack of strong SST front in the region where the SASD occurs and/or due to overwhelming influence of remote forcing such as ENSO and SAM.

9.2 Predictability of the subtropical dipoles Considering the widespread impacts of anomalous regional rainfall over southern Africa on sectors such as agriculture and water management, skillful seasonal predictions of this variability may be of immense value for planning and management purposes (Malherbe et al., 2013; Archer et al., 2017, 2018). To this end, it is important to investigate whether the occurrence and development of the SASD and IOSD can be skillfully predicted using state-of-the-art coupled general circulation models (CGCMs) at seasonal timescales (in addition to skillful predictions of the global

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modes of variability such as ENSO and the SAM). In order to predict the SASD and IOSD events in advance, the subtropical high variations and the associated mixed-layer thickness variations should be well represented in the CGCMs. For example, a recent study by Yuan et al. (2014) has reported that both the SASD and IOSD can be skillfully predicted up to one season ahead using their CGCM called the SINTEX-F1. They have discussed that the prediction skills for the SASD slightly exceed those for the IOSD, probably due to better representation of subtropical high variations associated with atmospheric teleconnection from ENSO. A subsequent study by Doi et al. (2016) has suggested that their upgraded CGCM called the SINTEX-F2, in which the horizontal and/or vertical resolutions in both the atmosphere and ocean models are increased and a dynamic sea-ice model has been newly incorporated, represents higher skills in predicting the IOSD. This is probably due to better representation of local air
sea interaction process near the strong SST front in the Agulhas Return Current region that affects the location and amplitude of the storm tracks and the upper-level westerly winds (as per the earlier discussion). However, the prediction accuracy for the SASD does not show much improvement, because the SINTEX-F2 model is initialized using the observed SST dataset only, so that the influence of the observed sea-ice anomalies (e.g., SIC) may be underestimated in the CGCM. To address this remaining issue on a potential role of sea-ice variability in predictability of the SASD, we performed further experiments using the same SINTEX-F2 model, in which both the SST and the SIC are initialized using the observed data. A comparison of the predicted SST anomalies between the model experiments with and without the SIC initialization (Fig. 9
4A and B) reveals that warm SST anomalies associated with the SASD are better predicted in the experiment where both SST and SIC are initialized. This can be evaluated by an increase in the pattern correlation from 0.61 to 0.65 and a decrease in the root-mean-squared errors from 0.55 C to 0.53 C, over the analysis domain. Also, the warm SST anomalies associated with the IOSD become well separated from those associated with the SASD, indicating a possible role of sea-ice initialization in improving the atmospheric variability over the South Atlantic. Other experiments, in which the model’s subsurface ocean temperature and salinity are additionally initialized using threedimensional ocean data assimilation (3DVAR; Storto et al., 2011, 2014) scheme, modify the amplitude of warm and cold SST anomalies associated with the SASD and IOSD, reflecting a role of mesoscale eddies in the subsurface ocean (Fig. 9
4C). However, the pattern correlation (0.63) and the root-mean-squared error (0.55) do not substantially change compared to the experiment with the SST and SIC initializations. These results indicate that accurate prediction of spatial pattern and amplitude of the SST anomalies requires both the SST and SIC initializations. Continuous improvement in predicting the SASD and IOSD should eventually translate in more skillful predictions of atmospheric variability and hence rainfall variability over the southern Africa.

9.3 Decadal variability over the South Atlantic and Southern Indian Oceans The southern Indian Ocean is widely known to undergo decadal-multidecadal fluctuation with basin-wide warming and cooling associated with the overlying atmospheric circulation variability

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FIGURE 9–4 (A) 2010 December
February SST anomalies predicted from September 1, 2010 using the SINTEX-F2 model in which the SST is initialized with the observed data. A simple average of 12 ensemble members generated using different initial conditions is shown. (B) Same as in (A), but from the model in which the model’s SST and sea-ice concentration (SIC) are initialized. (C) Same as in (A), but from the model in which the model’s SST, SIC, subsurface ocean temperature, and salinity are initialized.

(Allan et al., 1995). During the warm phase of the southern Indian Ocean, the Mascarene High becomes stronger and brings more moisture from the southern Indian Ocean to southern Africa, resulting in anomalously high rainfall (Malherbe et al., 2014). A recent study by Behera et al. (2018) suggests that the decadal rainfall variations over southern Africa could further contribute to decadal variability in malaria incidences there. Although there are many studies that discuss potential sources of decadal SST variability from different perspectives such as remote forcing associated with interdecadal modulation of the Indonesian Throughflow (Reason et al., 1996), local air
sea interaction involving atmospheric circulation variability (Reason et al., 1998), and internally generated ocean mixed-layer variations (Yamagami and Tozuka, 2015). Another study

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(Morioka et al., 2015b) has provided new insights into remote influence from the South Atlantic, with a particular focus on eastward-propagating decadal SST variability along the ACC. Fig. 9
5 describes time evolution of 8-year high pass
filtered SST and sea-level pressure (SLP) anomalies during austral summer from 1990 to 2005. Warm SST anomalies started to appear over the South Atlantic in 1990, before slowly migrating eastward in the mid-latitudes along the ACC. After reaching the southwestern part of the southern Indian Ocean in 1999, the warm SST anomalies gradually decayed. Associated with the warm SST anomalies, high SLP anomalies also appeared over the South Atlantic in 1990 and slowly migrated eastward to the southwestern part of the southern Indian Ocean. This intriguing aspect of eastward propagation of the SST and SLP anomalies indicates that the decadal SST variability over the southern Indian Ocean can be remotely induced by zonal advection of the SST anomalies from the South Atlantic, while the local air
sea interaction can operate to amplify the decadal variability in the Agulhas Return Current region where the strong SST front exists. The warm SST anomalies, once they reach the SST frontal zone, act to shift the storm tracks southward by strengthening (weakening) the meridional temperature gradient to the south (north) of the SST front and hence increasing (decreasing) near-surface baroclinicity. Following the warm SST and high SLP anomalies, cold SST anomalies started to appear over the South Atlantic in 1996 and migrated eastward toward the southern Indian Ocean. Eastward propagation of the cold SST anomalies and the associated low SLP anomalies seem to play an important role in reversing the warm state of the southern Indian Ocean to the cold state after 2005. Decadal SST variability over the South Atlantic has received much attention in recent years due to a gradual increase in the number of in situ ocean observations. In contrast to the southern Indian Ocean experiencing the decadal-interdecadal SST variability, the South Atlantic shows distinct decadal variability with a meridional dipole of warm and cold SST anomalies (Venegas et al., 1996, 1997). Spectrum analysis of reconstructed SST and SLP data reveals decadal variability with a frequency of 14
16-year period (Venegas et al., 1997), while that of 300-year simulation using a CGCM suggests multidecadal variability with a typical periodicity of 25
30 years (Wainer and Venegas, 2002). During the warm phase of the southern South Atlantic, the overlying SLP anomalies show a southward shift and strengthening of the St. Helena High. Based on a 300-year simulation of the SINTEX-F2 CGCM, Morioka et al. (2017a) have examined mixed-layer heat balance to determine that the warm SST anomalies over the South Atlantic are mostly driven by changes in oceanic heat transport, in particular via meridional advection and vertical entrainment processes (Fig. 9
6A and B). The southward-shifted high SLP anomalies over the South Atlantic tend to increase southward Ekman transport that advects warm water from the subtropics and also induce Ekman downwelling that inhibits subsurface cold water from being entrained into the surface mixed layer. The decadal SST variability over the South Atlantic can thus be considered an atmosphere-driven variability, but it has alternatively been proposed that oceanic mesoscale eddies that can internally generate low-frequency variability through eddy
mean interaction (Le Bars et al., 2016). Relative roles of atmosphere-driven variability and ocean intrinsic variability need to be investigated in further studies.

Chapter 9 • Interannual-to-decadal variability and predictability

FIGURE 9–5 SST (in  C) and sea-level pressure (SLP) anomalies (in hPa) observed during austral summer (December
February) from 1990 to 2005. Eight-year running mean filter was applied to the anomalies.

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FIGURE 9–6 (A) Composite time series of mixed-layer temperature tendency anomalies (Tend) and relative contribution from net surface heat flux (Qnet), advection (Adv), and entrainment (Entrain) over the warm SST anomaly pole (40
50 S, 0
20 W) in the South Atlantic obtained from 270-year simulation of the SINTEX-F2 CGCM. (B) Same as in (A), but for anomalous contribution from the total advection (Total), zonal advection (Uadv), and meridional advection (Vadv). Adapted from Morioka, Y., Taguchi, B., Behera, S.K., 2017a. Eastward propagating decadal temperature variability in the South Atlantic and Indian Oceans. J. Geophys. Res. Ocean. 122, 5611
5623.

There are several potential sources of decadal SLP variability that induce decadal SST variability over the South Atlantic, but detailed mechanisms on generation of the decadal SLP variability are not well investigated. As a key climate driver on a decadal timescale, the Interdecadal Pacific Oscillation (IPO; Power et al., 1999) can induce atmospheric teleconnections such as the PSA pattern on a decadal timescale and trigger the decadal SLP variability over the South Atlantic, in a similar manner to the interannual climate variability over the South Atlantic such as the SASD (Rodrigues et al., 2015). Also, a decadal fluctuation of the atmospheric intrinsic variability in the mid-high latitudes, the SAM, may have links with the decadal SLP variability over the South Atlantic through meridional shift of the westerly jets, but detailed mechanisms and relative importance of the IPO and the SAM remain unclear and require further studies.

9.4 Predictability of the South Atlantic and Indian Ocean decadal variability Decadal climate predictability over the South Atlantic and southern Indian Oceans has received comparatively less attention due to relatively poor prediction skills compared to the other oceans (Guemas et al., 2013). Most of predictability in the decadal SST variability arises from skillful prediction of basin-scale warming related to global warming induced by greenhouse gas forcing, but few studies to date have examined skill in predicting internally driven decadal variability. A recent study by Morioka et al. (2018a) has identified that the decadal SST variability in the southwestern part of the southern Indian Ocean can be skillfully predicted 6
10 years ahead using the SINTEX-F2 CGCM in which only the SST is initialized with the

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231

FIGURE 9–7 SLP anomalies (in hPa) during 1994
98 and 1999
2003. From left to right, SLP anomalies from the atmospheric reanalysis and the anomalies predicted from March 1, 1994 using the SINTEX-F2 model in which the model’s SST is initialized with the observed data, respectively. The predicted anomalies based on a simple average of 12 ensemble members generated using different initial conditions are shown. Black boxes correspond to the center of decadal warm SST anomalies in the southern Indian Ocean. Adapted from Morioka, Y., Doi, T., Behera, S.K., 2018a. Decadal climate predictability in the southern Indian Ocean captured by SINTEX-F using a simple SST-nudging scheme. Sci. Rep. 8, 1029.

observed data. Decadal reforecast experiments initiated from 1994 and 1999 are found to successfully capture decadal warming of the southern Indian Ocean during late 1990s and early 2000s and its phase change into the colder state in the late 2000s, respectively. Along with the skillful prediction of decadal SST variability in the southern Indian Ocean, eastward propagation of decadal SLP variability from the South Atlantic to the southern Indian Ocean is well reproduced in the CGCM (Fig. 9
7). Since the model’s SST is only initialized using the observed SST data, skillful prediction of decadal SST and SLP variability over the southern Indian Ocean may stem from better representation of local air
sea interaction in the SST frontal regions where the SST and SLP anomalies travel from the South Atlantic. Predictability of the internally driven decadal SST variability over the South Atlantic has been recently demonstrated in the same SINTEX-F2 CGCM by additionally initializing subsurface ocean temperature and salinity (Morioka et al., 2018b). Fig. 9
8 describes spatial patterns of the SST anomalies during 2006
10 and 2011
15. The observed data shows that warm SST anomalies appeared in the South Atlantic during 2006
10 and slowly migrated eastward during 2011
15. The development and eastward migration of warm SST anomalies are not well captured by decadal reforecast experiments in which the model’s SST is only initialized in 2006 (middle panels in Fig. 9
8). On the other hand, decadal reforecast experiments, in which both the model’s SST and subsurface ocean temperature and salinity are initialized, represent skillful predictions in the development and eastward migration of the warm SST anomalies over the South Atlantic. The marked difference in the predicted SST anomalies between the two reforecast experiments indicates that the decadal SST variability over the South Atlantic can be predicted 6
10 years ahead due to improvement of initial

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FIGURE 9–8 SST anomalies (in  C) during 2006
10 and 2011
15. From left to right, SST anomalies from the observation and the anomalies predicted from March 1, 2006 in the SINTEX-F2 CTR experiments in which the SST is initialized and in the SINTEX-F2 3DVAR experiments in which the model’s SST and subsurface ocean temperature and salinity are initialized, respectively. Black boxes indicate the center of decadal warm SST anomalies in the South Atlantic. Modified from Morioka, Y., Doi, T., Storto, A., Masina, S., Behera, S.K., 2018b. Role of subsurface ocean in decadal climate predictability over the South Atlantic. Sci. Rep. 8, 8523.

subsurface ocean structure and the associated oceanic heat transport. In contrast to the decadal SST variability over the southern Indian Ocean, the role of local air
sea interaction is relatively weak over the warm SST anomalies in the southeastern part of the South Atlantic, probably due to lack of the strong SST fronts. Therefore, the subsurface ocean initialization in a CGCM is indispensable for skillful prediction of the decadal SST variability over the South Atlantic. Since there remains some disagreement in the amplitude of the SST anomalies between the observation and the predicted results (Fig. 9
8), further efforts on improving the model physics and initialization schemes are required for better prediction of the decadal SST variability over the South Atlantic.

9.5 Summary This chapter reviews the interannual climate variability in South Atlantic and southern Indian Oceans, particularly the two modes of variability, namely SASD and IOSD, embedded in those two basins. In addition, a decadal signal that originates in the southeastern South Atlantic Ocean and moves eastward to southern Indian Ocean is also discussed. These interannual to decadal variations in the South Atlantic and southern Indian Oceans have substantial influence on austral summer rainfall over southern Africa through modulation of atmospheric circulations. CGCM experiments together with observed data are used to understand the underlying physical processes for interannual and decadal climate variations. The subtropical highs in

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both basins are found to be the major contributors for the generation of SASD and IOSD on interannual timescales. There are multiple factors that excite subtropical high variations: atmospheric teleconnections from ENSO, SAM, and recently found sea-ice variability over the Weddell Sea (Morioka et al., 2017b). The sea-ice variability is also seen to contribute to the decadal climate variability in the South Atlantic that propagates eastward to develop decadal variability in the southwestern Indian Ocean. The SINTEX-F2 CGCM has good skills in the prediction of the SASD and IOSD at one season lead time. The model also shows reliable skills in predicting the internally generated decadal signal, especially with subsurface data assimilation, in the South Atlantic.

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10 The other coastal Niño/Niña—the Benguela, California, and Dakar Niños/Niñas Pascal Oettli1, Chaoxia Yuan2, Ingo Richter1 1

APPLICAT ION LABORATORY, RE SEARCH INSTITUTE FOR VALUE-ADDED-INFORM ATION GENE RATION, JAP AN AGENCY FOR MARINE-EARTH SCIENCE AND TECHNOLOGY,

YOKOHAMA, JAPAN 2 KEY LABORATORY OF M ETEOROLOG ICAL DISASTER OF MINISTRY OF EDUC ATION, COLLABORATIVE INNOVATION CENTER ON FORE CAST AND EVALUATION OF ME TEOROLOGICAL DISASTERS, NANJING UNIVERSITY OF INFORMATION S CIENCE & T ECHNOLOGY, NANJING, CHINA

10.1 Introduction Coastal upwelling is a remarkable expression of the influence of the wind on the coastal oceans. This phenomenon is particularly prevalent along eastern ocean boundaries, where the alongshore surface winds (ASWs) related to the subtropical highs blow equatorward and drive an offshore Ekman transport toward the ocean interior (Ekman, 1905), as a result of the balance between the Coriolis and turbulent drag forces (Kämpf and Chapman, 2016). The transported coastal surface water is replaced by the upwelled cold subsurface water (Brink, 2004; Hill et al., 1998; Mason et al., 2011; Sverdrup, 1938). Hence, the sea surface temperatures (SSTs) in the coastal upwelling regions are colder than those in the interior ocean at the same latitude. Also, the upwelled subsurface water is rich in nutrients and supports an abundance of marine species at different trophic levels (Carr, 2001; Carr and Kearns, 2003; Dragesund, 1971; Hill et al., 1998; Kämpf and Chapman, 2016; Mittelstaedt, 1991; Pelegrí et al., 2005; Van Camp et al., 1991). Coastal upwelling regions are characterized by high annual variability in catches (FAO, 2018), which are, apart from changes in the exploitation techniques, the result of the natural variability of upwelling intensity. The near-shore SST is considered a good indicator of the coastal upwelling variability (Lathuilière et al., 2008). Any change in the upwelling intensity will be visible in the SST (McLain et al., 1985), giving clues to their potentially deleterious impacts on the marine ecosystem. Regionally coupled oceanalandaatmosphere phenomena similar to El Niño/Southern Oscillation (ENSO) in the tropical Pacific (Philander, 1990) have been described in eastern Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00010-1 © 2021 Elsevier Inc. All rights reserved.

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ocean boundaries: the Benguela Niño in the southeastern subtropical Atlantic (Shannon et al., 1986), the Ningaloo Niño in the southeastern subtropical Indian Ocean (Feng et al., 2013; Kataoka et al., 2013), the California Niño in the northeastern subtropical Pacific (Yuan and Yamagata, 2014), and the Dakar Niño in the northeastern subtropical Atlantic (Oettli et al., 2016). In the present review we focus on the Benguela Niño, California Niño, and Dakar Niño, while the Ningaloo Niño is reviewed by Tozuka et al. (2020) in Chapter 8, The Ningaloo Niño/Niña: Mechanisms, Relation with Other Climate Modes and Impacts of this book. Of the three coastal Niños to be discussed here, the Benguela Niño has the longest research history, with the first dedicated studies appearing in the 1980s. While near-shore variability off the coasts of California and Northwest Africa certainly has been of interest for a long time, the importance of local air
sea feedbacks and their independence from ENSO have only been realized since the mid-2010s. As such, research into the California and Dakar Niños has a much shorter history than that into the Benguela Niño and, consequently, there is less literature on these two phenomena. Furthermore, climate model simulations experience far greater difficulties in the southeast Atlantic than in the other two regions (Richter, 2015), a problem that has garnered much attention and led to a rich literature. The present review reflects the imbalance in the literature and therefore devotes more time to the Benguela Niño than to the California and Dakar Niños. While there exist some asymmetries, Niñas are in general opposite of Niños and hence we discuss the Niños in most part of this chapter. In the following section, we present the climate of the upwelling systems and the underlying dynamics. In Section 10.3, the representation of coastal Niños in climate models is examined, while in Section 10.4, the future of upwelling regions in the context of climate change is discussed. Finally, this review is summarized and discussed in Section 10.5.

10.2 The upwelling regions and their variability 10.2.1 Benguela system The Benguela region off the coast of southwestern Africa, extending roughly from 15 S to 35 S, is subject to intense upwelling that is active year round and supports a rich marine ecosystem and one of the world’s largest fisheries (Carr, 2001; Chavez and Messié, 2009; Rossi et al., 2008). While the Benguela upwelling system (BUS) shares many of the characteristics of other eastern boundary upwelling systems, such as subtropical location and upwelling-favorable ASW, it also displays some unique features, including a poleward coastal current off Angola (Peterson and Stramma, 1991) that flows against the prevailing surface wind direction, and the influx of Indian Ocean water at its southern boundary through Agulhas leakage (de Ruijter et al., 1999). Upwelling strength in the Benguela region is subject to pronounced variability on interannual time scales (Florenchie et al., 2003; Shannon et al., 1986), which can have severe impacts on fisheries (Boyer et al., 2001; Gammelsrød et al., 1998). The major driver of upwelling in the BUS is the ASW (Fig. 10
1; Fennel et al., 2012; Nelson and Hutchings, 1983; Shannon, 1985), which leads to offshore Ekman transport near the surface, with cold, nutrient-rich water at depth welling up to the surface to provide mass

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FIGURE 10–1 Climatological averages of SST (shading;  C) and near-surface winds (vectors; m s21) for (A) February
March
April (FMA), and (B) July
August
September (JAS). SST and wind data are from the Scatterometer Climatology of Ocean Winds (SCOW; Risien and Chelton, 2008), which is based on satellite observations for the period September 1999
October 2009.

continuity. The ASWs also drive an equatorward ocean flow, the Benguela Current, which advects cold surface waters into the region (Charney, 1955; Philander and Yoon, 1982; Yoshida, 1955). These winds form the eastern flank of the South Atlantic anticyclone (also called St. Helena High), which is linked to the pronounced subsidence in the subsiding

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branch of the Hadley cell and climate variability in the southern Atlantic (Morioka et al., 2020 reviewed it in Chapter 9: Interannual-to-decadal variability and predictability in South Atlantic and southern Indian Oceans, of this book). The subsiding air gives rise to stable conditions in the lower troposphere and is thus conducive to the formation of marine stratocumulus clouds, which are prevalent in the southeastern tropical Atlantic (Klein and Hartmann, 1993). This type of low-level cloud shields the ocean surface from incoming solar radiation while emitting infrared radiation at relatively high temperature, leading to a net cooling effect on the ocean surface (Hartmann et al., 1992). Further, cooling is provided by the latent heat flux that accompanies the high wind speeds on the eastern flank of the St. Helena High. Thus, a variety of processes contribute to cooling in the BUS, with the stratocumulus and latent heat flux effects extending far into the ocean interior. The impact of this cooling is evident in Fig. 10
1, where cool SSTs stretch from the coast of southwestern Africa toward the center of the basin, with the coolest SSTs along the coastline. A remarkable feature of the current system along the southwest African coast is the existence of a poleward coastal current, the Angola Current, which originates at around 5 S (Kopte et al., 2017; Peterson and Stramma, 1991; Rouault et al., 2007; Yamagata and Iizuka, 1995). This current is relatively poorly observed but both research cruise data during 1995
2017 (Tchipalanga et al., 2018) and mooring data during 2013
2015 (Kopte et al., 2017) suggest annual mean poleward flow extending to 100
300 m depth depending on the season, with occasional episodes of flow reversal. At around 16 S, the poleward Angola Current meets the equatorward Benguela Current leading to a strong meridional SST gradient called the Angola-Benguela frontal zone (ABFZ; Fig. 10
1). The existence of the poleward Angola Current poses an apparent conundrum, as it flows against the generally equatorward directed local winds (Fig. 10
1). Closer consideration of the underlying dynamics shows that the coastal currents are sensitive not only to the strength of the ASWs but also to the gradient of these winds in the offshore direction (Capet et al., 2004; Fennel et al., 2012; Junker, 2014; McCreary and Chao, 1985; Renault et al., 2012; Small et al., 2015). More specifically, if the maximum of the equatorward surface winds is located offshore, it will lead to cyclonic wind stress curl that is associated with Ekman pumping-induced upwelling offshore and poleward flow along the coast. Thus, the precise structure of the near-shore winds is important in determining the balance between Ekman pumping and Ekman divergence-driven upwelling. As can be seen in Fig. 10
1, the maximum winds tend to be very close to the coast south of 18 S but offshore to the north. This explains, at least in part, the existence of the poleward Angola Current. As will be discussed in detail in Section 10.4, the sensitivity of upwelling and coastal currents to the precise structure of the near-shore winds also plays a key role in the persistent biases of GCMs in the BUS. The annual cycle of SST along the southwest African coast (Fig. 10
2A) shows the coldest SST in the BUS occurring in August and September, with SSTs at 25 S dropping below 14 C. Farther toward the equator, at 10 S, the seasonal cycle is more pronounced, with SSTs dropping by approximately 6 C from April through August. This is consistent with the contemporaneous strengthening of the equatorial trade winds, which excites equatorial Kelvin waves

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FIGURE 10–2 Annual climatological cycle of SST (shading;  C) along the coasts of (A) southwest Africa, (B) western North America, and (C) northwest Africa, averaged between the coast and 2 degree offshore, and plotted as latitude-time sections. The bundled contour lines around 23 N in panel (B) are due to the jump from the mainland to the Pacific coast of Baja California. SST and wind data are from SCOW.

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that subsequently are transmitted into coastally trapped waves at the eastern boundary and lead to changes in the upwelling (Ostrowski et al., 2009; Rouault, 2012). While the latitudinal position of the ABFZ is relatively stable it does display seasonality, attaining its southernmost position at 17 S from January through March and its northernmost position at 15 S from July through September (Meeuwis and Lutjeharms, 1990; Shannon et al., 1987). Variability in the BUS occurs on a range of time scales, with peaks at the synoptic, intraseasonal, and interannual time scales (Bachèlery et al., 2016). Major warming events last several months and occur about once a decade, recent examples being the 1984, 1995, and 2011 warming events. Such major warming events are termed Benguela Niños (Shannon et al., 1986) and can have devastating effects on local fisheries. This was particularly evident during the 1995 event when there were reports of fishery collapses (Gammelsrød et al., 1998). There is, however, no commonly agreed definition of Benguela Niños and some authors may include, into the definition, the events that are weaker or occur on shorter time scales. There is a clear preference for Benguela Niños to occur in austral fall, when the background SSTs in the region are warmest. This is evident in Fig. 10
3A, which shows the annual cycle of the standard deviation of SST anomalies in the Angola-Benguela area (ABA; defined as the region 20-10 S, 8 E-coast), which is a commonly used index (Florenchie et al., 2003). The variability peaks in April at about 1.2K, which is quite comparable to the amplitude of El Niño in the tropical Pacific. The temporal evolution of Benguela Niños is shown in Fig. 10
4. Here, events have been composited on 1 standard deviation of March
April
May (MAM) SST anomalies in the ABA. Coastal warming of up to 0.5K is already present in January with attendant northerly surface wind anomalies along the African coast between the equator and 10 S. In February, surface wind anomalies become more widespread, with pronounced westerly anomalies over the western equatorial Atlantic, while SST anomalies in the ABA continue to grow. As the SST anomalies in the ABA continue to grow from February through April, positive rainfall anomalies develop to the northeast, including the coastal regions of Angola. Starting from May, surface wind anomalies weaken, particularly in the eastern part of the basin, and the SST anomalies in the ABA decline. In the eastern equatorial Atlantic, on the other hand, moderate positive SST anomalies develop and peak around 0.5K in June. Early studies on the generation mechanisms of Benguela Niños have emphasized the role of remotely forced Kelvin waves (Florenchie et al., 2003; Rouault et al., 2007). According to this paradigm, westerly wind anomalies in the western equatorial Atlantic (Fig. 10
4) force downwelling Kelvin waves that propagate toward the eastern boundary, where they are partly reflected into equatorial Rossby waves, and partly transmitted into coastally trapped waves that travel toward the BUS, where they reduce upwelling efficiency and also drive the Angola Current further poleward (Rouault, 2012; Rouault et al., 2007), leading to the intrusion of warm, nutrient-poor water into the BUS. A study by Richter et al. (2010), on the other hand, pointed to the potential importance of local alongshore winds in the generation of Benguela Niños. Based on GCM simulations, satellite observations, and reanalysis data, these authors suggest that SST anomalies in the ABA are preceded not only by westerly surface wind anomalies on the equator but also by northerly surface wind anomalies along the southwest African coast (Fig. 10
4). Analysis of sea-level pressure (SLP) in the developing phase of Benguela

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FIGURE 10–3 Annual cycle of SST standard deviation in the (A) Angola-Benguela upwelling region (ABA), (B) California Niño (CN) region, and (C) Dakar Niño (DN) region, calculated from the Optimally Interpolated SST (OISST) data set (Reynolds et al., 2007) for the period 1982
2018. See the main text for the geographical definitions of the upwelling regions.

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FIGURE 10–4 Benguela Niño composites showing the temporal evolution of European Centre for Medium-Range Weather Forecasts (ECMWF) Interim reanalysis (ERA-Interim; Dee et al., 2011) SST (shading; K), ERA-Interim surface winds (vectors; reference 0.5 m s21), and Global Precipitation Climatology Project (GPCP; Adler et al., 2003) precipitation (contours; interval 0.5 mm day21). The compositing criterion is 0.5 standard deviations of March
April
May average SST in the ABA region. The years selected from the 1979
2016 analysis period are 1984, 1986, 1995, 1996, 1999, 2001, 2006, 2007, 2011. The linear trend has been removed from all fields.

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Niños suggests a basin-wide weakening of the St. Helena High (Lübbecke et al., 2010; Richter et al., 2010; also suggested by the surface wind anomalies in Fig. 10
4), which indicates that downwelling Kelvin waves may not only be generated in the western equatorial Atlantic but also to the east. Furthermore, alongshore wind anomalies may also excite coastally trapped waves (Clarke and Brink, 1985) and changes in the coastal currents (Junker et al., 2017). The notion of local forcing is also partially supported by the results of Polo et al. (2008) who showed that coastally trapped waves tend to dissipate south of 15 S. Several recent studies examined in more detail the generation of Benguela Niños and provided strong evidence for the remote forcing mechanism (Bachèlery et al., 2016; Illig and Bachèlery, 2019; Imbol Koungue et al., 2017; Rouault et al., 2018), albeit with some qualifications. Bachèlery et al. (2016) performed a comprehensive suite of sensitivity tests with a regional ocean model forced with varying combinations of lateral and surface boundary conditions for the period 2000
2008. Since their model domain is limited to the eastern tropical Atlantic, east of 10 W, they mimic the influence of remote forcing by supplying either observed or climatological fields at the open western boundary. Their results suggest that, on interannual timescales, sea-level, thermocline, and current variability in the BUS are dominated by remote forcing. At intraseasonal timescales, however, the local forcing appears dominant, consistent with the analysis of daily mean satellite data by Richter et al. (2010). Imbol Koungue et al. (2017) examined in detail the evolution of cold and warm events in the BUS for the period 1998
2012. They found that two-thirds of pronounced BUS warming and cooling events are preceded by equatorial sea surface height (SSH) anomalies. In difference to some previous studies, they suggest that SSH in the eastern equatorial Atlantic may be a better predictor of BUS events than the wind stress forcing in the western equatorial Atlantic. Junker et al. (2017) found that variations in BUS water mass characteristics are dominated by alongshore advection, consistent with the results of Rouault (2012), but do not reach a definitive conclusion on whether this advection is driven locally [as suggested by Junker (2014)] or remotely. Additionally, they point out the persistence of alongshore wind anomalies in the coastal regions off Namibia and South Africa. While the preponderance of the literature supports the notion that Benguela Niños are remotely forced from the equator, the debate continues and several questions remain unanswered. Lübbecke et al. (2019) examined the 2016 BUS warm event and presented evidence that several local processes played a crucial role in its development. These processes include the formation of a shallow mixed layer (ML) due to enhanced river runoff but also reduced upwelling due to a weakening of the alongshore winds. While the 2016 event was not a typical Benguela Niño event, due to its short duration and early termination, the study suggests that development of Benguela Niños may often rely on more than just a single process. Kobayashi and Tozuka (2019) attempt to classify BUS events depending on whether they are locally amplified or not. They suggest that land surface temperature anomalies over southern Africa can influence the St. Helena High and significantly alter the cross-shore pressure gradient that drives alongshore winds. This mechanism has been termed the coastal Bjerknes feedback and has been found to play an important role in several studies of other coastal upwelling regions (Kataoka et al., 2013; Oettli et al., 2016; Yuan and Yamagata, 2014; see Section 10.2.2). Sun et al. (2019) suggest that variations in the position and strength of the

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St. Helena High are the driver of both land
sea contrast and alongshore wind variations, though they find that this mechanism is mostly active south of 20 S. On the oceanic side, Siegfried et al. (2019) suggest that in addition to the equatorial/coastal waveguide, water masses in the BUS may also be influenced from the southeastern equatorial Atlantic through variations in the south-equatorial undercurrent and the south-equatorial countercurrent. This would suggest that variability in the BUS depends not only on equatorial wind stress forcing but also on the wind patterns in the wider tropical Atlantic area. How and where Benguela Niños are generated has important implications for their predictability. Wind stress forcing from the western equatorial Atlantic would provide potential predictability of up to 2 months, assuming that the second baroclinic Kelvin mode is dominant (Polo et al., 2008), with Imbol Koungue et al. (2017) suggesting skill at 1
2 months. Variations in the St. Helena High could provide even longer lead times if they were linked to ENSO. Current results, however, suggest that this link is tenuous (Sun et al., 2019). Local forcing may provide the least predictability as it likely contains a large stochastic component. GCM-based prediction systems from the climate historical forecast project (CHFP; Kirtman and Pirani, 2009) do not provide skill above the persistence forecast (Fig. 10
5). This may be due to the fact that the representation of the BUS in these models is subject to severe biases (e.g., Xu et al., 2014a) that may deteriorate prediction skill. Imbol Koungue et al. (2017) construct a simple prediction model based on observed SSH on the equator and a linear wave model. Based on this model, they suggest prediction skill at 1
2 months lead time but a rigorous verification of this model is outstanding at the time of writing. In addition to having a strong influence on the local marine ecosystem, variability in the BUS may also have an impact on precipitation over land. While the composite analysis presented here shows that rainfall anomalies above 0.5 mm day21 are confined to the coastal area off Angola, northeast of the ABA, a study by Rouault et al. (2003) suggests that rainfall

FIGURE 10–5 Prediction skill for SST in the ABA region, as measured by the anomaly correlation coefficient (ACC), and plotted against lead time (x-axis). The solid line denotes models participating in the Climate-system Historical Forecast Project (CHFP; Kirtman and Pirani, 2009), while the dashed line denotes the SINTEX-F1 seasonal prediction model (Luo et al., 2003). All models were initialized on February 1 of each year. The evaluation period is 1979
2012, and the reference data are ERA-Interim SST.

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anomalies may extend to coastal Namibia and South Africa as well, depending on the largescale conditions. This is also supported by a study by Reason and Smart (2015). More work remains to be done in this area. There are possible links between the Benguela Niño and other variability patterns in the tropical Atlantic. The equatorial Kelvin wave mechanism suggests a link between Benguela Niños and interannual variability in the eastern equatorial Atlantic, named “Atlantic Niño” or “Atlantic Zonal Mode” (AZM) because any wave generated in the western equatorial Atlantic would have to pass through the eastern equatorial Atlantic and depress the thermocline there, as suggested by Florenchie et al. (2003). Accordingly, one would expect that AZM events precede BUS events. Lübbecke et al. (2010), however, found that the ATL3 index for Atlantic Niños (SST anomaly averaged over the region 20 W to 0 and 3 S to 3 N) and the ABA index have their highest correlation of 0.7 when the ABA leads by one month, consistent with our Benguela Niño composite (Fig. 10
3). This leads to an apparent conundrum that has not been fully resolved yet. Lübbecke et al. (2010) offer as an explanation the seasonal migration of the outcropping latitude, but the relatively stable latitude of the ABFZ, which should be regarded as the outcropping region, suggests that this is a minor effect. Furthermore, equatorial wind anomalies tend to peak in April and May, just 1
2 months after the peak of Benguela Niños, which does not allow a large seasonal northward shift in the outcropping region. More detailed analysis will be needed to resolve this issue. Hu and Huang (2007) suggest an atmospheric bridge between the tropical southeastern and equatorial Atlantic, in which warm SST anomalies off southwestern Africa induce a southward shift of the intertropical convergence zone (ITCZ) that weakens the equatorial trades. This induces warming on the equator that is subsequently amplified by local feedbacks. One could develop this idea further by hypothesizing that the downwelling Kelvin waves excited by the westerly wind stress reinforce the coastal SST anomalies, though it is not clear whether the observed evolution of ATL3 and ABA SST anomalies supports such a feedback loop. The SST anomalies associated with Benguela Niños are not confined to the coast (Fig. 10
3) but extend into the open ocean. This can be explained by offshore advection but also by the widespread trade wind weakening that leads to reduced latent heat loss at the ocean surface. The latter process has been associated with the wind-evaporation-SST (WES) coupled air
sea feedback and is thought to underpin the Atlantic meridional mode (AMM) of variability (Huang et al., 2004). A recent study by Myers et al. (2018b) suggests that the impact of SST on stratocumulus clouds might further amplify the AMM. Thus, the Benguela Niño could provide the initial kick for the development of the AMM.

10.2.2 Baja California system In the northeast Pacific Ocean, the California Current flows along the western coast of North America roughly from southern British Columbia to southern Baja California and brings cold water southward (Hickey, 1979). The upwelled water in the current is enriched with nutrients from the subsurface and supports abundant plankton, fish and sea birds (Hill and Wheeler,

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2002). The upwelling is generated by the southward ASW stress related to the North Pacific subtropical high and has distinct seasonality. While the subtropical high tends to be most pronounced in boreal summer, when the air
sea contrast is strongest, the alongshore winds are most pronounced in boreal spring (Bakun and Nelson, 1977). The SSTs off the California coast reach a minimum in boreal spring (Fig. 10
2B), consistent with the most intense alongshore winds and associated upwelling. The annual cycle of SST is also influenced by the net surface solar radiation, which in turn depends on the seasonal march of the sun and the incidence of low-level cloud (Klein and Hartmann, 1993). SSTs in the coastal ocean along California and Baja California are mainly influenced by the strength of local upwelling, cold advection by the California Current (Lynn and Simpson, 1987), surface turbulent heat fluxes that depend on the surface wind speed, and stratus cloud cover (Myers et al., 2018a). In some years, the coastal SSTs are much warmer/cooler than normal and significantly influence the local marine ecosystem (e.g., Balech, 1960; Berner, 1960; Pearcy, 2002; Reid, 1960; Roemmich and McGowan, 1995). This anomalous warming/ cooling can be largely attributed to the tropical ENSO through its atmospheric and oceanic teleconnections (e.g., Enfield and Allen, 1980; Schwing and Moore, 2000; Strub and James, 2002). Recent research, however, shows that local air
sea interaction between the ASWs and the coastal SSTs also plays an important role in the coastal SST variations (e.g., Feng et al., 2013; Kataoka et al., 2013; Oettli et al., 2016; Yuan and Yamagata, 2014). If the ASWs are weaker than normal, they will reduce the coastal upwelling and raise the coastal SSTs. The higher SSTs then heat the overlying atmosphere, decrease the SLP, and generate an anomalous land
sea pressure gradient that can further weaken the ASWs. The positive feedback is analogous to the Bjerknes feedback in the tropical oceans responsible for ENSO generation and thus is called the coastal Bjerknes feedback. Accordingly, the resultant air
sea coupled phenomena are called coastal Niño/Niña (Feng et al., 2013; Yuan and Yamagata, 2014). While compared to the tropical ENSO, the coastal Niño/Niña events have a much shorter time scale and smaller spatial scale, they tend to occur in boreal summer, when ENSO activity is usually weak. This is reflected in the annual cycle of SST variability in the CNI region (Fig. 10
3B), which shows a peak in August. The secondary peak in February is associated with ENSO variability and disappears once the influence of ENSO is linearly removed (Yuan and Yamagata, 2014). Observational evidence suggests that the coastal Bjerknes feedback is operative in the coastal ocean off California and Baja California, and that it provides a major contribution to the coastal air
sea coupled phenomenon known as the California Niño/Niña (Yuan and Yamagata, 2014). As shown in Fig. 10
6, anomalous cyclonic circulation anomalies first appear off the coast of California in boreal spring 3 months before the peak of the California Niño. The poleward alongshore wind anomalies reduce the coastal upwelling, weaken the southward advection of cold water from higher latitudes, and contribute to the coastal-positive SST anomalies. The anomalous warm SST anomalies in turn heat the overlying atmosphere, reduce the coastal SLP, generate an anomalous offshore pressure gradient, and enhance the initial poleward alongshore wind anomalies (Yuan and Yamagata, 2014). This positive feedback operates most effectively in boreal summer when the coastal ocean has the shallowest ML and the ASWs reach their seasonal

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FIGURE 10–6 Lead-lag correlation coefficients between the July
September California Niño/Niña indices and 3-month-running mean anomalies in SST (shading, OISST; Reynolds et al., 2002), SLP (contour, NCEP/NCAR reanalysis 1; Kalnay et al., 1996), and 10-meter-height wind (vector, NCEP/NCAR reanalysis 1). Negative (positive) numbers in the top of each panel denote the months that the July
September California Niño/Niña indices lag (lead) (courtesy of Yuan and Yamagata 2014).

maximum. It is estimated that the coastal Bjerknes feedback contributes to about a half of the total variance of the California Niño/Niña in boreal summer (Yuan and Yamagata, 2014). In the following autumn, the ML becomes deeper, the impacts of upwelling variations on the SSTs become less efficient, and thus the contribution of the coastal Bjerknes feedback to the coastal SST variations becomes smaller. It is worth mentioning that some California Niño/Niña cooccurs with the tropical ENSO since the coastal SSTs and alongshore wind anomalies induced by the tropical ENSO teleconnection can serve as the trigger for the coastal Niño/Niña. However, there are some cases of California Niño/Niña events that are independent to ENSO.

10.2.3 Dakar system In the northern tropical Atlantic Ocean, the North Atlantic gyre is roughly bounded by the Gulf Stream to the west (Stommel, 1965), the North Atlantic Current to the north (Krauss, 1986), the Canary Current to the east (Arístegui et al., 2006; Mittelstaedt, 1991), and the North Equatorial Current to the south (Bourlès et al., 1999). The eastern boundary extends from the north of the Iberian Peninsula at 43 N to the south of Senegal at 10 N (Arístegui et al., 2006). From 30 N to 10 N, the Canary Current continuously flows all year-round toward the equator (Fedoseev, 1970; Wooster et al., 1976), which is associated with climatological ASWs blowing southward along the Northwest African coast (Mittelstaedt, 1991). From about 5 N to 20 N, the alongshore winds are most upwelling favorable in early spring and least favorable in late summer, when

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they become southerly (not shown). This is reflected in the SST along the coast, which is coolest in March and warmest in September (Fig. 10
2C). The annual cycle of shortwave radiation may further contribute to the seasonality of SST. The relative abundance of marine resources off Senegal (between 12 and 16 N; Binet et al., 1998; Fréon, 1984; Fréon and Stéquert, 1979; Voituriez and Herbland, 1982) helps the local fisheries to generate a high economic value for local communities (Belhabib et al., 2015, 2014). Fisheries sector provides employment for over 600,000 individuals and generates revenue of almost $340 million annually (USAID, 2015). Aside from overfishing issues (Baldé et al., 2018), anomalous weakening of the upwelling may be a threat to the ecosystem and, by extension, to fisheries. Conversely, anomalous strengthening of the upwelling is generally favorable toward ecosystems. As noted before, weaker than normal upwelling is associated with SST warming (so-called coastal Niño events), while stronger than normal upwelling is associated with SST cooling (“coastal Niña” events). Along the Northwest African coast climatological surface winds are blowing southward, generating one of the major eastern boundary upwelling systems (Arístegui et al., 2006; Benazzouz et al., 2015; Wooster et al., 1976). Between 20 and 25 N, the upwelling is found throughout the year, intensifying in spring and autumn (Cropper et al., 2014; Van Camp et al., 1991). North of 25 N, the upwelling is generally weaker, with seasonal intensification in summer and autumn (Van Camp et al., 1991). Due to the seasonal migration of the ITCZ and the relaxation of the trade winds at the surface of the ocean, the upwelling taking place between 10 and 20 N is marked by a strong seasonality, which is reflected in the alongshore SST (Fig. 10
2C) as well as in the primary production (Lathuilière et al., 2008). Variability on interannual and longer time scales has been observed in the Dakar upwelling system. In the northern part of the system, the North-Atlantic Oscillation (NAO, Hurrell et al., 2003) plays a role in the temporal variability of the upwelling along the western African coast (Borges et al., 2003; Cropper et al., 2014) by modulating the wind conditions, especially during the cold season months (November
April). The AMM (Chiang and Vimont, 2004) is another mode of variability and the dominant source of coupled ocean
atmosphere variability in the Atlantic. In the southern part of the upwelling system, a periodicity of around 16 years exists in the surface wind (Teisson, 1982), compatible with the influence of the AMM in this region. Between March 1971 and June 1984, McLain et al. (1985) identified positive anomalies occurring in 1978, 1979
80, 1981
82, and 1983
84 in the Canary Current, relative to a general warming trend between 1976 and 1984. To explain these warm events, different factors have been proposed, including anomalous local onshore transport, propagation of coastally trapped waves, and anomalous solar radiation. Focusing on late boreal winter-early boreal spring (February to April), that is, the season with the largest SST variability (Fig. 10
3C; Fig. 10
7, shading), Oettli et al. (2016) also found an anomalous warming of the SST in the southern part of the Canary Current, near the Cape Verde in Senegal in 1983
84 (not shown). In this region, the maximum of regional variability in SST is collocated with the strongest alongshore gradient of SST (Fig. 10
7, contour) and equatorward climatological surface winds (Fig. 10
7, vector). For the period 1982
2011, Oettli et al. (2016) identified six warm events, named “Dakar Niños,” with 1983 and 1984 being particularly strong.

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FIGURE 10–7 Climatological standard deviation of SST (shading; K) and averages of SST (contour; K) and 10-m winds (vectors; m s21) for February
March
April. Data are from ERA-Interim (Dee et al., 2011)

FIGURE 10–8 Composite analysis of SST (shading; K) and 10-m winds (vectors; m s21) anomalies (departure from the 1981
2000 climatology) from February to April for Dakar Niño (A
C) and Niña (D
F). For the period 1982
2011, 6 years are selected for the warm composite (1983, 1984, 1997, 1998, 2008, and 2010), and five years for the cold composite (1985, 1986, 1999, 2003, 2009), that is, when the February
March
April (FMA) average of SST in DN region is above (below) 0.8 (20.8) standard deviation. Data are from ERA-Interim (Dee et al., 2011). The linear trend has been removed from all fields.

The existence of an ocean
land
atmosphere coupled feedback in the region off Senegal is involved in the local anomalous warming. Related to a modification of the large-scale circulation during late boreal winter-early boreal spring, alongshore wind is weakened (Fig. 10
8A
C, vectors), reducing the coastal upwelling and the evaporation at the surface of the coastal ocean.

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FIGURE 10–9 Mixed-layer heat balance (A) and surface heat flux contribution (B) during composited Dakar Niño (1983, 1984, 1997, 1998, 2008, and 2010) in the region 21
17 W, 9
14 N. Data are from Global Ocean Data Assimilation System (GODAS; Behringer, 2007; Behringer and Xue, 2004) and NCEP-DOE Reanalysis 2 (NCEP2; Kanamitsu et al., 2002). Adapted from Oettli, P., Morioka, Y., Yamagata, T., 2016. A regional climate mode discovered in the North Atlantic: Dakar Niño/Niña. Sci. Rep. 6, 18782.

Following the reduction in the upwelling intensity, the oceanic ML becomes thinner and more susceptible to warming. An ML heat budget performed in the Dakar Niño (DN) region (21
17 W, 9
14 N) shows that warming is developing mainly under the influence of net surface heat flux (Fig. 10
9A). In particular, the effect of the climatological solar radiation on the anomalously thin ML is large (Fig. 10
9B) and leads to anomalous warming of the ML and, consequently, SST (Fig. 10
8A
C, shading). In their turn, warm SST anomalies generate both local air temperature and pressure anomalies in the lower troposphere. The existence of an atmospheric pressure anomaly above the SST anomaly may generate an anomalous ocean
land pressure contrast, locally maintaining the alongshore wind anomaly, completing the coupled feedback. For the same period, five cold events also have been identified (Oettli et al., 2016). A cold event is called “Dakar Niña” and involves an intensification of the climatological situation, that is, an intensification of the coastal wind (Fig. 10
8D
F, vectors) and the coastal upwelling, an increase of the evaporation, a thickening of the ML (not shown), a cooling of the SST (Fig. 10
8D
F, shading), and a reduction in the local cross-shore gradient.

10.3 Representation of coastal Niños in climate models It has long been recognized that the simulation of eastern ocean boundaries in GCMs is subject to severe warm SST biases (e.g., Large and Danabasoglu, 2006; Mechoso et al., 1995; Zuidema et al., 2016; also see reviews by Richter, 2015). While biases in the southeastern and northeastern Pacific have seen some improvement due to increased resolution (e.g., Delworth et al., 2012;

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Gent et al., 2010; McClean et al., 2011), the southeast Atlantic has largely resisted improvement efforts (Small et al., 2015), though some recent results are encouraging (Harlaß et al., 2018; Kurian et al., 2019; Milinski et al., 2016). Typical GCM biases in the BUS are shown in Fig. 10
10 for austral fall. The ensemble mean SST biases exceed 3K close to the coast (Figs. 10
10) and 5K right on the coast (not shown). This is accompanied by surface wind biases that are indicative of a weakening of the St. Helena High across the entire basin, including westerly biases on the equator and northerly biases along the southwest African coast. SST variability in the BUS is much lower than observed, and this is likely related to the deeper than observed thermocline (not shown), among other reasons. The cause of warm SST biases in the southeast Atlantic, as well as in the southeast Pacific, was initially hypothesized to lie in the model representation of marine stratocumulus (e.g., Huang et al., 2007; Ma et al., 1996). This cloud type is notoriously underestimated in

FIGURE 10–10 Biases of March
April
May (MAM) average SST (shading; K), surface winds (vectors; reference 2 m s21), and standard deviation of SST (contours; interval 0.2K) for an ensemble of preindustrial control simulations in the CMIP5 archive (Hurrell et al., 2011). The reference data are ERA-Interim for the period 1979
2016. The CMIP5 ensemble consists of the following 25 models: ACCESS1-0, ACCESS1
3, bcc-csm1-1, BNU-ESM, CanESM2, CCSM4, CSIRO-Mk3
6-0, EC-EARTH, FGOALS-g2, FGOALS-s2, FIO-ESM, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H, GISS-E2-R, HadGEM2ES, inmcm4, MIROC4h, MIROC5, MIROC-ESM, MPI-ESM-LR, MPI-ESM-M, MRI-CGCM3, and NorESM1-M.

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GCMs (Mechoso et al., 1995; Zuidema et al., 2016), which leads to excessive downward solar radiation warming up the ocean. Subsequent studies suggested, however, that, while the simulated downward shortwave radiation is excessive in the southeast Atlantic and Pacific, this error is overcompensated by excessive upward latent and sensible heat flux (de Szoeke and Xie, 2008; Toniazzo and Woolnough, 2014; Xu et al., 2014a; Zheng et al., 2011). Thus, even though the underrepresentation of stratocumulus is problematic, it cannot explain the current GCM biases. Several recent studies have pointed to errors in the local wind stress forcing as an essential element (Harlaß et al., 2018; Koseki et al., 2018; Kurian et al., 2019; Milinski et al., 2016; Small et al., 2015). The southern portion of the BUS features an equatorward low-level jet, called the Benguela low-level coastal jet (BLLCJ; Lima et al., 2019; Nicholson, 2010; Patricola and Chang, 2017). In GCMs, the BLLCJ appears to be poorly resolved (Koseki et al., 2018; Xu et al., 2014a). A detailed analysis with a GCM coupled to a regional ocean model Small et al. (2015) showed that the BLLCJ is too weak and displaced offshore, which is associated with a cyclonic wind stress curl off the Namibian coast that induces poleward flow and therefore shifts the ABFZ too far south. Small et al. (2015) found that the SST bias in their model could be substantially alleviated when they artificially shifted the jet core eastward by 1 degree. Further modeling studies showed the importance of adequate atmospheric resolution to represent the BLLCJ and its effect on upwelling (Martin Krebs, personal communication; Harlaß et al., 2018; Kurian et al., 2019; Milinski et al., 2016). These studies also suggest that oceanic resolution plays a secondary role, with moderate improvements when ocean resolution is increased to 1/10 degree but not beyond that. Increasing atmospheric resolution, on the other hand, leads to substantial bias alleviation even when the oceanic resolution is on the order of 1 degree. An additional contribution to the BUS SST biases may come from insufficient offshore transport associated with poorly resolved eddies in coarse-resolution ocean models (Toniazzo et al., 2010). The equatorial westerly wind bias commonly found in GCMs (Richter et al., 2014; Richter and Xie, 2008) may also contribute remotely through adjustment of the BUS thermocline via Kelvin waves (Xu et al., 2014b). The seasonality of SST biases in the ATL3 and ABA lends support to this notion because the former peaks 1 month before the latter (not shown), consistent with a wave signal propagating along the equatorial/coastal wave guide. The severe biases in the BUS likely also have a strong influence on its variability patterns, though few studies have examined this. Fig. 10
6 shows that variability in austral fall (the peak season of the Benguela Niño) is severely underestimated between 20 S and 10 S in the CMIP5 ensemble. Consistently, Richter et al. (2010) found variability patterns displaced southward in their GCM. This may have further consequences, as the influence of coastal Kelvin decreases with latitude (Polo et al., 2008) and thus a southward shift of the variability may lead to a regime that is unrealistically dominated by local forcing. Moreover, the coarse resolution of GCMs may not be sufficient to adequately resolve coastal Kelvin waves (Veitch et al., 2010). This may explain the underestimation of SST variability in the BUS. On the other hand, the coarse atmospheric resolution and offshore displacement of the BLLCJ in GCMs likely lead to weak and unrealistic variability of alongshore winds (Harlaß et al., 2018; Kurian et al., 2019; Small et al., 2015), which could be another contribution to the weak SST variability.

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The California Niño/Niña has significant impacts on the land surface temperature and precipitation in the nearby continent (Yuan and Yamagata, 2014). Hence, successful prediction of such events at sufficient lead time is beneficial to the coastal fisheries and the social activities in the nearby continent. Doi et al. (2015) have investigated the seasonal predictability of California Niño/Niña using the SINTEX-F1 seasonal prediction system. They showed that some events that co-occurred with ENSO can be successfully predicted at least two seasons ahead, while those independent of ENSO cannot be well predicted. This is probably because the coarse resolution of SINTEX-F1 (1.125 degree in the atmospheric component and 2 degree in the oceanic comment) cannot resolve well the coastal Bjerknes feedback. Increasing the spatial resolution of the seasonal prediction system or applying additional regional dynamical downscaling may be helpful for increasing the seasonal prediction skills of California Niño/Niña.

10.4 Future of the upwelling regions Examining the period 1945
1985, Bakun (1990) pointed to an increase in upwelling strength in the world’s major upwelling regions (i.e., “Bakun Hypothesis” or “Upwelling Intensification Hypothesis,” e.g., Cropper et al., 2014), due to the increased land
sea contrast under global warming strengthening the low-level coastal jets and the local acceleration of alongshore winds (Bakun et al., 2010; Cropper et al., 2014; Diffenbaugh et al., 2004). Sydeman et al. (2014) conducted a systematic review of the literature dealing with coastal upwelling systems and reported a general agreement of positive trends in upwelling-favorable wind intensity in Benguela and California coastal systems. In the Canary region, results are more ambiguous, without clear trend. Vizy et al. (2018) examined decadal changes in the ABFZ latitude and found that it shifted slightly southward since 1980, accompanied by warming to the north and cooling to the south. They ascribe this to the southward shift of the St. Helena High over the same period, which would be consistent with the notion of a Hadley cell expansion under global warming (Lu et al., 2007), and more generally consistent with the anticipated poleward displacement of the mid-latitude high-pressure systems (e.g., Bakun et al., 2015; García-Reyes et al., 2015; Rykaczewski et al., 2015; Wang et al., 2015) inducing a poleward shift of the favorable winds. A study by Lima et al. (2019) supports the idea of a poleward shift of the St. Helena High and intensification of the BLLCJ. However, issues in the maritime wind data and the seasonality of the phenomenon have been questioned (e.g., Cropper et al., 2014). Also, recent works contradicted the “Bakun Hypothesis,” with no evidence for a general intensification of upwelling, and a more latitudedependent intensification, enhanced in higher latitudes, and weakened in lower latitudes (e.g., Barton et al., 2013; Belmadani et al., 2014; Varela et al., 2015). While the above studies relied on observations of past trends, climate change projections have to rely on model simulation, particular from GCMs. Under the RCP6.0 and RCP8.5 climate change scenarios, Arellano and Rivas (2019) suggest an intensification of the coastal upwelling in Baja California, due to a change in the large-scale surface atmospheric

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circulation in spring and to stronger alongshore winds. However, the response of the marine ecosystem to the upwelling intensification is still uncertain, considering the complex interaction of involved processes (Xiu et al., 2018). Thus, the effects on fisheries in the region remain to be clarified. The impact of future climate change on the southern BUS was investigated by Ortega-Cisneros et al. (2018), who use an ecosystem model forced by a climate change scenario obtained from a GCM. They find that effects of temperature warming dominate over upwelling intensification, with likely negative effects on fisheries. In the Dakar system, it is not yet clear how climate change would affect the occurrence of coastal Niño/Niña, considering the importance of both the regional scale (cross-shore pressure gradient) and the large scale (position of the ITCZ and phase of the NAO). While Oettli et al. (2016) did not analyze in detail the role of land surface processes on the cross-shore pressure gradient this aspect deserves more attention, as the large-scale land
sea contrast and associated pressure gradients are expected to intensify. Based on the Special Report on Emission Scenarios (SRES) A1 scenario, Lam et al. (2012) project a 21% drop in annual fish landed value, 50% decline in fisheries-related jobs, and a total annual loss of US$ 311 million in West Africa. Because of the severe biases of these models and due to their coarseness relative to the processes of interest, there is much uncertainty regarding future projections for coastal upwelling regions. Much more work remains to be done to arrive at reliable climate change projections for these regions.

10.5 Summary and outlook Coastal upwelling systems have been the subject of intense study, particularly in recent years. Thus, the commonalities and differences among these upwelling regions and their variability patterns are now fairly well documented (see summary in Table 10
1). There is also a better understanding of various mechanisms involved in generating SST anomalies in these regions. In terms of the mean state in the BUS, for example, there is now a better theoretical understanding of the local wind stress forcing that is needed to maintain the upwelling, the position of the ABFZ, and the poleward Angola Current. In terms of interannual variability in coastal upwelling regions, there is now a better understanding of the contribution of the coastal Bjerknes feedback to SST variations, a common feature among the three regions (Table 10
1). Progress has also been made in understanding the relative contributions of equatorial and coastal wind forcing to the generation of variability on interannual and shorter time scales. Furthermore, it appears, there is a consensus emerging regarding the root causes of the biases in these regions that have plagued climate models for decades. On the other hand, many problems remain unsolved or only partially answered, leaving much room for future research. For example, the predictability of Benguela Niños, and more generally coastal Niños, remains largely unexplored. While GCMbased prediction systems currently have no useful skill, it remains to be seen whether this is due to insufficient model resolution, inherent predictability limits, or systematic model biases.

Chapter 10 • Benguela, California, and Dakar Niños/Niñas

Table 10–1 Benguela

257

Unique and common features of coastal Niño/Niña. California

Dakar

• Occurs in boreal summer, when SSTs are • Occurs in late boreal winter/early • Occurs in austral fall, when boreal spring when SSTs are warmest; weak phase locking (may SSTs are warmest; pronounced coolest; pronounced phase partially be masked by ENSO-related phase locking; • Occur in frontal zone between locking; variability); • Strong influence of solar radiation • Direct influence of El Niño-Southern poleward (Angola) and and ocean mixed-layer thickness oscillation during years of occurrence; equatorward (Benguela) • Potential influence of multiannual North currents; • Warm events marked by Pacific variability (“blob”) poleward intrusion of Angola Current • Subtropical latitude; • Eastern ocean boundary/upwelling region; • Coastal Bjerknes feedback SST, sea surface temperature; ENSO, El Niño/Southern Oscillation.

The relation between Atlantic Niño (Richter and Tokinaga 2020 reviewed it in Chapter 7: The Atlantic zonal mode: dynamics, thermodynamics, and teleconnections, of this book) and Benguela Niño needs to be better understood, in particular why the latter, despite being downstream in the wave guide, typically precedes the former. While hypotheses have been put forward to explain this behavior (see Section 10.2.1), more work remains to test and refine these. Much work also remains to be done to explore the existence of local coupled feedbacks between the low-level coastal jet, upwelling, and air
sea contrast. This could further the understanding of many aspects, including the mean state, model biases, and variability on synoptic, interannual and longer timescales. Zuidema et al. (2016) suggest that a better representation of specific processes in the upwelling regions might help to reduce model biases. Richter (2015) emphasizes the need to enhance ocean and atmosphere observations of the eastern boundary regions, as well as the coordination of multimodel experiments, in order to mitigate error source. In regard to these suggestions, a better representation of local ocean
land
atmosphere coupled feedback (“coastal Niño/Niña”) might be key to resolve this issue. Lastly, future climate change in the region remains largely unexplored, mostly because the severe climate model biases in the region undermine the credibility of projections. Regarding the California Niño, there is an open question whether this seasonal phenomenon may be linked to longer-term warm SST anomalies that are sometimes observed off Baja California. The so-called Pacific warm blob (Bond et al., 2015) is a possible source of this persistent warming, though the linkage is still under debate (Robinson, 2016; Zaba and Rudnick, 2016). Thus, the influence of warm anomalies in the northeast Pacific on the occurrence of California Ninõs remains to be explored. While we have focused on the physical aspects of upwelling systems, their marine productivity is certainly an area of major interest. For a few major coastal Niño events, the negative impact on marine productivity has been well documented. The impact of minor coastal

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Niños and that of coastal Niñas, on the other hand, has received much less attention. Quantifying the impact of interannual variability on marine productivity, let alone fisheries, remains a major challenge, due to the complexity of biological systems and external influences. To give just one illustrative example, it is thought that negative impacts on marine productivity are not limited to weak upwelling but may also occur during strong upwelling (e.g., Menge and Menge, 2013). This is because strong upwelling and its associated offshore transport tend to flush larvae into the open ocean too soon, leading to reductions in recruitment, though the importance of this process is still under debate (Shanks and Morgan, 2018). Close synergy among various scientific disciplines will be needed to arrive at a more comprehensive understanding and useful predictions of the coastal marine ecosystem.

Acknowledgment We thank the reviewer for constructive comments. We also thank Prof. Toshio Yamagata for his insight and guidance on the coastal Niño/Niña research. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Fig. 10
10 of this review chapter) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also acknowledge WCRP/CLIVAR Working Group on Seasonal to Interannual Prediction (WGSIP) for establishing the Climate-system Historical Forecast Project (CHFP). The authors are also grateful to Met Office Hadley Centre, European Centre for Medium-Range Weather Forecasts (ECMWF) and NOAA/OAR/ESRL PSD for providing reanalysis dataset.

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11 Impacts of strong warm ocean currents on development of extratropical cyclones through the warm and cold conveyor belts: A review Hidetaka Hirata1, Masami Nonaka2 1

FACULTY OF G EO-ENVIRONMENTAL SCIENCES, R ISSHO UNIVERSITY, KUMAGAYA, J AP AN 2 APPLICATION LABORATORY, RESE ARCH INSTITUTE FOR VALUE-ADDEDINFORMATION GENERATION, JAPAN AGEN C Y FO R MA R I N E - E A RT H S CI E N CE AN D TECHNOLOGY, Y OKOHAMA, JAPAN

11.1 Introduction Ocean
atmosphere coupled system in the tropics is the dominant mechanism for interannual variability not only in the tropics but also large part of the extratropics through atmospheric/oceanic teleconnections as discussed in other chapters of this book. In contrast, oceans in mid-latitude have been considered to be passive to atmospheric variability. Recent high-resolution ocean/atmosphere satellite observational and numerical simulation data, however, have revealed that mid-latitude ocean also influences atmosphere locally and remotely in mesoscale to hemispheric scales (Kwon et al., 2010; Nakamura et al., 2015; Minobe et al., 2016; Czaja et al., 2019, for recent reviews). In the strong current regions, such as the Kuroshio/Kuroshio Extension (KE), the Gulf Stream, the Agulhas Current, and the Antarctic Circumpolar Current, frontal structures of sea surface temperature (SST) and their variability are dominated by the currents and its variability rather than atmospheric thermal forcing, and the variability can affect atmosphere aloft actively, causing air
sea interactions including not only thermodynamics but also dynamics. Among those strong currents, we focus on the Kuroshio/KE in the present chapter. The Kuroshio is the western boundary current of the subtropical gyre in the North Pacific. It originates from the Philippine Sea and flows north/northeastward along the western edge of the North Pacific Ocean and goes into the East China Sea (ECS). Around the southern tip of Kyusyu Tropical and Extratropical Air
Sea Interactions. DOI: https://doi.org/10.1016/B978-0-12-818156-0.00014-9 © 2021 Elsevier Inc. All rights reserved.

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Island, Japan, the Kuroshio turns eastward and goes out from ECS into the Pacific Ocean, and flows to the south of Japan. At the eastern edge of the Honshu Island, Japan, around Cape Inubo, it leaves from the western boundary and further flows eastward as the KE. Small horizontal-scale SST structures of oceanic fronts and eddies associated with the Kuroshio and other strong currents are found to influence not only atmospheric boundary layer (Nonaka and Xie, 2003; Tanimoto et al., 2003; O’Neill et al., 2003; Small et al., 2008) but also free troposphere (Tokinaga et al., 2009; Liu et al., 2007; Minobe et al., 2008). In addition to climatological SST frontal structures, interannual-to-decadal variability in SST front are also found to affect latitude of atmospheric storm track and further large-scale atmospheric circulation (Tanimoto et al., 2003; Frankignoul et al., 2011; Taguchi et al., 2012; Révelard et al., 2018), and the resultant sea surface wind anomalies can induce variability in ocean circulation (e.g., Qiu et al., 2014). The SST fronts associated with strong warm currents also affect path of extratropical cyclones (Nakamura et al., 2012; Hayasaki et al., 2013) and development of atmospheric fronts associated with them (Parfitt et al., 2016), and then statistics of rapidly developing extratropical cyclones (i.e., bomb cyclones), large-scale precipitation distribution, and atmospheric circulation (O’Reilly and Czaja, 2015; Kuwano-Yoshida and Minobe, 2017). Extratropical cyclones develop in the mid-latitudes, causing heavy snowfall and rainfall (e.g., Hayasaki and Kawamura, 2012; Yamazaki et al., 2015; Honda et al., 2016), strong winds (e.g., Clark et al., 2005; Schultz and Sienkiewicz, 2013; Hart et al., 2017), and high waves (e.g., Powers and Stoelinga, 2000; Zhang et al., 2006; Kita et al., 2018), especially in winter. It is well known that the rapid development of extratropical cyclones is frequently observed around the warm currents in winter (Fig. 11
1; e.g., Sanders and Gyakum, 1980; Chen et al., 1992; Lim and Simmonds, 2002; Yoshiike and Kawamura, 2009; Kuwano-Yoshida, 2014; Seiler and Zwiers, 2016). These observations suggest that the strong warm currents play active roles in the rapid development of extratropical cyclones. In this chapter, we mainly review how the warm ocean currents affect the development of extratropical cyclones in winter. To promote understanding the effect of warm ocean current on extratropical cyclones, we briefly explain factors related to the development of extratropical cyclones. Now, we consider those from a viewpoint of potential vorticity (PV; e.g., Hoskins et al., 1985). Under the dry

FIGURE 11–1 Frequency of rapidly developing extratropical cyclones, which are called bomb or explosive cyclones, during 40 winters (DJF; December, January, and February) from 1979/1980 to 2018/2019. The data and algorithm for tracking cyclones utilized to make this figure are the ERA-interim reanalysis (Dee et al., 2011) and the algorithm used in Tsukijihara et al. (2019), respectively.

Chapter 11 • Impacts of strong warm ocean currents on development of extratropical cyclones 269

condition, the cyclone development is considered as the interaction between an upper-level disturbance and a near-surface baroclinic zone (Fig. 11
2). When an upper-level disturbance associated with positive PV anomaly approaches a near-surface baroclinic zone, it induces a southerly wind over the baroclinic zone and then causes positive advection of potential temperature at the surface. Consequently, a positive potential temperature anomaly, being equivalent to a positive PV anomaly at the surface (Bretherton, 1966), is generated. This corresponds to the growth of a surface cyclone. In turn, the surface cyclone induces a northerly wind in upper troposphere, leading to the further enhancement of the upper-level disturbance via positive PV advection. Moreover, under the realistic condition including moist processes, latent heat release is also an important factor (e.g., Kuo et al., 1991; Reed et al., 1992; Davis et al., 1993; Ahmadi-Givi et al., 2004; Kuwano-Yoshida and Asuma, 2008; Willison et al., 2013). Latent heating creates a positive PV anomaly in the lower troposphere (Fig. 11
2). The diabatic PV anomaly directly intensifies the cyclonic circulation of the surface cyclone. Additionally, the diabatic PV anomaly induced circulation can increase the amplitude of the positive PV anomalies associated with the upper-level and the surface disturbances (Fig. 11
2). Thus an upper-level disturbance, baroclinicity near the surface, and latent heating are the essential factors of the development of extratropical cyclones. The objective of this chapter is to review the relationship between the warm strong ocean currents and the development of extratropical cyclones in winter, different from other recent review papers on mid-latitude air
sea interactions. Recent studies pointed out that warm ocean

FIGURE 11–2 Schematic showing the role of latent heat release in storm intensification in the potential vorticity (PV) framework. The figure is an update of a schematic for dry circulation, from Hoskins et al. (1985), showing the arrival of an upper-level trough over a low-level baroclinic region (indicated by the black solid curves). The orange “plus” indicates the positive PV anomaly associated with the upper-level trough, and the orange arrows show the circulation associated with this anomaly. The red “plus” and arrow indicate the positive PV and circulation associated with the warm temperature anomaly induced by low-level advection. The gray cloud indicates the region of maximum latent heating, and the black “plus” and “minus” are the PV anomalies associated with the heating gradient. At low-levels, the positive PV anomaly is associated with a stronger cyclonic circulation (black arrows), while aloft the negative PV anomaly can be seen as a shift in the location of the tropopause (black dashed line). Adapted from Colle, B.A., Booth, J.F., Chang, E.K., 2015. A review of historical and future changes of extratropical cyclones and associated impacts along the US East Coast. Curr. Clim. Change Rep. 1, 125
143. doi: 10.1007/s40641-015-0013-7, licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/legalcode).

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currents affect the cyclone development through the maintenance of near-surface baroclinicity and the enhancement of latent heat release. Therefore we review the roles of the warm ocean currents and associated SST structures in the maintenance of baroclinicity and the enhancement of latent heating in Sections 11.2 and 11.3, respectively. Moreover, we introduce the mechanisms of mid-latitude SST structures and their variability in Section 11.4. Section 11.5 is for summary.

11.2 Role of warm currents in the maintenance of baroclinicity As mentioned in Section 11.1, extratropical cyclones frequently develop around warm currents associated with oceanic fronts (Fig. 11
1). Atmospheric environmental fields over the oceanic fronts are characterized by strong near-surface baroclinicity (Fig. 11
3). Such a strong baroclinicity provides a favorable condition for the development of cyclones as mentioned earlier. On the other hand, when extratropical cyclones grow, the baroclinicity is relaxed due to the poleward heat transport caused by the cyclones. Thus a unique mechanism maintaining the strong baroclinicity should be operated over those oceanic frontal regions. Nakamura et al. (2004) focused on the correspondence of the oceanic frontal zone with the strong baroclinic zones and suggested that the SST distribution associated with oceanic frontal zone plays an important role in the maintenance of the strong baroclinicity. Sensible heat supply from warmer SST regions on the equatorward side of oceanic fronts to the atmosphere aloft is larger than that from the poleward-side cold water. Consequently, the crossfrontal contrast in surface sensible heating becomes evident along oceanic fronts. Such a sensible heating pattern enhances meridional air temperature gradients near the surface, contributing to the maintenance of the baroclinicity along the oceanic fronts. This mechanism of the maintenance of the baroclinicity is referred as to “oceanic baroclinic adjustment” (e.g., Nakamura et al., 2008; Sampe et al., 2010; Ogawa et al., 2012). Validity of the oceanic baroclinic adjustment mechanism was examined by a series of aquaplanet experiments using an atmospheric general circulation model. Nakamura et al. (2008) and Sampe et al. (2010) conducted aquaplanet experiments giving SST distributions with and without oceanic frontal zones. In the experiments with the SST frontal zone, the activity of the cyclones

FIGURE 11–3 Near-surface baroclinicity over ocean averaged over 40 winters (DJF; December, January, and February) from 1979/1980 to 2018/2019. The baroclinicity is defined by the magnitude of horizontal gradient of potential temperature (K 100 km21) at 925 hPa. Data used are the ERA-interim reanalysis data (Dee et al., 2011).

Chapter 11 • Impacts of strong warm ocean currents on development of extratropical cyclones 271

concentrated near the SST frontal zones, which is consistent with the observation. In contrast, in the experiments without the SST frontal zones, the cyclone activity was weaker and less concentrated. Moreover, the sharp cross-frontal contrast in sensible heat flux from the ocean was simulated in the experiments with the SST frontal zones, while such a heat flux pattern was not seen in the experiments without the SST frontal zones. The results indicated that the meridional contrast in the sensible heating associated with the SST frontal zones maintains the surface baroclinicity against its relaxation by baroclinic eddies (Fig. 11
4), supporting the oceanic baroclinic

FIGURE 11–4 Schematic diagrams showing the restoration off near-surface baroclinicity by an oceanic front through surface sensible heat flux from the ocean against the relaxation by eddy heat transport. (A) A strong surface air temperature gradient formed above an oceanic front favors baroclinic eddy growth. (B) Poleward eddy heat flux relaxes the surface air temperature gradient and then heat flux from the ocean corresponding to the air
sea temperature difference acts to restore the surface air temperature gradient. Adapted from Sampe, T., Nakamura, H., Goto, A., Ohfuchi, W., 2010. Significance of a midlatitude oceanic frontal zone in the formation of a storm track and an eddy-driven westerly jet. J. Clim. 23, 1793
1814. doi: 10.1175/2009JCLI3163.1. © American Meteorological Society. Used with permission.

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adjustment mechanism. Ogawa et al. (2012) further performed aquaplanet experiments using six SST profiles, in which only the latitude of oceanic frontal zone differs, to investigate the response of baroclinicity and cyclone activity to oceanic frontal zone. In all experiments, the strong surface baroclinic zones formed near the SST frontal zones. Moreover, the activity of cyclones also became maximum roughly over the SST frontal zones. These results also demonstrated that the SST frontal zone significantly influences the baroclinicity and cyclones’ activity. Although the conditions of the aquaplanet experiments are not realistic, the importance of oceanic fronts in the maintenance of baroclinicity was also verified by realistic numerical simulations. Nonaka et al. (2009) focused on the SST front along the warmer flank of the Antarctic Circumpolar Current in the south Indian Ocean and explored its impact on the overlaying atmosphere using a coupled general circulation model. The results showed that the meridional contrast in sensible heat fluxes across the SST frontal zone maintained the near-surface baroclinicity along it, again supporting the oceanic baroclinic adjustment mechanism. Moreover, they estimated that the timescale of the restoring of the near-surface baroclinicity along the SST front is about 1 day, which is consistent with another estimate (about 2 days) by Vannière et al. (2017). Taguchi et al. (2009) examined the influence of oceanic fronts in the Kuroshio and Oyashio Extension region on the atmosphere aloft in spring and winter using a regional atmospheric model. They showed that the oceanic baroclinic adjustment was applicable in the oceanic region in both spring and winter. The patterns of the sensible heat fluxes, however, differed in winter and spring. In spring, the pattern was characterized by the upward (downward) sensible heat fluxes on the warmer (cooler) side of the SST frontal zone because baroclinic disturbances predominated the atmospheric circulation field. The situation is similar to the situation in the south Indian Ocean (Nonaka et al., 2009). On the other hand, in winter, the cold and dry flow associated with Asian winter monsoon from the Eurasian continent prevailed over the Kuroshio and Oyashio Extension region. Consequently, the upward sensible heat fluxes occurred even from the cooler side of the SST frontal zone, although it was weaker than that from the warmer side. They pointed out that the strong influence of the East Asia winter monsoon is a unique feature in the Kuroshio and Oyashio Extension region. Although the aforementioned studies pointed out that sensible heating plays an essential role in the maintenance of the baroclinicity through the oceanic baroclinic adjustment, Hoskins and Valdes (1990) suggested another mechanism of the maintenance of the baroclinicity. Hoskins and Valdes (1990) proposed that the moisture supply from warm currents enhances latent heating within the warmer side of extratropical cyclones developing along the oceanic fronts, and the enhanced latent heating maintains the baroclinicity along the oceanic front. Moreover, they suggested that surface sensible heat supply weakens cyclones by heating the air of their colder side. To reveal the relative contributions of sensible and latent heating to the maintenance of the baroclinicity, Hotta and Nakamura (2011) examined steady linear responses of a planetary wave model to individual components of diabatic heating. The experimental results showed that the contribution of the sensible heating to the formation of near-surface baroclinicity is much larger than that of the latent heating over the North Atlantic, the North Pacific, and the south Indian Ocean. Based on these results, Hotta

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and Nakamura (2011) concluded that the role of sensible heating is essential in the maintenance of the strong near-surface baroclinicity over the oceanic frontal zones. More recently, Papritz and Spengler (2015) investigated the processes related to the maintenance of baroclinicity around the Gulf Stream utilizing the slope of isentropic surfaces as a measurement of baroclinicity. Climatologically, the lower tropospheric baroclinic zone defined by the slope of isentropic surfaces was evident along the Gulf Stream. To understand its formation mechanism, they analyzed the tendency equation of the slope of isentropic surfaces and showed that diabatic processes were the main cause of the generation of the slope along the Gulf Stream. Moreover, they indicated that the sensible and latent heat fluxes from the Gulf Stream led to the enhancement of the diabatic processes, and the contribution of latent heating to the diabatic tendency was larger than that of sensible heating, consistent with Hoskins and Valdes (1990) and in contrast to Hotta and Nakamura (2011). Why did such a discrepancy in the conclusions of these studies occur? It is difficult to simply compare the results of Papritz and Spengler (2015) with those of Hotta and Nakamura (2011), as the highlighted heights of baroclinicity in those studies are different. Hotta and Nakamura (2011) focused on the near-surface baroclinicity below 900-hPa level because it is essential for the development of extratropical cyclones from a viewpoint of PV thinking (Hoskins et al., 1985) as introduced in Section 11.1. In contrast, Papritz and Spengler (2015) focused on the lower tropospheric baroclinicity defined as the slope of isentropic surfaces averaged between 900- and 600-hPa levels. From the perspective of the slope of isentropic surface, baroclinicity in the entire troposphere seems to be important in the development of extratropical cyclones (Papritz and Spengler, 2015). Additionally, they eliminated the baroclinicity below 900-hPa level from their analyses due to numerical issues about steep isentropic surfaces. Such a difference in the highlighted height may lead to the discrepancy of the conclusions of these studies. To solve this issue, further studies about the maintenance of the baroclinicity are required.

11.3 Role of moisture and heat supply from warm currents in cyclone development When extratropical cyclones develop over warm currents, moisture and sensible heat supply from the ocean become active (e.g., Neiman and Shapiro, 1993; Reed et al., 1993b; Takayabu et al., 1996; Zhang et al., 2006; Hirata et al., 2015, 2016, 2018, 2019). Latent and sensible heat fluxes from warm currents often approach or exceed 1000 W m22 near the centers of cyclones during their development stage (e.g., Fig. 10 in Neiman and Shapiro, 1993). On the contrary, when cyclones migrate over colder SST regions to the north of warm currents, downward surface heat fluxes occur around their centers (e.g., Fig. 10d in Neiman and Shapiro, 1993; Fig. 20 in Kuo et al., 1991). These indicate that SST distribution under cyclones is an important factor determining direct supply of moisture and heat from ocean to cyclone systems, and that warmer SST associated with warm currents provides a favorable condition for the increase in that supply. Previous studies suggested that moisture and sensible heat released around warm currents

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contribute to the intensification of cyclones (e.g., Nuss and Kamikawa, 1990; Reed et al., 1993b; Takayabu et al., 1996; Gyakum and Danielson, 2000). Thus to comprehend the relationship between warm currents and the development of extratropical cyclones, we should clarify the role of moisture and heat supply from warm currents in their development processes. In this section, we review the recent progress regarding this topic. From statistical perspective, Kuwano-Yoshida and Minobe (2017) examined the impact of SST distribution around warm currents over the northwestern Pacific region on the activity of extratropical cyclone. They conducted 20-year numerical experiments with real SST and smoothed SST data using the atmospheric general circulation model for the Earth Simulator (AFES; Ohfuchi et al., 2004) with 50-km horizontal resolutions. The difference in SST between the two experiments was evident especially around the Kuroshio/KE (see their Fig. 1c). Comparisons between the two experiments showed that the explosive development of cyclones over the northwestern Pacific region in January was reduced with the smoothed SST. Analyses based on the pressure tendency equation (Fink et al., 2012) revealed that the cyclone development difference resulted from suppression of latent heating in the smoothed SST experiment. Moreover, they showed that the suppression of latent heating was due to the weaker surface evaporation from the Kuroshio and its Extension during the development stage of cyclones. Their results indicated that the moisture supply from the warm currents plays an important role in the explosive development of cyclones over the northwestern Pacific region by enhancing latent heat release. To better understand the relationship among the moisture and heat supply from warm currents, latent heat release, and the development of extratropical cyclones, we should consider roles of synoptic-scale near-surface airflows associated with extratropical cyclones (Fig. 11
5). This is because that the airflows transport humid and/or statically unstable air, which causes latent heating, into near the cyclone center. The warm conveyor belt (WCB) and cold conveyor belt (CCB) are well known as the near-surface airflows associated with extratropical cyclones (Fig. 11
5). The WCB is a poleward flow associated with warm and moist air that prevails in the warm sector of cyclones (e.g., Carlson, 1980; Whitaker et al., 1988; Browning and Roberts, 1994). The CCB is an easterly flow accompanied by cold and dry air that passes beneath the warm-frontal zone of

FIGURE 11–5 Schematic showing warm conveyor belt (WCB) and cold conveyor belt (CCB).

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cyclones (e.g., Carlson, 1980; Whitaker et al., 1988; Schultz, 2001). Recent studies examined the impact of warm currents on the WCB (Booth et al., 2012; Sheldon et al., 2017) and also CCB (Hirata et al., 2015, 2018, 2019). In the remaining of this section, we review main findings of these studies and compare those. Booth et al. (2012) studied the influence of SST around the Gulf Stream on the development of cyclones using the Weather Research and Forecasting model (Skamarock et al., 2008) with 12- and 36-km horizontal resolutions. They conducted multiple numerical experiments of an extratropical cyclone using different SST conditions and investigated the cyclone’s response to SST changes. This study paid a special attention to the role of WCB because previous studies suggested that latent heating related to WCB brings the cyclone development (e.g., Fantini, 1990; Reed et al., 1993a, b; Wernli and Davies, 1997; Ahmadi-Givi et al., 2004; Brennan and Lackmann, 2005). The experimental results of Booth et al. (2012) showed that the increase in SST under the cyclone enhanced the cyclone intensification and latent heating within the WCB. Their results also indicated that the cyclone intensity is not sensitive to the strength of meridional SST gradient associated with the Gulf Stream. Moreover, using sensitivity experiments without surface sensible and latent heat fluxes and latent heat release, they showed that latent heating induced by moisture evaporated from ocean was a cause leading to the response of the cyclone intensity to the SST changes. Thus they concluded that SST changes under warm sector of cyclones regulate the surface evaporation and the associated latent heating within the WCB, affecting the cyclones’ development. Sheldon et al. (2017) examined the impact of the warm SST tongue associated with the Gulf Stream (Fig. 11
6A) on upward motion in the WCB of an extratropical cyclone using the Met Office Unified Model (Martinez-Alvarado et al., 2014) with a 12-km horizontal resolution. In addition to a control (CNTL) experiment with observed SST (Fig. 11
6A), they conducted two SST sensitivity experiments, which are referred to as SMTH and COOL. To see the effect of the warm tongue, they provided smoothed SST data without the warm tongue structure to the SMTH experiment (Fig. 11
6B). To investigate the role of the absolute temperature of the warm tongue, as opposed to the role of the SST gradient north of it, in the COOL experiment, they used the SST data created by cooling the SST by 3K over the entire domain (Fig. 11
6C). To explore the response of upward motion associated with the WCB to the SST distribution around the Gulf Stream, Sheldon et al. (2017) conducted backward trajectory analyses. They computed trajectories of more than 70,000 parcels that were initially released around the core of the updraft around the cyclone center between 4 and 8 km in height and extracted trajectories that passed over the Gulf Stream at low levels from all (Fig. 11
7). The total number of the extracted trajectories was 1178, 275, and 625 in the CNTL, SMTH, and COOL experiments, respectively. The number of the trajectories reaching a height of 5 km (7 km) or more was 780 (167), 27 (0), and 335 (29) in the CNTL, SMTH, and COOL experiments, respectively. The total number of trajectory trajectories in the two SST sensitivity experiments are much smaller than that in the CNTL experiment. Especially, the trajectories exceeding a height of 5 km were hardly seen in the SMTH experiment, which is related to

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FIGURE 11–6 Sea surface temperature (contoured every 2 C) used in experiment: (A) CNTL, (B) SMTH, and (C) COOL. Panel (D) gives the difference CNTL-SMTH (contoured every 1.5 C, positive continuous, negative dashed). The color curves in panels (A), (B), and (C) are the average horizontal trajectories for the respective experiments. The color indicated the height of air parcels at the initial time of the backward trajectory analyses (z0) (magenta: z0 $ 7 km, green: 5 km # z0 , 7 km, blue: z0 , 5 km). The diamonds on these curves indicate the average locations when parcels leave the boundary layer. The color curves in (D) indicate the CNTL (continuous lines) and SMTH (dashed lines) mean trajectories, reproduced from panel (A) and (B), respectively. Adapted from Sheldon, L., Czaja, A., Vannière, B., Morcrette, C., Sohet, B., Casado, M., et al., 2017. A ‘warm path’ for Gulf Stream-troposphere interactions. Tellus A Dyn. Meteorol. Oceanogr. 69. doi: 10.1080/16000870.2017.1299397, licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/legalcode).

the absence of both the warmer SST and strong SST gradient associated with the Gulf Stream warm tongue (Fig. 11
6D). From these results, they considered that both the two oceanic features created by the warm tongue play crucial roles in reinforcing the upward motion in the WCB. Sheldon et al. (2017) proposed two mechanisms to explain how the Gulf Stream warm tongue affects the upward motion in the WCB. One is the thermodynamic mechanism in which the warmer SST is a vital player as described later in this chapter. When highequivalent potential temperature (θe) air associated with the WCB flows near the warm tongue as in the CNTL experiment (Fig. 11
6A), the warmer SST condition acts to maintain the high θe, then leading to the stronger ascending motion. The other is the dynamical mechanism including two processes related to the strong SST gradient. The first process is that the

Chapter 11 • Impacts of strong warm ocean currents on development of extratropical cyclones 277

FIGURE 11–7 Back trajectories from the core of ascending motion at the initial time of the analyses (t 5 24 h) at mid-levels (the “release volume” in black) in (A) the CNTL, (B) the SMTH, and (C) the COOL experiments. The corresponding sea surface temperature is shown in black with a contour interval of 2℃. Note that only trajectories originating from low levels over the ocean at t 5 0 h are shown. The color coding refers to different sets of trajectories, depending on the height z0 at t 5 24 h: z0 $ 7 km (magenta), 5 km # z0 , 7 km (green), and z0 , 5 km (blue). Adapted from Sheldon, L., Czaja, A., Vannière, B., Morcrette, C., Sohet, B., Casado, M., et al., 2017. A ‘warm path’ for Gulf Stream-troposphere interactions. Tellus A Dyn. Meteorol. Oceanogr. 69. doi: 10.1080/ 16000870.2017.1299397, licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/legalcode).

SST gradient drives a thermally direct cell enhancing the frontal circulation. The second process is that the SST gradient destabilizes the frontal circulation by enhancing the vertical wind shear (Bennetts and Hoskins, 1979; Czaja and Blunt, 2011). Since the viewpoint of Booth et al. (2012) differs somewhat from that of Sheldon et al. (2017), we cannot simply compare these two studies. However, it is worth to note brief comparisons between the two studies about the WCB. Both the two studies agree that warmer SST intensifies extratropical cyclones. On the other hand, their conclusions about the impact of SST gradients on cyclones differ. Booth et al. (2012) indicated that the SST gradient associated with the Gulf Stream did not significantly influence the cyclone intensity. On the other hand, Sheldon et al. (2017) showed that the SST gradient can intensify updraft around the cyclone center through the dynamic mechanism. Although Sheldon et al. (2017) did not examine the cyclone intensity, the upward motion intensified by the SST gradient seems to enhance the cyclone development because stronger upward motion usually attends stronger latent heating. The discrepancy between the two studies may relate to the difference in the experiment designs for investigation of impacts of SST gradient. Booth et al. (2012) changed

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the strength of SST gradients by adding SST anomalies to the north of the Gulf Stream. On the other hand, Sheldon et al. (2017) removed the presence of the SST gradient by smoothing SST data spatially. Therefore Booth et al. (2012) focused on the strength of the SST gradient, while Sheldon et al. (2017) focused on the presence of the SST gradient, and then magnitudes of the SST anomalies in those experiments are also different. This may lead to different conclusions. Thus in the studies on the response of cyclones to SST gradients, we may require a special care to select SST boundary conditions. As reviewed earlier, importance of the WCB for development of extratropical cyclones and influence of SST anomalies on it have been revealed. Recent studies, however, pointed out that the CCB also plays active roles in the cyclone development over warm ocean currents (Hirata et al., 2015, 2016, 2018, 2019). Hirata et al. (2015, 2018) focused on the same cyclone rapidly developed over the Kuroshio/KE in January 2013 and examined the impact of moisture and sensible heat supply from the warm currents on the cyclone development, respectively. They hypothesized that mesoscale structures of extratropical cyclones are the key to elucidate the relationship between the warm currents and the cyclone development because the generation of latent heat release around the cyclone center seems to be related to the mesoscale structures. Thus, to represent the mesoscale structures, they conducted numerical experiments using a regional cloud-resolving model, the Cloud Resolving Storm Simulator (CReSS; Tsuboki and Sakakibara, 2002), with 0.05 (about 5 km) horizontal resolutions. Their results showed that the latent and sensible heat fluxes from the warm currents were enhanced along the CCB north of the cyclone center during the cyclone development stage (Fig. 11
8). In this case, the CCB, which is characterized by strong surface wind and cold and dry air, just overlapped with the warmer SST region along the Kuroshio and its Extension. Such an overlap between the CCB and the warm currents is very suitable for the increase in surface heat fluxes. Then, the CCB plays a vital role in the enhancement of

FIGURE 11–8 Maps of surface turbulent latent heat flux (shaded), 10-m horizontal wind (vectors), and sea level pressure (contours) at 0900 UTC 13 January, 0000 UTC 14 January, and 1500 UTC 14 January 2013 (the time of the maximum deepening rate). The shaded interval is 200 W m22. The reference arrow is 30 m s21. Winds of less than 10 m s21 are suppressed. The contoured interval is 4 hPa. Adapted from Hirata, H., Kawamura, R., Kato, M., Shinoda, T., 2015. Influential role of moisture supply from the Kuroshio/Kuroshio Extension in the rapid development of an extratropical cyclone. Mon. Weather. Rev. 143, 4126
4144. doi: 10.1175/MWR-D-15-0016.1. @American Meteorological Society. Used with permission.

Chapter 11 • Impacts of strong warm ocean currents on development of extratropical cyclones 279

surface moisture and heat supply from the warm currents. In contrast, the heat fluxes around the WCB are weak compared to those along the CCB (Fig. 11
8). The enhanced moisture and sensible heat supply from the warm currents modify the air associated with the CCB as below. The moisture supply enhances the convectively unstable conditions near the surface along the CCB, and so does the sensible heat supply also. Moreover, the sensible heating leads to an increase in the water vapor content of the saturated air related to the CCB through an increase in the saturation mixing ratio. The CCB also transports the air modified by the warm currents into a frontal zone around the cyclone center (Fig. 11
9). An atmospheric mesoscale frontal zone characterized by the zonal thermal gradient was seen around the western edge of the bent-back front just north of the cyclone center (Fig. 11
10). Along this front, positive two-dimensional frontogenesis is evident (Fig. 11
10C), which forces upward motion (Fig. 11
10B) on the warm side of the front (e.g., Martin, 2006). The air modified by the warm currents is imported into the front by the CCB and then lifted by the forced updraft associated with the frontogenesis. As a result, the moisture evaporated from the warm currents condenses, causing the release of convective instability and generation of the strong mesoscale latent heating associated with the convection. The increased latent heating further intensifies the cyclone and its associated CCB, which was confirmed by diagnostic analyses using PV (Hirata et al., 2015) and numerical experiments without surface heat fluxes from the warm currents (Hirata et al., 2018). On the basis of their results,

FIGURE 11–9 (A
F) Locations of air parcels (closed circles) calculated by a forward trajectory analysis at 0300 UTC 14 January, 0900 UTC 14 January, 1500 UTC 14 January, 2100 UTC 14 January, 0300UTC 15 January, and 0900 UTC 15 January 2013. The colored circles indicate the heights (m) of the parcels. The surface turbulent latent heat flux (shaded), the sea level pressure (contours), and 10-m horizontalwinds (vectors) are also shown. The shaded interval is 250 W m22. The contoured interval is 10 hPa. The reference arrow is 40 m s21. Winds of less than 5 m s21 are suppressed. Adapted from Hirata, H., Kawamura, R., Kato, M., Shinoda, T., 2015. Influential role of moisture supply from the Kuroshio/Kuroshio Extension in the rapid development of an extratropical cyclone. Mon. Weather. Rev. 143, 4126
4144. doi: 10.1175/MWR-D-15-0016.1. @American Meteorological Society. Used with permission.

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FIGURE 11–10 (A) Maps of the horizontal gradient of 950-hPa potential temperature (shading), 950-hPa horizontal wind (vectors), and sea level pressure (contours) at 1400 UTC 14 January 2013 in the CNTL run. The shading interval is 1.0 3 1021 K km21. The reference arrow is 40 m s21. The contour interval is 5 hPa. (B) Longitude
height cross-sectional maps of potential temperature (shading) and zonal and vertical winds (vectors) along the blue line illustrated in (A). The shading interval is 4K. The reference arrows for the zonal and vertical winds are 40 m s22 and 1 m s21, respectively. (C) As in (A), but for the 950-hPa two-dimensional frontogenesis (shading). The unit of frontogenesis is 1025 K km21 s21. Adapted from Hirata, H., Kawamura, R., Kato, M., Shinoda, T., 2018. A positive feedback process related to the rapid development of an extratropical cyclone over the Kuroshio/Kuroshio Extension. Mon. Weather. Rev. 146, 417
433. doi: 10.1175/MWR-D-17-0063.1. @American Meteorological Society. Used with permission.

Hirata et al. (2015, 2018) proposed that the moisture supply and sensible heat from warm ocean currents accelerate the rapid development of extratropical cyclones through a positive feedback process between a CCB and latent heating (Fig. 11
11), referred to as the cold conveyor belt (CCB)-latent heating (LH) feedback process. Although Hirata et al. (2015, 2018) focused on one case, Hirata et al. (2016) suggested that the CCB-LH feedback process also plays an important role in the rapid development of other extratropical cyclones over the Kuroshio/KE region. Moreover, Hirata et al. (2019) showed that the CCB-LH feedback is a key process in the extremely rapid intensification of an extratropical cyclone over the Gulf Stream in early January 2018. These studies did not pay attention to the role of the WCB because the surface heat fluxes around the WCB are much weaker than those around the CCB (Fig. 11
8). However, although surface moisture fluxes under WCB are small, its effect may be nonnegligible because the evaporated moisture integrated over the wide region of WCB can contribute to the total moisture around cyclones (Dacre et al., 2015). Moreover, it is not denied that warmer SST associated with the warm currents contributes to the maintenance of high θe around the WCB and to the enhancement of latent heating via the thermodynamic mechanism proposed by Sheldon et al. (2017). On the other hand, since the horizontal pattern of the heat fluxes shown in those studies (e.g., Fig. 11
8) acts to reduce meridional atmospheric thermal gradients around the cyclone center, the dynamic process suggested by Sheldon et al. (2017) might not operate in their cases. Recent studies introduced here indicated that both the WCB (Booth et al., 2012; Sheldon et al., 2017) and the CCB (Hirata et al., 2015, 2018, 2019) play key roles in the development of cyclones over warm ocean currents. To further deepen our understanding of the relationship between warm ocean currents and the cyclone development, as the next step, we

Chapter 11 • Impacts of strong warm ocean currents on development of extratropical cyclones 281

FIGURE 11–11 Schematic diagram representing the revised cold conveyor belt (CCB)-latent heating (LH) feedback process in relation to the explosive development of an extratropical cyclone over the Kuroshio/Kuroshio Extension. Dashed arrows denote part of the CCB-LH feedback process related to moisture supply from the warm currents proposed by Hirata et al. (2015). Solid arrows account for the additional roles of sensible heat supply from the warm currents highlighted in Hirata et al. (2018). Adapted from Hirata, H., Kawamura, R., Kato, M., Shinoda, T., 2018. A positive feedback process related to the rapid development of an extratropical cyclone over the Kuroshio/ Kuroshio Extension. Mon. Weather. Rev. 146, 417
433. doi: 10.1175/MWR-D-17-0063.1. @American Meteorological Society. Used with permission.

should estimate relative contributions of the WCB and CCB to the cyclone intensification, and we need to construct a framework to compare quantitatively both contributions. Moreover, those studies pointed out that high-horizontal resolutions are required to reproduce the impacts of warm ocean currents on climate and the rapid development of cyclones in numerical models. Sheldon et al. (2017) pointed out that climate models with horizontal resolutions of several 100 km cannot accurately simulate the climatic impact of the Gulf Stream warm tongue through the warm sector of cyclones on the basis of their results. Additionally, to correctly simulate the rapid development of cyclones associated with the CCB-LH feedback process, we require high-resolution cloud-resolving simulations, which can resolve convection. This is because latent heating associated with convection plays an

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essential role in that process (Hirata et al., 2015, 2018, 2019). Such high-resolution numerical simulations are useful to further comprehend the impacts of warm currents on climate and individual cyclones.

11.4 The Kuroshio and Kuroshio Extension, and their variability As discussed in the previous sections, SST distributions driven by oceanic frontal structures and eddies associated with strong warm ocean currents can affect atmospheric cyclone developments and then large-scale atmospheric circulations. This means that understanding of formation and variability mechanisms of such currents and eddies is also crucial to understand climate system in mid-latitude. In this section, we review recent studies on such mechanisms focusing on the Kuroshio and mainly on the KE as examples of such strong warm currents that can affect the atmosphere aloft. As described later in the chapter, the Kuroshio takes multiple possible paths to the south of Japan, and it is a unique property compared to other strong warm currents such as the Gulf Stream. In ECS, the Kuroshio generally flows along the southeastern edge of the continental shelf. Then, while there are substantial eddy activities, the current path is basically stable and associated with the current warm SST tongue structure and strong SST gradient is formed. With those stable SST frontal structures, cyclone developments (Xie et al., 2002) and enhanced rainfalls (Sasaki et al., 2012; Miyama et al., 2012; Sasaki and Yamada, 2018) have been observed. Understandings of the mean structures and variability of the Kuroshio in ECS have been much improved with enhanced observational studies in the last decades (see recent review by Andres et al., 2015) and eddy-resolving ocean general circulation model (OGCM) studies (e.g., Nakamura et al., 2010). To the south of Japan, the Kuroshio is known to have three kinds of possible paths, the Large Meander, Nonlarge meander near-shore, and Nonlarge meander off-shore paths (Kawabe, 1995). The path strongly affects SST distribution and then the surface wind (Nonaka and Xie, 2003), rainfall (Xu et al., 2010), and even paths of cyclones (Nakamura et al., 2012; Hayasaki et al., 2013). Understandings of mechanisms of transition of the Kuroshio path have much improved in the last decade with developments of high-resolution satellite observations, high-resolution OGCMs, and oceanic data assimilation system thanks to much developed computational resources. Based on the detailed analyses of such model outputs, Usui et al. (2013) have pointed out importance of three key phenomena, an eddy propagating from off Taiwan through ECS, sea surface height (SSH) off Kuroshio, and state of the KE, while importance of bottom topography is also suggested (Endoh and Hibiya, 2001). For maintenance of the Large Meander, importance of strength of the Kuroshio is shown by numerical experiment: the Large Meander can be advected downstream with too strong Kuroshio and can propagate upstream (westward) when the Kuroshio is too weak (Usui et al., 2013). As the Kuroshio is the western boundary current of the wind-driven subtropical gyre, this implies the Kuroshio Large Meander could be controlled by wind-stress

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variability to some extent. Indeed, Tsujino et al. (2013) succeeded to reproduce interannualto-decadal variability in the Kuroshio path in an OGCM driven by wind fields based on an atmospheric reanalysis. These improvements of understandings of mechanisms have made possible real-time prediction of variability in the Kuroshio (Miyazawa et al., 2005). With the Kuroshio Large Meander started in 2017, the second Large Meander in the enhanced satellite observation era, the understandings are further validated (Usui, 2019). The KE flows eastward around 35N, much south of the boundary between the North Pacific subtropical and subpolar gyres, where the eastward current is expected theoretically from sea surface wind distributions. While the structure of coastline of Japan can have some impacts, Nakano et al. (2008) showed that it is mainly due to existence of small cyclonic gyre in the northern part of the subtropical gyre along the western boundary. Idealized experiments by Sue and Kubokawa (2015) support this and suggest possible relationship with meridional profiles of wind-stress curl. In KE, it has been shown that decadal-scale variability is dominant rather than interannual one (e.g., Qiu and Chen, 2005). Variability in KE can be a combination of meridional shift of its axis and variability in its strength (Taguchi et al., 2007), and both are basically driven by atmospheric variability in the central/eastern parts of the North Pacific. The wind-driven signals propagate westward as Rossby waves (Deser et al., 1999) and affect the KE strength through its effect on strength of the southern recirculation gyre (Qiu and Chen, 2005), connecting to variability in the eastern North Pacific (Ceballos et al., 2009). Especially for meridional shift in KE, Rossby waves along the mean KE are shown to be important (Sasaki and Schneider, 2011; Sasaki et al., 2013). Rossby wave propagation into the western boundary can further induce KE jet speed anomalies that tend to propagate downstream (Sasaki et al., 2013). As the signals driven in the central/eastern parts of the North Pacific Ocean take several years to propagate to its western part, KE variability could be predicted based on the signals in the central/eastern parts of the basin with several-year lead time on broad meridional scale (Schneider and Miller, 2000) and also on the jet scale (Nonaka et al., 2012). At the same time, however, dynamics of the strong jet of KE is nonlinear and some parts of its variability are not atmospheric-driven and induced by oceanic internal dynamics (Dijkstra and Ghil, 2005; Pierini 2006, 2014). This intrinsic interannual-to-decadal variability in KE jet has substantial amplitude in OGCM experiment even if it is driven by interannualy varying atmospheric reanalysis field, and can induce uncertainty in its prediction (Nonaka et al., 2016). Associated with the strong current, highest oceanic eddy activities are observed in the KE region over the North Pacific Ocean (e.g., Qiu and Chen, 2005). Recent studies have shown that, in addition to oceanic fronts, eddies are important to induce anomalies in SST and sea surface turbulent heat flux in the western North Pacific (Sugimoto and Hanawa, 2011) and could affect atmospheric storms (O’Reilly and Czaja, 2015; Ma et al., 2015). Heat flux anomalies released to the atmosphere associated with eddies also affect dissipation of eddies themselves, and then the KE jet through eddy
mean flow interactions (Ma et al., 2016) and impacts on the recirculation gyres to the north of KE (Taguchi et al., 2010). Detailed properties of eddies in the western North Pacific are investigated well based on satellite SSH observations, and highest amplitude of anticyclonic (cyclonic) eddies are

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observed to the north (south) of KE (Itoh and Yasuda, 2010). From the KE jet, anticyclonic (cyclonic) rings are shed to the north (south) of it most frequently from the downstream region around the Shatsky Rise (upstream around the steady meander of the path; Sasaki and Minobe, 2015). Eddy activities also indicate decadal variability. In the KE upstream region (141E-153E), satellite observations showed eddy activities were high when the KE jet speed was low, but the relation was not clear in the downstream region (Qiu and Chen, 2005). In the downstream region, Taguchi et al. (2010) show that eddy activities tend to high when current speed in the region is high based on an eddy-resolving OGCM. For mechanisms for the variability in eddy activities in the KE region, while possible importance of bottom topography such as the Izu-Ogasawara Ridge (Qiu and Chen, 2005) and Shatsky Rise (Qiu and Chen, 2010) is suggested, it is still under debate. Such Kuroshio and KE jet speed can affect SST along the axis (e.g., Nishikawa and Yasuda, 2011; O’Reilly and Czaja, 2015), and high (low) eddy activities weaken (strengthen) SST gradient of the frontal zone associated with the strong current (e.g., Sasaki and Minobe, 2015). Then, as discussed in the previous sections, the oceanic variability can affect development of cyclones through their influences on the SST structures. Also, decadal variability in the eddy activities can affect not only SST and atmospheric aloft but also subsurface and remote oceanic water mass (Qiu et al., 2007; Oka et al., 2015) and biogeochemical properties (Oka et al., 2019) through variability in exchanges of water mass across the KE front associated with formation of eddies there. Around the KE region, submesoscale variability is also active, especially in late winter with deeper oceanic mixed layer (Sasaki et al., 2014). Highresolution in situ observations showed that atmospheric boundary layer can respond to SST streamer with several tens kilometer scales (Kawai et al., 2019). It is also an open question if such submesoscale oceanic structures are also possible to affect atmospheric cyclones and upper troposphere.

11.5 Summary and conclusion 11.5.1 Summary Thanks to recent rapid developments of high-resolution satellite observations and numerical simulations, active ocean
atmosphere interactions in mid-latitude have been revealed and its mechanisms have been investigated. In the present chapter, we review recent studies on those topics especially focusing on roles of strong oceanic warm currents and associated SST frontal structures in the development of extratropical cyclones, which can cause extreme weather events such as heavy rainfall/snowfall. One important role of such SST structure is formation/ maintenance of baroclinicity of the near-surface atmosphere. Numerical simulations clearly indicate that while cyclones reduce the baroclinicity by poleward heat flux associated with them, resultant enlarged atmosphere-ocean temperature differences can enhance sensitive heat flux contrasting across the SST front and restore baroclinicity in the near-surface atmosphere. For the restoring of baroclinicity in the lower troposphere, possible importance of latent heating is also suggested.

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Another crucial role of the SST frontal structure and especially its warmer side is the enhancement of evaporation from the ocean and resultant latent heating. Enhanced moisture supply from warm ocean in the wide region in the warmer side of cyclones and latent heating in the WCB are found to be important for intensification of updraft to mid/upper troposphere. Further, recent atmospheric mesoscale resolving numerical simulation studies have shown warm SSTs associated with strong warm ocean currents can affect also heat and moisture supplies from the ocean to lower atmosphere and enhanced development of cyclone through latent heating related to convection in the CCB. There can be positive feedback among heating, moisture supplies, and convection. Magnitude of warm strong ocean currents such as the Kuroshio/KE and activities of associated eddy can affect SST and its gradient in the oceanic frontal zone, which have the key roles for development of extratropical cyclones. The currents are basically driven by wind-stress and their variability in the strength and meridional positions are also strongly affected by atmospheric variability. Recent studies, however, have also revealed important roles of oceanic mesoscale eddies. Variability of oceanic eddies relates to variability in current magnitude, but its mechanisms are still under debate and further studies are necessary.

11.5.2 Open questions As reviewed in this chapter, a possible key role of the processes in the CCB for development of cyclones responding to warm SST associated with the strong warm currents has been recently revealed by mesoscale resolving simulations in addition to the widely recognized importance of those in the WCB. While those processes in the two conveyor belts are expected to be able to cooccur, enhanced heat supply in the CCB might reduce meridional temperature gradient around the cyclone center, weakening the dynamical mechanism to develop the cyclones through the WCB. However, if the CCB is over the polar side of the SST frontal zone and only the WCB covers the warm SST region, the dynamical mechanism will efficiently operate, whereas the CCB-LH feedback will not work. Thus we suppose that the strength of the WCB and CCB processes is sensitive to relative position of the two conveyor belts to the SST fronts. To clarify this sensitivity and relative importance of the processes in the WCB and CCB to the development of various extratropical cyclones, further investigations based on models with enough horizontal resolutions are necessary. Considering the horizontal scale of the atmospheric mesoscale structures, which have been revealed to play crucial roles in development of cyclones through heat and moisture supplies from the ocean especially through the CCB, oceanic mesoscale and further submesoscale structures might be important for prediction of cyclones’ development and extreme heavy rainfall/snowfall. Such possible importance of oceanic fine structures for weather forecast has not been investigated yet. Three-dimensional detailed structures of oceanic submesoscale structures, horizontal distributions, and temporal variability of activities of such structures have not been described yet sufficiently due to difficulty of observations and numerical simulations of them. Also, how such rapid development of extratropical cyclones can affect oceanic

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mesoscale/submesoscale structure should be further investigated (e.g., Kuwano-Yoshida et al., 2017). With their horizontal scales, the atmospheric mesoscale structures have large windstress curl and can affect oceanic fine structures dynamically.

11.5.3 Conclusion While importance of SST frontal zone and associated warm waters for development of extratropical cyclones have been revealed as reviewed here, we need further understandings of detailed mechanisms for response of cyclones to oceanic fine structures to answer questions, for example, as follows: How can oceanic eddies of B100 km horizontal scale affect cyclones of B1000 km horizontal scale? What spatial structures and scales of SST most significantly affect cyclones? Recent simultaneous in situ observation by three vessels in the KE region revealed that fine-scale variability of the strong current and associated SST structure is still difficult to be captured by oceanic reanalysis systems even after assimilating satellite observations (Kawai et al., 2014). To what extent the reanalysis systems should be improved to forecast development of extratropical cyclones and associated severe rainfall/snowfall are of importance for not only scientific but also socioeconomic purposes.

Acknowledgment The authors would like to thank the anonymous reviewer and Dr. Arnaud Czaja for their valuable comments. Collaboration with Dr. Czaja was supported by JSPS Bilateral Joint Research Projects, “A novel modeling technique to understand the impact of the Gulf Stream and Kuroshio on climate.” This work was partly supported by JSPS KAKENHI (grant nos. JP19H05701, JP20H05170, and JP19K14794).

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3652. Available from: https://doi.org/10.1175/JCLI-D-11-00641.1. Nonaka, M., Sasai, Y., Sasaki, H., Taguchi, B., Nakamura, H., 2016. How potentially predictable are midlatitude ocean currents? Sci. Rep. 6, 20153. Available from: https://doi.org/10.1038/srep20153. Nuss, W.A., Kamikawa, S.I., 1990. Dynamics and boundary layer processes in two Asian cyclones. Mon. Weather Rev. 118, 755
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Useful resources Common data and information URLs useful for all chapters of the bookIt may be noted that all these website URLs were up-to-date as of June 3, 2020 but those link addresses might change in future.

Observed, reanalyses, and model data NOAA/OISST v2 (sea surface temperature): https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html COBE SST (sea surface temperature): https://ds.data.jma.go.jp/tcc/tcc/products/elnino/cobesst/cobe-sst.html https://psl.noaa.gov/data/gridded/data.cobe.html Hadley Centre sea ice and sea surface temperature (HadISST): https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html Hadley Centre ocean data EN4: https://www.metoffice.gov.uk/hadobs/en4/download-en4-1-1.html Global Precipitation Climatology Project V2 (GPCP): https://psl.noaa.gov/data/gridded/data.gpcp.html Available data sets at PSD/ESRL/NOAA: COBE SST, ERSST, and NOAA OIV2: https://psl.noaa.gov/data/gridded/index.html IRI/LDEO climate data library: https://iridl.ldeo.columbia.edu/index.html?Set-Language 5 en ERA-5 products: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 Japanese 55-year Reanalysis (JERA-55): https://jra.kishou.go.jp/JRA-55/index_en.html SINTEX-F coupled general circulation model (CGCM): http://www.jamstec.go.jp/aplinfo/sintexf/e/seasonal/overview2.html SINTEX-F CGCM prediction results: http://www.jamstec.go.jp/virtualearth/general/en/ North American Multi-Model Ensemble (NMME): https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/ Climate-System Historical Forecast Project (CHFP): http://chfps.cima.fcen.uba.ar/shfp.php Asia-Pacific Data-Research Center (APDRC): http://apdrc.soest.hawaii.edu/data/data.php

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Useful resources

Analyses tools Ferret: https://ferret.pmel.noaa.gov/Ferret/ Ferret scripts: https://github.com/NOAA-PMEL/Ferret/tree/master/jnls/examples Climate Data Operators (CDO): https://code.mpimet.mpg.de/projects/cdo/ https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf NCAR Command Language (NCL): https://www.ncl.ucar.edu/index.shtml netCDF Operator (NCO): http://nco.sourceforge.net/ Grid Analysis and Display System (GrADS): http://cola.gmu.edu/grads/ Useful GrADS scripts: https://www.cpc.ncep.noaa.gov/products/wesley/grads_scripts.html http://kodama.fubuki.info/wiki/wiki.cgi/GrADS/script?lang 5 en https://github.com/CoDNEXLAB/grads Python in ocean and climate data analyses: https://gist.github.com/tmiyama/30c3a069e5e391012e7dcaa6e7b5d7f2 R: https://www.r-project.org/ R script discussions: https://stackoverflow.com/questions/tagged/r Climate Explorer: https://climexp.knmi.nl/start.cgi

Additional resources specific to some of the chapters Resources for Chapter 2 MJO observation and forecasts [Climate Prediction Center (CPC)]: https://www.cpc.ncep.noaa.gov/products/precip/CWlink/MJO/mjo.shtml NCL scripts: https://www.ncl.ucar.edu/Applications/mjoclivar.shtml Simple models: https://github.com/masamatt/mjo_eq-wave_model Python package: https://github.com/cghoffmann/mjoindices

Useful resources

297

Resources for Chapter 3 Nino3.4 time series and forecasts [Japan Agency for Marine-Earth Science and Technology (JAMSTEC)]: http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#nino34 Nino3.4 forecasts [Bureau of Meteorology (BOM), Australia]: http://www.bom.gov.au/climate/enso/index.shtml#tabs 5 Outlooks Nino3.4 forecasts (CPC): https://www.cpc.ncep.noaa.gov/products/predictions/90day/tools/briefing/unger.pri.php Nino3.4 forecasts [International Research Institute for Climate and Society (IRI)]: https://iri.columbia.edu/our-expertise/climate/forecasts/enso/current/ Historical Nino3 index from 1870 prepared from HadISST: https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino3/ Japan Meteorological Agency (JMA) Nino3 index: https://www.data.jma.go.jp/gmd/cpd/db/elnino/index/nino3idx.html CPC Oceanic Nino index: https://catalog.data.gov/dataset/climate-prediction-center-cpcoceanic-nino-index Tropical Pacific data (NOAA PMEL TAO/TRITON, PIRATA, and RAMA): https://www.pmel.noaa.gov/tao/drupal/disdel/

Resources for Chapter 4 EOF in NCL: https://www.ncl.ucar.edu/Applications/eof.shtml EMI time series and forecasts (JAMSTEC): http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#emi

Resources for Chapter 5 IOD time series and forecasts (JAMSTEC): http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#dmi IOD forecasts (JMA): https://ds.data.jma.go.jp/tcc/tcc/products/model/indices/3-mon/indices1/shisu_forecast. php IOD time series and forecasts (BOM): http://www.bom.gov.au/climate/enso/indices.shtml?bookmark 5 iod http://www.bom.gov.au/climate/enso/index.shtml#tabs 5 Indian-Ocean IOD time series (PSL, NOAA): https://psl.noaa.gov/gcos_wgsp/Timeseries/DMI/

Resources for Chapter 6 JMA Indian Ocean Basin Wide index: https://www.data.jma.go.jp/gmd/cpd/db/elnino/index/iobwidx.html

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Resources for Chapter 7 ATL3 time series and forecasts (JAMSTEC): http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#atl3

Resources for Chapter 8 Ningaloo Nino time series and forecasts (JAMSTEC): http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#nni

Resources for Chapter 9 IOSD and SASD time series and forecasts (JAMSTEC): http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#siod http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#sasd

Resources for Chapter 10 California Nino and Dakar Nino time series and forecasts (JAMSTEC): http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#cni http://www.jamstec.go.jp/virtualearth/general/en/graph_SINTEX.html#dni

Resources for Chapter 11 ALERA2: AFES-LETKF experimental ensemble reanalysis 2 ALERA2 is an experimental atmospheric reanalysis dataset from January 1, 2008 to January 5, 2013 produced on the Earth Simulator with 63 ensemble members. http://www.jamstec.go.jp/alera/alera2.html OFES2: Ocean General Circulation Model for the Earth Simulator (OFES) version 2 OFES is a quasiglobal eddy-resolving simulation with 0.1-degree horizontal resolutions. In OFES ver. 2, sea-ice model and tidal mixing scheme were implemented and JRA55-do atmospheric data set was used to force. The hindcast simulation has been integrated from 1958 to the present year. http://www.jamstec.go.jp/ofes/ofes2.html List of regional atmospheric models used in studies reviewed in this chapter: Cloud Resolving Storm Simulator (CReSS): http://www.rain.hyarc.nagoya-u.ac.jp/Btsuboki/cress_html/index_cress_eng.html Weather Research and Forecasting (WRF) model: https://www.mmm.ucar.edu/weather-research-and-forecasting-model Met Office Unified Model: https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/ index

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively. A AAC. See Anomalous anticyclone (AAC) ABFZ. See Angola-Benguela frontal zone (ABFZ) Abnormal ocean warming, 61 ACC. See Anomaly correlation coefficient (ACC); Antarctic Circumpolar Current (ACC) AGCM. See Atmospheric general circulation model (AGCM) Agulhas Current, 221
222 Agulhas Return Current region, 228 Air
sea coupling, effects of, 36
37 Along-shore surface winds (ASWs), 237
240 AMM. See Atlantic meridional mode (AMM) Angola-Benguela frontal zone (ABFZ), 240 Anomalous anticyclone (AAC), 141
142, 148
149 in tropical Western North Pacific (WNP), 154
155 Anomalous atmospheric cyclonic circulation, 210
211 Anomalous moisture budget equation, 38
39 Anomalous shortwave radiation, 46 Anomaly correlation coefficient (ACC), 75
76 Antarctic Circumpolar Current (ACC), 221
222 in South Indian Ocean, 272 Anthropogenic global warming, 15 Anticyclone Philippine Sea, 141 South Atlantic, 238
240 Aquaplanet experiments, 272 Asian summer monsoon (ASM), 19, 151
152 ASWs. See Along-shore surface winds (ASWs) Atlantic basin, decadal-to-interdecadal variability in, 195
196 Atlantic cold tongue, 173 Atlantic meridional mode (AMM), 247, 250 tropical Atlantic variability linkage to, 186
187 Atlantic Niño, 171 Atlantic Niño II, 182

Atlantic-Pacific interbasin coupling, 161
162 Atlantic zonal mode (AZM), 171
172, 247 amplitude, 188 climatological annual cycle of equatorial Atlantic, 173
176 composite evolution of, 178
180, 179f, 180f data description and definition, 172, 173t El Niño-Southern oscillation impact on, 190
191 events initiation, 185
186 negative, 181, 182f, 183f, 184f, 185
186, 185f noncanonical, 182
184 impact on El Niño-Southern Oscillation, 189
190 linkage to tropical Atlantic variability, 186
188 link to Benguela Niño, 187
188 link to meridional mode, 186
187, 187f sea surface temperature (SST) variations, 171 warming, 196
197 thermodynamic, 184
185 Atmosphere, 1 and ocean circulations, 6
9, 8f, 9f Atmospheric general circulation model (AGCM), 150, 186, 191, 195, 210, 213 simulations, 155
157 Atmospheric heat budget, 3
6 Atmospheric intrinsic variability, decadal fluctuation of, 230 Atmospheric Rossby waves, 209
210 Atmospheric teleconnections, 141 Atmospheric waves, 63 AZM. See Atlantic zonal mode (AZM) B Baja California system, upwelling regions and variability, 239f, 247
249 Bakun Hypothesis, 255

299

300

Index

Baroclinicity, role of warm currents in maintenance of, 270
273, 271f Barrier layer, 24
25 Basin-wide mechanism, 65
66 BCMLE. See Benguela Current Large Marine Ecosystem (BCMLE) Benguela Current Large Marine Ecosystem (BCMLE), 13
14 Benguela low-level coastal jet (BLLCJ), 253
254 Benguela Niño, linkage to tropical Atlantic variability, 187
188 Benguela upwelling system (BUS), upwelling regions and variability, 238
247, 239f Bjerknes feedback, 61
62, 64, 66 Black body radiation, 3 BLLCJ. See Benguela low-level coastal jet (BLLCJ) Boomerang pattern, 72 Boreal summer, quasimonthly oscillation, 20
21, 21f Boreal summer, impact on northward propagation in, 37
44, 37f, 40f, 42f, 43f Boreal summer intraseasonal oscillation (BSISO), 19
20 convection, 37
38 life cycle, 20
24 northward propagation of, 18, 38 Boreal winter impact on eastward propagation in, 31
37, 32f, 33f, 34f, 35f, 36f intraseasonal oscillations (ISOs), 19
20 and summer ISOs, distinctive propagation characteristics, 18 BSISO. See Boreal summer intraseasonal oscillation (BSISO) C California Niño/Niña, 248
249, 255 Canonical El Niños (ELs), 93, 103
104 Carbon cycle, 99 CCB. See Cold conveyor belt (CCB) CGCM. See Coupled general circulation model (CGCM) CHFP. See Climate historical forecast project (CHFP) Clarke
Meyers effect, 208
209

Climate desert, 2 impacts, 106 Mediterranean, 2 tropical, 2 variations, 10
14 Climate change, 2 El Niño Modoki (EM), 105
106 natural, 2 Climate Forecast System Reanalysis-derived diabatic heating products, 38
39 Climate historical forecast project (CHFP), 246 Climate model, 84
85, 107
108 Coastal Niños in, 252
255 simulations, 237
238 Climate modes, Ningaloo Niño (Niña), 212
213 Climate Prediction and its Application to Society (CliPAS), 75
76 Climate prediction system, JAMSTEC, 76 Climatological annual cycle, of equatorial Atlantic, 173
176, 174f, 175f, 176f Climatology, 2 Cloud Resolving Storm Simulator (CReSS), 278 CMIP5. See Coupled Model Intercomparison Project phase 5 (CMIP5) CNN. See Convolutional neural network (CNN) CNRM model, 31 CNTL experiment, 275
277, 276f, 277f Coastal Bjerknes feedback, 210
211 Coastal Niño, 207, 215 in climate models, 252
255 Coastal Niño/Niña California Niño/Niña, 248
249 features of, 257t Coastal upwelling, 12
14, 237, 245
246, 248
249, 252 Coastal upwelling regions, sea surface temperatures (SSTs) in, 237 Coastal warming, 242 Cold conveyor belt (CCB), 274
275, 274f, 279 LH feedback process, 279
280, 281f Community Earth System Model Large Ensemble (CESM-LE) system, 126 Composite raw anomalies, wTemporal evolutions of, 22
24, 27f

Index

Convolutional neural network (CNN), 76 COOL experiment, 275
276 Cooling, sea surface temperature (SST), 116 Coriolis force, 11
12 Coupled general circulation model (CGCM), 75, 160
161, 184
185, 225
226, 228 Coupled Model Intercomparison Project phase 5 (CMIP5), 160 CReSS. See Cloud Resolving Storm Simulator (CReSS) Cyclone extratropical, 268
269, 268f, 273
274, 278 tropical, 155
157 activity in Western North Pacific (WNP), 142 D Dakar Niños, 250 Dakar system, 256 upwelling regions and variability, 249
252, 251f, 252f “Dateline ENSO”, 95 Decadal variability Indian Ocean, predictability of, 230
232, 231f, 232f South Atlantic, 226
230, 229f South Atlantic Ocean, predictability of, 230
232, 231f, 232f Southern Indian Oceans, 226
230, 229f Desert climate, 2 Diabatic PV anomaly, 268
269 Dipole mode index (DMI), 118
119 DMI. See Dipole mode index (DMI) Dynamical forecast systems, 76 E Earth climate system, 1 gravity, 7 Earth’s rotation, annual cycle of, 1
2, 2f East Asian summer monsoon, 155 East China Sea (ECS), 267
268 Eastern boundary upwelling systems, 238, 250 Eastern Pacific (EP), sea surface temperature (SST) variations in, 97 Eastward-propagating boreal winter, effects of air
sea coupling on, 36
37

301

Eastward propagation, in Boreal winter, 31
37, 32f, 33f, 34f, 35f, 36f Ecosystem, marine, 246
247 Ekman pumping, 11
12 El Niño, 61, 108, 143
144 canonical, 93, 103
104 El Niño Modoki (EM), 93
96, 99
100 climate change, 105
106 debate, 96
97 distinctions and nonlinearities, 97
101 dynamic coupling strength, 106
107 signal, 99
100 teleconnections, 101
105, 102f, 107 El Niño Modoki Index (EMI), 93
94 El Niño-Southern Oscillation (ENSO), 62f, 93, 104
105, 141, 161
162, 171 aperiodicity, 64
65 Atlantic zonal mode (AZM) impact on, 189
190 canonical picture of, 62 cycle, 61
62, 64 effect on SLP, 210 equatorial Atlantic influence on, 189 equatorial Pacific SST anomalies, 141 impact on Atlantic zonal mode, 190
191 global weather and climate, 84 Indian subcontinent, 123 Indian Ocean Dipole (IOD) interactions with, 125
127, 127f magnitude modulations, 160 mode, 50 model, 64
65 Modoki, 107 nonlinearities in the ENSO evolution, 100
101 in tropical Pacific, 115, 237
238 El Niño/Southern Oscillation (ENSO) theory, 63
69, 64f asymmetry, 67
69, 68f, 69f decadal and future climate, 81
83 diversity and flavors, 69
71, 69f oscillatory mode, 65
67 predictability, 75
81, 77f, 78f, 79f, 80f sporadic mode, 65 teleconnection, 72
75, 72f, 73f

302

Index

EM. See El Niño Modoki (EM) Empirical orthogonal function (EOF), 93, 96, 149
150 Energy radiative, 3 thermal, 3 transfer in global energy budget, 3, 4f turbulent, 3 ENSO Modoki, Indian Ocean Dipole (IOD) interactions with, 125
127, 127f EOF. See Empirical orthogonal function (EOF) Equatorial Atlantic climatological annual cycle of, 173
176, 174f, 175f, 176f influence on El Niño-Southern Oscillation (ENSO), 189 sea level pressure (SLP) gradient, 173 sea surface temperature (SST) variations, 171, 174f Equatorial Atlantic variability, 196
199 dynamical and thermodynamical elements of, 176
178, 177f, 178f negative Atlantic zonal mode events, 181, 182f, 183f, 184f, 185
186, 185f phase locking, 180f, 181 thermodynamic Atlantic zonal mode, 184
185 in global climate models errors in simulated variability, 192
193, 193f, 194f mean state biases, 191
192, 192f impact of climate change, 195
196 interannual variability, 176 low-frequency modulation of, 195
196 prediction of, 194
195 to terrestrial precipitation and remote basins, 188
191 impact of Atlantic zonal mode, 189
190 impact of El Niño-Southern oscillation, 190
191 impact on tropical precipitation, 188
189 Equatorial Kelvin waves, 181
184, 240
242 Equatorially trapped eastward-propagating convective anomalies, 17 Equatorial ocean dynamics, 62

Equatorial Rossby waves, 208
209, 242
245 Equatorial trades, 173 Equatorial westerly wind bias, 253
254 European Centre for Medium-Range Weather Forecasts (ECMWF), 242
245, 244f Extratropical cyclones, 268
269, 268f, 273
274, 278 F Forecast systems, dynamical, 76 G GCMs. See General circulation models (GCMs); Global climate models (GCMs) General circulation models (GCMs), 124
125, 130
131, 148 Gill-type Kelvin wave, 22
24 Global climate models (GCMs) atmospheric GCM (AGCM), 186, 191, 195 coupled GCM (CGCM), 184
185 equatorial Atlantic variability in errors in simulated variability, 192
193, 193f, 194f mean state biases, 191
192, 192f Global energy budget, energy transfer in, 3, 4f Global sea surface temperature (SST) climatology (1982
2011), 6, 6f Global warming, 2, 5 anthropogenic, 15 surface, 161
162 Gravity, Earth, 7 Greenhouse gases, 4
5 Gulf Stream, 273 Gyres, subtropical, 10 H Hadley cell, 7
8, 10 Heat flux forcing, 25
26 Heat waves, 154 marine, 215 Heavy rains, 154
155, 156f High-resolution numerical simulations, 281
282 I Indian Ocean, 207, 210
211 tropical, air
sea interactions in, 115
140

Index

Indian Ocean capacitor Kelvin wave-induced surface Ekman divergence, 148 persistent Indian Ocean warming, 145
148, 146f, 147f Indian Ocean decadal variability, predictability of, 230
232, 231f, 232f Indian Ocean Dipole (IOD), 97, 115
116, 122
125, 123f, 124f event of 2019, 117
122, 117f, 118f, 119f, 120f in future climate, 132
133 interactions between IOD and ENSO, 126 interactions with ENSO and ENSO Modoki, 125
127, 127f mode, 145
146 predictions, 130
132, 131f Indian Ocean subtropical dipole (IOSD), 221
225, 222f, 224f, 225f Indian summer monsoon rainfall (ISMR), 122
125, 123f, 124f Indo
Western Pacific Ocean capacitor (IPOC) effect, 141
142, 142f, 143f climate impacts, 152
157 extremes in Southeast and East Asia, 154
157 heat waves, 154 heavy rains, 154
155, 156f Pacific
Japan pattern, 152
154, 153f tropical cyclones, 155
157 future changes, 160
161 long-term modulations, historical changes, 158
160, 159f mechanism and predictability, 142
152 Indian Ocean capacitor, 145
148 Indo
Western Pacific Ocean capacitor mode, 148
150 seasonal predictions, 150
152, 151f South Asia, 157 Indo
Western Pacific Ocean capacitor (IPOC) mode, 141
142, 148
150, 149f, 154
155, 157, 161 Interbasin coupling, 161
162 Atlantic
Pacific, 161
162 Interdecadal Pacific Oscillation (IPO), 230 Intertropical convergence zone (ITCZ), 104
105, 173
174, 175f, 181, 186
187, 193, 247 seasonal migration of, 181

303

Intraseasonal oscillations (ISOs), 121
122 propagation, impact of air
sea interaction on, 31
44 tropical, 17 Intraseasonal oscillation
sea surface temperature anomaly relationship, 19
22, 19f, 20f, 21f, 22f, 23f, 24f Intraseasonal oscillation variance, role of air
sea interaction in affecting overall, 28
31, 29f, 30f Intraseasonal sea surface temperature anomaly, cause of, 22
28, 25f, 26f, 27f, 28f Intraseasonal timescale selection, air
sea interaction frameworks on, 50
52, 52f IOD. See Indian Ocean Dipole (IOD) IOSD. See Indian Ocean subtropical dipole (IOSD) IPO. See Interdecadal Pacific Oscillation (IPO) ISMR. See Indian summer monsoon rainfall (ISMR) ISOs. See Intraseasonal oscillations (ISOs) ITCZ. See Intertropical convergence zone (ITCZ) J JAMSTEC climate prediction system, 76 Japan Meteorological Agency (JMA), 151 K Kelvin wave-induced surface Ekman divergence, 148 Kelvin waves, 18, 62
65, 120
121, 173, 187
189, 196
197, 242
245 equatorial, 181
184, 240
242 Gill-type, 22
24 Kiel Climate Model, 105 Kuroshio, 267
268 Kuroshio and Kuroshio extension, 282
284 L La Niña (LN), 61
62, 62f, 67
68, 100
101, 142
143 amplitudes of, 69 event, 222
223 Leeuwin Current, 208
215 Local ocean-atmosphere coupled feedback, 210
212, 211f

304

Index

Longwave cloud-radiative forcing (LWCRF), 99 Longwave radiation, absorb and emit, 4
5 Lukas
Lindstrom “barrier layer” theory, 24
25, 28f LWCRF. See Longwave cloud-radiative forcing (LWCRF) M Madagascar orography, 221
222 Madden
Julian oscillation (MJO), 17
20, 121
122 eastward propagation, effect of air
sea interactions on, 34
35 Initiation, role of ocean feedback in, 44
50, 45f, 46f, 47f, 49f Marine ecosystem, 246
247 Marine heatwaves, 215 Matsuno
Gill-type response, 209
210 Matsuno
Gill-type Rossby
wave, 141
142 Mediterranean climate, 2 Meiyu
Baiu rainband, 154
155 Met Office Unified Model, 275 Mid-latitude air
sea interactions, 269
270 Mixed layer depth (MLD), 25
26 annual variation of, 48 MJO. See Madden
Julian oscillation (MJO) MLD. See Mixed layer depth (MLD) Moistening, planetary boundary layer (PBL), 17
18 Moist static energy (MSE) tendency asymmetry theory, 17
18 Monsoon winds, 115
116 Mozambique Channel, 221
222 MSE tendency asymmetry theory. See Moist static energy (MSE) tendency asymmetry theory Multimodel ensemble (MME) prediction system, 75
76 N NAO. See North
Atlantic Oscillation (NAO) Natural climate change, 2 Negative Atlantic zonal mode events, 181, 182f, 183f, 184f, 185
186, 185f Ningaloo Niño (Niña), 207, 208f climate modes, 212
213 impacts, 214 local ocean-atmosphere coupled feedback, 210
212, 211f

remote atmospheric forcing, 209
210, 210f remote oceanic forcing, 208
209, 209f SST anomalies, 207, 208f thermodynamics, 212 “NoENSO” simulation, 149
150, 149f North Atlantic gyre, 249
250 North
Atlantic Oscillation (NAO), 250 North Pacific Gyre Oscillation, 105
106 North Pacific Meridional Modes (NPMM), 82
83 Northward propagation in boreal summer, impact on, 37
44, 37f, 40f, 42f, 43f of convective anomalies, air
sea interaction, 22
24, 26f NPMM. See North Pacific Meridional Modes (NPMM) O Ocean-atmosphere coupled system, 267 Ocean circulations, 10
14 atmosphere and, 6
9, 8f, 9f Ocean dynamics, 184
185, 193 equatorial, 62 Ocean general circulation model (OGCM), 181, 282
283 Oceanic baroclinic adjustment, 270
272 Oceanic fronts and eddies, 268 Oceanic warm currents, 284 Ocean warming, abnormal, 61 Off-equatorial Rossby wave, 22
24 OGCM. See Ocean general circulation model (OGCM) One-dimensional (1D) heat balance, 24
25 One-dimensional (1D) heat budget analysis, 53 Oscillation See also specific types of oscillation Southern, 61
62 tropical intraseasonal, 17 Oscillator delayed, 66 mechanisms, recharge, 99 recharge-discharge, 67 P Pacific, air
sea interaction in tropical, 61
92 Pacific
Atlantic relation, 190
191

Index

Pacific Decadal Oscillation (PDO), 105
106 Pacific
Japan (PJ) teleconnection pattern, 152
154 Pacific
North American (PNA) pattern, 72
74 PBL. See Planetary boundary layer (PBL) PDO. See Pacific Decadal Oscillation (PDO) Perturbation, quasiperiodic surface flux, 24
25 Peruvian coast, 61 Philippine Sea anticyclone, 141 pIOD. See Pole of the positive IOD (pIOD) Planetary boundary layer (PBL) moistening, 17
18, 33
34 moisture asymmetry, 31
33 momentum equation, 35
36 Pole of the positive IOD (pIOD), 116 events, 116
119 Positive potential temperature anomaly, 268
269 Potential vorticity (PV), storm intensification in, 268
269, 269f Q Quasiperiodic surface flux perturbation, 24
25 R Radiation, 3 longwave, absorb and emit, 4
5 shortwave. See Shortwave radiation solar, 3 Radiative energy, 3 Recharge-discharge oscillator, 67 Recharge-discharge process, 67, 84 Recharge oscillator mechanisms, 99 Remote atmospheric forcing, Ningaloo Niño (Niña), 209
210, 210f Remote basins, 188
191 Remote oceanic forcing, Ningaloo Niño (Niña), 208
209, 209f Rossby waves, 18, 22
24, 63
64, 64f, 66, 120
121, 283 atmospheric, 209
210 equatorial, 208
209, 242
245 Matsuno
Gill-type, 141
142 off-equatorial, 22
24 Rotational EOF (REOF), 96
98

305

S SAM. See Southern Annular Mode (SAM) SASD. See South Atlantic subtropical dipole (SASD) Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F), 119
120, 225
226 model, 76, 77f SCS. See South China Sea (SCS) Sea-ice concentration (SIC), 223
224 Sea level pressure (SLP) anomalies, remote atmospheric forcing, 209
210 ENSO effect on, 210 gradient, equatorial Atlantic, 173 Seasonal migration of intertropical convergence zone (ITCZ), 181 Seasonal predictions, 149
152 Sea surface height (SSH) anomalies, 245 Sea surface temperature (SST), 115, 221
222 anomalies, Ningaloo Niño (Niña), 207, 208f climatological standard deviation of, 250, 251f climatology (1982
2011), 6, 6f cooling, 116 frontal zones, 270
272 horizontal gradient of, 24
25 interannual SST variations, 222
223 June
September composite anomalies of, 123, 124f latent heat flux (LHF) feedback, 99 sensible heat flux (SHF) feedback, 99 surface fluxes and SST variations, 22
24, 25f variations in Eastern Pacific (EP), 97 Sea surface temperature anomalies (SSTAs), 18, 20
21, 24f, 34, 93 positive, 36
37 winter-summer asymmetry of, 50 Sea surface temperatures (SSTs), 61
62, 65
66 biases of March
April
May (MAM) average, 237, 253f in coastal upwelling regions, 237 cooling, 249
250 ENSO impact on global, 72 warming, 249
250 Self-organizing map (SOM) analysis, 100
101 Shortwave cloud-radiative forcing (SWCRF), 99

306

Index

Shortwave radiation, 30
31, 45
47, 50, 53
54 anomalous, 46 net, 47f surface, 48 SINTEX-F. See Scale Interaction ExperimentFrontier Research Center for Global Change (SINTEX-F) SMTH experiment, 275
276, 276f SOI. See Southern Oscillation Index (SOI) Solar energy, 5 Solar radiation, 3 South Asian summer monsoon, 157 South Atlantic anticyclone, 238
240 South Atlantic, decadal variability, 226
230, 229f South Atlantic Ocean decadal variability, predictability of, 230
232, 231f, 232f South Atlantic subtropical dipole (SASD), 221
226, 222f, 224f, 225f South China Sea (SCS), 17 Southern Annular Mode (SAM), 223
224 Southern Indian Oceans, decadal variability, 226
230, 229f Southern Oscillation, 61
62 Southern Oscillation Index (SOI), 122, 123f South Indian Ocean, Antarctic Circumpolar Current in, 272 South Pacific Meridional Modes (SPMM), 82
83 Southwest African coast, annual cycle of SST, 240
242, 241f Spatio-temporal variability, 11 SPB. See Spring predictability barrier (SPB) Special Report on Emission Scenarios (SRES), 256 SPMM. See South Pacific Meridional Modes (SPMM) Spring predictability barrier (SPB), 106 SSTAs. See Sea surface temperature anomalies (SSTAs) SSTs. See Sea surface temperatures (SSTs) Stefan-Boltzmann law, 3 Storm intensification, in potential vorticity (PV), 268
269, 269f Subtropical dipoles Indian Ocean, 221
226, 222f, 224f, 225f predictability of, 225
226, 227f South Atlantic, 221
226, 222f, 224f, 225f

Subtropical gyres, 10 Summer ISOs and boreal winter, distinctive propagation characteristics, 18 Surface cyclone, 268
269 Surface evaporation, 22
24, 33
34 Surface global warming, 161
162 Surface latent heat flux (LHF) anomaly, 26
28 Surface shortwave radiative flux anomaly, 26
28 Sverdrup balance, 10 SWCRF. See Shortwave cloud-radiative forcing (SWCRF) T TAO/TRITON. See Tropical Atmosphere Ocean/ Triangle Trans-Ocean Buoy Network (TAO/TRITON) TC. See Tropical cyclone (TC) Teleconnections, 127
130, 128f, 129f, 131f atmospheric, 141 Terrestrial precipitation equatorial Atlantic variability to, 188
191 impact on, 188
189 Thermal energy, 3 Thermocline anomalies, 179 Thermodynamic Atlantic zonal mode, 184
185 Thermodynamics, Ningaloo Niño (Niña), 212 Thermohaline circulation, 10 Three-dimensional ocean data assimilation (3DVAR), 226 TIO. See Tropical Indian Ocean (TIO) TP. See Tropical Pacific (TP) Tropical Atlantic variability linkage to, 186
188 Benguela Niño, 187
188 meridional mode, 186
187, 187f Tropical Atmosphere Ocean/Triangle TransOcean Buoy Network (TAO/TRITON), 63 Tropical climate, 2 Tropical cyclone (TC), 155
157 activity in Western North Pacific (WNP), 142 genesis, 104 Tropical Indian Ocean (TIO), 17
18 air
sea interactions in, 115
140

Index

Tropical intraseasonal oscillation, 17 air
sea interaction on, 28
52 pronounced seasonality of, 19 Tropical Ocean-Global Atmosphere (TOGA) program, 63 Tropical Pacific (TP), 93 Air
sea interaction in, 61
92 El Niño/Southern Oscillation (ENSO) in, 237
238 El Niño/Southern Oscillation (ENSO) of, 115 subsurface temperature anomalies, 99
100, 100f wind-induced thermocline variations, 106 Tropical Western North Pacific (WNP), 141
142 wind-evaporation-sea surface temperature feedback in, 143
145, 144f, 145f Tropospheric circulation anomalies, 141 Turbulent energy, 3 U Upper-ocean dynamics, 25
26 Upwelling, coastal, 12
14 Upwelling Intensification Hypothesis, 255 Upwelling regions, 255
256 Upwelling systems, Eastern boundary, 238, 250 V Velocity, vertical pressure, 38
39

307

W Walker circulation, 61
62 Warm conveyor belt (WCB), 274
275, 274f, 278, 280
281 Warming, abnormal ocean, 61 Warm sea surface temperature anomalies, 18, 20
21 Warm water, accumulation of, 221
222 WCB. See Warm conveyor belt (WCB) WES feedback, 143
145, 148, 150, 186 in tropical Western North Pacific (WNP), 149
150 Westerly wind bursts (WWBs), 64
65 Western North Pacific (WNP), 141 tropical, 141
142 tropical cyclone (TC) activity in, 142 Wind bias, equatorial westerly, 253
254 Wind-evaporation-sea surface temperature feedback in tropical western North Pacific, 143
145, 144f, 145f Wind-induced thermocline variations, Tropical Pacific (TP), 106 WNP. See Western North Pacific (WNP) World Meteorological Organization, 1 World Weather Watch program, 1 WWBs. See Westerly wind bursts (WWBs)