Global Three-Dimensional Atmospheric Structure of the Atlantic Multidecadal Oscillation As Revealed by Two Reanalyses

Global Three-Dimensional Atmospheric Structure of the Atlantic Multidecadal Oscillation as Revealed by Two Reanalyses

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the Graduate School of The Ohio State University

Scott Seele Stuckman

Graduate Program in Environmental Science

The Ohio State University

2016

Dissertation Committee:

Jialin Lin, Advisor

Desheng Liu

Bryan G. Mark

Jeffrey C. Rogers

Scott Stuckman

2016

Abstract

The Atlantic Multidecadal Oscillation (AMO) is one of the dominant multidecadal modes of the global climate system, shaping global and regional climate and extreme events through interactions with other climate modes operating on similar timescales. These include the Interdecadal Pacific Oscillation (IPO), which appears to have some intriguing connections with the AMO, and anthropogenic global warming

(AGW), the detection and attribution of which can be confounded by the AMO. The relatively short observational record limits confident characterization of the AMO structure, contributing to the difficulty of climate models to more realistically simulate the AMO and compromising the accuracy of climate change projections on multidecadal timescales. Analyses of the AMO have been restricted primarily to the surface or certain regions exclusively during the extreme (warm and cool) phases, despite the existence of transition (cool-to-warm and warm-to-cool) phases for years to a decade or more.

Extending the AMO characterization to the upper air and to the transition phases provides a more comprehensive assessment of the AMO life cycle important for understanding

AMO dynamics and global teleconnections, separating the AMO-AGW signals, and providing a baseline for evaluating climate model simulations of the AMO.

This study is a first documentation of the structure of the entire AMO life cycle, including extreme and transition phases, throughout the global troposphere. The extreme phase climate signature is constructed based on the strongest and most robust patterns identified by two methods (linear correlation and composite analyses), two reanalysis datasets (the National Centers for Environmental Prediction/National Center for

Atmospheric Research and Twentieth Century Reanalysis, supplemented with precipitation data from the University of Delaware dataset) and data from two consecutive AMO cycles. The first characterization of the AMO transition phases uses a transition index based on the time derivative of AMO index. When trying to compare the zonal mean structure of AMO with the El Niño-Southern Oscillation (ENSO), a literature search showed the zonal mean structure of ENSO remained unpublished, despite the otherwise generally well-characterized horizontal structures. Therefore this study includes a seasonal analysis of the ENSO zonal mean structure during boreal winter

(DJF) and summer (JJA).

The AMO extreme phase is characterized by a blend of low and middle latitude centers of action, with the associated tilt of geopotential height anomaly patterns consistent with off-equatorial heating patterns generated by the Held idealized model.

The surface climate signature is connected to the upper air with baroclinic vertical structure over the North Atlantic but barotropic structures elsewhere. The associated zonal mean circulation features three circulation cells globally with strong inter- hemispheric mixing that suggests the traditional view of the AMO involving a Northern-

Southern Hemisphere asymmetry is accurate only near the surface.

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The AMO transition phase features a more equatorial-based climate signature and associated geopotential height anomaly patterns consistent with the Matsuno-Gill idealized model. The zonal mean circulation of the transition phases features six, rather than three, circulation cells globally. The only baroclinic structure, over North America, and several barotropic structures are positioned west of corresponding similar structures during the AMO extreme phase, suggesting an eastward evolution of climate anomalies as the AMO progresses from a cool-to-warm transition phase to warm phase. The Pacific- based climate signature resembles the IPO warm phase and it is proposed the AMO and

IPO are different basin-wide expressions of a single multidecadal oscillation. The identification of an AMO transition phase climate signature distinct from the extreme phase suggests transition phases are not neutral and may provide an additional source of information for characterizing climate cycles.

Dedication

Dedicated to My Families and Friends, especially Mengling

Acknowledgements

I would like to acknowledge my sincere gratitude and respect to my advisor, Dr.

Jialin Lin, and committee members Dr. Desheng Liu, Dr. Bryan Mark and Dr. Jeffrey

Rogers.

Vita

1994....…………………...………………B.S. Microbiology and Psychology,

B.A. English, The Ohio State University

2007 to present…………………………Graduate Fellow, Graduate Teaching Assistant,

Instructor at OSU-Lima, Department of

Geography, The Ohio State University

Fields of Study

Major Fields: Environmental Science

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Table of Contents

Abstract ...... ii

Dedication ...... v

Acknowledgements ...... vi

Vita ...... vii

Table of Contents ...... viii

List of Tables...... xiii

List of Figures ...... xiv

CHAPTER 1: LITERATURE REVIEW OF THE ATLANTIC MULTIDECADAL

OSCILLATION (AMO) ...... 1

A. IDENTIFICATION AND DESCRIPTION ...... 1

B. CLIMATE IMPACTS ...... 7

C. OCEANIC STRUCTURE ...... 10

D. ATMOSPHERIC STRUCTURE ...... 13

E. EXISTING THEORIES AND PACIFIC CONNECTION ...... 14

CHAPTER 2: DATA AND METHODS ...... 20

A. DATASETS AND VARIABLES ...... 20

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B. METHODS ...... 24

B.1. AMO Index, Extreme Phase ...... 24 B.2. AMO Index, Transition Phase ...... 25 B.3. ENSO Index ...... 26 B.4. Data analysis ...... 27 B.4.a. Linear Correlation ...... 27 B.4.b. Composite Analysis ...... 28 C. PLOTS AND SCHEMATICS ...... 28

CHAPTER 3: AMO EXTREME PHASE NCEP/NCAR-LC RESULTS ...... 33

A. HORIZONTAL MAPS ...... 33

B. ZONAL MEAN CROSS-SECTION PLOTS ...... 37

CHAPTER 4: AMO EXTREME PHASE COMPARISON OF METHODS ...... 53

NCEP/NCAR Linear Correlation and NCEP/NCAR Composite Analyses ...... 53

A. HORIZONTAL PLOTS: NCEP/NCAR-LC and NCEP/NCAR-Comp ...... 54

A.1. Sea Surface Temperature Anomalies (SSTAs), annual mean and seasonal...... 54 Annual Mean ...... 54 Seasonal ...... 55 NCEP/NCAR-LC, JJA and DJF ...... 55 NCEP/NCAR-Comp, JJA and DJF ...... 55 Seasonal Summary ...... 56 A.2.Geopotential Height Anomalies 1000 mb, annual mean and seasonal ...... 57 Annual Mean ...... 57 Seasonal ...... 58 NCEP/NCAR-LC, JJA and DJF ...... 58 NCEP/NCAR-Comp, JJA and DJF ...... 59 Seasonal Summary ...... 61 A.3. Geopotential Height Anomalies 500 mb, annual mean and seasonal ...... 61 Annual Mean ...... 61 Seasonal ...... 63 ix

NCEP/NCAR-LC, JJA and DJF ...... 63 NCEP/NCAR-Comp, JJA and DJF ...... 64 Seasonal Summary ...... 66 A.4. Geopotential Height Anomalies 200 mb, annual mean and seasonal ...... 66 Annual Mean ...... 66 Seasonal ...... 67 NCEP/NCAR-LC, JJA and DJF ...... 67 NCEP/NCAR-Comp, JJA and DJF ...... 69 Seasonal Summary ...... 70 Annual Mean Overall Summary ...... 71

B. ZONAL MEAN CROSS-SECTIONS: NCEP/NCAR-LC and NCEP/NCAR-Comp .. 72

B.1. Zonal Mean Air Temperature Anomalies ...... 72 B.2. Zonal Mean Geopotential Height Anomalies ...... 73 B.3. Zonal Mean Zonal Wind Anomalies...... 73 B.4. Zonal Mean Meridional Wind Anomalies ...... 74 B.5. Zonal Mean Omega Anomalies ...... 74 Zonal Mean Summary ...... 75

CHAPTER 5: AMO EXTREME PHASE COMPARISON OF DATASETS ...... 93

NCEP/NCAR Composite and 20CR2c-Composite Analyses ...... 93

A. Sea Surface Temperature Anomalies (SSTAs), annual mean and seasonal ...... 93

Annual Mean ...... 93 Seasonal ...... 94 NCEP/NCAR-Comp and 20CR2c-Curr, JJA ...... 95 NCEP/NCAR-Comp and 20CR2c-Curr, DJF ...... 95 B. Geopotential Height Anomalies 1000 mb, annual mean and seasonal ...... 96

Annual Mean ...... 96 Seasonal ...... 98 NCEP/NCAR-Comp and 20CR2c-Curr, JJA ...... 98 NCEP/NCAR-Comp and 20CR2c-Curr, DJF ...... 99

C. Geopotential Height Anomalies 500 mb, annual mean and seasonal ...... 100

Annual Mean ...... 100 Seasonal ...... 101 NCEP/NCAR-Comp and 20CR2c-Curr, JJA ...... 101 NCEP/NCAR-Comp and 20CR2c-Curr, DJF ...... 101 D. Geopotential Height Anomalies 200 mb, annual mean and seasonal ...... 102

Annual Mean ...... 102 Seasonal ...... 103 NCEP/NCAR-Comp and 20CR2c-Curr, JJA ...... 103 NCEP/NCAR-Comp and 20CR2c-Curr, DJF ...... 104 Annual Mean Overall Summary ...... 104

CHAPTER 6: AMO EXTREME PHASE COMPARISON OF AMO CYCLES...... 119

A. Sea Surface Temperature Anomalies (SSTAs), annual mean and seasonal ...... 119

Annual Mean ...... 119 Seasonal ...... 120 20CR2c-Curr, JJA and DJF ...... 120 20CR2c-Prev, JJA and DJF ...... 121 B. Geopotential Height Anomalies 1000 mb, annual mean and seasonal ...... 121

Annual Mean ...... 121 Seasonal ...... 123 20CR2c-Curr, JJA and DJF ...... 123 20CR2c-Prev, JJA and DJF ...... 124 Seasonal Summary ...... 125 C. Geopotential Height Anomalies 500 mb, annual mean and seasonal ...... 125

Annual Mean ...... 125 Seasonal ...... 127 20CR2c-Curr, JJA and DJF ...... 127 20CR2c-Prev, JJA and DJF ...... 128 D. Geopotential Height Anomalies 200 mb, annual mean and seasonal ...... 129

Annual Mean ...... 129 Seasonal ...... 130 20CR2c-Curr, JJA and DJF ...... 130 20CR2c-Prev, JJA and DJF ...... 131 Annual Mean Overall Summary ...... 132

CHAPTER 7: AMO TRANSITION PHASE ...... 147

A. HORIZONTAL MAPS ...... 147

B. ZONAL MEAN CROSS-SECTION PLOTS ...... 152

CHAPTER 8: ENSO ZONAL MEAN STRUCTURE ...... 168

A. INTRODUCTION ...... 168

B. RESULTS ...... 170

B.1. Boreal Winter (DJF) ...... 170 B.2. Boreal Summer (JJA) ...... 172 C. DISCUSSION ...... 174

CHAPTER 9: DISCUSSION AND CONCLUSION ...... 189

A. DISCUSSION...... 189

A.1. AMO Extreme Phase ...... 189 A.2. AMO Transition Phase ...... 196 B. Conclusion ...... 203

Bibliography ...... 207

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List of Tables

Table 1: Datasets and variables used in this study to characterize the AMO ...... 31

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List of Figures

Figure 1: Schematic of construction of AMO transition index ...... 32

Figure 2: Linear correlation of annual mean sea surface temperature anomalies (SSTAs) with the annual mean AMO index using NCEP/NCAR dataset...... 42

Figure 3: Linear correlation of annual mean precipitation anomalies with the annual mean

AMO index using (a) NCEP/NCAR and (b) UDel datasets ...... 43

Figure 4: Linear correlation of annual mean geopotential height anomalies with the annual mean AMO index at 1000 mb (bottom), 500 mb (middle) and 200 mb (top)...... 44

Figure 5: Meridional mean cross-section of North Atlantic baroclinicity ...... 45

Figure 6: Linear correlation of zonal wind to annual mean AMO index at 1000, 500 and

200 mb...... 46

Figure 7: Linear correlation of meridional wind anomalies at 1000, 500 and 200 mb with the annual mean AMO index ...... 47

Figure 8: Linear correlation of annual mean zonal mean air temperature anomalies to the annual mean AMO index ...... 48

Figure 9: Linear correlation of annual mean zonal mean geopotential height anomalies to the annual mean AMO index ...... 49

Figure 10: Linear correlation of annual mean zonal mean zonal wind anomalies to the annual mean AMO index ...... 50 xiv

Figure 11: Linear correlation of annual mean zonal mean meridional wind anomalies to the annual mean AMO index ...... 51

Figure 12: Linear correlation of annual mean zonal mean omega anomalies to the annual mean AMO index ...... 52

Figure 13: Comparison of NCEP/NCAR annual mean SSTAs with the annual AMO index using NCEP/NCAR linear correlation (top) and composite (bottom) methods ...... 76

Figure 14: NCEP/NCAR linear correlation of SSTAs with AMO index for annual mean

(top), JJA (middle) and DJF (bottom) ...... 77

Figure 15: NCEP/NCAR composite analysis of SSTAs with AMO index for annual mean

(top), JJA (middle) and DJF (bottom) ...... 78

Figure 16: Comparison of NCEP/NCAR 1000 mb annual mean geopotential height anomalies with the annual mean AMO index using linear correlation (top) and composite

(bottom) methods ...... 79

Figure 17: NCEP/NCAR linear correlation of 1000 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 80

Figure 18: NCEP/NCAR composite analysis of 1000 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 81

Figure 19: Comparison of NCEP/NCAR 500 mb annual mean geopotential height anomalies with the annual mean AMO index using linear correlation (top) and composite

(bottom) methods ...... 82

Figure 20: NCEP/NCAR linear correlation of 500 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 83

Figure 21: NCEP/NCAR composite analysis of 500 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 84

Figure 22: Comparison of NCEP/NCAR 200 mb annual mean geopotential height anomalies with the annual mean AMO index using linear correlation (top) and composite

(bottom) methods ...... 85

Figure 23: NCEP/NCAR linear correlation of 200 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 86

Figure 24: NCEP/NCAR composite analysis of 200 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 87

Figure 25: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean air temperature anomalies to the annual mean AMO index ...... 88

Figure 26: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean geopotential height anomalies to the annual mean AMO index ...... 89

Figure 27: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean zonal wind anomalies to the annual mean AMO index...... 90

Figure 28: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean meridional wind anomalies to the annual mean AMO index ...... 91

Figure 29: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean omega anomalies to the annual mean AMO index ...... 92

Figure 30: Composite analysis of annual SSTAs during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) datasets ...... 107

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Figure 31: NCEP/NCAR composite analysis of SSTAs with AMO index for annual mean

(top), JJA (middle) and DJF (bottom) ...... 108

Figure 32: 20CR2c composite analysis of SSTAs with AMO index for annual mean (top),

JJA (middle) and DJF (bottom) ...... 109

Figure 33: Composite analysis of annual 1000 mb geopotential height anomalies during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) reanalysis datasets ...... 110

Figure 34: NCEP/NCAR composite analysis of 1000 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 111

Figure 35: 20CR2c composite analysis of 1000 mb geopotential height anomalies with

AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 112

Figure 36: Composite analysis of annual 500 mb geopotential height anomalies during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) reanalysis datasets ...... 113

Figure 37: NCEP/NCAR composite analysis of 500 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 114

Figure 38: 20CR2c composite analysis of 500 mb geopotential height anomalies with

AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 115

Figure 39: Composite analysis of annual 200 mb geopotential height anomalies during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) reanalysis datasets ...... 116

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Figure 40: NCEP/NCAR composite analysis of 200 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 117

Figure 41: 20CR2c composite analysis of 200 mb geopotential height anomalies with

AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 118

Figure 42: Comparison of 20CR2c annual mean SSTAs with annual AMO index for current (top) and previous (bottom) AMO cycles ...... 135

Figure 43: 20CR2c composite analysis of current cycle SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 136

Figure 44: 20CR2c composite analysis of previous cycle SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom) ...... 137

Figure 45: Comparison of 20CR2c annual mean 1000 mb geopotential height anomalies with annual AMO index for current (top) and previous (bottom) AMO cycles ...... 138

Figure 46: 20CR2c composite analysis of current cycle 1000 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)..... 139

Figure 47: 20CR2c composite analysis of previous cycle 1000 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)..... 140

Figure 48: Comparison of 20CR2c annual mean 500 mb geopotential height anomalies with annual AMO index for current (top) and previous (bottom) AMO cycles ...... 141

Figure 49: 20CR2c composite analysis of current cycle 500 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)..... 142

Figure 50: 20CR2c composite analysis of previous cycle 500 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)..... 143

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Figure 51: Comparison of 20CR2c annual mean 200 mb geopotential height anomalies with annual AMO index for current (top) and previous (bottom) AMO cycles ...... 144

Figure 52: 20CR2c composite analysis of current cycle 200 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)..... 145

Figure 53: 20CR2c composite analysis of previous cycle 200 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)..... 146

Figure 54: NCEP/NCAR linear correlation of sea surface temperature anomalies (SSTAs) with the annual mean AMO Transition index ...... 157

Figure 55: Linear correlation of precipitation anomalies with the annual mean AMO transition index using the (a) NCEP/NCAR and (b) UDel datasets...... 158

Figure 56: NCEP/NCAR linear correlation of air temperature anomalies with annual mean AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb ...... 159

Figure 57: NCEP/NCAR linear correlation of geopotential height anomalies with the annual mean AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb . 160

Figure 58: NCEP/NCAR linear correlation of zonal wind anomalies with the annual mean

AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb ...... 161

Figure 59: NCEP/NCAR linear correlation of meridional wind anomalies with the annual mean AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb ...... 162

Figure 60: NCEP/NCAR linear correlation of zonal mean air temperature anomalies to the annual mean AMO transition index ...... 163

Figure 61: NCEP/NCAR linear correlation of zonal mean geopotential height anomalies to the annual mean AMO transition index ...... 164

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Figure 62: NCEP/NCAR linear correlation of zonal mean zonal wind anomalies to the annual mean AMO transition index ...... 165

Figure 63: NCEP/NCAR linear correlation of zonal mean meridional wind anomalies to the annual mean AMO transition index ...... 166

Figure 64: NCEP/NCAR linear correlation of zonal mean omega anomalies to the annual mean AMO transition index ...... 167

Figure 65: NCEP/NCAR linear correlation of zonal mean air temperature anomalies to the Niño3.4 index during DJF ...... 177

Figure 66: NCEP/NCAR linear correlation of zonal mean geopotential height anomalies to the Niño3.4 index during DJF ...... 178

Figure 67: NCEP/NCAR linear correlation of zonal mean zonal wind anomalies to the

Niño3.4 index during DJF...... 179

Figure 68: NCEP/NCAR linear correlation of zonal mean meridional wind anomalies to the Niño3.4 index during DJF ...... 180

Figure 69: NCEP/NCAR linear correlation of zonal mean omega anomalies to the

Niño3.4 index during DJF...... 181

Figure 70: NCEP/NCAR linear correlation of zonal mean air temperature anomalies to the Niño3.4 index during JJA...... 182

Figure 71: NCEP/NCAR linear correlation of zonal mean geopotential height anomalies to the Niño3.4 index during JJA...... 183

Figure 72: NCEP/NCAR linear correlation of zonal mean zonal wind anomalies to the

Niño3.4 index during JJA ...... 184

Figure 73: NCEP/NCAR linear correlation of zonal mean meridional wind anomalies to the Niño3.4 index during JJA...... 185

Figure 74: NCEP/NCAR linear correlation of zonal mean omega anomalies to the

Niño3.4 index during JJA ...... 186

Figure 75: Schematic of stronger zonal mean climate anomalies associated with the

ENSO warm phase during boreal winter (DJF) using NCEP/NCAR linear correlation . 187

Figure 76: Schematic of stronger zonal mean climate anomalies associated with the

ENSO warm phase during boreal summer (JJA) using NCEP/NCAR linear correlation

...... 188

Figure 77: Schematic depiction of AMO extreme phase robust climate anomalies plotted on global horizontal maps at 1000, 500 and 200 mb and by zonal mean cross-section (see text for detailed discussion)...... 205

Figure 78: Schematic depiction of AMO transition phase climate anomalies plotted on global horizontal maps at 1000, 500 and 200 mb and by zonal mean cross-section (see text for detailed discussion)...... 206

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CHAPTER 1: LITERATURE REVIEW OF THE ATLANTIC MULTIDECADAL

OSCILLATION (AMO)

A. IDENTIFICATION AND DESCRIPTION

The Atlantic Multidecadal Oscillation (AMO) is one of the dominant multidecadal climate modes (Knight et al. 2005) which impacts billions via sustained influences on water availability, agriculture, fisheries and public health (Mehta et al.

2000). The quasi-periodic ~60 year cycle of alternating warm and cool North Atlantic sea surface temperature anomalies (SSTAs; Enfield et al. 2001) was originally detected as a global oscillation (Folland et al. 1984) before tracking the source to the North Atlantic

(Schlesinger and Ramankutty 1994). Several composite analyses had separately identified multidecadal North Atlantic SSTA oscillations over the course of the 20th century (e.g.,

Bjerknes 1964; Deser and Blackmon 1993; Kushnir 1994). Further, the switch to a North

Atlantic cool phase around 1970 appeared to involve temperature changes at greater oceanic depths as well (Levitus 1989a, 1989b, 1990), suggesting the North Atlantic warm and cool phases track changes in the intensity of the thermohaline circulation (THC;

Greatbatch et al. 1991). The THC is conceptualized as a global oceanic conveyor belt

(Broecker 1991) that in the Atlantic transports surface water poleward to the Arctic interface where the cooler dense water downwells primarily in the Greenland-Iceland-

Norway (GIN) and Labrador/Irminger Seas (Dickson and Brown 1994), reverses direction and flows equatorward down to ~ 3 km depth as the subsurface branch of the

Atlantic THC (Medhaug and Furevik 2011).

Despite being one of the dominant multidecadal climate modes, the AMO is only recently named (Kerr 2000), with both its discovery and ongoing characterization a gradual process due to the short observational record, reliance on statistical methods for signal detection, inability to achieve complete separation of the signal from the effects of other climate signals operating on similar timescales (e.g., global warming and sulfate aerosols), and incomplete understanding of the climate system. The short observational record presents a particularly important and enduring problem. Meteorological and statistical standards suggest observational coverage of six complete cycles to confidently characterize a climate mode. Extending only to the 1850s and assuming a 60-70 year climate cycle, the instrumental record provides coverage of only two full cycles, limiting the ability to characterize this as a persistent natural climate mode as opposed to a pattern of fluctuations confined to the instrumental period. This instrumental record also contains spatial shortcomings that are pervasive before the 1970s satellite era, particularly in the high latitudes and Southern Hemisphere and throughout the upper troposphere, that increase back through time. Improved understanding of the AMO will continue to be driven by the construction of more accurate and better quality-controlled global datasets, but a proper-length observational record will not be available for at least two more centuries.

Proxy reconstructions of past climate and virtual reconstructions using centuries- long general circulation model (GCM) integrations have been used to address some of the spatiotemporal limitations of the observational record. Proxy analyses extend the AMO characterization further back in time, although proxy data alone cannot distinguish between external forcing (e.g., solar, volcanic, aerosols, anthropogenic) and the internal climate variability of the climate system. A widely-adopted AMO index applies a ten- year running mean to detrended North Atlantic SSTAs from 0 to 60°N (Enfield et al.

2001), and the first proxy reconstruction of an AMO index was based on tree-ring records from eastern North America, Europe, Scandinavia and the Middle East (Gray et al. 2004).

The 400-year proxy record captures six full cycles of the AMO and suggests a larger range of AMO variability in the pre-instrumental record: for example, the 18th century is notable for a lack of significant AMO activity from 1709-1763 followed for a decade and a half by the weakest-intensity cool phase of the entire reconstruction. Other proxy

(Poore et al. 2009; Wang et al. 2011) and proxy-based network (Mann et al. 1995; Mann et al. 1998; Delworth and Mann 2000) studies also show a persistent oscillation before the instrumental record. Although the AMO can also be referred to as Atlantic

Multidecadal Variability (AMV), the term ‘oscillation’ is used here to indicate the climate mode is characterized by a certain timescale of variability associated with an established mean and variance (Knight 2009) persisting at least centuries (Gray et al.

2004) and possibly throughout the past 8,000 years (Knudsen et al. 2011).

Several ocean-atmosphere coupled GCMs spontaneously generated multidecadal variability in the strength of the THC and associated oscillation of North Atlantic SSTs in

the absence of external forcing, suggesting a naturally emergent AMO signal. Princeton’s

Geophysical Fluid Dynamics Laboratory (GFDL) GCM featured a 40-60 year oscillation persisting throughout a 600 year integration (Delworth et al. 1993) that was consistent with SST and salinity observations (cf Levitus 1989a) and displayed a spatial pattern similar to observations (cf Kushnir 1994). Variations in the strength of the model THC were attributed to internal oceanic processes driven by density variations in the North

Atlantic deep water formation induced by the THC itself. Other coupled GCMs featuring a spontaneous AMO-like signal include a 35-year North Atlantic-centered oscillation by the coupled GCM from Max Plank Institute (MPI; Timmermann et al. 1998), a 70-120 year oscillation in the U.K. Hadley Centre’s Meteorological Office, HadCM3 (Knight et al. 2005), and a shorter 300-year integration with a 30-50 year oscillation in the NCAR

CSM (Capotondi and Holland 1997).

Although the HadCM3, MPI and GFDL models produce broadly similar spatial patterns of SST and SLP associated with this multidecadal oscillation – e.g., compare

Delworth et al. Figures 6 and 20 (1993) to Timmermann et al. Figure 20 (1998) - they do so through different mechanisms. The GFDL signal appears to be an ocean-only mode

(Delworth et al. 1993) in contrast to the air-sea coupled mode in the MPI and HadCM3 simulations (Timmermann et al. 1998; Knight et al. 2005). Still other analyses rely on a three-way ocean-atmosphere-cryosphere interaction in which THC intensity is modulated by an AMO-induced change of Arctic sea ice export that interferes with deep water formation in the northern North Atlantic to trigger critical thresholds (Dong and

Sutton 2005; Jungclaus et al. 2005; Dima and Lohmann 2007).

Coupled GCMs also vary widely in the timing, amplitude, phasing and climate effects of the model AMO signal (Guan and Nigam 2009; Knight 2009; Ting et al. 2011;

Kavvada et al. 2013; Ruiz-Barradas et al. 2013). Skill remains particularly low in simulating hydroclimate effects with few improvements from CMIP3 to CMIP5 model versions (Ruiz-Barradas et al. 2013) due in part to inadequate representation of oceanic heat content and geopotential height anomalies (Kavvada et al. 2013). Ensemble simulations of the AMO have mixed success in deriving a realistic signal (Lee et al.

2011), with the IPCC AR4 model simulations underestimating the multidecadal amplitude of 20th century oscillations. This is partly because the model-generated multidecadal signals are canceled out after applying the ensemble mean (Knight 2009;

Ting et al. 2009) and is likely a reason contributing to the failure of earlier ensemble simulations to detect the observed AMO (Knight 2009). Continuing routine model underestimation of the AMO period and magnitude of surface warming in particular decreases the accuracy of climate change forecasts (Kavvada et al. 2013).

Like all climate cycles, characterization of the AMO focuses on the extreme

(warm and cool) phases. However, the AMO is also known to progress through transition phases between the extreme phases. These transition phases (warm-to-cool and cool-to- warm) are likely related to adjustment of the THC (Dima and Lohmann 2007) and remain unstudied. Proxy reconstructions suggest these transition phases persist years to a decade or more (Gray et al. 2004; Hubeny et al. 2006), with the North Atlantic switching regimes only after progressing through a transition (or “organizational”) phase. Traditional analyses of climate cycles conflate the two transition phases into one, with the effects of

each canceling the other, to establish baseline conditions that are removed to identify climate effects of the extreme phases. This method assumes transition phases are

‘neutral’ and exert little influence on climate, an assumption embedded in the ‘La Nada’

(‘the Nothing’) moniker for El Niño-Southern Oscillation (ENSO) transition phases. The extent to which this assumption is accurate remains unclear, however, and the longer- lived transition phases of the AMO may facilitate sustained climate effects.

The short observational record limits understanding of the AMO in the physical world and presents difficulties in reconciling model inconsistencies that compromise the accuracy of climate change forecasts on multidecadal time scales. Although the AMO surface structure has been fairly well characterized (especially SST, pressure and precipitation), there has only recently become available high-quality reanalysis datasets providing global climate coverage of at least one full AMO cycle, including throughout the troposphere and lower stratosphere. Reanalysis datasets, or reanalyses, provide observationally-constrained model results and minimize the perceived climate ‘jumps’ associated with using multiple sources of data. A comprehensive global analysis of

AMO-related climate anomalies throughout the entire troposphere should provide a more comprehensive understanding of the AMO and may contribute to improvements in model simulations and climate change projections. Extending the characterization beyond the extreme phases to the AMO transition (cool-to-warm and warm-to-cool) phases could provide a more dynamic understanding and further insight into the evolution of the AMO life cycle.

B. CLIMATE IMPACTS

The AMO influences global mean temperatures (Schlesinger and Ramankutty

1994; Knight et al. 2005; Lu 2005; Zhang and Delworth 2007) and has been implicated in the early 21st century slowdown of surface global warming (Steinman et al. 2015). On smaller regional scales, the AMO influences mean temperature and hydroclimate variability over regions of nearly every continent (Enfield et al. 2001; Gray et al. 2004;

Knight et al. 2006; Baines and Folland 2007). The AMO also modulates the frequency of extreme events, such as Atlantic hurricanes (Goldenberg et al. 2001; Bell and Chelliah

2006; Wang et al. 2008), eastern U.S. intense storms (Tootle et al. 2005; Curtis 2008),

African dust storms (Prospero and Lamb 2003), North American wildfire activity

(Kitzberger et al. 2007) and pest outbreaks (Hart et al. 2013).

The AMO can strongly influence precipitation and monsoon regimes on decadal to multidecadal timescales. Two particularly sensitive regional climates respond to

AMO-associated changes in the position of the interhemispheric tropical convergence zone (ITCZ) with multidecadal periods oscillating between drought and wet conditions: the African Sahel (Rowell et al. 1992; Knight et al. 2006) and the Drought Polygon area of northeast Brazil (Nobre and Shukla 1996; Hastenrath 2012). The AMO also affects the

Indian and Asian monsoons, with observational analyses showing distinct wet and dry regimes on multidecadal timescales in which all these monsoonal circulations remain correlated (Kripalani and Kulkarni 2001). The wet and dry periods last around 30 years over India and China, with China lagging India by about 10 years (Kripalani and

Kulkarni 2001). Multidecadal fluctuations in the intensity of these monsoon regimes 7

appears to be associated with the AMO (Kripalani and Kulkarni 2001; Goswami et al.

2006), with observational analysis (Goswami et al. 2006) and GCM experiments (Zhang and Delworth 2005; Lu et al. 2006) suggesting an AMO warm (cool) phase enhances

(diminishes) monsoon intensities.

Over much of the U.S. an AMO warm (cool) phase is associated with increased

(decreased) temperatures and decreased (increased) precipitation (Enfield et al. 2001;

McCabe and Wolock 2002; Rogers and Coleman 2003; Sutton and Hodson 2005; Curtis

2008). An AMO warm phase increases the likelihood of drought over North America, including the 1930s Dust Bowl and 1950s drought (Fye et al. 2003; Schubert et al. 2004a,

2004b), and synchronizes large-scale wildfires across western North America by modulating the strength and spatial extent of ENSO and PDO, the main drivers of fire on interannual to decadal timescales (Kitzberger et al. 2007).

A number of climate variables in and near the Arctic also vary on 50-90 year timescales fairly coherent with AMO phase changes. These include Arctic sea ice and surface air temperatures (Polyakov et al. 2002) as well as Fram Strait Sea Ice Export

(Schmith and Hansen 2003). Specifically, AMO warm (cool) phases are associated with lower (higher) Arctic sea ice extent on multidecadal timescales (Venegas and Mysak

2000).

However, specific multidecadal trends, including Arctic changes, are shaped through AMO interactions with other climate modes operating on similar timescales, including AGW and the Interdecadal Pacific Oscillation (IPO; Parker et al. 2007;

Schubert et al. 2009; Wang et al. 2013; Dong and Zhou 2014), and it is currently not

possible to perfectly disentangle these signals. So although the rapid warming of the

Arctic that began in the late 20th century (Serreze and Francis 2006) and the recent accelerated Greenland melting (Nghiem et al. 2012) are attributable to a combination of

AGW and the AMO (Polyakov and Johnson 2000) it is unclear to what extent each of these climate modes are responsible. Although one study proposes the recent warming of coastal Greenland is primarily due to AMO-associated enhanced northward heat transport, based in part on a similar 1930s rapid warming when the effects of AGW are considered negligible (Chylek et al. 2009), other studies apportion more of the responsibility to AGW while still suggesting a role for the AMO (Ting et al. 2009).

Since the AMO warm phase and AGW generate many similarities in climate (e.g., increased frequency of major hurricanes, Northern Hemispheric warming), the AMO can confound the detection and attribution of AGW (Hegerl et al. 1997), with an AMO warm

(cool) phase enhancing (mitigating) some of the expected climate effects of AGW.

However, not all climate effects of the AMO and AGW add linearly: for example, if

AGW weakens the Atlantic THC this could favor a shift to an AMO cool phase that is associated with decreased temperatures in the U.S. and Europe (Sutton and Hodson

2005). Therefore AMO characterization is important for more accurate detection and attribution of climate change and more accurate climate forecasts on shorter (decadal-to- multidecadal) timescales and smaller (regional) spatial scales (Stott et al. 2004).

C. OCEANIC STRUCTURE

The SST oscillations associated with the AMO have a larger spatial extent than

ENSO and, based on accumulated SST amplitude, may project a global intensity roughly twice that of ENSO (see Fig 5 of Chen et al. 2010a). The spatial patterns vary somewhat outside the North Atlantic depending on method but share several common features. An

AMO warm phase is characterized by a fairly coherent warming throughout the Northern

Hemisphere with relatively more neutral to weakly cool SSTAs in the Southern

Hemisphere (e.g., Knight et al. 2005). The signal is particularly robust with maximum amplitude throughout the North Atlantic and Mediterranean Sea, with changes in the

North Atlantic generally more pronounced at high latitudes and less so in the tropics

(Delworth and Mann 2000; Knight et al. 2005; Parker et al. 2007; Enfield and Cid-

Serrano 2010). The Atlantic Ocean is characterized by a hemispheric dipole, with South

Atlantic SSTAs opposite-sign and weaker than North Atlantic SSTAs (e.g., Enfield et al.

2001). There are also consistent and significant co-oscillations in the North Pacific

(Timmermann et al. 1998; Delworth and Mann 2000; Enfield et al. 2001; Knight et al.

2005; Sutton and Hodson 2005; Parker et al. 2007; Zhang and Delworth 2007; Chen et al.

2010a) likely important for extending the influence of the AMO on global climate

(Enfield et al. 2001).

Although the AMO tracks changes in the intensity of the North Atlantic branch of the thermohaline circulation (Delworth et al. 1993; Delworth and Mann 2000; Knight et al. 2005), the oceanic characterization of the AMO is primarily surface-based despite the

AMO being driven through both surface and subsurface components of the THC. This is 10

because the strength of the THC has not been assessed directly and observational estimates of the Atlantic meridional overturning circulation (the thermohaline circulation plus wind-based ocean gyre circulation and tides) are based on just a few hydrographic sections with continuous measurements only after 2004 (Marsh et al. 2005). The North

Atlantic THC, a crucial regulator of the global THC (Polyakov et al. 2010; Hu et al.

2011) and potential trigger for abrupt climate change (Broecker 1997; Hu et al. 2010), is responsible for most of the northward heat transport in the Northern Hemisphere (Hall and Bryden 1982) and includes the only deep water formation sites in the Northern

Hemisphere. The Atlantic THC is characterized by pan-hemispheric northward-traveling warm surface waters in the upper 1000 meters. At high latitudes, intense heat loss to the atmosphere increases the density of these currents that then sink primarily in the

Greenland-Iceland-Norway (GIN) and Labrador/Irminger Seas (Dickson and Brown

1994). The northward heat transport that extends to the GIN Seas constrains the winter sea ice extent (Rahmstorf 1997). These downwelling water masses, together with subsurface polar water masses flowing south through Fram Strait, eventually reverse direction and flow equatorward down to ~ 3 km depth as the subsurface branch of the

Atlantic THC (Medhaug and Furevik 2011). These waters mix with other deepwater masses in the Antarctic Circumpolar Current, cycle through the deep Pacific and Indian

Oceans and eventually upwell to the surface, although the latter process is not localized and difficult to observe (Schmittner et al. 2007). The strength of the THC depends on heat- and salinity-driven density differences that reflect a balance between high-latitude cooling and freshwater input from precipitation, river runoff and Arctic sea ice export.

At high latitudes, a freshening of the upper layers can inhibit deep water formation and lead to critical thresholds that dramatically weaken the THC.

This freshening of the upper layers and weakening of the Atlantic THC is explored in GCM water-hosing experiments in which the addition of freshwater in the subpolar North Atlantic substantially weakens the Atlantic THC (similar to an extreme version of an AMO cool phase). Although these experiments involve modulations of

THC intensity that are greatly amplified relative to the modern-day AMO, they generate teleconnections and spatial patterns similar to those associated with the AMO. To compare water-hosing results with the AMO warm phase the anomaly sign can be reversed. These experiments produce extremely robust associations that are also consistent with AMO-associated observations, including: a southward (northward during an AMO warm phase) shift in the ITCZ over the Atlantic and eastern Pacific and a meridional dipole between the North and South Atlantic (Zhang and Delworth 2005;

Stouffer et al. 2006; Dong and Sutton 2007; Timmermann et al. 2007; Wu et al. 2008); an

El Niño-like warming (La Niña-like cooling during an AMO warm phase) of the tropical eastern Pacific and strong co-oscillation of SSTAs between the North Atlantic and extratropical North Pacific (Zhang and Delworth 2005; Dong and Sutton 2007;

Timmermann et al. 2007; Wu et al. 2008); and a cooling (warming during an AMO warm phase) of the western Pacific and maritime continent (Zhang and Delworth 2005; Dong and Sutton 2007; Wu et al. 2008).

D. ATMOSPHERIC STRUCTURE

Characterization of observed AMO-related atmospheric anomalies is generally restricted to surface variables (e.g., temperature, sea level pressure and precipitation) with analyses above the surface limited to regions of notable importance (e.g., winds over the

Atlantic hurricane Main Development Region). Although GCM analyses can be useful for identifying causal mechanisms and teleconnections, significant inter-model variability in AMO amplitude, periodicity and even driving mechanisms warrants caution in generalizing results to the physical world. Understanding the interaction of the SST component of the AMO with the overlying atmosphere remains challenging yet is necessary to connect the results of idealized models and coupled GCMs and better understand the AMO in the physical world (Dijkstra et al. 2006).

The AMO involves an oceanic forcing of the atmosphere coherent over much of the Northern Hemisphere (Sutton and Hodson 2005; Knight et al. 2006; Arguez et al.

2009), with deepened low sea level pressure (SLP) anomalies especially over the North

Atlantic and much of Eurasia (Enfield et al. 2001; Goswami et al. 2006; Lu et al. 2006) and modulations of surface precipitation in the African Sahel (Folland et al. 1986), northeast Brazil (Nobre and Shukla 1996; Folland et al. 2001) and North America

(Enfield et al. 2001; McCabe and Wolock 2002; Shabbar and Skinner 2004; Sutton and

Hodson 2005). The vertical structure over the North Atlantic and Eurasia during an AMO warm phase is baroclinic, with cyclonic anomalies at the surface and anticyclonic anomalies in the upper troposphere (Enfield et al. 2001; Goswami et al. 2006; Lu et al.

2006; Wang et al. 2009). 13

Modulation of semi-permanent pressure cells extends the influence of the AMO, with a weakened North Atlantic subtropical high enhancing Atlantic hurricane activity

(Wang et al. 2008) and a weakened Siberian High causing warm winters in China (Li and

Bates 2007). Over the Pacific, a likely mechanistic center of action for AMO-North

Pacific climate variability is the Aleutian Low, which has been implicated in multidecadal North Pacific SST variability (Delworth et al. 1993; Delworth et al. 1997;

Timmermann et al. 1998; Minobe 1999; Dima and Lohmann 2007). Studies consistently feature positive height anomalies over the North Pacific, although the locations of these are somewhat susceptible to differences in analytical method and AMO definition (e.g.,

Parker et al. 2007; Guan and Nigam 2009; Ting et al. 2011).

The AMO-related surface patterns of temperature, pressure and precipitation have been analyzed with various statistical methods, datasets and models, and specific tropospheric regions have been well characterized above the surface, particularly the baroclinicity of the North Atlantic. In contrast there have been few observationally-based analyses of the mid- to upper troposphere. Although Kavvada et al. (2013) characterized the 500 mb geopotential height anomalies, the analysis was restricted to a portion of the

Northern Hemisphere. A comprehensive global reconstruction of AMO-associated climate anomalies throughout the troposphere has not yet been reported.

E. EXISTING THEORIES AND PACIFIC CONNECTION

Extratropical SSTAs, unlike tropical SSTAs dominated by ENSO and interannual variability, are predominantly influenced by multidecadal climate cycles, and most large-

scale multidecadal climate cycles are likely associated with variations of the oceanic thermohaline circulations, particularly in the Atlantic (Gray et al. 1997). The two leading natural modes of multidecadal variability are the AMO and the Interdecadal Pacific

Oscillation (IPO), the pan-Pacific manifestation of the North Pacific-based Pacific

Decadal Oscillation (PDO). Both are coupled atmosphere-ocean oscillations indexed by basin-wide changes in SSTAs that, together with AGW operating on similar timescales, influence regional and global climate (Parker et al. 2007; Schubert et al. 2009; Wang et al. 2013; Dong and Zhou 2014).

Despite the limited observational record, a conceptual framework has been proposed consistent with observation and theory to explain the AMO life cycle and phase switching (Dima and Lohmann 2007). Beginning arbitrarily with a strong THC and associated AMO warm phase, the Dima-Lohmann model shows warm North Atlantic

SSTAs generating low SLP anomalies over the North Atlantic and Eurasia. The AMO signal is transferred to the Pacific via the tropics (where the signal propagates westward with the ITCZ acting as a zonal waveguide) and possibly via the mid/high-latitudes, weakening the Aleutian Low and, with a lag time of 10-15 years, generating high SLP anomalies in the North Pacific. The resulting hemispheric wavenumber-1 SLP structure increases Arctic sea ice and freshwater export to the North Atlantic after a 10-20 year adjustment period by projecting onto Fram Strait (Dima and Lohmann 2007), a region featuring the largest export of Arctic freshwater, two-thirds in the form of sea ice

(Aagaard and Carmack 1989), primarily driven by intensified northerlies over the GIN

Seas (Dickson et al. 1988; Aagaard and Carmack 1989; Belkin et al. 1998). The

associated freshening of the North Atlantic interferes with deep water formation after a

10-15 year lag, weakening the THC and switching to an AMO cool phase. Summing the lag times due to oceanic inertia results in each AMO warm and cool phase lasting between 30 and 35 years.

There remain some uncertainties with this model. Although this and other studies suggest involvement of the cryosphere via Arctic sea ice and freshwater export (Delworth et al. 1997; Jungclaus et al. 2005; Dima and Lohmann 2007) other model analyses suggest this is not essential (Delworth et al. 1993; Knight et al. 2005). There are also opposite-sign uncertainties regarding observational-based estimates of the intensity of

GIN Seas convection, which may be weakening (Alekseev et al. 2001) or intensifying

(Ronski and Budeus 2005).

However, AMO-associated involvement of the Pacific on multidecadal time scales has been suggested by several studies. The 1970 switch to an AMO cool phase was followed by a step change in numerous climate variables over the Pacific and Indian

Oceans within several years, perhaps attributable to a series of global adjustments in response to a weakening of the Atlantic THC (see Gray et al. 1997 and references within). These include increases in equatorial west Pacific SSTs, tropospheric temperatures and humidity (Graham 1990; Gaffen et al. 1991), tropospheric thickness anomalies (Gray et al. 1997) and tropopause heights (Reid and Gage 1993), as well as changes in the associations between SLP and Indian monsoon precipitation

(Parthasarathy et al. 1991) and between ENSO and western Pacific warm pool variability

(Gaffen et al. 1991). The relatively short lag time for these Indo-Pacific changes suggests

North Atlantic SSTAs may influence the Pacific via large-scale atmospheric circulation anomalies (e.g., trade wind anomalies in the subtropical central and east Pacific and/or variations in the tropospheric polar vortex) generated and maintained by ocean- atmosphere interactions rather than strictly oceanic interactions involving global THC adjustments (Gray et al. 1997; Mestas-Nunez and Enfield 1999; Dong and Sutton 2002).

These atmospheric connections can efficiently transport SST-associated changes to remote locations and may act as a teleconnection bridge by which to extend the influence of the AMO (Dong and Sutton 2002).

Links between tropical Pacific and North Atlantic SSTAs exist on various time scales and are bidirectional, with some analyses suggesting the former leading by a few months (Lanzante 1996; Enfield and Mayer 1997) to several decades (Latif 2001) and other studies suggesting it is North Atlantic SSTAs that lead the Pacific by a few months

(Wu et al. 2005) to years (Dima and Lohmann 2007). The ITCZ, for example, may act as a zonal waveguide for the Atlantic-to-Pacific signal propagation (White and Cayan 2000;

Dima and Lohmann 2004). Intensified Atlantic trades can cross Central America and cool the tropical northeast Pacific by increasing evaporation (Wu et al. 2005). Once transferred to the tropical Pacific, the signal could be amplified by wind-evaporative stress-SST (WES) feedback while propagating westward via air-sea interactions (Dong and Sutton 2002; Zhang and Delworth 2005). The tropical Pacific signal may also be transmitted to the mid-latitude North Pacific via atmospheric teleconnections (Seager et al. 2001) where it is amplified by feedback processes after several years (Peng and

Robinson 2001; Wu et al. 2003). An AMO-induced weakening of the Icelandic Low may

weaken the Aleutian Low through tropical Atlantic-Pacific interactions (Mestas-Nunez and Enfield 1999), a mechanism proposed to result in a shift to an IPO cool phase (Dima and Lohmann 2007; Grossmann and Klotzbach 2009). In fact, the ECHAM-3 MPI coupled GCM features AMO-driven Pacific SSTAs and corresponding weakened

Aleutian Low and SST patterns resembling the IPO cool phase (see Figure 21d of

Timmermann et al. 1998).

Other analyses suggesting an AMO/IPO relationship include an EOF-based analysis suggesting either the AMO leads the PDO by 13 years or the AMO lags the PDO by 17 years, with the AMO index strongly correlated with the PDO index at these time lags (see Figure 4a of d'Orgeville and Peltier 2007). A separate EOF analysis suggests the

AMO is the leading signal, with the observed unfiltered PDO index lagging the unfiltered

AMO index by 12 years (Zhang and Delworth 2007). An AMO warm phase/IPO cool phase association has also been suggested by regressing detrended 10-year low-pass filtered SSTAs from the NOAA Extended Reconstruction SST (ERSST.v3b) data set onto an AMO index (Schneider and Noone 2012). And a multicentury integration of the

U.K. Met Office Unified Model (HadCM3) finds a small but statistically significant covariability between the Atlantic THC and the IPO, with the coefficient peaking at 0.27

(significant at the 99% level, accounting for the impact of serial correlation) when the

Atlantic THC index leads the IPO index by 1 year (Rashid et al. 2010). The HadCM3 results show AMO-generated changes in air temperature, SLP and precipitation throughout the Indo-Pacific region and an AMO cool phase associated with stronger and more frequent La Niña-like IPO cool events. Nevertheless, due to the short and sparse

instrumental record and inconsistencies in mechanisms driving AMO signals in GCMs, the extent of an AMO-IPO relationship as well as confident validation of the Dima-

Lohmann conceptual model exceeds current capabilities.

CHAPTER 2: DATA AND METHODS

A. DATASETS AND VARIABLES

The climate variables used to reconstruct the AMO extreme and transition phases in this study are anomalies of sea surface temperature (SST), precipitation, air temperature, geopotential height, zonal wind, meridional wind and vertical pressure velocity (omega). All variables are analyzed using two reanalysis datasets: the National

Centers for Prediction/National Center for Atmospheric Research (NCEP/NCAR)

Reanalysis dataset and the National Oceanic and Atmospheric Administration (NOAA)

20th Century Reanalysis version 2c (20CR2c). For both reanalyses, the baseline period used to calculate the anomaly has been updated to 1981-2010 to match the new climate normal time period. Land-based precipitation is also analyzed using the University of

Delaware (UDel) precipitation dataset. All datasets span over 60 years to provide coverage for at least one complete AMO cycle and are available at the National Oceanic and Atmospheric Administration’s Office of Oceanic and Atmospheric Research, from the Physical Sciences Division of the Earth System Research Laboratory

(NOAA/OAR/ESRL PSD) at Boulder, Colorado, USA from their website at http://www.esrl.noaa.gov/psd/. A listing of each dataset and associated climate variables is found in Table 1.

The NCEP/NCAR Reanalysis dataset (Kalnay et al. 1996) provides global atmospheric data from 1948 through 2012. SSTAs are obtained from the Global Ocean

Surface Temperature Atlas (GOSTA) monthly averages, version MOHSST5, which incorporate the Comprehensive Ocean-Atmosphere Data Set (COADS) for areas lacking

GOSTA data. Before 1981 monthly SSTAs were produced from the U.K. Meteorological

Office’s Global sea-Ice and Sea Surface Temperature (GISST) dataset by computing empirical orthogonal functions (EOFs) from the 1982-1993 monthly analyses and temporally weighting each function by fitting to a 2° grid. After 1981 the Reynolds SST

Reanalysis, which includes the Advanced Very High Resolution Radiometer (AVHRR) satellite instrument data, generates monthly SSTAs using optimal interpolation (OI) on a

1° gridded SST analysis produced daily and weekly.

The atmospheric variables in the NCEP/NCAR dataset are categorized according to the degree of model influence. Category A variables are strongly influenced by observation and most reliable. Less reliable are Category B variables, directly affected by observation but with a strong influence from the model climatology, and Category C variables that are completely model-derived during data assimilation. Air temperature, geopotential height, zonal wind and meridional wind are all Category A variables with global gridded spatial coverage of 2.5° longitude x 2.5° latitude (144 x 73) from 90°N to

90°S and 0°E to 357.5°E and 17 pressure levels from 1000 to 10 mb. The daily and monthly temporal resolution is available from 1948-2011. The NCEP/NCAR system uses complex quality control (CQC) of atmospheric temperature, height and wind data to accept, reject or correct data based on horizontal, vertical and temporal interpolation,

followed by optimal interpolation quality control (OIQC) of all observations before data assimilation. The analysis module uses spectral statistical interpolation (SSI), with optimal averaging for temperature and zonal and meridional winds over certain areas.

After each month, output are checked by an automatic monitoring system for quality- control of the temperature, geopotential height, zonal wind and meridional wind time series at all standard pressure levels for every 6-hour period. Sources of uncertainty in the

NCEP/NCAR dataset increase with altitude and back in time, especially over the

Southern Hemisphere. Before 1979 and satellite data, areas with sparse data coverage, especially above 200 mb and south of 50°S are more influenced by the model climatology and may contain spurious trends (Kistler et al. 2001). Atmospheric coverage is more limited from 1948-1957 before the establishment of an upper-air network as part of the International Geophysical Year. The present study uses monthly anomalies of climate variables, derived in the NCEP/NCAR Reanalysis by removing the 1981-2010 base period monthly means.

Vertical pressure velocity, or omega, is a Category B variable, partially defined by observations but also strongly influenced by the model. The spatial resolution and temporal coverage and resolution is the same but spatial coverage is available only for 12 pressure levels with data available to 100 mb rather than 10 mb. Omega is derived from the NCEP/NCAR forecast model based on four times daily pressure observations.

Precipitation is a Category C variable, model-derived and least reliable (Kalnay et al. 1996). Surface coverage is available at the same spatial resolution, temporal coverage and temporal resolution of other NCEP/NCAR variables. Precipitation rate is based on a

six hour forecast average valid for six hours after the reference time during which the data assimilation nudges the variable to remain close to the atmosphere. It is this model forcing that can produce biased results due to model climatology (Kalnay et al. 1996).

Climate variables are also analyzed using the NOAA-based Twentieth Century

Reanalysis version 2c (20CR2c) dataset (Compo et al. 2011): surface skin temperature, geopotential height, zonal wind, meridional wind and omega. The Twentieth Century

Reanalysis provides the first observation-based estimate of global tropospheric variability extending to the mid-1800s, with support for the Twentieth Century Reanalysis Project dataset provided by the U.S. Department of Energy, Office of Science Innovative and

Novel Computational Impact on Theory and Experiment (DOE INCITE) program, the

Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office. The global gridded spatial coverage of 2° longitude x 2° latitude (180 x 91) is available from 90°N to 90°S and from 0 to 358°E with 24 pressure levels from 1000 to 10 mb. The daily and monthly temporal resolution covers 1851 to 2011. The 20CR2c ingests surface pressure and sea level pressure observations every six hours from three datasets: the International Surface

Pressure Databank station component version 2 (ISPD, the world’s largest collection of pressure observations), International Comprehensive Ocean-Atmosphere Data Set

(ICOADS) and the International Best Track Archive for Climatic Stewardship

(IBTrACS). This pressure data, with observed monthly SST and sea-ice distributions used as boundary conditions, is ingested into the atmospheric component of NCEP’s

Climate Forecast System model, which uses a deterministic Ensemble Kalman Filter data

assimilation method, a type of Monte Carlo analysis, to optimally combine observations and estimates of the background using a least-squares method. This generates an a posteriori distribution of the state of the atmosphere in the form of a 4-dimensional weather map every 6 hours. Despite being based on surface-only observations the upper atmosphere data has compared favorably with independent radiosonde data (Compo et al.

2011).

A more observational-based assessment of terrestrial precipitation is provided by the University of Delaware precipitation dataset (Matsuura and Willmott UDel website) compiled from a large number of stations, including the Global Historical Climate

Network and the archive of Legates & Willmott. This land-based dataset provides global gridded spatial coverage with 0.5° longitude x 0.5° latitude (720 x 360) resolution from

89.75°N to 89.75°S and 0.25°E to 359.75°E. The monthly mean data from 1901-2010 is cross-validated and interpolated from a compilation of monthly rain-gauge precipitation totals, with a range of between 4,100 and 22,000 stations globally.

B. METHODS

B.1. AMO Index, Extreme Phase

This study of AMO-related climate anomalies uses the traditional AMO index

(Enfield et al. 2001). The timeseries is calculated at NOAA PSD using the Kaplan

Extended SST V2 dataset (Kaplan et al. 1998), which relies on U.K. Meteorological

Office SST data and provides global gridded spatial coverage with 5° by 5° (72 x 36) resolution with monthly coverage from 1856-2014. The area weighted average over the 24

North Atlantic from 0 to 60°N is computed from this dataset, and the time series linearly detrended by subtracting the monthly mean global average SSTAs to remove a long-term signal and focus on multidecadal variability. Though this linear detrending contains a residual component of the climate change signal, alternative methods of deriving an

AMO index (Mann and Emanuel 2006; Trenberth and Shea 2006; Parker et al. 2007) substitute different imperfect assumptions (Knight 2009; Enfield and Cid-Serrano 2010) and are also incapable of perfect AMO-climate change signal separation, unavoidable since this would require calculating the North Atlantic SST response to all climate forcings.

B.2. AMO Index, Transition Phase

For the first-ever characterization of the transition phases, an AMO Transition

Index is generated by calculating a time derivative of the AMO Smoothed Index using a finite difference method, the Central Difference Scheme (Figure 1). The Central

Difference Scheme optimizes the approximation of the differential operator and provides a numerical solution for the differential equation for the central grid point under consideration. Since the necessary grid points are unavailable for the first and last data points, these are calculated using the forward and backward difference schemes respectively.

The AMO Transition index time series is correlated with the time series of monthly climate variable anomalies from the NCEP/NCAR and UDel datasets to construct the global fingerprint of the AMO transition phase. Correlation coefficient

values of -1 and +1 suggest a perfect linear relationship, with a positive sign indicating an increase in the dependent variable (the climate anomaly time series) with an increase in the independent variable (the AMO transition index). The time series for the AMO transition index and each climate variable are correlated on a global grid of 144 longitudes x 73 latitudes (10,512 data points) from 1948-2008 (61 years or 732 months).

B.3. ENSO Index

The anomalous global zonal mean structure of ENSO is constructed using the

Niño3.4 climate index, defined as the average of sea surface temperature anomalies

(SSTAs) over the east central tropical Pacific region from 5°N - 5°S and 170° - 120°W available through the National Oceanic and Atmospheric Administration (NOAA)

Climate Prediction Center (CPE) at http://www.esrl.noaa.gov/psd/data/correlation/nina34.data. To construct the global circulation cells associated with the ENSO warm and cool phases, the Niño3.4 index time series is correlated with the time series of monthly zonal mean temperature, geopotential height, zonal wind, meridional wind and omega anomalies from the NCEP/NCAR dataset on a global grid of 144 longitudes x 73 latitudes (10,512 data points) from 1948-2013 during two seasons, boreal winter (DJF) and summer (JJA).

B.4. Data analysis

Two data analysis methods are used to generate global horizontal and vertical reconstructions of AMO-related climate anomalies: linear correlation and composite analyses.

B.4.a. Linear Correlation

Conventional linear correlation analysis measures the degree of relationship between the AMO index reference time series and the time series of climate variable anomalies. Correlation is a measure of covariability calculated by multiplying the covariance of the two presumed-random variables and dividing by the square root of the multiplied variance of the two variables. Values of -1 and +1 for the correlation coefficient suggest a perfect linear association and values in between the extent to which a linear relationship exists. A positive sign indicates the dependent variable (e.g., the

NCEP/NCAR climate anomaly time series) increases with an increase in the independent variable (the AMO index). The time series for the AMO index and each climate variable are correlated on a global grid of 144 longitudes x 73 latitudes for 10,512 data points, with 64 years (768 months) of data spanning 1948-2011. A two-tailed test at the 95% confidence level suggests correlations involving 64 years of data would be significant above 0.25, but this is a measure of local significance levels only that would generate at least .05 x (144 x 73) = 526 grid points significant just by chance if extrapolated to this analysis. Serial correlation of variables such as SST, which features year to year

correlations at a region, reduces the actual degrees of freedom and requires higher correlation values for significance.

B.4.b. Composite Analysis

Composite analysis is a signal analysis technique to identify correlations between two time series and isolate periodic signals (related variations in the time series) from noise (unrelated variations), especially those that may go undetected in spectral analyses when the signal is small compared to the noise. Compositing involves superposing two time series (AMO warm phase years and AMO cool phase years) by adding them, which tends to average out the unrelated noise in order to isolate AMO-associated signals with fixed phase.

The NCEP/NCAR and 20CR2c composite analyses use the Kaplan SST dataset to identify the 10 highest AMO index years and 10 lowest AMO index years over the course of an AMO cycle. All composite analyses use the unsmoothed AMO index. The smoothed AMO index is less appropriate since the source data remains unsmoothed and results in the inclusion of non-high AMO index years. Consistent with this, the anomaly patterns generated using a smoothed AMO index (not shown) display less fidelity to previously reported expected patterns and to the NCEP/NCAR linear correlation results.

C. PLOTS AND SCHEMATICS

To visualize the spatial pattern of correlations, or teleconnections, between the

AMO index and the global gridded sets of climate variable anomalies, two types of plots

are constructed using tools provided by the NOAA/OAR/ESRL PSD at http://www.esrl.noaa.gov/psd/: global horizontal maps and zonal mean cross-sections.

For both, the sign of the climate anomaly shown represents the AMO warm phase. For correlations of climate variables to the AMO cool phase, the anomaly sign is multiplied by -1. Global horizontal maps are constructed for monthly anomalies of surface-only variables of SST and precipitation and at the 1000, 500 and 200 mb pressure levels for air temperature, geopotential height, zonal wind and meridional wind using the

NCEP/NCAR, 20CR2c and, for precipitation, the UDel datasets. Climate anomaly correlations are plotted using zonal mean cross-sections, with latitude on the x-axis and height (mb) on the y-axis from the surface to 10 mb for air temperature, geopotential height, zonal wind, meridional wind and vertical velocity using the NCEP/NCAR-based linear correlation and composite analyses.

Reconstruction of the AMO extreme phase involves comparisons of two data analysis methods, two reanalysis datasets and two consecutive AMO cycles using a series of three comparisons. The first compares different methods, linear correlation and composite analysis, using the NCEP/NCAR dataset. The second compares different datasets, with composite analyses of the NCEP/NCAR and 20CR2c reanalyses. These two comparisons are restricted to the current AMO cycle, whereas the third comparison involves a composite analysis of the previous AMO cycle using the longer 20CR2c dataset.

A schematic of robust AMO-related climate anomalies is constructed based on results from the global horizontal maps and zonal mean cross-section plots. The

horizontal component of the schematic features three pressure levels at 1000, 500 and

200 mb with correlation coefficients of anomaly patterns generally above 0.4 (0.3) for the extreme (transition) phases. These patterns are useful for investigating the details of the vertical schematic, which extends from the surface to lower stratosphere. Intended to provide a first approximation of the global circulation of the AMO, vertical schematic correlation coefficients of the anomaly patterns do not all reach the threshold of the global horizontal maps but dynamic consistency among climate anomalies improves confidence in the general structure.

Data Set Variables Temporal Temporal Spatial References Coverage Resolution Resolution NCEP/NCAR SST, SLP, air 1948-2011 Daily and 2.5° lat x (Kalnay et Reanalysis 1 temperature, monthly 2.5° long al. 1996) precipitation, geopotential heights, zonal wind, meridional wind, omega 20th Century Surface skin 1871-2010 Daily and 2° lat x 2° (Compo et Reanalysis V2c temperature, monthly long al. 2011) SLP, air temperature, geopotential heights, zonal wind, meridional wind, omega

University of Land Only: 1900-2010 Monthly 0.5° lat x http://climat Delaware Air surface air temp 0.5° long e.geog.udel. Temperature and precipitation edu/~climat and e/publicatio Precipitation n_html/inde x.html

Table 1: Datasets and variables used in this study to characterize the AMO

Figure 1: Schematic of construction of AMO transition index

CHAPTER 3: AMO EXTREME PHASE NCEP/NCAR-LC RESULTS

A. HORIZONTAL MAPS

Figure 2 shows the global map of linear correlation of annual mean SSTAs with the annual mean AMO index. The AMO warm phase is characterized by warm SSTAs that are near-global in extent, though more robust in the Northern Hemisphere, with cool

SSTAs confined to the eastern Pacific basins and Southern Hemisphere mid-latitudes.

Three major regions of strong anomalous warming include the North Atlantic, the main center of action with the most robust signal in the tropics and eastern basin. The warm

SSTAs are slightly cross-equatorial and extend to adjacent bodies of water, especially the

Mediterranean Sea, consistent with previous studies (Marullo et al. 2011), and Hudson and Baffin Bay. The western North Pacific is the second region, consistent with nearly every study showing a strong North Pacific co-oscillation (e.g., Enfield et al. 2001;

Zhang and Delworth 2007). The warm SSTAs are also cross-equatorial and extend throughout the maritime continent. A similar pattern in the South Pacific, though with weaker correlations, suggests the western North Pacific signal may be the Northern

Hemisphere component of a larger horseshoe-shaped structure of warm SSTAs (see also

Figure 4g of Chen et al. 2010a). The third region is the central Arctic, in which

anomalous pan-Arctic warming is consistent with previous studies (Polyakov and

Johnson 2000) and strongest near the date line. Cool SSTAs are weakly correlated in the low latitude Pacific basins east of the date line and stronger in the southern South

Atlantic as part of a nearly circumglobal zonal band of cool SSTAs centered near 60°S latitude. Although some studies indicate these cool SSTAs extend to the highest latitudes in an out-of-phase Arctic-Antarctic relationship (Chylek et al. 2010), this analysis suggests warm SSTAs in the Southern Ocean, although results from this data-sparse region should be interpreted cautiously.

Despite some regional variations between the NCEP/NCAR and UDel (land- based only) precipitation datasets (Figure 3), both feature increased precipitation over most of Eurasia and northern Africa, west Australia and Antarctica, and decreased precipitation over the central and western United States and northeast Brazil. These results are consistent with previous studies of hydroclimate sensitivity of the African

Sahel (Folland et al. 1986) and of northeast Brazil (Folland et al. 2001) to the AMO.

Over the oceans, the NCEP/NCAR reanalysis shows the most robust increase of precipitation over the warm SST anomaly in the North Atlantic Ocean and weaker increased precipitation over the warm SST anomalies over the western Pacific and central

Arctic Ocean.

Figure 4 shows the global 3-dimensional structure of geopotential heights at three vertical levels: 1000 mb (bottom), 500 mb (middle), and 200 mb (top). The two regions of warm SSTAs and increased precipitation anomalies are consistent with negative surface height anomalies over the North Atlantic (with extensions to the Middle East) and

near the central Arctic over the Bering Strait and northwest North America (with similar though slightly weaker anomalies extending from the Bering Strait over coastal eastern

Asia). The strongest positive height anomalies are over the mid-latitude South Atlantic and correspond to the most robust cool SSTAs. This pattern extends over southern South

America (co-localized with strong cool air temperature anomalies, not shown) to the eastern South Pacific. Positive height anomalies throughout the Pacific basin, especially over the low latitudes, are strongest over the central North Pacific centered around the

Hawaiian Islands consistent with cool SSTAs and decreased precipitation anomalies east of the date line, but also extend over the western North Pacific warm SSTAs (a region featuring inconclusive precipitation results). A warm SSTA-positive height anomaly relationship also occurs over the western Arctic and Greenland (which features warm air temperature anomalies at 1000 mb, not shown), though these western Arctic SSTAs are only weakly warm and relatively cooler than some adjacent regions.

In the vertical, the negative surface height anomaly pattern over the North

Atlantic, northern Africa and Middle East becomes increasingly positive from 500 to 200 mb (Figure 3) associated with the total latent heating (Figure 2) and warm air temperature anomalies aloft (not shown). The upper-level warming and positive height anomalies reinforce negative height anomalies at the surface by removing air from the column as it expands upward and outward, generating a baroclinic vertical structure. A zonal mean cross-section plot of the North Atlantic region (Figure 5) between 40 and 50°N throughout the troposphere shows the characteristic east-west tilt of a baroclinic structure,

with the upper-level positive height anomalies positioned west of the negative height anomalies at the surface.

Globally, the North Atlantic is the only region of baroclinicity. Vertical structures in regions away from this main center of action are all barotropic with positive height anomalies. All but one are Pacific-based. The most consistent positive height anomaly signals throughout the troposphere occur over the western Arctic, subtropical North

Pacific near the Hawaiian Islands, low latitude western North Pacific and low latitude eastern South Pacific. Two other regions feature mostly positive tropospheric height anomalies: over the mid-latitude North Pacific west of the dateline (though with weak correlations at 1000 mb) and over the mid-latitude South Pacific east of the dateline

(though with weak anomalies at 200 mb).

A previously unreported feature is the organization of geopotential height anomalies into patterns that are tilted diagonally (southeast-to-northwest) at the surface.

Over the main center of action, the negative height anomalies over the North Atlantic and northern Africa align diagonally with the tilted negative height anomalies near the central

Arctic over northwest North America and Bering Strait, separated by a localized region of neutral anomalies. A similar orientation and tilt of positive height anomalies occurs north of the main center of action over the western Arctic and Greenland. In the Southern

Hemisphere, the positive height anomaly pattern similarly aligns with the diagonally- tilted anomalies over the eastern South Pacific. Height anomaly patterns from 500 to 200 mb, however, are zonally-oriented rather than tilted, especially throughout the subtropics of both hemispheres. The only exception is the pattern over the western Arctic and

Greenland, which retains a diagonal tilt aloft. Also unlike the surface patterns, height anomalies aloft are generally symmetric about the equator, particularly over the low latitudes. These features resemble the patterns and orientation of dynamical responses generated from a linear shallow water model by Held et al. (2002).

The zonal wind (Figure 6) and meridional wind (Figure 7) anomalies are dynamically consistent with the strong geopotential height anomaly patterns throughout the troposphere. For example, North Atlantic anomalous cyclonic circulation includes strong westerly (easterly) anomalies in the low (middle) latitudes whereas the anomalous anticyclonic circulation in the North Pacific is consistent with the pattern of strong easterly (westerly) anomalies in the low (middle) latitudes.

B. ZONAL MEAN CROSS-SECTION PLOTS

The global zonal mean circulation is also affected by the AMO. The AMO warm phase is associated with warm zonal mean air temperature anomalies throughout the global troposphere (Figure 8), with the exception of the Southern Hemisphere mid- latitudes, overlaid by stratospheric cool temperature anomalies. The strongest warm anomalies are in the Northern Hemisphere, with the most robust surface signal centered near 30°N associated primarily with the main center of action in North Atlantic Ocean.

Above 500 mb, warm air temperature anomalies throughout the Northern Hemisphere are consistent with precipitation anomalies associated with the main center of action and, at higher latitudes, near the central Arctic. The extent to which this Northern Hemisphere anomalous warming extends cross-equatorially over the Southern low latitudes between

about 700 and 200 mb appears inconsistent with the traditional characterization of the

AMO as a North-South hemispheric temperature asymmetry. This is especially the case if the warm air temperature anomalies throughout the data-sparse Southern high latitude troposphere are accurate. At the surface, cool air temperature anomalies are localized around 40°S and correspond to the zonal band of cool SSTAs throughout the Southern mid-latitudes, especially in the South Atlantic. Globally above 100 mb are cool air temperature anomalies, with a downward extension to 250 mb over the Southern high latitudes.

Zonal mean geopotential height anomalies (Figure 9) are consistent with anomalous air temperatures. Near the surface, the North Atlantic (and eastward to the

Middle East) generates negative height anomalies over the Northern low and mid- latitudes. Above this, in the mid- and upper troposphere, are the most robust positive height anomalies, reflecting the strong baroclinicity of the main center of action identified in the global horizontal plots. North and south of the main center of action at the surface are positive zonal mean height anomalies. Over the highest Northern latitudes, these correspond to the positive height anomalies over the western Arctic and Greenland.

Over the Southern mid-latitudes, the positive height anomalies correspond to the cool

SSTAs and positive heights in the South Atlantic, Pacific and Indian Oceans. In the stratosphere, a negative zonal mean height anomaly over the Southern high latitudes extends to 150 mb over 60°S latitude and is unusually co-localized with cool air temperature anomalies.

The zonal mean zonal wind anomaly pattern (Figure 10) is generally hemispherically symmetric, with easterly anomalies over the low and high latitudes and neutral-to-weak westerly anomalies aloft over the mid-latitudes. The North Atlantic is an exception to the low latitude symmetry, with the anomalous cyclonic circulation driving zonal mean westerly anomalies from the surface to 700 mb strong enough to obscure the opposing signal at similar latitudes over the central North Pacific. With altitude, however, the barotropic central North Pacific and baroclinic North Atlantic to Middle East both promote anomalous anticyclonic circulation and strong zonal mean easterly anomalies throughout the Northern low latitude troposphere. In the Southern subtropics positive height anomalies drive zonal mean easterly anomalies over the low latitudes at the surface and aloft, with a similar upper-level pattern over the Northern subtropics. In both hemispheres, these subtropical-centered positive height anomalies aloft generate stronger anomalies in the low latitudes than the mid-latitudes in the zonal mean, due in part to signal cancellation especially in the Northern Hemisphere. Easterly anomalies over the

Northern and Southern high latitudes from the surface to the tropopause are consistent with positive height anomalies and anomalous anticyclonic circulation over the western

Arctic and Greenland and continental Antarctica, respectively.

Zonal mean meridional wind anomaly patterns (Figure 11) are also dynamically consistent with the robust cyclonic and anticyclonic anomalies and useful for identifying potential areas of north-south convergence and divergence. Three patterns are detected at the surface and aloft. At the surface, southerly anomalies over the low latitudes extend cross-equatorially from the Southern to the Northern Hemisphere, with northerly

anomalies over the higher latitudes in both hemispheres. This pattern suggests surface convergence around 30°N, consistent with the negative height anomalies and cyclonic circulation over the main center of action, and divergence around 40°S associated with the positive height anomalies and anticyclonic flow over the South Atlantic and southern

South America. Relative to the surface, zonal mean meridional wind anomalies between about 300 and 200 mb are opposite-signed, with southerly (northerly) anomalies over the high (low) latitudes. This pattern suggests upper-level divergence overlays surface convergence around 30°N, consistent with the strong baroclinic structure over the North

Atlantic to the Middle East. In the Southern Hemisphere, upper-level convergence over the mid-latitudes, consistent with negative height anomalies, is also vertically aligned with the surface divergence at 40°S. A prominent feature throughout the troposphere is the inter-hemispheric circulation, with anomalous southerly (northerly) intrusions into the

Northern (Southern) Hemisphere at the surface (aloft).

Vertical pressure velocity (Figure 12) is useful for detecting large-scale ascending and descending anomalies though it is a Category B variable in reanalysis subject to considerable model influence. There are four main areas, two each of positive and negative, in which zonal mean omega anomalies of consistent sign extend throughout the troposphere, and these provide a vertical component linking the surface and upper-level areas of convergence and divergence. Negative omega anomalies are most prominent over the Northern mid-latitudes from 30-40°N, consistent with the baroclinic vertical structure of the North Atlantic. Although nearby latitudes also feature negative omega anomalies (over the Northern low latitudes and equator) as well as positive (over the

subtropics), these patterns do not extend throughout the entire tropospheric column. The other region of negative omega anomalies, the Southern high latitudes, is associated with warm tropospheric temperature anomalies over Antarctica and the Southern Ocean.

Positive omega anomalies over the Southern mid-latitudes are co-localized with the cool

SSTAs and positive surface height anomalies over the South Atlantic and vertically link the upper-level convergence and surface divergence implied by the zonal mean meridional anomalies. Positive omega anomalies over the Northern high latitudes are consistent with the strong positive surface height anomalies over the western Arctic and

Greenland.

Figure 2: Linear correlation of annual mean sea surface temperature anomalies (SSTAs) with the annual mean AMO index using NCEP/NCAR dataset.

Figure 3: Linear correlation of annual mean precipitation anomalies with the annual mean AMO index using (a) NCEP/NCAR and (b) UDel datasets

Figure 4: Linear correlation of annual mean geopotential height anomalies with the annual mean AMO index at 1000 mb (bottom), 500 mb (middle) and 200 mb (top).

Figure 5: Meridional mean cross-section of North Atlantic baroclinicity

Figure 6: Linear correlation of zonal wind to annual mean AMO index at 1000, 500 and 200 mb.

Figure 7: Linear correlation of meridional wind anomalies at 1000, 500 and 200 mb with the annual mean AMO index

Figure 8: Linear correlation of annual mean zonal mean air temperature anomalies to the annual mean AMO index

Figure 9: Linear correlation of annual mean zonal mean geopotential height anomalies to the annual mean AMO index

Figure 10: Linear correlation of annual mean zonal mean zonal wind anomalies to the annual mean AMO index

Figure 11: Linear correlation of annual mean zonal mean meridional wind anomalies to the annual mean AMO index

Figure 12: Linear correlation of annual mean zonal mean omega anomalies to the annual mean AMO index

CHAPTER 4: AMO EXTREME PHASE COMPARISON OF METHODS

NCEP/NCAR Linear Correlation and NCEP/NCAR Composite Analyses

This section compares the results of the NCEP/NCAR reanalysis dataset using two methods, linear correlation (NCEP/NCAR-LC) and composite analysis

(NCEP/NCAR-Comp). The composite analysis uses the Kaplan SST dataset to identify the 10 highest AMO index years (1998, 2001-2007 and 2010-2011) and 10 lowest AMO index years (1971-1977, 1985-1986 and 1993) of the most recent AMO cycle. All composite analyses use the unsmoothed AMO index since the source data remains unsmoothed. Use of the smoothed index results in the inclusion of non-high AMO index years and generates anomaly patterns (not shown) less comparable to previously published patterns and to the NCEP/NCAR-LC results. Annual mean anomalies of sea surface temperature and geopotential heights, zonal winds and meridional winds at 1000,

500 and 200 mb are shown, with comparison of seasonal (JJA and DJF) anomalies of these climate variables to the annual mean results.

A. HORIZONTAL PLOTS: NCEP/NCAR-LC and NCEP/NCAR-Comp

A.1. Sea Surface Temperature Anomalies (SSTAs), annual mean and seasonal

Annual Mean

The annual mean SSTA patterns (Figure 13) are highly consistent between the

NCEP/NCAR-LC and NCEP/NCAR-Comp analyses, with no opposite-sign anomalies.

Robust warm SSTA patterns include the North Atlantic, with near-neutral anomalies around 30°N in the western basin, and the Pacific mostly west (east) of the dateline in the

North (low and mid-latitude South) Pacific. However, the composite South Pacific patterns are zonally elongated and extend further eastward than suggested by linear correlation. Over the tropical Indian Ocean the composite method shows stronger warm

SSTAs than linear correlation as well as previous studies (e.g, Enfield et al 2001).

Both analyses show warm SSTAs throughout the high latitudes, though the composite pattern is more extensive in the Northern Hemisphere to include the eastern

Arctic, a region of near-neutral anomalies in the NCEP/NCAR-LC analysis. Cool SSTAs are consistently strongest and most extensive throughout the 60°S latitude belt with weaker localized anomalies in the eastern Pacific basins: in the low and mid-latitude

North Pacific and subtropical South Pacific. The composite analysis suggests an additional eastern Pacific pattern in the mid-latitude South Pacific off southwest South

America, a region of neutral anomalies in the linear correlation results.

Seasonal

NCEP/NCAR-LC, JJA and DJF

The NCEP/NCAR-LC JJA patterns (Figure 14) are essentially equivalent to the annual mean, with no opposite-sign anomalies. Differences are limited to changes in signal strength, with JJA featuring slightly weaker anomalous warming over the Northern

Hemisphere mid-latitudes (in the western Pacific) and high latitudes (in the western/central Arctic).

The NCEP/NCAR-LC DJF results are mostly consistent with the annual mean over the Northern low and mid-latitudes, with warm SSTAs over the North Atlantic and western Pacific. Outside these latitudes the DJF analysis shows weaker and less extensive regions of anomalous warming than the annual patterns, with most regions featuring neutral or cool SSTAs. This generates some weak opposite-sign anomalies over the

Northern high latitudes (in the western and central Arctic) and throughout the Southern

Hemisphere latitudes: over the low latitude South Atlantic, mid-latitude central Pacific and, centered around 60°S, more extensive cool SSTAs that extend further poleward to

Antarctica.

NCEP/NCAR-Comp, JJA and DJF

The JJA composite analysis (Figure 15) features no opposite-sign anomalies compared to the annual mean results. The biggest difference is in the Northern high latitudes, with neutral anomalies during JJA that are strongly warm in the annual mean

(and DJF) analyses. The JJA patterns also feature slightly weaker warm SSTAs in the

Northern mid-latitudes, including the North Atlantic and western Pacific, but are very consistent with the annual mean results in the Northern low latitudes and throughout the

Southern Hemisphere.

The NCEP/NCAR-Comp DJF results retain the strong anomalous warming characteristic of the annual mean results (unlike the neutral anomalies predominant during DJF in the NCEP/NCAR-LC analysis) and feature patterns consistent with the annual mean except in the equatorial Pacific and throughout the Southern Ocean. In the

Pacific, the DJF composite shows warm SSTAs directly over the equator east of the dateline, rather than the off-equatorial anomalous warming in the annual mean results.

Like the linear correlation comparisons, the cool SSTAs centered near 60°S are stronger and more extensive than the annual mean patterns, producing opposite-sign cool SSTAs off coastal Antarctica compared to the warm SSTAs in the annual mean results.

Seasonal Summary

Overall, all seasonal plots in both NCEP/NCAR-LC and NCEP/NCAR-Comp show a robust North Atlantic pattern of strong anomalous warming (except for the western basin around 30°N) and co-oscillation of warm SSTAs in the western North

Pacific, with cool SSTAs in the eastern Pacific basins and in a nearly circumglobal zonal band centered around 60°S. Although the JJA results generally more closely approximate the annual mean results in both analyses, the composite-associated DJF patterns are more similar to the annual mean results over the Northern mid- to high latitudes.

A.2.Geopotential Height Anomalies 1000 mb, annual mean and seasonal

Annual Mean

Both methods generate consistent 1000 mb geopotential height patterns (Figure

16). The biggest contrast is poleward of 50°S with linear correlation showing mostly neutral anomalies in contrast to the globally strongest negative height anomalies suggested by the NCEP/NCAR-Comp and the associated zonal partitioning of height anomaly sign mostly around that latitude. Both analyses show regional positive height anomalies over east Antarctica from 0 to 60°E. In the Southern mid-latitudes, the composite analysis suggests positive height anomalies over the three ocean basins: the strongest over the South Atlantic and most extensive over the South Pacific (both regions consistent with linear correlation), and the weakest over the southern Indian Ocean

(inconsistent with NCEP/NCAR-LC neutral anomalies).

The remaining differences are weak and localized. Two regions of weak anomaly sign differences are over the African continent - weakly negative (positive) in the

NCEP/NCAR-LC (NCEP/NCAR-Comp) - and the high latitude northeastern-most North

America, with weakly positive (negative) anomalies using NCEP/NCAR-LC

(NCEP/NCAR-Comp). Both analyses show positive height anomalies over the North

Pacific, with the NCEP/NCAR-LC (NCEP/NCAR-Comp) patterns strongest (weak) over the low latitudes and near-neutral (stronger) over the mid-latitude central North Pacific. 57

The strongest NCEP/NCAR-Comp positive height anomaly in the Northern Hemisphere is over central Asia, a region of weakly positive anomalies using linear correlation.

The diagonal tilt of two of the three surface height anomaly patterns identified in the NCEP/NCAR-LC results is replicated in the composite analysis: the negative height anomalies near the Bering Strait and positive anomalies in a grouping of localized patterns centered over southern South America from the South Atlantic to eastern South

Pacific. Over the western Arctic, the negative height anomaly over northeastern-most

North America bisects the otherwise positive height anomalies from Greenland to the dateline, disrupting the diagonal tilt suggested by linear correlation.

Seasonal

NCEP/NCAR-LC, JJA and DJF

The NCEP/NCAR-LC JJA results (Figure 17) suggest the 1000 mb height anomalies more closely resemble the annual patterns, consistent with the SSTA patterns.

There are no opposite-sign anomalies, with the main differences involving signal strength in the high latitudes. Compared to the annual AMO patterns, there are relatively weaker positive height anomalies over the western Arctic and stronger negative (rather than weakly negative to neutral) height anomalies over the Southern high latitudes. Positive height anomalies over the Pacific during JJA are stronger and more extensive (weaker and less extensive) over the North (South) Pacific.

The DJF-associated height anomalies are similar to the annual mean, though with signals that are generally weaker and less extensive, especially over the western Pacific 58

and in the Northern high latitudes, consistent with the seasonal SSTA patterns. The 1000 mb geopotential height anomalies over the Pacific during DJF are consistent with the

SSTA results showing mostly weaker (stronger) North (South) Pacific involvement relative to the annual mean. Northern Hemisphere inconsistencies during DJF include: the low latitude western Pacific (from positive to neutral), mid-latitude central Eurasia

(from weak heterogeneous signals to the globally strongest positive height anomaly in the

NCEP/NCAR-LC DJF analysis) and throughout the high latitudes (from positive to near- neutral). Southern Hemisphere DJF-related discrepancies are mostly in the mid-latitudes, with DJF seasonal results featuring weaker positive height anomalies over the South

Atlantic but stronger positive height anomalies over the mid-latitude eastern Pacific. The

DJF-associated patterns over the Southern high latitudes are generally weaker and less extensive, especially over eastern Antarctica. Compared to the annual mean, the DJF results suggest neutral (rather than positive) height anomalies from 0 to 60°E and much less extensive negative height anomalies near 120°E.

NCEP/NCAR-Comp, JJA and DJF

The NCEP/NCAR-Comp 1000 mb geopotential height anomalies during JJA

(Figure 18) are very consistent with the annual mean patterns, with no opposite-sign anomalies. Three Northern Hemisphere regions of differing anomaly signal strength includes the North Atlantic, in which JJA anomalies are less extensive, and the other two over the North Pacific and eastern Arctic, in which JJA anomalies are stronger. The less

extensive North Atlantic negative height anomalies include neutral anomalies centered around 30°N over much of the eastern basin. During JJA, positive height anomalies over the mid-latitude central North Pacific are much stronger and negative height anomalies over the high latitude eastern Arctic stronger and more extensive. In the Southern

Hemisphere during JJA, negative height anomalies over the high latitudes are more extensive and extend to the southern South Atlantic, displacing relatively equatorward positive height anomalies compared to the annual patterns.

The NCEP/NCAR-Comp DJF patterns are similar to the annual mean patterns, although with generally stronger signals over the mid-latitudes and Northern high latitudes and two regions of opposite-sign anomalies: over the mid-latitude North Pacific mostly east of the dateline and the eastern Arctic. In the Northern Hemisphere during

DJF, the negative height anomaly in the mid-latitude North Pacific near the Bering Strait is stronger and extends throughout the mid-latitudes mostly east of the dateline in contrast to the more limited meridional extent of the annual mean pattern, which features an opposite-sign mid-latitude positive height anomaly. In the Northern high latitudes, the

DJF results show strongly positive pan-Arctic height anomalies. In the eastern Arctic this produces opposite-sign anomalies relative to the weakly negative heights in the annual mean. In the Southern mid-latitudes, the DJF results show three regions of positive height anomalies between 30 and 50°S that correspond to similar patterns in the annual mean analysis. The most consistent are over the South Atlantic and South Pacific, although the latter is less zonally-oriented and more diagonally tilted during DJF. The third region,

over the southern Indian Ocean, is of similar strength during DJF but relatively weaker than the other two regions in the annual mean results.

Seasonal Summary

Overall the seasonal analyses feature robust negative height anomalies over the

North Atlantic and western Europe, near the Bering Strait and, except for neutral anomalies in the NCEP/NCAR-LC DJF analysis, much of the Southern high latitudes, with positive height anomalies over the western Arctic, mid-latitude South Atlantic and eastern Antarctica (0 to 60°E). Although the specific locations of Pacific anomaly patterns vary between analyses, both consistently feature positive height anomalies that are generally stronger (limited) over the North (South) Pacific during JJA, a pattern that is reversed in DJF.

A.3. Geopotential Height Anomalies 500 mb, annual mean and seasonal

Annual Mean

At 500 mb both annual mean analyses (Figure 19) show near-global positive geopotential height anomalies with the exception of the 60°S latitude belt. The tropics consistently feature the weakest signals, with strong centers of action mainly over the subtropics (mid-latitudes) characterizing the NCEP/NCAR-LC (NCEP/NCAR-Comp) analysis. Both analyses show similar centers of action throughout the tropics (western

Pacific), subtropics (eastern Pacific basins, Middle East and Australia) and Northern high latitudes (western Arctic). 61

In the mid-latitudes, however, the NCEP/NCAR-Comp features three positive height anomaly centers of action in each hemisphere, with two patterns in each hemisphere consistent with linear correlation and the third corresponding to mostly neutral anomalies. The most consistent Northern mid-latitude pattern is over North

America, a region of positive height anomalies that is the southernmost lobe of a larger pattern extending throughout the western Arctic to the dateline. Both analyses suggest this mostly high latitude signal is the strongest and most extensive globally in the mid- troposphere. The other fairly consistent Northern mid-latitude pattern is over central

Asia. Although this is not a main center of action in the linear correlation analysis, it is nevertheless a region of positive height anomalies, with both analyses suggesting these anomalies vertically extend from the surface. The NCEP/NCAR-Comp also suggests positive height anomalies extend from the surface to 500 mb over the mid-latitude central

North Pacific, patterns inconsistent with NCEP/NCAR-LC neutral anomalies.

Of the three Southern mid-latitude positive height anomaly regions, the strongest and most extensive pattern in both analyses is over the South Pacific east of the dateline, a vertical extension of surface patterns that are consistently stronger and more zonally elongated in the composite analysis. The other fairly consistent pattern is over the mid- latitude Indian Ocean, with the composite-associated positive height anomalies stronger and more extensive. Although these extend from the surface in the composite analysis, the NCEP/NCAR-LC features neutral surface anomalies. The inconsistent Southern mid- latitude positive height anomaly pattern is over the South Atlantic, with anomalies extending from the surface in the NCEP/NCAR-Comp. Although linear correlation also

shows strongly positive height anomalies at the surface, the mid-troposphere near-neutral anomalies suggest a shallow signal. Both analyses show positive height anomalies over

Antarctica, especially over eastern Antarctica from 0 to 60°E, that vertical extend from the surface. Both analyses also show negative height anomalies generally centered around

60°S latitude. The location and extent of the main centers of action are mostly method- dependent, with NCEP/NCAR-LC indicating three weakly negative patterns between the continental landmasses and Antarctica and the stronger, more zonally elongated composite pattern suggesting the main center of action is centered around 120°W.

However, two regions consistent between methods are near the Antarctic Peninsula and, although the NCEP/NCAR-LC signal is weak, over the Southern Ocean off eastern

Antarctica between 120°E and the dateline. Unlike at the surface, where only the

NCEP/NCAR-LC results showed a clear diagonal tilt in the western Arctic pattern, both analyses feature diagonally tilted mid-troposphere positive height anomalies over the western Arctic.

Seasonal

NCEP/NCAR-LC, JJA and DJF

The NCEP/NCAR-LC JJA patterns at 500 mb (Figure 20) are very consistent with the annual mean results, though with positive height anomaly centers of action more extensive over the subtropics (especially over the North Atlantic) and weaker over the high latitudes, especially over the western Arctic and east Antarctica. Over the North

Pacific during JJA, positive height anomalies are stronger and extend further into the 63

mid-latitudes, similar to results at the surface. Over the South Pacific, the JJA-associated positive height anomalies are slightly stronger than the annual mean results over the low latitudes (compared to weaker at the surface) and weaker in the mid-latitudes, (consistent with the surface).

The NCEP/NCAR-LC DJF results at 500 mb show much weaker positive height anomalies but with similar centers of action as the annual mean results. There is one opposite-sign anomaly region, over the Northern high latitudes, with weakly negative

(rather than weakly positive) height anomalies during DJF from the 120°E to the dateline.

The Northern mid-latitudes feature stronger and more negative height anomalies during

DJF in regions corresponding to mostly neutral annual mean anomalies (i.e., over the

North Atlantic, western Europe and the mid-latitude eastern North Pacific). Over the

Southern mid-latitudes, the negative height anomalies are similarly positioned with the exception of a negative (rather than neutral) height anomaly over southern South America during DJF. At higher latitudes are neutral height anomalies over Antarctica during DJF, including from 0 to 60°E, a region of stronger positive height anomalies according to the annual mean results.

NCEP/NCAR-Comp, JJA and DJF

The NCEP/NCAR-Comp JJA (Figure 21) and annual mean patterns are mostly consistent, with the only opposite-sign anomaly over the eastern Arctic near 60°E, a region of weakly negative (rather than weakly positive) height anomalies during JJA. The strongest positive height anomalies outside the western Arctic are in the mid-latitudes of

both hemispheres, with similar centers of action in the Northern Hemisphere, although the JJA analysis suggests an additional pattern over the North Atlantic around 30°N, a region of neutral anomalies in the annual mean results, consistent with the NCEP/NCAR-

LC seasonal and annual mean comparisons. Positive height anomalies during JJA in the

Southern mid-latitudes are weaker and only somewhat consistently positioned relative to the annual mean. The South Pacific east of the dateline consistently features positive height anomalies, but the annual mean positive height anomalies east of South America shift west during JJA and the mid-latitude Indian Ocean anomalies are neutral during

JJA. Negative height anomalies over much of the Southern Ocean are less extensive than the annual mean patterns and the main center of action shifts west during JJA, from

120°W to over the dateline.

Unlike the linear correlation results, with generally weaker mid-troposphere signals during DJF, the 500 mb NCEP/NCAR-Comp DJF signals are stronger than the annual mean at all latitudes. Like linear correlation, the centers of action are similar between DJF and the annual mean. The Northern mid-latitudes feature three strong negative height anomalies during DJF, over the North Atlantic, western Europe and the eastern North Pacific. These locations are consistent with the NCEP/NCAR-LC seasonal results and, consistent with linear correlation results, correspond to regions of neutral annual mean anomalies. The Southern high latitudes are the only region featuring opposite-sign anomalies, with the negative height anomalies over the Southern Ocean extending to the highest latitudes over Antarctica during DJF rather than the positive annual mean height anomalies over Antarctica.

Seasonal Summary

Overall, linear correlation and composite analyses feature JJA patterns that more closely resemble the annual mean results, with near-global positive height anomalies except near 60°S latitude. Both analyses suggest the development of a positive height anomaly pattern near 30°N over the North Atlantic during JJA, a region of neutral to near-neutral anomalies in the annual mean results. During DJF there is a general shift to stronger and more extensive negative height anomalies. Consistent with the surface patterns, the positive height anomalies over the Pacific are generally stronger and more extensive (weaker and less extensive) over the North (South) Pacific during JJA, a pattern that is reversed in DJF. Like at the surface, both methods show more extensive 500 mb negative height anomalies over both the Northern mid-latitudes and Southern mid- to high latitudes during DJF. Unlike linear correlation, with generally weaker mid- troposphere signals during DJF compared to the annual mean, the 500 mb NCEP/NCAR-

Comp DJF signals are stronger than the annual mean at all latitudes.

A.4. Geopotential Height Anomalies 200 mb, annual mean and seasonal

Annual Mean

Both NCEP/NCAR-LC and NCEP/NCAR-Comp analyses feature consistent 200 mb geopotential height anomalies north of 30°S (Figure 22) but with differences poleward, especially over the Southern mid-latitudes. Positive height anomalies are globally predominant with the exception of the latitudes centered around 60°S.

Consistent throughout the troposphere, the linear correlation (composite) results suggest stronger positive height anomaly centers of action over the subtropics (mid-latitudes).

Nevertheless, the Northern Hemisphere main centers of action remain mostly consistent between methods: over the subtropical North Atlantic to Africa and the Middle East as well as the eastern North Pacific. Compared to linear correlation (in parentheses), the stronger mid-latitude positive height anomaly signals in the composite are over the central North Pacific (shifted slightly east compared to linear correlation) and central

Eurasia and in the Southern Hemisphere over the South Atlantic (neutral), South Pacific

(near-neutral) and Indian Oceans (near-neutral). The strong positive heights over the western Arctic are diagonally tilted in both analyses and extend over part of North

America. South of 30°S the composite is characterized by strongly positive (rather than near-neutral to weakly negative) height anomalies throughout the Southern mid-latitudes and weakly positive (rather than neutral) height anomalies throughout the Southern high latitudes.

Seasonal

NCEP/NCAR-LC, JJA and DJF

The NCEP/NCAR-LC JJA (Figure 23) and annual mean results are very consistent outside the Southern mid-latitudes, with similar centers of action over the

Northern Hemisphere subtropics (the North Atlantic, eastern North Pacific and from

Africa to the Middle East), mid-latitudes (part of North America and central Eurasia) and high latitudes (western Arctic). Both the JJA and annual mean results show positive 67

height anomalies in the Southern low latitudes stretching across the eastern South

Atlantic and southern Africa to Australia and, in a pattern stronger during JJA, between northern South America and the eastern South Pacific. And both the JJA and annual mean patterns include weakly positive height anomalies over Antarctica, excepting weakly negative (annual mean) or neutral (JJA) anomalies over the Antarctic Peninsula.

The Southern mid-latitudes are also consistent, though with a shift to slightly more positive during JJA: neutral (rather than near-neutral negative) height anomalies between the continental landmasses and Antarctica and positive (rather than near-neutral) height anomalies over the mid-latitude Indian Ocean and South Pacific east of the dateline.

The NCEP/NCAR-LC DJF analysis features a general shift to a more negative anomaly sign globally, similar to other tropospheric levels: from near-neutral to strongly negative during DJF poleward of 30°S and in the Northern mid-latitudes, and from strongly to weakly positive height anomalies north of 30°S. Despite this, both the DJF and annual mean analyses show similar centers of action, with positive height anomalies over the Northern low latitudes (over the North Atlantic to Africa, the eastern North

Pacific), middle latitudes (central Eurasia, North America) and high latitudes (western

Arctic) as well as the Southern low latitudes (subtropical Indian Ocean).

Three negative height anomalies over the Northern mid-latitudes unique to DJF are consistent with lower tropospheric levels to form barotropic structures of negative height anomalies over the North Atlantic, western Europe and the eastern North Pacific.

Negative height anomalies over the Southern mid- to high latitudes are much stronger and more extensive during DJF but with similar centers of action between the continental

landmasses and Antarctica, especially poleward of Australia and South America. The

DJF analysis shows an additional negative height anomaly pattern over mid-latitude

South America, a region of neutral annual mean anomalies.

NCEP/NCAR-Comp, JJA and DJF

The JJA composite patterns (Figure 24) are very consistent with the annual mean, although with slightly weaker (stronger) Northern low (middle) latitude signals suggesting a JJA-associated northward shift of many positive height anomaly centers of action: from the Northern low to middle latitudes (over the North Atlantic, Africa and the

Middle East), from lower to higher Northern middle latitudes (over the central North

Pacific and central Eurasia), and an equatorward-shifted Southern Hemisphere pattern over the mid-latitude South Atlantic. The JJA patterns in the Southern high latitudes are stronger than the annual mean, particularly the negative (rather than neutral) height anomaly centered over the dateline in the Southern Ocean and the positive height anomalies throughout eastern Antarctica.

The NCEP/NCAR-Comp DJF analysis is fairly consistent with the annual mean results, with increased low latitude involvement suggested by the stronger positive height anomalies throughout the Northern and Southern low latitudes. The centers of action are similar except for opposite-sign anomalies in the Northern mid-latitudes and especially throughout the Southern high latitudes, consistent with the NCEP/NCAR-LC DJF comparison to annual mean. These DJF-associated negative height anomaly regions comprise barotropic vertical structures: in the Northern mid-latitudes over the North

Atlantic, eastern North Pacific and western Europe (consistent with NCEP/NCAR-LC

DJF patterns) and throughout the Southern high latitudes (inconsistent with

NCEP/NCAR-LC DJF) with the exception of eastern Antarctica from 0 to 60°E due to positive surface height anomalies. Composite DJF patterns over the Northern extratropics feature a diagonal tilt, particularly over the western Arctic and mid-latitude eastern North

Pacific, but are zonally oriented at other latitudes.

Seasonal Summary

Overall, JJA results more closely resemble the annual mean patterns, consistent with lower tropospheric levels. During DJF there are weaker positive height anomalies globally, with much stronger and more extensive negative height anomalies located over the Northern mid-latitudes and Southern high latitudes. Both methods show three

Northern mid-latitude barotropic structures during DJF, with negative height anomalies over the North Atlantic, eastern Pacific and western Eurasia. During DJF the linear correlation and composite analyses are least consistent over the Southern mid-latitudes, with opposite-sign anomalies throughout: unlike the zonal partitioning of anomaly sign around 30°S using NCEP/NCAR-LC, with negative to neutral height anomalies south of that latitude, the NCEP/NCAR-Comp DJF analysis suggests positive height anomalies throughout the Southern mid-latitudes that are among the strongest globally and a barotropic structure of negative height anomalies over the Southern high latitudes with the exception of eastern Antarctica from 0 to 60°E.

Annual Mean Overall Summary

Anomalous patterns of SSTs and geopotential heights are considerably consistent throughout the troposphere using linear correlation (NCEP/NCAR-LC) and composite

(NCEP/NCAR-Comp) analyses, with differences mostly in the Southern mid-to high latitudes. There is a general poleward shift in the strongest centers of action from the subtropics in the linear correlation analysis to the mid-latitudes in the composite analysis.

The linear correlation and composite analyses have several vertical structures in common, including the baroclinic structure over the North Atlantic. The most consistent barotropic structures of positive height anomalies are over the western Arctic and subtropical North Pacific near the Hawaiian Islands. Three other NCEP/NCAR-LC- associated regions of barotropicity are mostly, but not entirely, consistent in the composite analysis which features either weak anomalies at certain tropospheric levels or a slight shift in position: over the mid-latitude North Pacific west of the dateline (shifted to east of the dateline), the low latitude eastern South Pacific (shifted poleward to 30°S) and the low latitude western North Pacific (with weak 1000 mb anomalies). The mid- latitude South Pacific structure east of the dateline, with weak 200 mb anomalies in the linear correlation results, is one of three strong Southern mid-latitude regions of barotropicity in the NCEP/NCAR-Comp analysis, the other two over the South Atlantic

Ocean (positive surface heights but neutral aloft using linear correlation) and southern

Indian Ocean (positive at 500 mb but neutral at other tropospheric levels using linear correlation).

The composite analysis retains most of the diagonal tilt of the three height anomaly patterns suggested by linear correlation, including the two surface-only patterns: near the Bering Strait and centered over southern South America. The third pattern, positive height anomalies over the western Arctic diagonally tilted throughout the troposphere, remains diagonally tilted aloft in the composite analysis but bisected by weakly negative height anomalies at the surface that interfere with tilt determination.

B. ZONAL MEAN CROSS-SECTIONS: NCEP/NCAR-LC and NCEP/NCAR-

Comp

B.1. Zonal Mean Air Temperature Anomalies

The zonal mean cross-section patterns of the linear correlation and composite analyses (Figure 25) are broadly consistent, with the biggest difference throughout the

Southern mid-latitudes. Both analyses feature predominantly warm tropospheric temperature anomalies, but only the composite results extend these throughout the

Southern mid-latitude troposphere (compared to linear correlation-associated neutral anomalies), except from the surface to 900 mb. In contrast to the stronger anomalous warming over the Southern mid-latitudes, the composite suggests weaker Northern mid- latitude signals, from centered at the surface near 30°N to throughout the middle and upper troposphere. The middle troposphere signal in particular is also weaker over the low latitudes of both hemispheres. There is increased vertical extent of composite- associated warm tropospheric anomalies over all latitudes (e.g., from 200 to 100 mb over 72

the low latitudes), displacing the cool stratospheric anomalies especially over the low and middle latitudes of both hemispheres.

B.2. Zonal Mean Geopotential Height Anomalies

Both analyses suggest globally positive zonal mean geopotential height anomalies

(Figure 26) with the exception of two regions: near the surface over the low latitudes and

Northern mid-latitudes, with neutral (composite) to weakly negative (linear correlation) height anomalies, and centered over 60°S throughout the troposphere. This Southern

Hemisphere region, consistent with zonal mean air temperature anomaly results, features the biggest geopotential height difference between methods: linear correlation-associated neutral anomalies are strongly negative in the NCEP/NCAR-Comp results. Elsewhere the height anomaly patterns are generally consistent. Both methods show positive height anomalies increasing in strength with increasing altitude, although in the NCEP/NCAR-

LC results these generate three distinct centers of action aloft centered between 300 and

200 mb. In order from the weakest and least extensive to strongest and most extensive these are positioned over the Southern low latitudes, the Northern subtropics and centered over 60°N. In contrast, the NCEP/NCAR-Comp features more homogenous height anomalies stratified by altitude rather than distinct centers of action.

B.3. Zonal Mean Zonal Wind Anomalies

As with previous variables, the biggest difference in zonal mean zonal wind patterns (Figure 27) is over the Southern mid-latitudes. The NCEP/NCAR-LC anomalies

are neutral throughout the lower and middle troposphere but positive in the upper troposphere whereas the NCEP/NCAR-Comp suggests strongly positive height anomalies throughout the tropospheric column between 60 and 40°N. An opposite-sign anomaly also occurs between 40 and 30°N in the mid- to upper troposphere, a region of positive

(negative) height anomalies in the linear correlation (composite) analysis. Elsewhere anomaly patterns are same-sign and similarly-positioned although the NCEP/NCAR-

Comp results suggest generally weaker signals north of 30°S throughout the Southern low latitudes and Northern Hemisphere.

B.4. Zonal Mean Meridional Wind Anomalies

Both methods suggest fairly consistent centers of action for the zonal mean meridional wind anomalies (Figure 28), excepting the Southern mid-latitudes in which the NCEP/NCAR-Comp results show neutral anomalies inconsistent with the strongly negative anomalies associated with NCEP/NCAR-LC. Although the composite results are highly consistent with linear correlation over the low latitudes, the NCEP/NCAR-Comp anomalies are much weaker and mostly neutral in the middle and upper troposphere outside the low latitudes.

B.5. Zonal Mean Omega Anomalies

The zonal mean omega anomalies (Figure 29) are mostly consistent between methods. The biggest difference may be over the Southern low latitudes, with the composite analysis suggesting mostly neutral (rather than positive) anomalies away from

the surface. Although the Northern Hemisphere anomalies feature similar centers of action between analyses, the NCEP/NCAR-Comp shows weaker anomalies throughout the middle and especially upper troposphere outside the low latitudes.

Zonal Mean Summary

Overall the NCEP/NCAR-LC and NCEP/NCAR-Comp results show consistent zonal mean anomaly patterns except throughout the Southern mid-latitudes. In this region the NCEP/NCAR-Comp results suggest warm (rather than neutral) air temperature anomalies above 900 mb, strongly negative (rather than neutral) geopotential height anomalies centered around 60°S from the surface through mid-troposphere, strong positive (rather than neutral) zonal mean zonal wind anomalies and mostly neutral (rather than negative) zonal mean meridional wind anomalies. In the composite analysis mid- to upper troposphere zonal mean circulation anomalies, both meridional and zonal winds as well as omega anomalies, are generally much weaker than the NCEP/NCAR-LC signals.

Figure 13: Comparison of NCEP/NCAR annual mean SSTAs with the annual AMO index using NCEP/NCAR linear correlation (top) and composite (bottom) methods

Figure 14: NCEP/NCAR linear correlation of SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

Figure 15: NCEP/NCAR composite analysis of SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

Figure 16: Comparison of NCEP/NCAR 1000 mb annual mean geopotential height anomalies with the annual mean AMO index using linear correlation (top) and composite (bottom) methods

Figure 17: NCEP/NCAR linear correlation of 1000 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom) 80

Figure 18: NCEP/NCAR composite analysis of 1000 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom)

Figure 19: Comparison of NCEP/NCAR 500 mb annual mean geopotential height anomalies with the annual mean AMO index using linear correlation (top) and composite (bottom) methods

Figure 20: NCEP/NCAR linear correlation of 500 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom)

Figure 21: NCEP/NCAR composite analysis of 500 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom)

Figure 22: Comparison of NCEP/NCAR 200 mb annual mean geopotential height anomalies with the annual mean AMO index using linear correlation (top) and composite (bottom) methods

Figure 23: NCEP/NCAR linear correlation of 200 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom)

Figure 24: NCEP/NCAR composite analysis of 200 mb geopotential height anomalies with the AMO index for annual mean (top), JJA (middle) and DJF (bottom)

Figure 25: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean air temperature anomalies to the annual mean AMO index

Figure 26: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean geopotential height anomalies to the annual mean AMO index

Figure 27: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean zonal wind anomalies to the annual mean AMO index

Figure 28: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean meridional wind anomalies to the annual mean AMO index

Figure 29: Linear correlation (top) and composite analysis (bottom) of annual mean zonal mean omega anomalies to the annual mean AMO index

CHAPTER 5: AMO EXTREME PHASE COMPARISON OF DATASETS

NCEP/NCAR Composite and 20CR2c-Composite Analyses

This section examines the composite analyses of the NCEP/NCAR

(NCEP/NCAR-Comp) and 20CR2c (20CR2c-Curr) datasets for the most recent AMO cycle. The 10 highest AMO index years are 1998, 2001-2007, 2010-2011, and the 10 lowest AMO index years are 1971-1977, 1985-1986 and 1993. Note the discussion of seasonal results is arranged differently in this section, with the two reanalysis datasets compared within each season. The seasonal analysis of each method can be found elsewhere: see Chapter 4 for the NCEP/NCAR-Comp JJA and DJF results and Chapter 6 for the 20CR2c-Curr JJA and DJF results.

A. Sea Surface Temperature Anomalies (SSTAs), annual mean and seasonal

Annual Mean

The two reanalysis datasets feature highly consistent annual mean SSTA patterns

(Figure 30), with the exception of opposite-sign anomalies throughout the Southern high latitudes and regionally in the western Arctic. Pan-Arctic warm SSTAs are consistent between datasets except for regional opposite sign cool SSTAs in the 20CR2c analysis in the western Arctic north of North America. Anomalous warming in the three extrapolar 93

oceans is strongest in the North Atlantic, except for near-neutral anomalies in the western basin centered around 30°N.

The Pacific patterns are consistent, with warm SSTAs in the western basin extending to the Northern mid-latitudes just beyond the dateline, throughout the tropical

South Pacific and east of the dateline in the mid-latitude South Pacific, with weakly cool anomalies in the eastern basins (in the mid-latitudes of both hemispheres and the

Southern low latitudes). Both analyses also suggest warm SSTAs in the Indian Ocean north of 20°S that are strongest over the equator.

In the Southern Hemisphere both datasets show cool SSTAs in a zonal band centered around 60°S, but these are much stronger and extensive in the 20CR2c composite. In contrast to the NCEP/NCAR-associated warm SSTAs off coastal

Antarctica, the 20CR2c results suggest opposite-sign cool SSTAs throughout the

Southern high latitudes except for a localized region around the Antarctic Peninsula.

Seasonal

Overall, both NCEP/NCAR (Figure 31) and 20CR2c (Figure 32) seasonal analyses are very consistent with annual mean patterns. Consistent seasonal differences include over the Northern high latitudes, with neutral (rather than warm) SSTAs during

JJA. During DJF both datasets suggest stronger and more extensive cool SSTAs over the mid-latitude eastern Pacific basins in both hemispheres and warm equatorial (rather than off-equatorial) SSTAs in the Pacific east of the dateline. The NCEP/NCAR-Comp

uniquely suggests opposite-sign (cool) SSTAs during DJF over the Southern high latitudes.

NCEP/NCAR-Comp and 20CR2c-Curr, JJA

With the exception of the Northern high latitudes, the JJA patterns are more consistent with (and nearly identical) to the AMO annual mean patterns. Over the Arctic, however, the JJA analyses show neutral or even opposite-sign weakly cool SSTAs rather than the strongly warm annual mean SSTAs.

NCEP/NCAR-Comp and 20CR2c-Curr, DJF

Among the biggest inconsistencies between the DJF and annual mean patterns are the Pacific patterns. The most notable difference is in the low latitude South Pacific east of the dateline, a region of warm SSTAs in the annual mean results that is shifted northward to become equatorially-based during DJF, with mainly neutral to cool (rather than weakly warm) SSTAs in the subtropical South Pacific. Two regions feature opposite-sign anomalies between DJF and JJA that tend to cancel in the annual mean results, which shows weak-to-neutral anomalies. One region is the mid-latitude eastern

South Pacific off southwest South America. Although cool SSTAs in both mid-latitude eastern Pacific basins are generally stronger and more extensive during DJF, the South

Pacific pattern is the only one of these to feature opposite-sign weakly warm SSTAs in

JJA. The second region of seasonal opposite-sign anomalies involves skin temperatures on land rather than SSTAs, with northernmost Eurasia strong cool anomalies during DJF

largely cancelling strong warm anomalies in JJA to produce near-neutral anomalies in the annual mean results. The Indian Ocean features minor differences in position and relative strength of signal, with stronger warm anomalies during DJF shifted southward from equatorial-based to the tropical southern Indian Ocean. Unlike the pan-Arctic warm

SSTAs in the annual mean and NCEP/NCAR DJF analysis, the 20CR2c-Curr DJF results uniquely suggest opposite-sign cool SSTAs in the central and western Arctic. In contrast, the NCEP/NCAR-Comp DJF analysis uniquely suggests opposite-sign cool SSTAs over the Southern high latitudes.

B. Geopotential Height Anomalies 1000 mb, annual mean and seasonal

Annual Mean

Geopotential height anomalies at 1000 mb are consistent between datasets (Figure

33), with no opposite-sign anomalies. Both datasets feature the weakest signals over the tropics, with nearly all centers of action outside the North Atlantic in the mid- to high latitudes. The Southern Hemisphere features slightly more extensive strong anomalies and with a more zonal orientation than the Northern Hemisphere.

Only a few regional differences occur, located over the high latitudes and mid- latitude Eurasia. Over the high latitudes the 20CR2c analysis suggests a shift to less positive height anomalies. In the Northern high latitudes there are more extensive negative (rather than neutral) anomalies over the eastern Arctic and mostly neutral (rather than positive) anomalies over the western Arctic, and over eastern Antarctica neutral

(rather than positive) anomalies from 0 to 60°E. The position of a strong teleconnection 96

over Eurasia is inconsistent, with the NCEP/NCAR (20CR2c) composite showing positive height anomalies over central (northwest) Eurasia. The 20CR2c also shifts the anomaly sign to less positive over the African continent, with weakly negative (rather than weakly positive) height anomalies.

Consistent between datasets are negative height anomalies over the North

Atlantic, and centered over the Bering Strait from northwest North America to northeast

Eurasia, although these extend further throughout the eastern Arctic in the 20CR2c results. The positive height anomalies over the western Arctic are spatially limited in the

20CR2c composite but both datasets show positive height anomalies over much of

Greenland. The Pacific patterns in both datasets are consistent: positive height anomalies are weaker in the North Pacific (from the subtopics over Hawaii and into the mid- latitudes to the dateline) and stronger and more zonally oriented throughout the mid- latitude South Pacific between 30 and 50°S. This South Pacific pattern is a component of a larger group of positive height anomaly patterns in a circumglobal band at these latitudes, including over the South Atlantic and South America and, more weakly, the

Indian Ocean. Both datasets also feature a zonal partitioning of anomaly sign around

50°S, with negative height anomalies poleward with two exceptions: over eastern

Antarctica from 0 to 60°E, with positive (neutral) height anomalies in the NCEP/NCAR

(20CR2c) analysis, and near the Antarctic Peninsula, with the South Atlantic positive height anomalies extending further poleward in this region.

The 20CR2c results mostly reproduce the three diagonally tilted patterns in the

NCEP/NCAR analyses. The positive heights over the western Arctic, though spatially

limited primarily to Greenland, still suggest a diagonal orientation, as does the negative height anomaly pattern around the Bering Strait extending from the mid-latitude eastern

North Pacific. The tilted cluster of positive height anomalies centered around southern

South America is tilted in the 20CR2c composite but differently, with the signals east

(west) of South America characterized by a greater meridional (zonal) orientation than the NCEP/NCAR composite.

Seasonal

Overall, the 1000 mb geopotential height anomalies during JJA more closely resemble the annual mean patterns in both the seasonal NCEP/NCAR (Figure 34) and

20CR2c (Figure 35) composites, with the DJF patterns featuring opposite-sign positive

(negative) height anomalies over the eastern Arctic (mid-latitude North Pacific east of the dateline) and increasingly diagonally-tilted positive height anomalies throughout the

Southern mid-latitudes.

NCEP/NCAR-Comp and 20CR2c-Curr, JJA

The composite analyses suggest the JJA-associated 1000 mb geopotential height anomalies are highly consistent with the annual mean, with no opposite-sign anomalies.

However, the North Atlantic pattern is somewhat modified, with neutral (rather than negative) height anomalies throughout the central basin. Differences in the relative strength of anomalies in the Northern Hemisphere include stronger mid-latitude positive height anomalies over the central North Pacific and eastern Asia. In the high latitudes, the

JJA analyses are amplifications of the annual mean: stronger negative height anomalies over the eastern Arctic and stronger positive heights over Greenland (east of Greenland) in the NCEP/NCAR (20CR2c) analyses.

NCEP/NCAR-Comp and 20CR2c-Curr, DJF

The 1000 mb geopotential height anomaly patterns during DJF are similar to the annual mean in the Southern Hemisphere but less so in the Northern Hemisphere, with opposite-sign anomalies in the mid-latitude North Pacific and eastern Arctic. The analyses consistently feature three centers of negative height anomalies in the Northern mid-latitudes: over the North Atlantic (a seasonal DJF pattern that more closely resembles the annual mean), over the Pacific east of the dateline (consistent with a deepened anomalous Aleutian Low and a region of opposite-sign positive height anomalies in the JJA and annual mean results) and over northwest Eurasia. Although slightly weaker in the 20CR2c results, the DJF analyses feature the strongest positive pan-Arctic height anomalies. These extend over the eastern Arctic, a region of strongly negative height anomalies in JJA that cancels in the annual mean to produce neutral to weakly negative height anomalies. The tropical Pacific signals, though still weak, are stronger than the JJA and annual mean results, with weakly positive anomalies in the western North Pacific and, in the 20CR2c (NCEP/NCAR) composites weakly negative

(neutral) anomalies in the equatorial eastern Pacific.

The Southern Hemisphere mid-latitude positive height anomaly patterns are strongest and most extensive in DJF, especially over the southern South Atlantic and

Indian Oceans. Poleward of about 50°S are negative height anomalies, with the only exception the opposite-sign positive height anomalies over eastern Antarctica in the

NCEP/NCAR composite. There is a more pronounced diagonal tilt of height anomaly patterns during DJF, including the patterns previously identified using linear correlation

(negative height anomalies over the North Atlantic and near the Bering Strait and positive height anomalies centered around South America) and another unique to this season

(positive height anomalies over the mid-latitude South Pacific east of the dateline).

C. Geopotential Height Anomalies 500 mb, annual mean and seasonal

Annual Mean

At 500 mb both datasets (Figure 36) feature consistent patterns outside the

Southern high latitudes, with stronger signals north of 50°S in the NCEP/NCAR composite. The only opposite-sign anomalies are the 20CR2c-associated negative (rather than positive) height anomalies over Antarctica, an extension of the pattern over the

Southern Ocean common to both datasets. A regional difference is the 20CR2c- associated negative (rather than neutral) height anomaly over the mid-latitude eastern

North Pacific, the only negative height anomaly north of 50°S in either analysis. Like at the surface, most of the centers of action are located outside the low latitudes, with the exception of positive height anomalies over the subtropical North Pacific over the

Hawaiian Islands. Northern Hemisphere positive height anomalies are consistent in the mid-latitudes over North America and the central North Pacific. Consistent with the surface results, the 20CR2c composite does not reproduce the positive height anomalies 100

over central Eurasia. The strongest signal in both datasets is in the Northern high latitudes, with the center of action over the western Arctic remaining mostly diagonally tilted. Of the three NCEP/NCAR-associated positive height anomaly patterns in the

Southern mid-latitudes, the South Pacific pattern east of the dateline is the most consistent, with the South Atlantic pattern shifted poleward to the Antarctic Peninsula and the Indian Ocean signal much weaker in the 20CR2c composite.

Seasonal

NCEP/NCAR-Comp and 20CR2c-Curr, JJA

The 500 mb JJA geopotential height anomaly patterns in the NCEP/NCAR

(Figure 37) and 20CR2c (Figure 38) seasonal composites are highly consistent with the annual mean patterns. Both datasets show JJA results with mostly positive (negative) anomalies equatorward (poleward) of about 50°S and strengthened Northern mid-latitude positive height anomaly patterns, especially over the central Pacific.

NCEP/NCAR-Comp and 20CR2c-Curr, DJF

The largest discrepancies in the composite DJF analyses relative to the annual mean are in the Northern mid- to high latitudes, consistent with the 1000 mb results. In the Northern mid-latitudes during DJF there are three centers of negative height anomalies that are weakly positive/near neutral in the annual mean (positive in JJA) results: over Europe, the North Pacific east of the dateline and over the North Atlantic.

The Northern high latitude response is strongest in DJF, with positive height anomalies 101

throughout the Arctic, rather than more regionally confined to Greenland and the western

Arctic during JJA. The Southern Hemisphere patterns are less variable. Positive height anomalies throughout the mid-latitudes north of 50°S are consistent between analyses, with the main centers of action shifting seasonally (unlike the Northern Hemisphere seasonal oscillations between anomaly sign) and located over each of the ocean basins: the South Atlantic, Indian (around 90°E) and South Pacific Ocean east of the dateline.

The Southern high latitudes feature relatively stronger (NCEP/NCAR and 20CR2c) and more extensive (NCEP/NCAR) negative height anomalies during DJF.

D. Geopotential Height Anomalies 200 mb, annual mean and seasonal

Annual Mean

The biggest discrepancies between the NCEP/NCAR and 20CR2c composite analyses are at 200 mb (Figure 39). North of 30°S regions of positive height anomalies are generally consistent, including over the subtropical North Atlantic and Middle East and in the Northern mid-latitudes over North America and the central North Pacific. The localized North American pattern features relatively weak but consistent positive height at anomalies at the surface that generally increase in intensity and extent (less so in the

20CR2c composite) with increasing altitude. Over the Northern high latitudes, all analyses show pan-Arctic positive height anomalies that are strongest over the western

Arctic, although the strongest 20CR2c signal has reduced spatial extent, from western

Greenland to northeast Canada.

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The Southern mid- to high latitudes are the least consistent between analyses, with opposite-sign anomalies poleward of 50°S. Of the three strong Southern mid- latitude positive height anomaly patterns in the NCEP/NCAR-Comp results – over the

South Atlantic, Pacific and Indian Oceans - the only one consistent with the 20CR2c analysis is a much weaker and less extensive South Pacific pattern. Instead the 20CR2c results suggest a zonal partitioning of anomaly sign in the Southern mid-latitudes, with positive (opposite-sign negative rather than weakly positive) heights equatorward

(poleward).

Seasonal

NCEP/NCAR-Comp and 20CR2c-Curr, JJA

At 200 mb both the NCEP/NCAR (Figure 40) and 20CR2c (Figure 41) seasonal composites show JJA-associated positive geopotential height anomalies north of about

50°S, with many of the same centers of action. The 20CR2c-Curr JJA results uniquely suggest a localized opposite-sign negative height anomaly over the eastern Arctic around

60°E. Northern mid-latitude positive height anomalies are strengthened during JJA with particularly consistent patterns over the central North Pacific and North America.

Consistent centers of action at other latitudes include positive height anomalies over the subtropical North Atlantic and in the western Arctic over Greenland. Over the Southern latitudes the JJA results are very consistent with the annual mean within each dataset.

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NCEP/NCAR-Comp and 20CR2c-Curr, DJF

The 200 mb DJF geopotential height anomalies show increased involvement of the low latitudes, with more centers of action and a higher degree of symmetry about the equator, and Northern high latitudes, with stronger pan-Arctic positive height anomalies.

The Northern mid-latitudes feature three negative height anomaly centers of action during DJF that are regions of opposite-sign positive height anomalies during JJA, consistent with the mid-troposphere DJF results: the strongest is over the North Pacific east of the dateline and the other two over the North Atlantic and northern Europe. Over the Southern mid-latitudes are three positive height anomaly centers of action that are more distinct and well-developed during DJF (especially in the 20CR2c-Curr DJF analysis compared to the annual mean): over the South Pacific east of the dateline, South

Atlantic and southern Indian Ocean. Although both composite analyses suggest negative height anomalies throughout the Southern high latitudes during DJF, the NCEP/NCAR-

Comp DJF patterns are unique in being opposite-sign anomalies compared to the annual mean anomalies.

Annual Mean Overall Summary

Outside the high latitudes, the SSTA patterns are very consistent between datasets. The 20CR2c analysis suggests opposite-sign cool SSTAs throughout the

Southern high latitudes and a regional opposite-sign cool SSTA pattern in the western

Arctic north of North America.

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Tropospheric differences increase with altitude. The annual mean 1000 mb geopotential height anomalies are mostly consistent between datasets, with no opposite- sign anomalies. Only a few regional differences exist, over the high latitudes (regions of the Arctic and east Antarctica) and mid-latitude Eurasia, with no opposite-sign anomalies. Both datasets show the weakest signals over the tropics, with nearly all centers of action outside the North Atlantic in the mid- to high latitudes. Strong AMO teleconnection patterns are a bit more extensive and much more zonally oriented in the

Southern Hemisphere. The 20CR2c reproduces to some degree the three diagonally tilted patterns identified in the NCEP/NCAR analyses: the positive height anomalies over the western Arctic (with limited spatial extent in the 20CR2c results, confined to Greenland), negative height anomalies around the Bering Strait extending from the mid-latitude eastern North Pacific, and positive height anomalies centered around southern South

America whose tilt is modified with a more meridional (zonal) orientation in the patterns east (west) of South America.

The annual mean AMO-associated 500 mb geopotential height anomalies are also fairly consistent between datasets outside the Southern high latitudes. North of 50°S the

20CR2c results are characterized by relatively weaker anomaly signals but similar centers of action. Throughout the Southern high latitudes the 20CR2c analysis features opposite- sign negative height anomalies.

The upper troposphere comparisons are least consistent. The 20CR2c composite suggests opposite-sign negative (rather than positive) anomalies throughout the Southern high latitudes and positive height anomalies that are strongest (rather than weakest) over

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the low latitudes and weakest (rather than strongest) over the mid- to high latitudes. The orientation of the patterns also differs somewhat, with 20CR2c-associated patterns generally more zonal.

There remain several consistent vertical structures in addition to the robust North

Atlantic baroclinicity. Both analyses feature barotropic positive height anomaly structures over the western Arctic (though the 20CR2c anomalies are weak at 1000 mb), the subtropical North Pacific near the Hawaiian Islands, the mid-latitude central North

Pacific and the mid-latitude South Pacific east of the dateline. The 20CR2c results are also mostly consistent with the barotropicity over the mid-latitude southern Indian Ocean, although the 1000 mb anomalies are weak. The three remaining NCEP/NCAR-associated barotropic regions are less consistent with the 20CR2c patterns: over the low latitude western North Pacific (neutral 1000 mb anomalies), low latitude eastern South Pacific

(shifted to the mid-latitudes with a shallow vertical signal) and mid-latitude South

Atlantic (shallow vertical signal). Both analyses generate a unique putative barotropic structure. The NCEP/NCAR-Comp suggests a localized barotropic region of positive height anomalies over eastern Antarctica between 0 and 60°E whereas the 20CR2c composite suggests a barotropic structure of negative height anomalies throughout most of the Southern high latitudes. Neither pattern is associated with the NCEP/NCAR-LC results, which feature shallow vertical signals over the Southern high latitudes.

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Figure 30: Composite analysis of annual SSTAs during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) datasets

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Figure 31: NCEP/NCAR composite analysis of SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 108

Figure 32: 20CR2c composite analysis of SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

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Figure 33: Composite analysis of annual 1000 mb geopotential height anomalies during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) reanalysis datasets 110

Figure 34: NCEP/NCAR composite analysis of 1000 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 111

Figure 35: 20CR2c composite analysis of 1000 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 112

Figure 36: Composite analysis of annual 500 mb geopotential height anomalies during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) reanalysis datasets 113

Figure 37: NCEP/NCAR composite analysis of 500 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

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Figure 38: 20CR2c composite analysis of 500 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

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Figure 39: Composite analysis of annual 200 mb geopotential height anomalies during the most recent AMO cycle using the NCEP/NCAR (top) and 20CR2c (bottom) reanalysis datasets 116

Figure 40: NCEP/NCAR composite analysis of 200 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 117

Figure 41: 20CR2c composite analysis of 200 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 118

CHAPTER 6: AMO EXTREME PHASE COMPARISON OF AMO CYCLES

20CR2c Composite Analyses of two cycles of AMO warm (1995-2011 and 1930-

1962) and cool (1960s-1994 v 1900-1930s) phases

This section examines composite analyses of the 20CR2c reanalysis dataset for two different time periods corresponding to two consecutive AMO cycles. The 20CR2c-

Curr composite compares the post-1995 AMO warm phase (with the 10 highest AMO index years 1998, 2001-2007, 2010-2011) and the 1960s-1994 cool phase (with the 10 lowest AMO index years 1971-1977, 1985-1986 and 1993). A composite analysis of the previous AMO cycle, 20CR2c-Prev, compares the 1930-1962 warm phase (10 highest index years 1937-1938, 1943-1945, 1951-1953, 1955 and 1960) and 1900-1930 cool phase 10 lowest AMO index years of 1904-1905, 1910, 1912-1914, 1917, 1919-1920 and

1922.)

A. Sea Surface Temperature Anomalies (SSTAs), annual mean and seasonal

Annual Mean

The SSTA patterns of both AMO cycles are broadly consistent outside the opposite-sign anomalies poleward of about 40°S (Figure 42). Although the 20CR2c-Curr composite suggests more extensive cool SSTAs throughout the Southern high latitudes

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and much of the Southern mid-latitudes, the 20CR2c-Prev analysis shows more regions of anomalous cooling north of 40°S. Regional opposite-sign anomaly differences include

20CR2c-Prev cool SSTAs over the low latitude southern Indian Ocean and equatorial

Pacific east of the dateline. Two other Northern Hemisphere opposite-sign regions occur due to cool surface temperature anomalies positioned further east from the current to previous AMO cycle: from the mid-latitude eastern North Pacific to western North

America and in the western Arctic from north of North America to over Greenland. Over the Pacific the warm SSTA pattern is more hemispherically symmetric in the 20CR2c-

Prev composite, with the North Pacific component extending throughout the mid- latitudes and the Southern low latitude anomalies displaced somewhat poleward by the cool equatorial Pacific SSTA pattern. The resulting pattern more closely resembles a canonical La Niña-like horseshoe shape.

Seasonal

20CR2c-Curr, JJA and DJF

The 20CR2c-Curr seasonal results (Figure 43) show JJA patterns highly consistent with the annual mean patterns. The biggest difference is the JJA-associated weakening of warm SSTAs over the Northern high latitudes, with near-neutral (rather than strongly warm) SSTAs especially over the central and eastern Arctic.

The DJF analysis also resembles the annual mean, except for a Northern high latitude anomaly shift to opposite-sign cool temperature anomalies. Although not opposite-signed, the tropical Pacific features a modified anomaly pattern east of the 120

dateline, with warm SSTAs in the tropical South Pacific increasing in strength and extent to form an equatorially-centered pattern during DJF.

20CR2c-Prev, JJA and DJF

The 20CR2c-Prev seasonal analyses (Figure 44) also suggest JJA patterns more closely resemble the annual mean, with no opposite-sign anomalies. Two slight regional differences include strong (rather than weak) warm SSTAs in the western North Atlantic around 30°N, forming a more homogeneous basin-wide SSTA pattern during JJA. In the

Southern Hemisphere the JJA analysis restricts cool SSTAs to west of the Antarctic

Peninsula around 120°W rather than throughout the 60°S latitude belt, a region of mostly neutral anomalies during JJA.

The 20CR2c-Prev DJF patterns also resemble the annual mean, but with more extensive opposite-sign cool (rather than warm) SSTAs over the western Arctic to the dateline. In contrast to the JJA results, the 60°S latitude belt features stronger and more extensive cool SSTAs during DJF.

B. Geopotential Height Anomalies 1000 mb, annual mean and seasonal

Annual Mean

The 1000 mb geopotential height anomalies are somewhat consistent between

AMO cycles (Figure 45), but with opposite-sign anomalies in the eastern Arctic and

Southern mid-latitudes and increased low latitude involvement in the 20CR2c-Prev analysis. The 20CR2c-Prev analysis shows pan-Arctic positive height anomalies 121

extending over the eastern Arctic to generate opposite-sign anomalies compared to the

20CR2c-Curr negative height anomalies. The less extensive western Arctic pattern, with neutral anomalies over southern Greenland and northeast North America, lacks the diagonal tilt observed in the current AMO cycle.

Opposite-sign anomalies also occur throughout the Southern mid-latitudes, with the 20CR2c-Prev analysis showing more extensive, less zonally oriented negative height anomalies that extend meridionally to weaken and/or displace equatorward the three

Southern mid-latitude positive height anomaly patterns in other analyses: over the South

Pacific east of the dateline and the mid-latitude South Atlantic and Indian Oceans. The first two of these three patterns nevertheless remain mostly consistent between AMO cycles, with the 20CR2c-Prev composite showing positive height anomalies over the mid-latitude South Pacific east of the dateline (though positioned differently and part of a larger pattern connected to low latitude centers of action inconsistent with the 20CR2c-

Curr results) and mid-latitude South Atlantic. Both regions, as well as the mid-latitude southern Indian Ocean, are characterized by weaker and less extensive positive height anomalies in the 20CR2c-Prev analysis.

Robust patterns include consistent positive height anomalies in the subtropical

North Pacific near the Hawaiian Islands, although this is part of a larger, differently- shaped pattern in the 20CR2c-Prev results that connects to low latitude centers of action inconsistent with the other analyses. The other consistent patterns are strongly negative height anomaly patterns: over the North Atlantic (and antisymmetric positive height anomalies in the mid-latitude South Atlantic), Middle East and near the Bering Strait.

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However, the Bering Strait-associated pattern differs in three ways from those in the current AMO cycle. The anomalies are less extensive and do not project over the mid- latitude eastern North Pacific, which instead features a strong opposite-sign positive height anomaly. Second, the anomaly pattern is not contiguous from North America to

Asia, with the pattern bisected by an opposite-sign positive height anomaly. Third, although the discontinuous negative heights over northeast Asia are diagonally tilted, the

Bering Strait/North America pattern is zonally oriented, lacking the diagonal tilt suggested by current AMO cycle analyses. The 20CR2c-Prev composite also suggests localized opposite-sign negative height anomalies off southwest South America that disrupt the diagonally tilted group of patterns centered over southern South America suggested by current AMO cycle analyses.

Seasonal

20CR2c-Curr, JJA and DJF

The 20CR2c-Curr seasonal results (Figure 46) suggest JJA is highly consistent with the annual mean, with generally stronger positive height anomalies throughout the mid-latitudes, especially in the Northern Hemisphere including over the central Pacific, much of Eurasia and western North America. During DJF the biggest difference is over the Arctic, with opposite-sign positive (rather than negative) height anomalies compared to the annual mean. Stronger more extensive negative height anomalies over the mid- latitude North Pacific replace the mid-latitude positive height anomalies. Two low latitude Pacific patterns are unique to DJF, with neutral annual mean anomalies: the 123

positive height anomalies over the western North Pacific and weakly negative height anomalies over the equatorial Pacific east of the dateline.

20CR2c-Prev, JJA and DJF

The 20CR2c-Prev JJA 1000 mb height anomalies (Figure 47) are more consistent with the annual mean, with the exception of the Southern mid- to high latitudes. Although the JJA results show a regional opposite-sign anomaly in the Northern high latitudes, with negative (rather than positive) height anomalies south of Greenland, the most extensive differences are in the Southern mid- to high latitudes. Negative height anomalies are much less extensive during JJA, with patterns generally restricted to western Antarctica (centered around the Antarctic Peninsula) and two mid-latitude regions: over the southern Indian Ocean and central South Pacific. These latter two patterns are part of an alternating zonal pattern of negative and positive heights over the mid-latitude South Pacific during JJA in which the positive height anomalies are opposite-sign the annual mean patterns centered near 60°S: over the South Pacific/

Southern Ocean east of the dateline and poleward of Australia.

The 20CR2c-Prev DJF results feature more extensive negative height anomalies throughout the Southern mid-latitudes that generate two regions of opposite-sign anomalies. One is over the South Pacific, with the annual mean positive height anomalies displaced equatorward to the tropics. The other is over the South Atlantic, with a negative

(rather) than positive height anomaly in the central to eastern basin. Over the Northern high latitudes, pan-Arctic positive height anomalies are stronger during DJF and extend

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further into the mid-latitudes, especially over the northern North Atlantic and central

Eurasia.

Seasonal Summary

Overall, unlike the JJA-dominant annual mean patterns in the 20CR2c-Curr analysis, the 20CR2c-Prev results suggest more balanced contributions from JJA and DJF in the annual mean patterns, although these differ by hemisphere with JJA (DJF) patterns more closely resembling the Northern (Southern) Hemisphere annual mean results. Both analyses, especially the 20CR2c-Curr results, suggest increased low latitude involvement during DJF and less during JJA.

C. Geopotential Height Anomalies 500 mb, annual mean and seasonal

Annual Mean

Both AMO cycles suggest 500 mb geopotential height anomalies (Figure 48) are weakest throughout the low latitudes. The biggest discrepancy is poleward of 50°S.

Although both analyses show negative surface height anomalies, at 500 mb these remain negative in the 20CR2c-Curr results but change to mostly opposite-sign positive anomalies in the 20CR2c-Prev analysis with negative patterns restricted to two regions: around 60°S poleward of Africa and over the Southern Ocean west of the Antarctic

Peninsula. These 20CR2c-Prev Southern Hemisphere patterns more closely resemble the

NCEP/NCAR analyses, rather than the strict zonal orientation and partitioning of anomaly sign in the 20CR2c-Curr composite, and account for the increased global 125

hemispheric symmetry of mid-troposphere height anomaly patterns in the 20CR2c-Prev analysis.

Of four 20CR2c-Prev centers of action in the Northern mid-latitudes, all are vertical extensions of surface anomaly patterns and none entirely consistent with the

20CR2c-Curr results. The most extensive is the positive (rather than weaker near-neutral) height anomaly pattern over the North Atlantic between 40-60°N. Two are opposite-sign anomalies that are vertical extensions of opposite-sign surface anomalies compared to the

20CR2c-Curr results: the negative (rather than positive) height anomalies over western

North America and positive (rather than negative) anomalies to the west over the mid- latitude eastern North Pacific. The fourth, negative height anomalies over the mid- latitude western North Pacific, is a region of near-neutral anomalies in the current AMO cycle.

The strongest, most extensive positive height anomalies are over the Northern high latitudes in both analyses, but the 20CR2-Prev pattern somewhat reverses regions of high and low anomaly strength, with stronger signals in the central and eastern (rather than western) Arctic. The near-neutral height anomalies over Greenland ensure the western Arctic pattern remains without the diagonal tilt characteristic of the current AMO cycle.

The 20CR2c-Prev analysis features three Southern mid-latitude positive height anomaly signals mostly consistent with the 20CR2c-Curr results. The most robust is over the South Pacific east of the dateline, consistent among all four analyses. The strongest in the 20CR2c-Prev is over the South Atlantic, and the weakest in both 20CR2c composites

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is over the mid-latitude Indian Ocean, another pattern that is shifted eastward (from 60 to

90°E) in the 20CR2c-Prev analysis.

Seasonal

20CR2c-Curr, JJA and DJF

Like the surface, the 20CR2c-Curr seasonal results (Figure 49) suggest the JJA patterns are most consistent with the annual mean at 500 mb, with a localized opposite- sign negative height anomaly over the eastern Arctic at 60°E. The biggest seasonal changes are over the Northern mid-latitudes, with positive height anomalies during JJA that are mostly consistent but amplified versions of the annual mean patterns over North

America and the central North Pacific. The Southern Hemisphere JJA patterns are nearly identical to the annual mean patterns.

The 20CR2c-Curr DJF patterns closely resemble the annual mean outside the

Northern mid-latitudes. There are no opposite-sign anomalies but several Northern mid- latitude DJF patterns are stronger and more extensive than the annual mean: the negative

(rather than neutral) height anomalies over the North Atlantic, much of the mid-latitude

North Pacific and western Europe. More extensive negative height anomalies over the

Southern high latitudes displace northward the positive height anomaly that is over the

Antarctic Peninsula in the annual mean results.

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20CR2c-Prev, JJA and DJF

The 20CR2c-Prev seasonal results (Figure 50) suggest the JJA (DJF) patterns contribute most to the annual mean patterns in the Northern (Southern) Hemisphere. The

JJA-associated patterns suggest two opposite-sign anomaly regions over the Northern high latitudes, with negative geopotential height anomalies over the much of the eastern

Arctic (and northernmost Eurasia) and western Arctic centered over Greenland. The

Southern Hemisphere centers of action are highly consistent with the annual mean results but with stronger, more extensive positive height anomalies over the Southern mid- to high latitudes.

The 20CR2c-Prev DJF results show much more extensive negative height anomalies throughout the Southern high latitudes, with the strongest annual mean positive height anomalies over the mid-latitude South Pacific and South Atlantic regions of near-neutral anomalies during DJF, though there are positive height centers of action over the mid-latitude South Pacific (west, rather than east, of the dateline), southern

Indian Ocean and southern South America that are mostly consistent with the annual mean results. Positive height anomalies are more extensive over the Northern high latitudes during DJF and the annual mean mid-latitude anomalies are stronger, especially over the Pacific with more extensive negative (positive) height anomalies over the western (eastern) North Pacific.

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D. Geopotential Height Anomalies 200 mb, annual mean and seasonal

Annual Mean

The 200 mb geopotential height anomaly patterns (Figure 51) feature large differences between the two AMO cycles at all latitudes. Throughout the high latitudes, the 20CR2c-Prev shows opposite-sign negative height anomalies over the Northern

Hemisphere and opposite-sign positive height anomalies poleward of 50°S. Similar to the mid-troposphere results, the 200 mb 20CR2c-Prev patterns are inconsistent with the

20CR2-Curr zonal partitioning of negative height anomalies poleward of about 50°S, with positive height anomalies over Antarctica more closely resembling the

NCEP/NCAR analyses.

The 20CR2c-Prev results also suggest positive height anomalies are stronger in the low latitudes and weaker in the mid-latitudes, opposite the 20CR2c-Curr results, with global height anomaly patterns lacking the extreme zonal orientation of the 20CR2c-Curr analysis. The 20CR2c-Prev results suggest two opposite-sign anomaly regions over the mid-latitude North Pacific inconsistent with other analyses, with positive (rather than neutral to negative) height anomalies over the mid-latitude eastern North Pacific and negative (rather than near-neutral) height anomalies over the mid-latitude northwest

Pacific extending into northeast Eurasia. The latter pattern is a component of a larger center of action with negative height anomalies extending throughout the Arctic (neutral over the central Arctic), opposite-sign anomalies unique to the 20CR2c-Prev analysis compared to the other analyses.

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Though more localized and distinct in the 20CR2c-Prev results, both analyses feature several similar regions of positive height anomalies. These include over the subtropical North Atlantic with eastward extensions over Africa and the Middle East around 30°N. A few Pacific patterns are also consistent with the 20CR2c-Prev suggesting positive height anomalies situated near the Hawaiian Islands (though shifted north and east) and over the equatorial western Pacific and mid-latitude South Pacific east of the dateline. The 20CR2c-Prev composite further suggests two additional positive height anomaly patterns in the Southern mid-latitudes that are relatively weak in the 20CR2c-

Curr results: over the South Atlantic and southern Indian Oceans.

Seasonal

20CR2c-Curr, JJA and DJF

The 20CR2c-Curr JJA results (Figure 52) more closely resemble the annual mean results outside the low latitudes, with the only opposite-sign anomaly a localized region of negative heights over the eastern Arctic near 60°E. Unlike the annual mean results, with zonally-oriented homogenous positive height anomalies throughout the low latitudes, the JJA results show strengthened positive height anomaly centers of action with less zonal orientation and increased involvement of the Northern mid-latitudes.

The 20CR2c-Curr DJF results are more consistent with the annual mean throughout the low latitudes, with zonally-oriented, equatorially-centered positive height anomalies throughout most of the global low latitudes. The biggest difference relative to the annual mean is over the Northern mid-latitudes, with three negative (rather than 130

neutral to positive) height anomaly regions over the North Atlantic, western Europe and, most prominently the North Pacific east of the dateline. The DJF results more clearly show the three regions of Southern mid-latitude positive height anomalies identified in other analyses but often with a shallower signal, indicating a greater vertical extent of anomalies during DJF over the mid-latitude South Pacific east of the dateline, southern

Indian Ocean and, to a lesser extent, South Atlantic. Over the high latitudes, anomaly strength increases during DJF.

20CR2c-Prev, JJA and DJF

The 200 mb 20CR2c-Prev JJA results (Figure 53) are highly consistent, with similar centers of action as the annual mean but increased anomaly strength at nearly all latitudes. The DJF results are mostly consistent with the annual mean, but with opposite- sign positive anomalies over part of the eastern Arctic. Compared to the annual mean and especially JJA results, the DJF results feature centers of action shifted away from the subtropics, except over the North Atlantic, and to the mid-latitudes in both hemispheres.

The Southern mid-latitudes feature three regions of positive height anomalies that are observed in the other analyses often as shallower vertical signals, but the locations of two are shifted west relative to the annual mean. The most consistently positioned positive height anomaly pattern is over the mid-latitude Indian Ocean, with the South Pacific pattern west of the dateline and the South Atlantic pattern centered over southern South

America.

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Annual Mean Overall Summary

This comparison of two different AMO cycles (20CR2-Curr and 20CR2-Prev) features the most discrepancies. Contributing factors likely involve increasingly incomplete observational data back through time, a reliance solely on surface-based observations in the 20CR2 dataset, introduction of model biases, and a confounder specific to this comparison regarding the unknown potential for and degree of regional differences between AMO cycles. That is, to what extent there are different ‘flavors’ of the AMO just as there are different flavors of ENSO associated with variations in regional signals.

Inconsistent with other analyses, the 20CR2-Prev results feature cool SSTAs in the tropics (in the equatorial eastern Pacific and southern Indian Ocean) and large-scale opposite-sign discrepancies in the high latitudes: cool SSTAs in the western and central

Arctic and warm SSTAs zonally near 60°S with cool SSTAs west from the Antarctic

Peninsula. In the Pacific, the 20CR2c-Prev is characterized by more hemispherically symmetric warm SSTAs that more closely resemble a La Niña-like horseshoe-shaped pattern.

The annual mean 1000 mb geopotential height anomaly patterns are broadly consistent between AMO cycles except for opposite-sign anomalies in the Northern high latitudes and Southern mid-latitudes and increased low latitude involvement. Pacific patterns are mostly same-signed but differently-positioned, with an apparent eastward shift. The diagonally tilted surface patterns identified in the other analyses – over the

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western Arctic, near the Bering Strait from northwest North America and centered around southern South America – are not diagonally tilted in the 20CR2c-Prev results.

The mid-troposphere patterns are more symmetric about the equator in the

20CR2c-Prev results, with Southern mid- and high latitude patterns that more closely resemble the NCEP/NCAR datasets, suggesting the zonal orientation and partitioning of anomaly sign around 50°S in the 20CR2c-Curr analysis may be an outlier. All mid- troposphere analyses suggest the weakest signals are over the low latitudes.

The 200 mb annual mean differences between AMO cycles are substantial, with

20CR2c-Prev opposite-sign anomalies throughout the high latitudes – negative heights in the Northern Hemisphere and positive heights in the Southern Hemisphere - and over regions of the mid-latitudes in both hemispheres. The 20CR2c-Prev height anomaly patterns are more diagonally tilted, unlike the extreme zonal orientation of the 20CR2c-

Curr patterns, and, opposite the 20CR2c-Curr patterns, are characterized by stronger positive height anomaly centers of action in the mid-latitudes and weaker signals over the low latitudes.

Despite the discrepancies, there remain some shared vertical structures between AMO cycles in addition to the North Atlantic baroclinicity. The most consistent positive height anomaly barotropic structures are over the subtropical North Pacific near the Hawaiian Islands and the mid-latitude South Pacific east of the dateline, with the mid- latitude central North Pacific structure also similar but shifted east of the dateline. Other

Pacific-based structures are less consistent: over the low latitude western North Pacific

(opposite-sign negative anomalies at the surface) and the low latitude eastern South

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Pacific (near-neutral 1000 and 500 mb anomalies). In the Northern high latitudes, the western Arctic pattern is somewhat consistent although slightly westward-shifted with near-neutral anomalies at 200 mb. Over the Southern mid-latitudes the 20CR2c-Prev shows a barotropic structure over the southern Indian Ocean (though with weak 1000 mb anomalies consistent with the 20CR2c-Curr pattern) and a strong barotropic structure over the South Atlantic (rather than the shallower vertical signal in the 20CR2c-Curr results).

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Figure 42: Comparison of 20CR2c annual mean SSTAs with annual AMO index for current (top) and previous (bottom) AMO cycles 135

Figure 43: 20CR2c composite analysis of current cycle SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

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Figure 44: 20CR2c composite analysis of previous cycle SSTAs with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 137

Figure 45: Comparison of 20CR2c annual mean 1000 mb geopotential height anomalies with annual AMO index for current (top) and previous (bottom) AMO cycles

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Figure 46: 20CR2c composite analysis of current cycle 1000 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 139

Figure 47: 20CR2c composite analysis of previous cycle 1000 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 140

Figure 48: Comparison of 20CR2c annual mean 500 mb geopotential height anomalies with annual AMO index for current (top) and previous (bottom) AMO cycles 141

Figure 49: 20CR2c composite analysis of current cycle 500 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 142

Figure 50: 20CR2c composite analysis of previous cycle 500 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom) 143

Figure 51: Comparison of 20CR2c annual mean 200 mb geopotential height anomalies with annual AMO index for current (top) and previous (bottom) AMO cycles

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Figure 52: 20CR2c composite analysis of current cycle 200 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

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Figure 53: 20CR2c composite analysis of previous cycle 200 mb geopotential height anomalies with AMO index for annual mean (top), JJA (middle) and DJF (bottom)

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CHAPTER 7: AMO TRANSITION PHASE

This section discusses the NCEP/NCAR linear correlation results between monthly climate anomalies and the annual mean AMO transition index. Correlations between the AMO transition index and monthly climate anomalies are generally less strong than those of the AMO extreme phase but still suggest regional centers of action.

Results from this section correspond to the AMO cool-to-warm transition phase, with anomaly sign reversed during the AMO warm-to-cool transition phase.

A. HORIZONTAL MAPS

The SSTA patterns suggest the main center of action associated with the AMO transition phase is not the Atlantic but the Pacific (Figure 54). Three main regions of warm SSTAs include the western Pacific/eastern Indian Ocean, the eastern Pacific and the western Arctic. The strongest correlations are in the far western Pacific and maritime continent, extending poleward into the mid-latitudes of both hemispheres to form a boomerang pattern along the Asian coast in the Northern Hemisphere and beyond

Australia to the date line in the Southern Hemisphere. In the low latitudes the warm

SSTAs extend west into the tropical Indian Ocean, especially the eastern basin and Bay of Bengal. The second region of warm SSTAs is in the low latitude eastern Pacific.

Though slightly more extensive in the Southern Hemisphere, the pattern is generally

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symmetric about the equator. The third region of warm SSTAs is in the western Arctic and is most robust above northwest North America. Cool SSTAs are most extensive near

60°S latitude, especially centered around the date line between 120°E to 120°W, with similar though weaker anomalies at the same latitude in the South Atlantic. This nearly circumglobal zonal band of cool SSTAs resembles the pattern associated with the AMO warm phase (Stuckman and Lin, submitted). One difference, however, is the location of the strongest correlations: in the AMO cool-to-warm transition phase the strongest correlations (near the date line) occur west of the strongest correlations in the AMO warm phase (in the South Atlantic). Cool SSTAs are also found in the central Pacific in both hemispheres and, like the cool SSTAs around 60°S, are situated west of a similar pattern in the eastern Pacific during the AMO extreme phase.

Anomalous precipitation (Figure 55) from the NCEP/NCAR Reanalysis dataset is relied on primarily for oceanic coverage and the more observationally-constrained UDel dataset for land-based precipitation. The NCEP/NCAR analysis suggests the most robust precipitation anomalies, like SSTAs, are associated with the Pacific Ocean. Increased precipitation anomalies are strongest over the tropical eastern Pacific, especially the eastern South Pacific, co-localized with warm SSTAs. The strongest decreases are over the central Pacific and associated with cool SSTAs. On the other hand, the warm SSTAs in the far western Pacific and eastern Indian Ocean may not be associated with strong precipitation anomalies. Although the NCEP/NCAR correlations suggest increased anomalous precipitation associated with the SSTA horseshoe-shaped pattern throughout the maritime continent and eastern Indian Ocean and extending northeast along the Asian

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coast and southeast over Australia, this is a bit inconsistent with the UDel dataset indicating increased precipitation anomalies only over northeast China (centered around

45°N, 120°E), with decreased precipitation anomalies in the maritime continent and only weak-to-neutral anomalies over the rest of coastal Asia and Australia.

The UDel dataset shows increased precipitation anomalies over northeast

Greenland, part of the western Arctic region with warm SSTAs, though the NCEP/NCAR dataset correlations are weaker. Elsewhere in the Northern Hemisphere, both

NCEP/NCAR and UDel datasets identify increased precipitation anomalies over the western U.S. and Bering Strait, regions with strong anomalous air temperature gradients

(Figure 56). Likewise, the UDel dataset suggests increased precipitation anomalies over

Scandinavia and northern Europe (centered around 60°N, 30°E) also co-localized with strong air temperature gradient anomalies in (Figure 56). In the Southern Hemisphere, both datasets reveal increased anomalous precipitation over southeastern Brazil, and the

UDel dataset includes northeast Brazil. This analysis suggests the hydroclimate regime of northeast Brazil, known to be extremely sensitive to the AMO extreme phases (Folland et al. 2001), may have an increased susceptibility to drought conditions even prior to the onset of an AMO warm phase.

Geopotential height anomalies (Figure 57) are generally dynamically consistent with anomalous precipitation and the distribution of latent heat. Surface height anomalies are generally positive between about 50°N and 50°S, with the exception of negative height anomalies over North America, and negative poleward of these latitudes. The

Pacific remains the main center of action. Positive height anomalies are co-localized with

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cool SSTAs and decreased anomalous precipitation throughout the western and central

Pacific in both hemispheres. The most robust correlations are near the date line, over the tropics and equator (subtropics) in the Northern (Southern) Hemisphere.

In the Northern Hemisphere, negative height anomaly patterns occur in three regions, all featuring increased anomalous precipitation. Of these, the strongest are centered over the Bering Strait. Also at similar latitude are slightly weaker negative height anomalies over northwest Eurasia. The third region is over the eastern U.S., where the negative height anomalies are co-localized with strongly warm air temperature anomalies. In the Southern Hemisphere extensive negative height anomalies poleward of

50°S are co-localized with warm air temperature anomalies, with the strongest correlations throughout the western sector of Antarctica and adjacent Southern Ocean to the date line.

In the vertical, only the North American region displays baroclinicity, with strong surface negative height anomalies over the eastern U.S. that become increasingly positive with altitude. This baroclinic vertical structure is tilted in the vertical to the south and west, with the strongest correlations at 200 mb stretching over Florida and Mexico. The moderately (strongly) positive height anomalies at 500 (200) mb are consistent with warm air temperature anomalies aloft (Figure 56) generated by the latent heat of condensation (Figure 55). Interestingly, the AMO extreme phase also featured only one baroclinic vertical structure, over the North Atlantic (Stuckman and Lin, submitted). This may be another example of AMO cool-to-warm phase patterns shifting eastward as the

AMO transitions to the warm phase. The other surface negative height anomaly patterns

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associated with the AMO transition phase, over the Bering Strait, northwest Eurasia and western Antarctica, are associated with cool air temperature anomalies aloft (Figure 56) with a negative height anomaly sign throughout the column that indicates a barotropic vertical structure. The two strongly positive surface height anomaly patterns, over the central North and South Pacific, are not hemispherically symmetric with altitude. The positive height anomalies over the central North Pacific become more spatially restricted and less robust with altitude, a relatively shallow vertical signal in contrast to the robust positive height anomalies comprising a barotropic structure over the central South

Pacific.

The stronger geopotential height anomaly patterns are dynamically consistent with the zonal (Figure 58) and meridional (Figure 59) wind anomalies. Driven by the anomalous pressure gradients and turned by the Coriolis force, the wind anomalies approximate geostrophic balance and exhibit the expected cyclonic or anticyclonic circulation. This confirmation of anomalous height-wind relationships increases confidence in the results by validating the consistency of the thermodynamic relationships between variables.

Idealized models suggest equatorial (non-equatorial) heat sources generate height patterns that are zonally-oriented (diagonally tilted) and symmetric (not symmetric) about the equator. These AMO transition phase patterns are predominantly zonally-oriented rather than tilted diagonally. The few exceptions include a slight diagonal tilt (from southeast-to-northwest) at 1000 mb in height anomalies over North America and at 500 and 200 mb over the Bering Strait and western/central Pacific. The anomalous height

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patterns are also generally symmetric about the equator at 1000 mb, with the exception over the Americas, and more so aloft. These mostly zonally-oriented and hemispherically-symmetric height anomaly patterns are consistent with idealized patterns generated in response to equatorial heating by the Matsuno-Gill model (Matsuno 1966;

Gill 1980) in which the shallow-water equations were later extended globally (Zhang and

Krishnamurti 1996).

B. ZONAL MEAN CROSS-SECTION PLOTS

An overall picture of the atmospheric climate signature associated with the AMO transition phases is provided by zonal mean cross-section plots. The AMO cool-to-warm transition phase features warm zonal mean air temperature anomalies throughout the troposphere over the low and into the middle latitudes (Figure 60). Near the surface these warm anomalies are attributable to the western Pacific and maritime continent, the eastern Pacific and eastern North America, with the warm anomalies aloft more localized and dispersed (Figure 57). A mid-troposphere warm air temperature anomaly over the

Northern high latitudes, mostly over the western Arctic and especially Greenland (Figure

56), is consistent with increased anomalous precipitation and the release of latent heat

(Figure 55). Cool stratospheric anomalies closely overlay the warm tropospheric anomalies, between about 50°S and 50°N, as well as over the highest latitudes in both hemispheres.

Positive zonal mean geopotential height anomalies (Figure 61) occur between

50°S and 50°N, co-localized with anomalous warm temperatures. Also consistent with

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the anomalous temperature signals, the strongest teleconnections near the surface occur over the central and western Pacific, with increasing contributions outside this area aloft.

Negative height anomalies centered at 60° latitude in both hemispheres extend, in the

Northern Hemisphere, from the surface to the mid-troposphere. Contributing to this zonal mean signal is the Bering Strait region and to a lesser extent northwest Eurasia. Above

400 mb the negative anomalies in these regions are weak and strong positive height anomalies over North America develop, resulting in zonal mean signal cancellation. The more expansive Southern Hemisphere pattern at 60°S extends over the highest latitudes and into the lower stratosphere, consistent with weakly warm air temperature anomalies near the surface and neutral-to-cool air temperature anomalies aloft (Figure 60). Over the

Northern high latitudes, a weakly positive height anomaly in the middle to upper troposphere is associated with the release of latent heat associated with increased anomalous precipitation over northern Greenland (Figure 55).

The zonal mean zonal wind anomaly pattern is generally symmetric about the equator (Figure 62), with westerly anomalies near the equator and 50° latitude in both hemispheres and easterly anomalies over the subtropics and high latitudes. The strongest westerly anomalies are centered near 50° latitude in both hemispheres where the strongest anomalous pressure gradients occur, with positive height anomalies equatorward

(especially over the central and western Pacific) and negative height anomalies poleward

(over the Bering Strait and northern Europe in the Northern Hemisphere and much of

Antarctica in the Southern). These regional height anomaly patterns have a barotropic vertical structure that promotes westerly anomalies throughout the tropospheric column.

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In contrast the only baroclinic signal, over North America, promotes anomalous cyclonic circulation in the lower troposphere and easterly anomalies near 50°N, a signal that is cancelled in the zonal mean by stronger opposite-signed anomalies at this latitude, but aloft promotes anticyclonic circulation that contributes to the westerly anomalies at this latitude.

The equatorial and low latitude westerly anomalies are notable. Idealized climate models indicate a region of equatorial westerlies is necessary for transmitting teleconnection signals across the equator from one hemisphere to the other (e.g.,

Branstator 1983), a condition that exists in certain Pacific regions especially during boreal winter (Webster and Holton 1982). The AMO cool-to-warm transition phase features equatorial westerly anomalies over the Pacific throughout the troposphere. Near the surface, these occur over the eastern and central Pacific, driven by positive height anomalies to the west to generate anomalous anticyclonic circulation in both hemispheres. The region of westerlies shifts westward with altitude, situated by 400 mb over the central and western Pacific and by 200 mb over the western Pacific. The presence of these anomalies suggests the potential for substantial inter-hemispheric circulation that could augment the influence of this AMO transition phase.

The subtropical easterly anomalies are more robust in the Southern Hemisphere, driven by the strong positive height anomaly in the subtropical central South Pacific that promotes anomalous anticylonic circulation. Over the high latitudes, easterly anomalies are generated by pervasive negative height anomalies and the associated cyclonic

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circulation. In the Northern (Southern) Hemisphere this is mainly attributable to the cyclonic circulation over the Bering Strait and northern Europe (western Antarctica).

The zonal mean meridional wind pattern is useful for characterizing regions of convergence and divergence and revealing large-scale anomalous circulation cells. Near the surface the anomaly patterns suggest three areas of convergence (Figure 63). The strongest of these occurs over the Southern low latitudes, driven by the main center of action in the Pacific. Northerly anomalies over the equator and southerly anomalies over the tropics are strongest in the central Pacific, consistent with the positive height anomalies and anomalous anticyclonic circulation. This cross-equatorial circulation is predicted by the presence of equatorial zonal mean westerly anomalies that idealized models suggest are necessary for inter-hemispheric signal transfer. This hemispheric mixing consists primarily of northerly anomalies (from the Northern to Southern

Hemisphere) throughout the troposphere co-localized with the anomalous westerlies generated in the central and eastern Pacific. In the mid-troposphere and near the tropopause the inter-hemispheric mixing is also promoted by equatorial southerly anomalies that penetrate from the Southern to Northern Hemisphere, at 700 mb over the central and eastern Pacific and between 200 and 100 mb over the eastern and western

Pacific basins.

A second area of convergence near 60°S features southerly anomalies equatorward, driven by positive height anomalies and anticyclonic circulation in the central and eastern Pacific, and northerly anomalies poleward generated by the negative height anomalies and cyclonic circulation over much of western Antarctica and the

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adjacent Southern Ocean. A corresponding though relatively weaker area of convergence in the Northern Hemisphere near 60°N is driven by negative height anomalies over the

Bering Strait, northwest Eurasia and the western Arctic that promote cyclonic circulation and southerly (northerly) anomalies poleward (equatorward). These three areas of surface convergence are overlaid by opposite-signed meridional anomalies aloft, indicating anomalous divergence consistent with the need for mass balance.

Omega, or vertical pressure velocity, anomalies (Figure 64) suggest areas of large-scale ascending and descending motions are strongest in the low and high latitudes and relatively weak over the mid-latitudes, consistent with the anomalous circulation suggested by meridional wind anomalies. That is, the areas of surface convergence and associated upper-level divergence identified by the meridional wind anomalies - over the

Southern low latitudes and 60° latitude in both hemispheres - are vertically linked by negative omega anomalies (anomalous rising air). Of these, the strongest signal is over the Southern Hemisphere low latitudes, driven primarily by warm SSTAs throughout the maritime continent with contributions from the eastern South Pacific. There are also three regions featuring positive omega (downward velocity) anomalies. The strongest correlations are over the Northern low latitudes, which may be influenced by the cool

SSTAs over the central Pacific. The other two regions are over the high latitudes near

80°N (associated with cool SSTAs above northwest Eurasia) and 80°S, both consistent with anomalous cool air temperatures aloft.

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Figure 54: NCEP/NCAR linear correlation of sea surface temperature anomalies (SSTAs) with the annual mean AMO Transition index

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Figure 55: Linear correlation of precipitation anomalies with the annual mean AMO transition index using the (a) NCEP/NCAR and (b) UDel datasets. 158

Figure 56: NCEP/NCAR linear correlation of air temperature anomalies with annual mean AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb 159

Figure 57: NCEP/NCAR linear correlation of geopotential height anomalies with the annual mean AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb

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Figure 58: NCEP/NCAR linear correlation of zonal wind anomalies with the annual mean AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb

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Figure 59: NCEP/NCAR linear correlation of meridional wind anomalies with the annual mean AMO transition index at 1000 (bottom), 500 (middle) and 200 (top) mb

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Figure 60: NCEP/NCAR linear correlation of zonal mean air temperature anomalies to the annual mean AMO transition index

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Figure 61: NCEP/NCAR linear correlation of zonal mean geopotential height anomalies to the annual mean AMO transition index

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Figure 62: NCEP/NCAR linear correlation of zonal mean zonal wind anomalies to the annual mean AMO transition index

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Figure 63: NCEP/NCAR linear correlation of zonal mean meridional wind anomalies to the annual mean AMO transition index

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Figure 64: NCEP/NCAR linear correlation of zonal mean omega anomalies to the annual mean AMO transition index

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CHAPTER 8: ENSO ZONAL MEAN STRUCTURE

A literature review associated with the construction of AMO-related zonal mean climate anomalies found no previous published study characterizing the global zonal mean structure of ENSO despite characterization of finer details over the decades. This global zonal mean characterization covers two seasons, boreal winter (DJF) and summer

(JJA), and is intended to complement the well-known horizontal structure.

A. INTRODUCTION

The dominant climate cycle on interannual timescales is the El Niño-Southern

Oscillation (ENSO), featuring extensive global teleconnections and societal impacts.

Many aspects of this coupled ocean-atmosphere cycle have been characterized and increasingly refined, including for example associated climate effects (e.g., Ropelewski and Halpert 1987), differing flavors of El Niño (Trenberth and Smith 2006; Sun et al.

2013) and interactions with other climate modes (McCabe et al. 2004; Hu and Huang

2009) including anthropogenic climate change (Yeh et al. 2009).

Previous studies have characterized certain zonal mean aspects of ENSO, including a reconstruction of zonal mean temperature anomalies with the European

Centre for Medium-range Weather Forecasting (ECMWF) Re-Analysis (ERA-40) dataset

(Trenberth and Smith 2006) and with higher-resolution though more temporally- 168

constrained GPS radio occultation data (Scherllin-Pirscher et al. 2012). To evaluate the skill of general circulation models (GCMs), the zonal mean anomalies of zonal wind and omega have been analyzed using the NCEP/NCAR and ERA-40 reanalysis datasets

(Joseph and Nigam 2006), and zonal mean divergent wind and vertical velocity associated with the Central Pacific versus Eastern Pacific El Niño were reconstructed from 30°N to 90°S (Sun et al. 2013).

An analysis of anomalous zonal mean circulation cells investigated zonal (Walker circulation and a midlatitude zonal cell over the North Pacific) and meridional (Hadley and Northern Hemisphere Ferrel) cells based on divergence and vertical pressure velocity anomalies in the NCEP/NCAR Reanalysis during different phases of the ENSO life cycle

(Wang 2002). Although the analysis was global, the published results were restricted to the tropics and Northern mid-latitudes (20°S - 60°N) with a focus on the Pacific (from

100°E - 80°W). The restricted regional analysis was due in part to the strong east-west contrast in the Pacific characteristic of ENSO that leads to signal cancellation in a zonal mean analysis (Wang 2002).

A comprehensive zonal mean analysis of global related climate anomalies has not previously been reported. Despite the limitation regarding zonal mean structures cancelling due to east-west Pacific contrasts, a big-picture context within which to consider the more detailed regional structures may still be useful. This study analyzes the global zonal mean structure of climate anomalies associated with ENSO during boreal winter (DJF) and summer (JJA), with the Niño3.4 index correlated with zonal mean monthly mean anomalies of climate variables from the NCEP/NCAR Reanalysis dataset

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to reconstruct the ENSO-associated climate. Anomalous circulation cells are identified using meridional wind anomalies (to distinguish regions of surface and upper-level convergence and divergence) and vertical pressure velocity (omega) anomalies to connect the meridional circulation through ascending and descending anomalies. Comparison to zonal mean anomalies of air temperature, geopotential height and zonal wind provides a check for dynamic consistency.

B. RESULTS

B.1. Boreal Winter (DJF)

During DJF the ENSO warm phase-associated main centers of action with respect to zonal mean air temperature anomalies (Figure 65) are the low latitudes. Strong zonal mean warm anomalies throughout the tropospheric column between 30°N and 30°S are overlaid by cool anomalies in the lower stratosphere, a pattern consistent with the reconstruction of zonal mean temperatures using ERA-40 (Trenberth and Smith 2006).

Weaker warm anomalies throughout the tropospheric column are located over 60° latitude in both hemispheres, and a cool anomaly localized in the Northern mid-latitude upper troposphere is an exception to the global hemispheric symmetry.

Zonal mean geopotential height anomalies (Figure 66) are uniformly positive throughout the low latitude atmospheric column, with correlations slightly stronger aloft than at the surface consistent with warm air temperature anomalies. Negative height anomalies over the mid-latitudes are strongest near the surface, associated with weakly

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warm air temperature anomalies, and in the Northern Hemisphere extend throughout the troposphere.

The low latitudes feature strong zonal mean zonal wind anomalies (Figure 67) consistent with the strongest anomalous pressure gradients and geostrophic balance, especially the westerly anomalies over the subtropics that strengthen with altitude. Over the equator, the sign of the anomaly changes with altitude with westerly (easterly) anomalies near the surface (aloft). Correlations over the extratropics are much weaker with the exception of easterly anomalies over the Northern high latitudes generated by the strong anomalous pressure gradient.

Consistent with other climate variables, the zonal mean meridional wind anomalies (Figure 68) are strongest over the low latitudes and decrease strength poleward. This variable is useful for revealing regions of anomalous surface and upper- level convergence and divergence to suggest the location and extent of global meridional circulation cells. At the surface are three zonal mean regions of convergence: over the equator, consistent with warm anomalies and convection, and around 50° latitude associated with weaker warm air temperature anomalies and negative height anomalies.

Anomalous surface divergence over the subtropics and high latitudes is consistent with positive height anomalies. The surface regions of convergence (divergence) are overlaid by anomalous meridional divergence (convergence) aloft.

Over the low latitudes, omega (vertical pressure velocity) anomalies (Figure 69) are negative (indicating upward velocity) over the equator and positive nearer the subtropics. Hemispherically symmetric negative (positive) omega anomalies are centered

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over 40° latitude (higher latitudes). The ascending and descending anomalies provide a link between the surface and upper-level regions of meridional convergence and divergence to suggest the location of anomalous zonal mean meridional circulation cells.

B.2. Boreal Summer (JJA)

Overall during boreal summer, ENSO climate correlations are weaker over the low latitudes, consistent with previous studies (e.g., Horel and Wallace 1981), though the low latitudes remain the main center of action. In contrast, several zonal mean extratropical teleconnections are slightly stronger during summer. The pattern of hemispheric symmetry over the low latitudes does not extend to the extratropics.

Zonal mean air temperature anomalies (Figure 70) remain strongest throughout the low latitude troposphere, though the seasonal northward shift following migration of the ITCZ produces fewer anomalies over the subtropical Southern Hemisphere. The warm tropospheric anomalies are overlaid by cool stratospheric anomalies. Correlations in the extratropics, slightly stronger relative to winter, feature cool (warm) zonal mean anomalies in the Northern (Southern) hemisphere. The location of the strong cool anomaly in the Northern mid-latitude upper troposphere during winter remains the same during summer though the pattern is much more extensive vertically, extending to the surface, with cool anomalies in the Northern high latitudes as well.

Positive zonal mean geopotential height anomalies (Figure 71) between the

Southern tropics and Northern subtropics strengthen with altitude, consistent with warm temperature anomalies. Extratropical correlations are stronger in the Northern

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Hemisphere, with cool tropospheric anomalies at the surface (aloft) generating positive surface (negative upper tropospheric) height anomalies between 60-70°N (over the mid- latitudes). Weakly warm air temperature anomalies throughout the Southern extratropical atmosphere drive the hemispherically antisymmetric negative height anomalies at the surface and positive anomalies aloft.

The strongest zonal mean zonal wind anomalies are the westerlies over the low latitudes throughout the troposphere (Figure 72), with equatorial easterly anomalies restricted to boreal winter. Anomalous pressure gradients drive mid-latitude easterlies between 50-60°N throughout the troposphere and lower stratosphere and between 30-

40°S in the mid- to upper troposphere, with few robust zonal mean anomalies over the high latitudes.

The zonal mean meridional anomalies (Figure 73) feature similar patterns over the low latitudes resembling those of boreal winter, though with a northward seasonal shift. Anomalous surface convergence (divergence) near the equator and mid-latitudes

(near the subtropics, though closer to the equator in the Southern Hemisphere) is overlaid by divergence (convergence) aloft. Correlations weaken outside the low latitudes, especially poleward of 50°S, but suggest anomalous surface convergence occurs near

40°N and in the Southern Hemisphere, associated with negative height anomalies, around

40°S and 70°S, with surface divergence near 50°S and 80°S latitudes. These zonal mean regions of surface convergence (divergence) correspond to upper-level divergence

(convergence).

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Omega anomalies are strongest over the low latitudes and Northern Hemisphere

(Figure 74). Anomalous equatorial warming and convection generates negative zonal mean omega anomalies, shifted seasonally slightly north of the equator, with strong positive omega anomalies over the Southern tropics and Northern extratropics. Over the extratropics, negative omega anomalies occur over 40°N and, in the Southern

Hemisphere consistent with negative surface height anomalies, over 40°S and 70°S.

Positive omega anomalies occur over 70°N, consistent with positive surface height anomalies, and over 50°S and 80°S latitude.

C. DISCUSSION

This global seasonal analysis characterizes the zonal mean structure of climate anomalies associated with ENSO and is intended to complement the more well-defined horizontal structure. Seasonal schematics during boreal winter (Figure 75) and summer

(Figure 76) are based on the stronger and dynamically consistent anomalies and suggest the ENSO warm phase is characterized by three circulation cells per hemisphere during

DJF that resemble the idealized 3-cell model, with an intensification of the anomalous

Hadley, Ferrel and polar cells. During JJA the Northern Hemisphere is comprised of three anomalous circulation cells but, highlighting the general summertime hemispheric asymmetry, the Southern Hemisphere may consist of five zonal mean circulation cells, depending on consideration of the weak but dynamically consistent high latitude meridional wind and omega anomalies.

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The low latitudes are the main center of action during both seasons, with global teleconnections suggested by the zonal mean patterns involving air temperature and circulation anomalies. Compared to boreal winter, JJA correlations tend to be slightly weakened (strengthened) over the low (higher) latitudes. The structure of ENSO- associated climate anomalies is hemispherically symmetric during winter, consistent with equatorial warming, but this symmetry is restricted primarily to the low latitudes during

JJA, with extratropical hemispheric antisymmetry due to cool (warm) air temperature anomalies over the Northern (Southern) Hemisphere.

During DJF the zonal mean meridional circulation cells reasonably approximate the idealized model of three cells per hemisphere and suggest an intensification of anomalous Hadley, Ferrel and polar cells. The strongest circulation cell features equatorial negative omega anomalies (upward vertical velocity) driven by anomalous equatorial heating, with warm air temperature anomalies throughout the low latitudes connecting anomalous meridional surface convergence (upper-level divergence) around a surface low (upper-level high) geopotential height center of action. The anomalous upper troposphere circulation diverging over the equator sinks over the subtropics of both hemispheres with strengthened anomalous positive height anomalies at the surface.

Negative surface height anomalies over the mid-latitudes around 50° form the ascending branch of the highest latitude circulation cells, with upper-air divergence and positive omega anomalies indicating the descending branches are located around 70°S and further poleward in the Northern Hemisphere around 85°N.

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During JJA the anomalous circulation cells become less well-defined over the high latitudes, especially in the Southern Hemisphere, and less hemispherically symmetric. Although there are stronger air temperature anomalies over the high latitudes during JJA, omega anomalies are weaker suggesting decreased involvement of both

Northern and Southern high latitude circulation cells during JJA. The Northern

Hemisphere remains characterized by three anomalous circulation cells, but with weaker high latitude anomalies shifted equatorward from about 85 to 70°N during JJA.

Discrepancies are larger over the Southern Hemisphere, including opposite-sign omega anomalies over the Southern mid-latitudes, a region characterized by negative (upward) omega anomalies during DJF that are positive (downward) during JJA around 50°S. The

Southern Hemisphere may be comprised of up to five anomalous circulation cells depending on the consideration of weak but dynamically consistent high latitude omega anomalies. Weak negative omega anomalies extend throughout the troposphere around

65°S, representing an ascending branch of a circulation cell corresponding to weak upper-level positive height anomalies and meridional anomalies indicating upper-level divergence. Poleward of this are positive omega anomalies around 80°S indicating potentially the descending branch of a weak anomalous circulation cell.

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Figure 65: NCEP/NCAR linear correlation of zonal mean air temperature anomalies to the Niño3.4 index during DJF

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Figure 66: NCEP/NCAR linear correlation of zonal mean geopotential height anomalies to the Niño3.4 index during DJF

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Figure 67: NCEP/NCAR linear correlation of zonal mean zonal wind anomalies to the Niño3.4 index during DJF

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Figure 68: NCEP/NCAR linear correlation of zonal mean meridional wind anomalies to the Niño3.4 index during DJF

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Figure 69: NCEP/NCAR linear correlation of zonal mean omega anomalies to the Niño3.4 index during DJF

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Figure 70: NCEP/NCAR linear correlation of zonal mean air temperature anomalies to the Niño3.4 index during JJA

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Figure 71: NCEP/NCAR linear correlation of zonal mean geopotential height anomalies to the Niño3.4 index during JJA

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Figure 72: NCEP/NCAR linear correlation of zonal mean zonal wind anomalies to the Niño3.4 index during JJA

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Figure 73: NCEP/NCAR linear correlation of zonal mean meridional wind anomalies to the Niño3.4 index during JJA

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Figure 74: NCEP/NCAR linear correlation of zonal mean omega anomalies to the Niño3.4 index during JJA

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Figure 75: Schematic of stronger zonal mean climate anomalies associated with the ENSO warm phase during boreal winter (DJF) using NCEP/NCAR linear correlation

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Figure 76: Schematic of stronger zonal mean climate anomalies associated with the ENSO warm phase during boreal summer (JJA) using NCEP/NCAR linear correlation

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CHAPTER 9: DISCUSSION AND CONCLUSION

A. DISCUSSION

A.1. AMO Extreme Phase

This series of comparisons suggests mostly consistent anomaly patterns. The most consistent comparison is between methods, with NCEP/NCAR-based linear correlation and composite analyses featuring minor differences mostly south of 50°S. Comparing different datasets, the NCEP/NCAR and 20CR2c results are mostly consistent but with opposite-sign anomalies poleward of 50°S that increase with altitude. Comparison of different AMO cycles shows a number of extrapolar regional differences with extensive opposite-sign anomalies over both high latitudes and the most inconsistencies among comparisons. This is not unexpected due to increasingly sparse observations back through time, increased model reliance and the unknown potential for and degree of regional differences between individual AMO cycles; that is, to what extent there are different

‘flavors’ of the AMO.

Figure 77 shows a schematic of the global 3-dimensional structure of the AMO warm phase based on the most robust climate anomaly patterns among the four analyses

(NCEP/NCAR-LC, NCEP/NCAR-Comp, 20CR2c-Curr and 20CR2c-Prev). The

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horizontal component of the schematic features three pressure levels at 1000, 500 and

200 mb with most correlation coefficients of anomaly patterns at least ±0.4. The vertical component shows the global zonal mean circulation anomalies extending from the surface to 10 mb based on the NCEP/NCAR-LC and NCEP/NCAR-Comp zonal mean cross-section plots. Intended to provide a first approximation of the global zonal mean circulation of the AMO, correlation coefficients of the zonal mean anomaly patterns do not all reach the ±0.4 level of the horizontal maps. Nevertheless, these patterns are dynamically consistent and similar between data analysis methods (linear correlation and composite), datasests (NCEP/NCAR and 20CR2c) and AMO cycles (current and previous), suggesting some confidence in the anomaly patterns.

Nearly all schematic patterns are consistent among all four analyses

(NCEP/NCAR-LC, NCEP/NCAR-Comp, 20CR2c-Curr and 20CR2c-Prev). However, two patterns are included despite consistency between only three of the four analyses and two other patterns are reduced in spatial extent in the schematic to accommodate the

20CR2c-Prev results. The two patterns consistent with three of the four analyses are included based on anomaly strength: the 200 mb positive height anomaly pattern over the mid-latitude South Pacific, a region of strong barotropicity in all composite analyses but a more shallow vertical signal in the linear correlation results, which show neutral anomalies in the upper troposphere. The other schematic pattern suggested by three of the four analyses is a Northern high latitude pattern over the western Arctic, a region of strong barotropic positive height anomalies except in the 20CR2c-Prev results which suggest much weaker mid-troposphere positive height anomalies and a switch to

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opposite-sign negative height anomalies in the upper troposphere. The schematic patterns with more restricted spatial extent to accomodate the 20CR2c-Prev results include negative height anomalies over northwest North America that would otherwise extend further over the mid-latitude eastern North Pacific. A second region is the cluster of positive height anomaly patterns around southern South America, which extend beyond western South America over the low latitude eastern South Pacific in all analyses except the 20CR2c-Prev composite, which shows opposite-sign negative height anomalies.

Five key findings emerge from this AMO extreme phase characterization:

1) Several barotropic and one baroclinic vertical structure consistent among

analyses;

2) Global modulation of anomalous pressure centers;

3) Three anomalous circulation cells globally;

4) The North-South asymmetry characteristic of the AMO reflects a surface

bias; and

5) Organization and tilt of height anomaly patterns are consistent with Held

model idealized patterns.

The first key finding is that only the main center of action generates a baroclinic vertical structure, with vertical structures outside the North Atlantic characterized by barotropicity. Four regions of tropospheric positive height anomalies suggesting barotropic vertical structures are mostly consistent between the NCEP/NCAR-LC,

NCEP/NCAR-Comp, 20CR2c-Curr and 20CR2c-Prev analyses: three patterns over the

Pacific and one over the Northern high latitudes. Two of the Pacific patterns are in the

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Northern Hemisphere, over the subtropical North Pacific near the Hawaiian Islands and the mid-latitude central North Pacific. The latter structure, though present in all composite analyses, is a region of near-neutral anomalies in the NCEP/NCAR-LC annual mean results but is present during JJA in all analyses, including linear correlation, suggesting at the least a seasonally persistent pattern. The third barotropic Pacific pattern is over the mid-latitude South Pacific east of the dateline, a prominent structure in all composite analyses but with a shallower vertical signal in the NCEP/NCAR-LC results, which feature positive height anomalies from the surface through mid-troposphere but mostly neutral anomalies at 200 mb. The Northern high latitude barotropic region is over the western Arctic and especially Greenland, characterized by positive surface height anomalies that increase in strength and extent with altitude except in the 20CR2c-Prev analysis, which show near-neutral to weakly negative 200 mb height anomalies.

The second key finding is the extent to which the AMO influences global climate through modulation of anomalous pressure cells globally, from the strong positive height anomalies over the western Arctic and throughout the Pacific east of the dateline in both hemispheres to the Southern extratropics. These AMO-responsive Pacific regions include: positive height anomalies over the subtropical North Pacific near the Hawaiian

Islands and mid-latitude South Pacific east of the dateline, and surface negative height anomalies over the mid-latitude eastern North Pacific, patterns consistent across all four analyses. The three composite analyses suggest two additional Southern mid-latitude pressure centers, with mostly positive height anomalies over the South Atlantic and

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southern Indian Ocean, although these are regions of mostly near-neutral anomalies in the

NCEP/NCAR-LC results.

Of these anomalous pressure centers, the Aleutian Low has been identified as a prominent feature of multidecadal Pacific variability (Minobe 1999) with links to the

Atlantic. Multidecadal variations in SST and SLP are highly correlated (0.98) and show pronounced variability over the 20th century (Mestas-Nunez and Enfield 1999; Dima and

Lohmann 2007) that have been associated with the phase of the IPO or PDO, with a weakening of the Aleutian Low corresponding to, and perhaps promoting a shift to, the

IPO cool phase (Dima and Lohmann 2007; Grossmann and Klotzbach 2009). The associated SSTAs, including a tropical east Pacific cooling, reduce El Nino events and promote cooler tropical Atlantic SSTAs via atmospheric teleconnections (Enfield and

Mayer 1997; Alexander et al. 2002). This has been suggested as potentially contributing to a transition of the AMO from a warm to a cool phase (Grossmann and Klotzbach

2009). One possibility, however, is that this weakening of the Aleutian Low and shift to an IPO cool phase may itself be a result of an AMO-induced weakening of the Icelandic

Low via atmospheric teleconnections (Dima and Lohmann 2007; Grossmann and

Klotzbach 2009) that may involve tropical Atlantic-Pacific interactions but also potentially polar vortex fluctuations (Mestas-Nunez and Enfield 1999). The ECHAM-3

MPI coupled GCM features AMO-driven Pacific SSTAs that correspond to a weakened

Aleutian Low and IPO cool phase (see Figure 21d of Timmermann et al. 1998). These

Atlantic-Pacific interbasin links are also prominent in an observational analysis indicating the Pacific Ocean is actually responsible for much of the conventional AMO signal.

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Isolating the influence of separate ocean basins through rotated EEOF decomposition suggests Pacific basin links and feedbacks explain up to 45% of the total variance associated with the conventional AMO and may even be responsible for generating much of the canonical AMO SSTA patterns in the tropical and subtropical North Atlantic

(Guan and Nigam 2009).

The third key finding is that the AMO generates three anomalous circulation cells globally: a primary inter-hemispheric cell and two secondary cells over the Northern and

Southern high latitudes. The primary circulation cell features an ascending branch centered near 30°N driven by warm SSTAs in the North Atlantic (and east to the Middle

East). The baroclinic structure is characterized by anomalous cyclonic circulation at the surface linked by negative omega anomalies to upper-level anomalous anticyclonic circulation driven by positive height anomalies in the low latitudes over the central North

Pacific east over the North Atlantic to the Middle East. This anomalous rising air diverges above 300 mb, with northerly anomalies crossing the equator and extending to the Southern mid-latitudes. Here the descending branch is characterized aloft by cool air temperature anomalies north of 50°S and anomalous convergence above 250 mb, with positive omega anomalies throughout the tropospheric column and anomalous anticyclonic circulation at the surface associated primarily with the South Atlantic.

The two secondary anomalous circulation cells are over the high latitudes. The

Northern high latitude cell shares the ascending branch of the dominant cell generated by the North Atlantic, and this is linked to the descending branch poleward of 60°N by northerly (southerly) anomalies at the surface (aloft). The barotropic vertical structure of

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the descending branch features positive height anomalies at the surface and aloft over the western Arctic. The Southern high latitude cell shares the descending branch of the dominant cell, with northerly (southerly) anomalies at the surface (aloft) linking this to an ascending branch over the high latitudes. At the surface the ascending branch is characterized by anomalous cyclonic circulation over Antarctica, with weakly negative height anomalies near the date line, a weakly warm troposphere and negative omega anomalies associated with increased precipitation anomalies (see Figure 78).

The fourth finding is that the traditional characterization of the AMO involving a

Northern-Southern Hemisphere asymmetry appears to reflect a surface bias. Extensive cross-equatorial flow includes zonal mean meridional wind anomalies above 700 mb that advect Northern Hemisphere warm air anomalies into the Southern Hemisphere. Similar climate anomalies aloft extending from the Northern Hemisphere to 30°S suggest limitations to the traditional description of the AMO as a North-South asymmetry, and a conceptual refinement may be appropriate.

A previously unrecognized finding is that the organization and tilt of geopotential height patterns in response to AMO-associated heating are consistent with idealized patterns generated by a linear shallow water model (Held et al. 2002). At the surface,

AMO-associated height anomaly patterns are diagonally tilted and positioned similar to patterns in the Held model associated with off-equatorial heating. However, the AMO patterns aloft are not tilted but zonally oriented (except over the western Arctic) with a hemispheric symmetry, a response more consistent with an equatorial heat source. This blended response suggests the AMO features both equatorial and off-equatorial warming

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and is consistent with an AMO index defined as heating anomalies from the equator to the high latitudes. The model results further suggest a differential atmospheric response, with the lower troposphere more responsive to off-equatorial heating and above this more responsive to an equatorial heating component of the AMO. This is consistent with the increasingly strong warm air temperature anomalies with height over the equator in the middle and upper troposphere advected cross-equatorially by northerly anomalies.

Connecting these observational-based results to an idealized model can inform modeling efforts and provide a basis for improved theoretical understandings of the AMO.

A.2. AMO Transition Phase

Figure 78 shows the first reconstruction of climate anomalies associated with the

AMO transition phase. Although observational analysis of six AMO cycles is ideal for generating statistical confidence, this will not be achieved for another two centuries.

Reanalysis datasets incorporating observational data provide quality-controlled global data for one complete AMO cycle, and this reconstruction of the AMO transition phases uses the NCEP/NCAR-LC analysis. It is encouraging for this analysis that the

NCEP/NCAR-LC characterization of the AMO extreme phases is largely consistent between methods (linear correlation and composite), datasets (NCEP/NCAR and

20CR2c) and consecutive AMO cycles, with the largest discrepancies generally confined to the Southern mid- to high latitudes.

Four key findings emerge from this AMO transition phase analysis:

1) Transition phases feature distinct climate signatures;

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2) Organization and tilt of height anomaly patterns consistent with Matsuno-Gill

idealized model patterns;

3) Potential eastward evolution of climate anomalies in progression of AMO life

cycle; and

4) Proposed inter-relatedness between AMO and IPO.

The first key finding is that the AMO transition phases are not neutral and feature a climate signature, one distinct from the extreme phases. This global characterization identifies the Pacific as the main center of action during AMO transition phases, with a quiescent Atlantic and more extensive Southern Hemisphere involvement relative to the

AMO extreme phases. Although significant anomaly patterns occur outside the low latitudes, the most extensive climate impacts are over the low latitudes at the surface that are increasingly displaced poleward with altitude. The SSTA pattern resembles the IPO warm phase, with warm SSTAs in the eastern basin and cool SSTAs in the central

Pacific. The only anomalous baroclinic pattern is over North America (rather than the

North Atlantic during the AMO extreme phase), which is tilted in the vertical towards the south and west. There are four barotropic regions globally according to this

NCEP/NCAR analysis. Two regions of tropospheric negative height anomalies (with corresponding increased anomalous precipitation) indicate barotropic structures centered near 60°S, over the Bering Strait and northwest Eurasia. The other two regions are over the Southern Hemisphere. One involves positive height anomalies over the South Pacific centered at the dateline (positioned over the subtropics at the surface by increasingly into the mid-latitudes with altitude) consistent with decreased precipitation anomalies. The

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other barotropic region is throughout the western half of the Southern high latitudes, with negative height anomalies throughout the tropospheric column. These AMO transition phase centers of action are notably distinct from the AMO extreme phases, with the main centers of action mostly Pacific-based.

This distinct AMO transition phase climate signature also extends to the anomalous global circulation cells. Rather than the three distinct cells in the AMO extreme analyses, the AMO transition phase may consist of six circulation cells globally.

Negative omega anomalies are off-equatorial over the Southern tropics indicate an ascending branch, consistent with meridional anomalies suggesting surface convergence and upper-level divergence, including northward over the equator with an anomalous descending branch over the Northern tropics consistent with surface zonal mean positive height anomalies. This circulation cell borders the most extensive circulation cell associated with the AMO transition phase, with the shared descending branch over the

Northern tropics and the ascending branch over the high latitudes near 70°N, the latter connected to the third Northern Hemisphere circulation cell with a descending branch around 85°N, consistent with negative surface height anomalies and meridional convergence connected to upper-level meridional divergence in the zonal mean patterns.

Not including the equatorial-based inter-hemispheric circulation cell throughout the tropics, the Southern Hemisphere features three circulation cells with dynamically consistent climate variable anomalies. The Southern tropical ascending branch is connected through surface and upper-level meridional flow to a mid-latitude descending branch over 40°S, a region featuring surface zonal mean positive height anomalies and

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meridional divergence. The surface flow converges with high latitude circulation anomalies around 60°S to form the Southern high latitude circulation cell, with positive omega anomalies indicating the descending branch around 80°S.

The second key finding is that the organization and tilt of geopotential height anomaly patterns resemble those generated by idealized models, but not the Held model describing the AMO extreme phases. Instead, the AMO transition phases feature zonally- oriented and hemispherically symmetric geopotential height anomaly patterns consistent with idealized patterns produced by the Matsuno-Gill model (Matsuno 1966; Gill 1980) in response to equatorial heating. These models provide a foundation for understanding tropical circulation, and the same shallow-water equations have been extended globally

(Zhang and Krishnamurti 1996). In contrast, the AMO extreme phases exhibit a hybrid response featuring height patterns associated with heat sources both equatorial

(hemispherically symmetric, zonally oriented) and off-equatorial (hemispherically asymmetric, diagonally tilted), consistent with a pan-North Atlantic basin source region and similar to those generated in a linear shallow model (Held et al. 2002). The equatorial-based AMO transition phase patterns provide indirect support for the fourth key finding of this section, the hypothesis that the AMO cool-to-warm transition phase is related to the El Niño-like equatorial-based IPO warm phase.

The third key finding is suggested from a more dynamic understanding of the

AMO life cycle through comparison of the transition and extreme phase anomalies. Many prominent patterns associated with the AMO cool-to-warm phase are similar to, but situated west of, similar patterns during the AMO warm phase, suggesting an eastward

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evolution of many climate impacts as the AMO cool-to-warm phase progresses to the warm phase. Anomaly patterns associated with the transition (extreme) phase that display this behavior include the only baroclinic vertical structure, over North America (the

North Atlantic); cool SSTAs in the central Pacific (eastern Pacific) and near 60°S at the date line (in the South Atlantic); warm SSTAs throughout the eastern Indian Ocean and maritime continent (west Pacific); easterly trade anomalies in the far western Pacific (east of that throughout the tropical Pacific); a barotropic positive height anomaly structure over the mid-latitude South Pacific over the dateline (east of the dateline) and a deepened anomalous Aleutian low over the Bering Strait (east of the Bering Strait).

The fourth key finding based on this more comprehensive characterization of the

AMO life cycle is a proposed relationship between the AMO and IPO, the pan-Pacific manifestation of the North Pacific-based Pacific Decadal Oscillation (PDO). Despite being derived differently, with the IPO the 3rd EOF of global SSTs and global night marine air temperatures using a 13-year low-pass filter (Folland et al. 2002) and the PDO the 1st EOF of annual North Pacific SSTs from 20-60°N after removing global mean

SSTAs (Power et al. 1999), the two time series are highly correlated at > 0.8 (Folland et al. 2002). Though essentially synonymous, the IPO term is chosen here to emphasize the global extent of these climate cycles. The IPO is more complex than the AMO, with different timescales of variability generating a cycle-within-a-cycle involving 20- and 50- year periodicities (Minobe 1997, 1999; Deser et al. 2004) and phase changes likely driven by both tropical and extratropical influences (see review in Alexander 2013).

These features suggest the IPO is regulated by several physical processes with different

200

dynamical origins, including the Atlantic, that operate on interannual to multidecadal timescales (Schneider and Cornuelle 2005; Deser et al. 2010; Alexander 2013).

Although confidence is limited by the short instrumental record, several observational lead-lag analyses identify a statistically significant correlation between the

AMO and IPO/PDO indices (d'Orgeville and Peltier 2007; Zhang and Delworth 2007;

Rashid et al. 2010; Li and Luo 2013). The 1995 beginning of an AMO warm phase was followed several years later by a step change in many Pacific climate variables

(McPhaden and Zhang 2004; Chikamoto et al. 2012a), similar to the previous IPO phase change and associated mid-1970s climate shift though with opposite sign (Burgman et al.

2008; Chen et al. 2008). Some of these changes appear distinct from the IPO, such as decadal sea level fluctuations and the intensification of easterly trades in the Pacific

(Merrifield 2011), and suggest source regions outside the Pacific (e.g., Xie et al. 2008;

Kucharski et al. 2011; Luo et al. 2012).

According to the NCEP/NCAR analyses many of these Pacific changes correspond to AMO-related patterns that shift eastward as the AMO progresses from the cool-to-warm to warm phase, including: a rapid warming of the Indo-Pacific warm pool

(Wang and Mehta 2008); strengthening of the easterly trades and associated Walker circulation (L'Heureux et al. 2013); and cooling of the eastern and central tropical Pacific due to the strengthened trades and increased upwelling (McPhaden and Zhang 2004;

England et al. 2014). This latter tropical Pacific cooling is qualitatively consistent with the weakly cool to near-neutral SSTAs in the NCEP/NCAR analysis in which the eastern and central Pacific are among the only tropical basins not to feature warm SSTAs.

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Together these changes can contribute to the La Niña-like SSTA regime shift associated with the AMO warm phase (Dong et al. 2006; Zhang and Delworth 2007; Kucharski et al. 2011; Dong and Lu 2013; McGregor et al. 2014) and are consistent with a CMIP5 hindcast in which ensemble members with larger amplitude AMO index more skillfully simulate the observed Pacific SST pattern (Chikamoto et al. 2012b). Alternative methods of deriving an AMO signal also reveal an associated Pacific SSTA pattern that strongly resembles the IPO (e.g., Fig 4 in Chen et al. 2010b; Fig 2 in Schneider and Noone 2012), suggesting a fairly robust response.

Based on these previous findings and the current analysis, it is proposed that the

AMO and IPO, at least the non-ENSO-dependent longer timescale component of the IPO, are not just correlated but are different basin-wide expressions of a single global multidecadal oscillation (MDO) in which each ocean basin influences the other with several years lag time. Previous studies and reanalysis show the most recent AMO cool- to-warm transition phase featured an El Niño-like IPO positive phase pattern that became established in the mid-1970s several years after the beginning of an AMO cool phase, not unlike the emergence of a La Niña-like IPO negative phase several years after the 1995 start of the AMO warm phase.

If this hypothesis of a single global MDO is correct and the AMO and IPO continue to operate similar to the most recent AMO cycle, within a decade or so the

AMO should enter the warm-to-cool transition phase, with a cooling of the tropical eastern Pacific and warming of the subtropical central Pacific in both hemispheres, and progress to an AMO cool phase that will trigger a return to an IPO positive phase after a

202

years-long adjustment, with a cooler western Pacific and relatively warmer eastern

Pacific. One caveat is that since the IPO is likely a sum of processes with diverse origins acting on various timescales, some differences should be expected between the IPO and the non-ENSO-dependent low-frequency component of the IPO we propose may be synonymous with the AMO. It is also possible that the El Niño- and La Niña-like patterns spatially resemble the IPO but are nevertheless distinct, which had been noted in one coupled GCM in which the simulated AMO signal produced an IPO pattern in the Pacific that did not appear related to the model IPO (Timmermann et al. 1998). However, the extensive suggestions of an AMO-IPO relationship reviewed above and by this study appear consistent with the concept of a single global multidecadal oscillation within which both the AMO and IPO are embedded, each influencing the other via bi-directional inter-basin teleconnections (e.g., McGregor et al. 2014).

B. Conclusion

Reconstruction of the AMO extreme phases is based on comparisons between two methods, two reanalysis datasets and two consecutive AMO cycles. A better understanding of the global atmospheric structure may inform modeling efforts and contribute to improved AMO signal representation. This in turn is expected to increase the accuracy of climate change forecasts, especially on smaller (regional) spatial scales and shorter (decadal to multidecadal) timescales. The NCEP/NCAR dataset provides the first analysis of the AMO transition phases. This study suggests these transition phases are not neutral, with a unique climate signature distinct from the extreme phase. Although

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this may especially be the case for longer climate cycles such as the AMO, due to the persistence of transition phases for years to a decade or more, this analysis suggests a re- conceptualization of transition phases may be justified, with potential benefits to revisiting the historical conflation of climate cycles’ two transition phases into one

‘neutral’ phase. Combined with the AMO extreme phase results, this study provides a more comprehensive and dynamic characterization of the AMO life cycle. The study of multidecadal climate remains in its early stages, due in large part to the lack of a multicentury observational record. However, the current situation resembles the early stage of ENSO research in the 1960s and 1970s when results based on only about two cycles of upper air observational data were used to construct the structure of ENSO, a structure later confirmed using longer datasets (Bjerknes 1969; Bjerknes 1972). This ability to discern large-scale dynamically-consistent atmospheric patterns based on limited temporal and spatial data provides some confidence in the general structure of the robust anomalies in the present study, even as finer details can be expected to be refined over time.

204

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Figure 78: Schematic depiction of AMO transition phase climate anomalies plotted on global horizontal maps at 1000, 500 and 200 mb and by zonal mean cross-section (see text for detailed discussion).

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