Mean-State Dependence of ENSO Atmospheric Feedbacks in 1

Climate Models 2

Tobias Bayr1, Mojib Latif

1,2, Dietmar Dommenget

3, Christian Wengel

1, Jan Harlaß

1 and Wonsun Park

1 3

1 GEOMAR Helmholtz Centre for Ocean Research, 4

Düsternbrooker Weg 20, 24105 Kiel, Germany. 5

Corresponding author (tbayr@geomar.de) 6

7

2 Cluster of Excellence “The Future Ocean”, University of Kiel, 24105 Kiel, Germany 8

9

3 School of Mathematical Sciences, Monash University, Clayton, Victoria, Australia. 10

Abstract 11

We investigate the dependence of ENSO atmospheric feedbacks on the mean state in a perturbed 12

atmospheric physics ensemble with the Kiel Climate Model (KCM) and in CMIP5 models. 13

Additionally, uncoupled simulations are conducted with the atmospheric component of the KCM to 14

obtain further insight into the mean state dependence. It is found that the positive zonal wind feedback 15

and the negative heat flux feedback are strongly linearly related through sea surface temperature (SST) 16

and differences in model physics are less important. In observations, strong zonal wind and heat flux 17

feedbacks are caused by a convective response in the western central equatorial Pacific (Niño4 region), 18

resulting from an eastward (westward) shift of the rising branch of the Walker Circulation (WC) during 19

El Niño (La Niña). Climate models with a La Niña-like mean state, i.e. an equatorial SST cold bias in 20

the Niño4 region, simulate a too westward located rising branch of the WC (by up to 30°) and only a 21

weak convective response. Thus, the position of the WC determines the strength of both the wind and 22

heat flux feedback, which also explains why biases in these two feedbacks partly compensate in many 23

climate models. Furthermore, too weak atmospheric feedbacks can cause quite different ENSO 24

dynamics than observed, while enhanced atmospheric feedbacks lead to a substantial improvement of 25

important ENSO properties such as seasonal ENSO phase locking and asymmetry between El Niño and 26

La Niña. Differences in the WC are suggested to be a major source of ENSO diversity in current 27

climate models. 28

29

1. Introduction 30

El Niño-Southern Oscillation (ENSO) is the most dominant climate variability on interannual time 31

scales. ENSO is characterized by variations of tropical Pacific sea surface temperature (SST), mainly in 32

the east and central equatorial Pacific and is caused by a complex interplay of oceanic and atmospheric 33

circulation via multiple amplifying and damping feedbacks (Philander 1990; Neelin et al. 1998; Jin et 34

al. 2006). During El Niño, for example, anomalous warm SSTs in the eastern and central equatorial 35

Pacific cause an eastward shift of the rising branch of Walker Circulation (Philander 1990; Bayr et al. 36

2014), which is normally located over the Maritime Continent. This is the atmospheric part of the 37

positive (amplifying) Bjerknes Feedback that in turn enhances SST warming due to changes in the 38

ocean circulation (Bjerknes 1969; Wang et al. 2012). On the other hand, warmer SSTs drive more 39

evaporation and more clouds, both contributing to a net heat flux (Qnet) damping due to more latent 40

heat (LH) release and reduced short-wave (SW) radiation (Lloyd et al. 2009). During La Niña the 41

situation is reversed, with a westward shift of the Walker circulation, which leads to ocean cooling by 42

stronger upwelling of cold subsurface water. Reduced evaporation and fewer clouds damp the SST 43

cooling. 44

Recent studies have shown that despite improvements in the last decades in simulating aspects of the 45

mean state and variability of the tropical Pacific Ocean, current state-of-the-art climate models, so 46

called Coupled General Circulation Models (CGCM), still have problems in simulating important 47

features of ENSO like amplitude, spatial structure, frequency, seasonal phase locking, asymmetry 48

between El Niño and La Niña or the strength of feedbacks (e.g. Wang and Picaut 2004; Guilyardi et al. 49

2009b; Bellenger et al. 2014). Also future projections about ENSO remain highly uncertain, as ENSO 50

representation strongly varies amongst current CGCMs (Van Oldenborgh et al. 2005; Meehl et al. 51

2007a; Latif and Keenlyside 2009; DiNezio et al. 2012; Stocker et al. 2013; Kim et al. 2014a). 52

The atmospheric components of CGCM has been identified as a major source of diversity in simulated 53

ENSO (Schneider 2002; Guilyardi et al. 2004; Kim et al. 2008; Sun et al. 2009; Lloyd et al. 2011). 54

Especially the positive (amplifying) zonal wind feedback (µ) and the negative (damping) net heat flux 55

feedback (α), both dependent on SST, are too weak in many CGCMs, but the two biases partly 56

compensate (Lloyd et al. 2009; Bellenger et al. 2014). 57

Lloyd et al. (2011) found that these two feedbacks are already biased in Atmospheric General 58

Circulation Models (AGCM) participating in the Atmospheric Model Intercomparison Project (AMIP), 59

which are forced by observed SSTs, but still better represented than in their coupled counterparts in the 60

Coupled Model Intercomparison Project (CMIP). They identified the SW feedback (αSW) as the major 61

source of the diversity in α, caused by the widely differing cloud responses to large-scale circulation 62

changes, indicating that cloud representation is still a major challenge in AGCMs. This is in line with 63

the findings of Guilyardi et al. (2009a) showing a strong sensitivity of α to the applied convection 64

scheme. 65

Biases in the mean state are another possible contributor to biases in ENSO atmospheric feedbacks. 66

Dommenget et al. (2014, hereafter D14) show that many CGCMs simulate an unrealistic positive αSW 67

in the eastern equatorial Pacific in the presence of a strong cold SST bias. Too cold SSTs favor 68

circulation regimes causing a positive αSW over the eastern equatorial Pacific, as low-level stratiform 69

clouds in the subsidence region of the Walker Circulation are overestimated, which dissolve when 70

SSTs rise, for example during an El Niño event (Lloyd et al. 2012). The equatorial cold bias is a 71

common problem in many state-of-the-art climate models and caused by several factors including too 72

strong mean easterly zonal winds, leading to increased ocean upwelling of cold subsurface water and 73

too weak mean SW radiation due to overestimated mean cloud cover and optical thickness (Davey et 74

al. 2002; Guilyardi et al. 2009b; Vannière et al. 2013). 75

Further, in the presence of a strong equatorial cold bias Dommenget (2010, hereafter D10) found an El 76

Niño-like SST variability in the ECHAM5 AGCM coupled to a slab ocean, in the absence of any 77

interactive ocean dynamics. This “Heat Flux El Niño” is driven by the interaction of a positive αSW and 78

the heat capacity of the ocean mixed layer, contradicting classical ENSO theory (e.g. Neelin et al. 79

1998). Heat Flux El Niño dynamics in the presence of a cold SST bias can be found in many CGCMs 80

participating in CMIP3 and CMIP5, as shown in D14. 81

From these studies we suggest two main reasons for biases in atmospheric ENSO feedbacks: First, a 82

direct effect of uncertainties in the model physics. Second, an indirect effect in the sense that biases in 83

model physics create mean state biases which in turn bias the feedbacks. From these considerations, the 84

following questions are addressed in this study: 85

How much feedback diversity among climate models is caused by the direct effect of 86

uncertainties in the model physics, and how much by the indirect effect of the uncertainties in 87

the model physics by altering the mean state? 88

What controls the strength of µ and α in climate model simulations? What is the role of 89

equatorial Pacific SST in the atmospheric ENSO feedbacks? Why have AGCMs in AMIP 90

experiments more realistic feedbacks as when it is coupled to an ocean model? 91

Are the strengths of the µ and α linked to each other and if so, by which mechanism? 92

To answer these questions we investigate µ and α in the CMIP5 models and in a set of perturbed 93

physics experiments with the Kiel Climate Model (KCM, which employs ECHAM5 as the AGCM), in 94

which convection scheme parameters and vertical resolution are changed. We untangle the relative 95

influences of perturbed physics and mean state differences in the KCM ensemble by repeating the 96

perturbed physics experiments with ECHAM5 with prescribed SST. The ECHAM5 is able to simulate 97

realistic µ and α in AMIP simulations (Lloyd et al. 2011), but the model also can produce positive α 98

and αSW that drives the Heat Flux El Niño if coupled to a slab ocean with strong SST biases (D10). 99

Furthermore, in the study of Lloyd et al. (2011) the ECHAM5 had the largest difference in µ and α 100

between AMIP and CMIP runs, but the reason for this was unclear. Thus, the ECHAM5 AGCM covers 101

a wide range of possible α values, from strongly damping to even amplifying. Therefore the ECHAM5 102

AGCM seems to be a good choice to investigate the origin of ENSO atmospheric feedback biases. 103

This paper is organized as follows: In Section 2 we describe the data and the methods used in this 104

study, followed by the analysis of the ENSO atmospheric feedbacks in Section 3. In Section 4 we 105

investigate the direct and indirect effects of perturbed physics on the feedbacks, and Section 5 106

elucidates the influence of the mean state on the feedbacks. A detailed analysis of the heat flux 107

components is presented in Section 6, and in Section 7 we show which ENSO properties depend on the 108

atmospheric feedbacks. Summary and discussion of the major results in Section 8 conclude the paper. 109

2. Data and Methods 110

This study is based on observations and reanalysis products, and on data from perturbed physics 111

experiments with the Kiel Climate Model (KCM) and uncoupled integrations of the ECHAM5 model, 112

the atmospheric component of the KCM, on 20th

century experiments employing historical forcing 113

from the CMIP5 database and a preindustrial control integration of the BCCR CM2.0 model from the 114

CMIP3 data base. 115

Observed SSTs for the period 1979-2015 are taken from HadISST (Rayner et al. 2003), and 10 m zonal 116

wind (U10), vertical wind (W) at 500 hPa and atmospheric temperature for the period 1979 - 2015 from 117

ERA-Interim reanalysis (Simmons et al. 2007). We use U10 instead of zonal wind stress, as wind stress 118

was not available for all models used in this study. Precipitation (precip) for the period 1979 - 2015 is 119

from CMAP (Xie and Arkin 1997), total cloud cover for the period 1984-2009 from ISCCP (Rossow 120

and Schiffer 1999). Heat fluxes for the period 1979-2015 are taken from ERA Interim. For comparison 121

the Woods Hole Oceanographic Institution dataset (1984-2009), also referred to as the OA Flux dataset 122

(Yu et al. 2008) and ERA40 reanalysis heat fluxes for the period 1958-2001 (Uppala et al. 2005) also 123

are used, as Lloyd et al. (2011) find some discrepancies between the heat flux products. We use ERA-124

Interim heat fluxes in most analyses, as the long wave (LW) component of the OA Flux data set has 125

some spurious jumps (Vinukollu et al. 2011) that originate from the use of different satellite 126

instruments. The choice of the heat flux dataset, however, only has minor influences on our findings. 127

The perturbed physics experiments (Tab. 1A) are performed with the KCM (Park et al. 2009) 128

consisting of the ECHAM5 AGCM (Roeckner et al. 2003) coupled to the NEMO ocean-sea ice general 129

circulation model (OGCM, Madec et al. 1998; Madec 2008). The ocean model has a ~2° horizontal 130

resolution, with a latitudinal refinement up to ~0.5° near the equator, and 31 levels in the vertical. The 131

atmospheric resolution is T42 (~2.8°) in the horizontal. We investigate a set of 40 “present day” (CO2 132

concentration of 348 ppm) integrations of the KCM, each 100 years long, from which we analyze the 133

last 80 years. Three different vertical resolutions were chosen: L19, L31, and L62, all with the same top 134

level. The experiments also differ in the parameters for cloud and radiation processes in the convection 135

parameterization. The following three parameters were changed: “convective cloud conversion rate 136

from cloud water to rain”, “entrainment rate for shallow convection” and “convective mass-flux above 137

level of non-buoyancy”. Mauritsen et al. (2012) provide a detailed discussion of these parameters. The 138

parameter values chosen here are in the range suggested by Mauritsen et al. (2012). 139

Additionally, we performed three sets of experiments with the AGCM ECHAM5 integrated in stand-140

alone mode (Tab. 1). The first set uses the standard values for the convection parameters and observed 141

monthly SSTs from 1980 to 2009 from HadISST, and the three vertical resolutions L19, L31 and L62 142

(Tab. 1B), named “AMIP-like” experiments. This set provides information about systematic errors in 143

the AGCM. The second set consists of perturbed physics AGCM experiments with the same 144

convection parameters and vertical resolutions as in the perturbed physics experiments with the CGCM 145

and forced by observed SSTs from 1980 to 2009 from HadISST. This set helps to isolate the direct 146

effect of the changed convection parameters on ENSO atmospheric feedbacks (Tab. 1C). The third set 147

of AGCM experiments employs the standard values for the convection parameters but uses the 148

modeled, interannually varying SSTs from the last 80 years of the coupled runs. This set provides 149

information about the indirect effect of changing the convection parameters via different mean states 150

and SST variability (Tab. 1D). For the second and third set of AGCM experiments, we chose the setup 151

of 28 out of the 40 CGCM experiments, considering that each vertical resolution and strength of µ and 152

α is represented in the AGCM experiments. 153

We also use data from experiments with the ECHAM5 model coupled to a slab ocean, in which D10 154

found the heat flux-driven El Niño mode (there named Slab Ocean El Niño) in the presence of a strong 155

equatorial cold bias in the Pacific (ECHAM5 Biased-Slab-Ocean experiments, Tab. 1E). In this 156

experiment, the SST is controlled to mimic the SST climatology of the CNRM-CM3 climate model 157

from the CMIP3 database, as this model has a large equatorial Pacific cold bias (see D10 for details). 158

Further, we analyze a set of historical simulations (1900-1999) of the CMIP5 database (Taylor et al. 159

2012). The data is interpolated on a regular 2.5°×2.5° grid and we used all models with relevant data 160

available (see Fig. 2a for a list of the models). We also show results from a preindustrial control 161

integration of the BCCR CM2.0 climate model from the CMIP3 database (Meehl et al. 2007b), as it is 162

among the models exhibiting the largest biases in ENSO atmospheric feedbacks, as shown in D14. 163

Monthly mean values are used here. We remove the climatological seasonal cycle and subtract the 164

linear trend for each month separately. The Niño1.2 region is defined as 80°W-90°W and 10°S-0°, the 165

Niño3 region as 90°W-150°W and 5°S-5°N, the Niño3.4 region as 120°W-170°W and 5°S-5°N, and 166

the Niño4 region as 160°E-150°W and 5°S-5°N. 167

To define ENSO events we use the criterion of Trenberth (1997): An El Niño (La Niña) event occurs if 168

the five month running mean SST of Niño3.4 is above 0.5 (below -0.5) times the standard deviations 169

for at least six consecutive months. To illustrate the time evolution of El Niño events we use composite 170

Hoevmoeller diagrams along the equatorial Pacific (5°S-5°N). For better comparison, all variables are 171

normalized with mean Niño3.4 SST anomalies three months before and after the maximum of all 172

events. Furthermore, all Hoevmoeller diagrams are centered in time on the month of the maximum of 173

the ENSO events (lag 0). The maximum of the El Niño (La Niña) event is defined for each event 174

individually as the month of maximum (minimum) in 5 month running mean Niño3.4 SST anomaly 175

during this event. Composite analysis enables studying El Niño and La Niña events separately to 176

highlight nonlinearities which is an important ENSO property (e.g. An and Jin 2004; An et al. 2005; 177

Frauen and Dommenget 2010; Dommenget et al. 2013; Zhang and Sun 2013). However, in the first 178

part of the paper we show the composites of all events together (El Niño and La Niña events). 179

As a measure for the zonal atmospheric circulation along the equator we use the zonal stream function 180

as defined in Yu and Zwiers (2010) and Yu et al. (2012): 181

Ψ = 2𝜋𝑎 ∫ 𝑢𝐷

𝑑𝑝

𝑔

𝑝

0

with uD the divergent component of the zonal wind, a the radius of the Earth, p the pressure and g the 182

constant gravity. The zonal wind is averaged over the latitude band 5°N-5°S and integrated from the 183

top of the atmosphere to surface. 184

3. Atmospheric ENSO feedbacks 185

3.1 Observations, CMIP5 and KCM 186

The mean time evolution of ENSO events in observations and reanalysis data is shown in Figure 1a-e) 187

as composite Hoevmoeller diagrams. A positive SST anomaly in the eastern and central equatorial 188

Pacific at lag 0 goes along with a weakening of U10 in the Niño4 region (the dashed vertical lines at 189

160°E, 150°W and 90°W indicate the boundaries of the Niño4 and Niño3 region) and is accompanied 190

with stronger convection identified through a negative W anomaly at 500 hPa, increased precipitation 191

and cloud cover with the maxima in the Niño4 region. This constitutes the atmospheric feedback part 192

of the Bjerknes Feedback, caused by a zonal shift of the rising branch of the Walker Circulation during 193

an ENSO event (Bayr et al. 2014). The atmospheric feedbacks start together with the SST anomaly 194

west of the Niño4 region at about lag -10, propagate eastward and remain active for about five months 195

after the maximum SST anomaly (lag 0). The Qnet damping (Fig. 1 f) is strongest in the Niño3 and 196

Niño4 region about three months before and after the maximum SST anomaly. The thermal damping is 197

mainly caused by a negative SW flux in the Niño4 region and to a lesser extent Niño3 region, and a 198

negative LH flux in the Niño3 region (Fig. 1f,g,j), in agreement with the results of Lloyd et al. (2009). 199

The sensible heat flux (SH) is negative in the Niño4 region (Fig. 1i), while the LW feedback is mostly 200

of opposite sign with regard to SW (Fig. 1h), but both feedbacks are much weaker in amplitude than 201

the SW and LH (note the different colorbar range in Fig. 1f-j). The negative Qnet acts to reduce SST 202

anomalies and is thereby counteracting the positive Bjerknes feedback. 203

We define the strength of the zonal wind feedback µ as the U10 response averaged over the Niño4 204

region three months before and after an ENSO event (black box in Fig. 1b). Similarly, we define the 205

net heat flux feedback α as the Qnet response averaged over the Niño3 and Niño4 region together (black 206

box in Fig. 1f). As these composites are normalized by the Niño3.4 SST anomaly, they represent 207

changes per Kelvin warming, comparable to a regression. Figure 2a) depicts the two feedback 208

parameters (µ and α) from ERA-Interim, the BCCR CM2.0 model (the model with the most biased 209

atmospheric feedbacks among the CMIP3 and CMIP5 models investigated in D14), and all available 210

CMIP5 models. First, all climate models underestimate the strengths of µ and α, with a large spread 211

ranging from close to ERA-Interim (e.g. CNRM-CM5 model) to very weak magnitudes close to zero 212

(e.g. CSIRO-Mk3.6). Second, there is a clear linear relation between µ and α (r² = 0.57), i.e. models 213

with a stronger µ tend to also have a stronger α and vice versa. 214

We depict the results for the perturbed physics experiments with the KCM in Fig. 2b, in which only 215

parameters in the convection parameterization have been varied. Interestingly, the KCM ensemble 216

exhibits a similar spread in µ and α as the CMIP5 ensemble. The linear relationship between the two 217

feedback parameters is stronger in the KCM (r² = 0.88) than in the CMIP5 models (r² = 0.57), as 218

expected given that the CMIP5 models differ in many more aspects. And in agreement with the study 219

of Lloyd et al. (2011), the ECHAM5 AMIP-type runs have roughly the same values of the feedback 220

parameters as ERA-Interim and even larger with 62 vertical levels, while there is no major difference 221

between the experiments with 19 and 31 levels. On the other hand, the ECHAM5 Biased-Slab-Ocean 222

run has very weak feedback parameters, as expected from the results of D10 and D14. 223

The strong linear relation seen in Fig. 2 for both the CMIP5 and KCM ensemble suggests an error 224

compensation between µ and α, which is a common problem in current climate models and is discussed 225

as a major contributor to the diversity of the simulated ENSO (Lloyd et al. 2009; Bellenger et al. 2014). 226

Therefore we next address the question what causes the differences between observed and modeled 227

feedbacks? 228

3.2 Strong vs. weak feedbacks 229

We define the atmospheric feedback strength (i.e. of µ and α together) as the average of the µ and α 230

(normalized here with respect to ERA-Interim, i.e. a value of unity corresponds to the ERA-Interim 231

value). To investigate the origin of the differences between observed and modeled feedback parameters 232

we define three sub-ensembles from the KCM runs: one with strong µ and α (atmospheric feedback 233

strength > 0.5 of the ERA-Interim value), one with weak µ and α (atmospheric feedback strength < 0.3 234

of the ERA-Interim value), and one with medium values of µ and α (hereafter STRONG, WEAK and 235

MEDIUM, respectively). The color in Fig. 2b indicates sub-ensemble. All three sub-ensembles contain 236

simulations with all three vertical resolutions, indicating that the vertical resolution has a weak 237

influence, which also is supported by the AMIP-type runs (Fig. 2b). 238

The average time evolution of ENSO events are shown in Fig. 3 for the ECHAM5 AMIP-type 239

ensemble, the KCM STRONG, MEDIUM and WEAK sub-ensembles and for the ECHAM5 Biased-240

Slab-Ocean simulation. The AMIP-type runs (Fig. 3a-e) show very similar patterns as ERA-Interim 241

(compare to Fig. 1), but much stronger amplitudes in W, precipitation and cloud cover, while U10 has a 242

similar amplitude. In all three KCM sub-ensembles, an unrealistic westward propagation of SST 243

anomalies is simulated (Fig. 3f,k,p), which is most pronounced in WEAK. In STRONG, the maximum 244

U10, W, precipitation and cloud cover anomalies is, as in ERA-Interim, in the Niño4 region (Fig. 3g-j), 245

but displaced to the west in MEDIUM (Fig. 3l-o) and largely outside the Niño4 region in WEAK (Fig. 246

3q-t), i.e. the convective signal shifts westward as µ and α become smaller, and also weakens. 247

This also is reflected in the heat flux composites (Fig. 4). The ECHAM5 AMIP-type runs (Fig. 4a-e) 248

show a pattern of Qnet similar to ERA-Interim. However, the amplitudes of the SW anomalies in the 249

Niño4 region and LH anomalies in the Niño3 region are stronger than in ERA-Interim, which is 250

compensated by stronger LW anomalies in the Niño4 region and slightly positive SW anomalies in the 251

Niño3 region. With regard to the three KCM sub-ensembles, the Qnet anomalies decrease from 252

STRONG (Fig. 4f-j) to WEAK (Fig. 4p-t), as expected from the selection criterion. Further, the 253

strongest negative Qnet anomalies shift from the Niño4 region in STRONG to the western edge of the 254

Niño4 region in MEDIUM, and to west of the Niño4 region in WEAK. The SW damping in the Niño4 255

region gets weaker and the unrealistic positive SW feedback in the Niño3 region gets stronger from 256

STRONG to WEAK, consistent with the cloud cover anomalies. 257

In the ECHAM5 Biased-Slab-Ocean simulation (Fig. 4u-y), positive Qnet flux anomalies appear before 258

and negative Qnet flux anomalies after an ENSO event, giving rise to the Heat Flux El Niño: The strong 259

positive SW forcing in the Niño3 region drives a positive SST anomaly. This fits to the cloud cover 260

anomalies that are about 90° out of phase with the SST anomalies, i.e. cloud cover is forcing the SST, 261

as described in D10 and D14. LW and LH counteract the SW forcing, and the positive SH anomalies to 262

the west of the SST anomaly causes westward propagating SST anomalies, as seen in Fig. 3u (for a 263

more detailed description of the Heat Flux El Niño dynamics see D10 and D14). The period of the Heat 264

Flux El Niño is longer than that of the ENSO in the coupled runs, but there is some similarity of the 265

heat fluxes of the Heat Flux El Niño with those in the WEAK sub-ensemble. This suggests that ENSO 266

in WEAK is partly driven by the positive αsw, as previously shown in D14 for a number of CMIP3 and 267

CMIP5 models. Indeed the ENSO in the WEAK sub-ensemble shares some important aspects with the 268

Heat Flux El Niño simulated in the ECHAM5 Biased-Slab-Ocean experiment (Fig. 3u-y): The 269

pronounced westward propagation of the SST anomalies (Fig. 3u), the weak U10, precipitation and 270

cloud cover anomalies in the Niño4 region, and the unrealistic cloud cover decrease in the Niño3 271

region. 272

In summary there is a systematic change in the anomalous atmospheric circulation from ECHAM5 273

AMIP-type to ECHAM Biased-Slab-Ocean, with the three KCM sub-ensembles fitting in between, 274

with the convective response shifting westward and an unrealistic cloud cover reduction and positive 275

SW flux in the Niño3 region. We obtain very similar results when clustering the CMIP5 models in the 276

same way as the KCM simulations (Fig. 5 and Fig. 6) with WEAK sharing similarities with the Heat 277

Flux El Niño of the Biased-Slab-Ocean run. This demonstrates that also in some CMIP5 models in the 278

presence of weak atmospheric feedback parameters, µ and α, the ENSO is partly heat flux driven, as 279

described in D14. 280

4. Direct vs. indirect effect of changed convection parameters 281

Next we investigate whether the differences among the three KCM sub-ensembles, WEAK, MEDIUM 282

and STRONG, is directly caused by altering the parameters in the atmospheric convection scheme or 283

due to changes in the mean state by changing the radiation balance as shown by Mauritsen et al. (2012). 284

We know from the AMIP-type runs that ECHAM5, when forced by observed SSTs, can produce values 285

of µ and α consistent with ERA-Interim. In one set of experiments, we perturb the physics in the 286

AGCM as in the perturbed physics experiments with the CGCM and force the model by observed SSTs 287

to isolate the direct effect of the changed convection parameters on atmospheric processes. A second 288

set uses the same convection parameters as those in the three AMIP-type runs that produce realistic µ 289

and α with specified observed SSTs, but the AGCM is forced by the SSTs simulated in the coupled 290

runs. This enables determining the indirect effect through the different mean states. The values of µ are 291

shown in Fig. 7a and of α in Fig. 7b. The coupled runs with the KCM are shown in blue, the first set of 292

experiments with ECHAM5 in red and the second set of experiments with ECHAM5 in green (the 293

experiments are ordered by their µ and α in the coupled runs). The direct effect of changing the 294

convection parameters (red bars) has only a weak influence on µ and α, as nearly most of the 295

experiments have feedbacks comparable to the AMIP-type runs. Especially the average over all 296

experiments fits quite well. However, the ECHAM5 integrations, in which we prescribe the simulated 297

SSTs from the coupled runs, have values of µ and α very similar to those in the coupled runs, which is 298

especially true for µ, suggesting that in the KCM it is the mean SSTs which dominate the differences in 299

the feedback strength and not the convection scheme parameters. 300

5. Mean state and feedback strength 301

5.1 Mean SST and background atmospheric state 302

The above results exhibit that the mean SSTs control the strength of µ and α. Therefore the spatial 303

distribution of the mean SSTs in the different sub-ensembles is investigated (Fig. 8). We compute the 304

departure from the SSTs averaged over the tropical Indo-Pacific region (40°E-70°W, 15°S-15°N). 305

Climates with different mean temperatures can be more easily compared when using relative SST. 306

More importantly, the atmospheric circulation in the tropics depends strongly on the relative 307

temperature distribution and to a lesser extent on absolute temperatures (Bayr and Dommenget 2013). 308

Compared to observations (Fig. 8a) the KCM has an extensive equatorial cold tongue bias in all three 309

sub-ensembles (Fig. 8b,d,f), mostly differing in the Niño4 region with the STRONG (WEAK) sub-310

ensemble exhibiting the smallest (largest) cold bias in this region. The three sub-ensembles of the 311

CMIP5 models show this behavior more clearly, as all sub-ensembles have a much weaker cold SST 312

bias in the Niño3 region than the KCM (Fig. 8c,e,g). We note that the CMIP5 models exhibit a larger 313

warm SST bias towards the coast. 314

The zonal structure of selected variables along the equator (5°N-5°S) further demonstrates that the 315

largest differences between the three sub-ensembles is in the Western Pacific. The mean SST, 316

precipitation and W at 500 hPa (Fig. 9a-c) most strongly differs in the Niño4 region, with STRONG 317

being closest to ERA-Interim. With regard to W at 500 hPa (Fig. 9c) STRONG has upward motion in 318

the Niño4 region, MEDIUM upward motion west and downward motion east of the dateline, and 319

WEAK has downward motion in the Niño4 region. The mean precipitation (Fig. 9b) and U10 (Fig. 9d) 320

fit well to the mean W. The mean Qnet in the KCM sub-ensembles (Fig. 9e) reflects enhanced (reduced) 321

cloud cover in the western (eastern) Pacific in STRONG relative to WEAK (Fig. 9f). 322

The KCM sub-ensembles are in a kind of “permanent” La Niña-like state with respect to many 323

variables, which is caused by the cold SST bias in the Niño4 region, which is most pronounced in the 324

WEAK sub-ensemble. The AMIP-type runs reveal another model bias in the Niño4 region: The 325

ECHAM5 model, when forced by observed SSTs, has atmospheric feedback strength parameters 326

similar to ERA-Interim, but stronger upward motion and more rainfall. Further, the relation between 327

the mean-state SST and atmospheric circulation seems to be model dependent: The CMIP5 models 328

depict a smaller equatorial Pacific cold SST bias in all three sub-ensembles in comparison to the KCM 329

(Fig. 10a), but similar atmospheric-state differences between the sub-ensembles (Fig. 10b-f). 330

Nevertheless, the CMIP5 results confirm that the atmospheric feedbacks strongly depend on the mean 331

SST and associated atmospheric state in the western central equatorial Pacific. 332

Variables averaged over the Niño4 region are plotted against the atmospheric feedback strength for 333

each individual experiment in Fig. 11, where the atmospheric feedback strength is defined as the mean 334

of µ and α (normalized with values from ERA Interim). We obtain from the KCM integrations strong 335

linear relationships between the atmospheric feedback strength and the mean SST (relative to the area 336

mean of the tropical Indo-Pacific), precipitation and W (r² = 0.86, 0.73, 0.67, respectively). The colors 337

of the numbers indicate the different vertical resolutions (L19 in black, L31 in magenta and L62 in 338

cyan) and reveal a separation, which is most obvious in precipitation and W. The correlation between 339

runs with the same vertical resolution is even higher than when considering all runs together (Fig. 11a-340

c). Thus, the relation between mean state and feedback strength depends on the vertical resolution. 341

We also obtain from the CMIP5 models significant linear relationships (Fig. 11d-f), but the correlations 342

are weaker than in the KCM (r² = 0.53|0.62|0.62 respectively). This is not surprising since the CMIP5 343

models considerably differ in model resolution (horizontal and vertical) and a variety of physical 344

parameterizations, whereas the KCM integrations only differ in vertical resolution and convection 345

scheme parameters. In summary, both ensembles support that the atmospheric feedback strength is 346

strongly controlled by the mean state in the western central equatorial Pacific. 347

5.2 Mean state of the Pacific Walker Circulation 348

We next analyze the Walker Circulation, as represented by the zonal stream function, from ERA-349

interim and the STRONG, MEDIUM and WEAK CMIP5 sub-ensembles (Fig. 12a,b,d,e). The most 350

striking difference in these figures is the position of the rising branch of the Pacific Walker Circulation 351

(zero line over the western Pacific, Yu and Zwiers 2010), as expected from the above results (Figs. 352

9,10). The rising branch of the Walker Circulation is the region of strongest convection in the 353

equatorial Pacific. The rising branch is centered at about 151°E in ERA-Interim (indicated by the 354

dashed vertical line), 145°E in STRONG, 139°E in MEDIUM and 131°E in WEAK. This explains why 355

in WEAK the convective response during ENSO events also is too far to the west (Figs. 3r, 5m), 356

having in mind that ENSO events are accompanied by a zonal shift of the main convection region 357

which is the rising branch of the Walker Circulation (Philander 1990; Bayr et al. 2014). In ERA-358

Interim, the main convection region shifts eastward by about 17° during El Niño (black dashed-dotted 359

line in Fig. 12c) and positioned in the Niño4 region. The same can be seen in STRONG (red dashed-360

dotted line in Fig. 12c). In WEAK, the main convection region is too far west and does not reach the 361

Niño4 region during El Niño (green dotted-dashed line in Fig. 12c). The correct position of the rising 362

branch of the Walker Circulation is essential for a realistic Bjerknes feedback (Neelin et al. 1998; 363

Jansen et al. 2009). Consistent with the results shown in Fig. 11 depends the position of the rising 364

branch of the Walker Circulation on the cold bias in Niño4 (Fig. 12f), since in the tropics convection is 365

strongest where it is relative warm and a cold bias hampers the convection (Bayr and Dommenget 366

2013). So in summary the position of the rising branch of the Walker Circulation explains the different 367

response of the convection and U10 during ENSO events as seen in Fig. 3 and 5. 368

6. Contribution of the heat fluxes 369

6.1 Decomposition of the net heat flux 370

The heat flux feedback is more complex than the wind feedback. Previous studies (e.g. Lloyd et al. 371

2009; Bellenger et al. 2014) define the heat flux feedback α over the Niño3 region, whereas we find 372

heat flux changes in both the Niño3 and Niño4 region important. 373

In the Niño4 region, αSW is clearly dominating the parameter α (Fig. 13a), with a systematic decrease of 374

α and αSW from ECHAM5 AMIP-type (having a stronger than observed α and αSW) to ECHAM5 375

Biased-Slab-Ocean run (having αSW of nearly zero and α even positive). In the Niño3 region (Fig. 13b), 376

in ERA-Interim αLH and αSW dominate the parameter α, with αLH being nearly twice as strong as αSW. In 377

the KCM (Fig. 13b), αSW and to a lesser extent αLH dominate the difference in the strength of α, and 378

also is systematically changing from ECHAM5 AMIP-type (being quite close to ERA-Interim) to 379

ECHAM5 Biased-Slab-Ocean run (having the largest bias). 380

The CMIP5 sub-ensembles, STRONG, MEDIUM and WEAK, yield a similar systematic change in 381

αSW from STRONG to WEAK in both the Niño4 and Niño3 region, with a larger αSW bias in the Niño4 382

region in comparison to the KCM but a smaller αSW bias in the Niño3 region (Fig. 13c,d). Thus αSW 383

dominates α in the Niño3 region in both CMIP5 and KCM, in agreement with Lloyd et al. (2009, 2011, 384

2012).The parameter αSW also dominates α in the Niño4 region and a link between αSW in the Niño3 385

and Niño4 region can be seen. Indeed, we get a significant correlation of 0.66 (0.71) for α (αSW) from 386

CMIP5 and KCM 0.86 (0.77). 387

6.2 Short-wave feedback 388

Lloyd et al. (2012) and Bellenger et al. (2014) argue that αSW and its nonlinearity is a major contributor 389

to the large diversity in simulated ENSO. Here we offer a new perspective on the role of αSW and 390

derive its relation to the Walker Circulation. The SW flux during El Niño events (Fig. 14a) can be 391

explained by the eastward shift of the main convection region, driving negative SW flux east of 150°E 392

(the mean position of the rising branch of the Walker Circulation in ERA-Interim) and positive SW 393

flux west of it. During, La Niña (Fig. 14b) the pattern is similar, but somewhat weaker and more to the 394

west (note the shifted sign due to normalization with Niño3.4 SST anomalies). The SW response is thus 395

stronger during La Niña west of the dateline and stronger during El Niño east of the dateline (Fig. 14c), 396

in agreement with the eastward shift of the Walker Circulation during El Niño and westward shift 397

during La Niña. In the ECHAM5 AMIP-type runs, a stronger negative SW flux in the Niño4 region is 398

simulated relative to ERA-Interim for both El Niño and La Niña, but during El Niño the negative SW 399

flux in the Niño3 region underestimated. In the KCM sub-ensembles, we find a westward shift of the 400

negative SW flux from STRONG to WEAK, for both El Niño and La Niña, in agreement with the 401

westward shift of the main convection region from STRONG to WEAK. 402

Interestingly, the further west the negative SW flux is located the stronger the positive SW flux in the 403

Niño3 region gets. This relationship can be explained with the Walker Circulation and its associated 404

cloud cover: Lloyd et al. (2009) report a positive SW feedback in observations in the eastern Pacific in 405

the cold tongue region, close to the coast of South America in the Niño1.2 region, where the 406

descending branch of the Walker Circulation is located and low-level stratiform clouds exist, that 407

dissolve when SST rise. Further, Lloyd et al. (2012) found that these low level clouds are 408

overestimated in many climate models. The KCM indeed simulates more low-level cloud cover in 409

WEAK than in STRONG (not shown). The strong correlation between αsw in Niño3 and Niño4 410

indicates a remote control, which can be explained by the Walker Circulation. Further, a positive SW 411

flux in the Niño3 region can also be found in the AMIP-type runs (Fig. 14d,e). This implies that this 412

bias is a systematic error in the AGCM, which is strongly enhanced by a La Niña-like mean state of the 413

Walker Circulation in the coupled models. This can be seen in the SW flux composites of the KCM as 414

well CMIP5 models (Fig. 15). Our results indicate that the SW feedback in the Niño4 and Niño3 region 415

is strongly controlled by the mean Walker Circulation. 416

Further, our results show a link between the SW non-linearity in the eastern equatorial and the location 417

of the main convection region: The SW non-linearity in Niño3 decreases the more the main convection 418

region moves to the west, since it is largest close to the main convection region (Fig. 14, 15, last 419

column). Thus we hypothesize that the strength of the SW non-linearity is related to the location of the 420

main convection region. 421

7. Feedback strength and ENSO properties 422

Two examples of ENSO properties that vastly differ among climate models are the seasonal ENSO 423

phase locking (Bellenger et al. 2014) and the asymmetry between El Niño and La Niña (Dommenget et 424

al. 2013). Wengel et al. (2016, submitted to Clim. Dyn.) suggest that the seasonal ENSO phase locking 425

in the Niño3.4 region is impacted by the cold SST bias. This study additionally suggests a link to the 426

atmospheric feedback strength. A phase locking index is defined according to Bellenger et al. (2014) as 427

the standard deviation of the Niño3.4 SST anomalies averaged over December to February (the months 428

with the highest SST variability in observations) divided by the SST anomalies averaged over April to 429

June (the months with the lowest SST variability in observations). We find a linear relation between the 430

phase locking index and the atmospheric feedback strength (average over µ and α) in the KCM (Fig. 431

16a, r² = 0.29) and CMIP5 (Fig. 16c, r² = 0.46) ensemble. A simple measure for the asymmetry 432

between El Niño and La Niña is the difference in skewness of the Niño3 and Niño4 SST anomalies 433

(Burgers and Stephenson 1999). As shown in Fig. 16b,d), the asymmetry strongly depends on the 434

atmospheric feedback strength with r²=0.63 (0.65) in the KCM (CMIP5) ensemble, as proposed by 435

Zhang and Sun (2013). Thus, the feedback strengths measured by µ and α is a key to improve 436

important ENSO properties in climate models. 437

8. Summary and Discussion 438

In this study we present a detailed analysis of the two most important atmospheric ENSO feedbacks, 439

the positive zonal wind feedback described by the parameter µ and the negative heat flux feedback 440

described by the parameter α. Climate models depict a large range of the feedback strength parameters, 441

often with compensating errors between these two. Two climate model ensembles were analyzed to 442

understand the origin of the diversity in atmospheric ENSO feedback parameters: one ensemble is 443

comprised of control integrations of the Kiel Climate Model (KCM) with differing physics and the 444

other of the models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). 445

Perturbed physics experiments with the KCM, in which three parameters of its atmospheric convection 446

scheme and vertical atmosphere model resolution have been varied, depict a spread in the strengths of 447

µ and α similar to that seen in the CMIP5 ensemble. Further, there is a strong linear relationship 448

between µ and α, in both the KCM and CMIP5 ensemble. 449

Companion perturbed physics experiments prescribing observed SSTs to ECHAM5, the AGCM of the 450

KCM, reveal that it is the atmospheric mean-state differences through differences in the SST that 451

explains the large range of feedback strengths in the KCM, while perturbed physics play a minor role. 452

Specifically, the equatorial SST influences the mean Walker Circulation and the associated deep 453

convection. The Niño4 region has been identified as a key region where mean-state SST controls the 454

strength of µ and α. Models with a relatively small cold equatorial SST bias simulate feedbacks more 455

consistent with reanalysis that models with a large cold bias. 456

More in detail, a systematic change in the mean state between the sub-ensembles of models with 457

STRONG, MEDIUM and WEAK feedbacks can be seen: The experiments with the weakest 458

atmospheric feedbacks have the largest cold bias in Niño4, i.e. the cold bias increases from STRONG 459

to WEAK and causes a westward shift of the area of mean convection from STRONG to WEAK, 460

which is the rising branch of the Walker Circulation. This is associated with mean descent in Niño4 in 461

the WEAK sub-ensemble in contrast to Niño4 mean ascent in the STRONG sub-ensemble. A 462

comparison with La Niña conditions in observations reveals that the strong cold bias in WEAK causes 463

a La Niña-like mean state of the Walker Circulation with a too west position of the rising branch of the 464

Walker Circulation (up to 30°). 465

We show that the mean state of the Walker Circulation determines the atmospheric response to SST 466

anomalies and explains the difference in convective response to SST anomalies over the western 467

Pacific in the climate models. The strong response of U10 during El Niño in the STRONG sub-468

ensemble is caused by a strong convective response to SST warming with a spatial maximum as found 469

in observations in the Niño4 region accompanied with ascending air, more cloud cover and 470

precipitation (Fig. 3f-j). In the WEAK sub-ensemble we only get a weak U10 response in Niño4 due to 471

a weak convective response to SST warming, with only weak ascending, increase in cloud cover and 472

precipitation (Fig. 3p-t). This is in line with the mean state position of rising branch of the Walker 473

Circulation. The results of the MEDIUM sub-ensemble fall in between those of STRONG and WEAK 474

which corroborates this identified systematic change between the sub-ensembles. 475

The strength of αSW dominates the strength of α and is strongly linked to the convective response in the 476

Niño4 region. The strong linear relation between µ and α can be explained as follows: The weakening 477

of the zonal winds in Niño4 in response to warmer SSTs during El Niño is caused by more convection 478

in Niño4, which in turn increases the cloud cover and reduces the SW flux there (vice versa for La 479

Niña). In STRONG the response of convection and SW to SSTs changes is much stronger than in 480

WEAK, leading to an overall stronger α in Niño4 in STRONG. 481

The heat flux response in Niño3 is a bit more complex compared to that in Niño4: The strongest 482

damping is the αLH, but it shows a much weaker difference between the sub-ensembles than αSW, which 483

can be explained by the difference in near surface humidity (not shown), in agreement with Lloyd et al. 484

(2011). So the diversity in strength of α is here also dominated by αSW, in agreement with (Lloyd et al. 485

2009, 2011, 2012) and Bellenger et al. (2014). Many CMIP5 models and nearly all KCM runs exhibit a 486

positive αSW, while it is negative in observations. As discussed in Section 6.1 there is a linear relation 487

between the strength of αSW in Niño3 and strength of αSW in Niño4, i.e. models with a stronger αSW 488

damping in Niño4 tend to have a less biased αSW in Niño3, in both KCM and CMIP5. Previous studies 489

(e.g. Lloyd et al. 2009; Bellenger et al. 2014) define α only over the Niño3 region, and not as we do 490

over Niño3 and Niño4 region. We define α over both Niño3 and Niño4 for several reasons: First the 491

feedback composites of observations show that the Qnet damping is strongest in Niño3 and in Niño4 492

region (with the maximum in Niño4), and that models with a weak heat flux damping fail to reproduce 493

the damping in both regions. Second, we get a higher linear relationship between µ and α in KCM 494

when considering α over both Niño3 and Niño4 (r² = 0.88) than only Niño3 (r² = 0.70). Third, this 495

strong linear relationship between α and αSW of these two regions exists not only in sub-ensembles, but 496

also in the individual CMIP5 models and KCM runs, as discussed in Section 6.1. Fourth this strong 497

relation can be explained by the mean state and response of the Walker Circulation, as discussed in 498

Section 5.2. Thus the linear relationship of the Qnet damping of these two regions exists not only in a 499

statistical sense and we suggest for future studies to define α over both Niño3 and Niño4 regions. The 500

different definition used here may explain why we get a stronger linear relationship between µ and α in 501

our Fig. 2a (r²=0.57) as Bellenger et al. (2014) in a similar analysis shown in their Fig. 9b (r² = 0.23). 502

In the ECHAM5 AMIP-type runs we could see that the STRONG sub-ensemble has a lot of similarities 503

with the AMIP-type runs, but shows weaker amplitudes. Thus the AMIP-type runs underline very well 504

the systematic change associated with the SST bias, indicating that KCM with prescribed SSTs from 505

observations would also have as strong feedbacks as observed. But we have to mention that the AMIP-506

type runs have feedback strengths close to observations for the wrong reason. The vertical wind and 507

precipitation are in the AMIP-type runs more in an El Niño-like mean state (Fig. 9), i.e. a very strong 508

ascending and much precipitation in Niño4, even more as during El Niño in observations. The response 509

of the vertical wind to SST changes is ~70% stronger as in observations (Fig. 3c), which leads to a 510

~50% stronger SW damping in Niño4 (Fig. 13a). But on the other hand in Niño3 the SW damping is 511

only half as strong as in observations (Fig. 13b), leading to a similar feedback strength as in 512

observations, when averaging over both Niño3 and Niño4 region. This seems to be a general problem 513

of ECHAM5 that the difference in αSW between Niño3 and Niño4 is more extreme as in observations or 514

the in CMIP5 (i.e. a stronger negative αSW in Niño4 but also a more biased (positive) αSW in Niño3). So 515

even ECHAM5 has in the absence of a SST bias quite good atmospheric feedbacks, the AMIP-type 516

runs indicate biases in models physics. 517

Further we find a lot of similarities between the WEAK sub-ensemble and the Heat Flux El Niño of the 518

Biased-Slab-Ocean run (Fig. 3u-y and 4u-y), e.g. the east to west propagation of SST anomalies, a 519

weak convective response too far in the west, a positive αSW in Niño3 and a positive sensible heat flux 520

response in Niño4. This is a clear indication that ENSO in WEAK is not only driven by ocean 521

dynamics, but rather at least partly driven by a positive heat flux feedback, as already pointed out in 522

D14. Thus our results confirm, that the cold bias favors the Heat Flux El Niño dynamics (D10, D14) 523

and hampers the ENSO atmospheric and oceanic feedbacks (Kim et al. 2014b), which means that 524

climate models create with very different feedbacks an ENSO variability that looks not too different 525

from observed ENSO SST statistics, but due to very different dynamics. 526

Further, the U10 response in WEAK is strongest west of Niño4 and much too weak in Niño4, which 527

can have significant impacts on the Bjerknes Feedback (Neelin et al. 1998; Jansen et al. 2009). We 528

therefore support the idea to quantify the role of the too weak atmospheric part of the Bjerknes 529

Feedback on the total Bjerknes feedback and the interaction of the subsurface heating of the Bjerknes 530

feedback and the amplifying heat fluxes in these runs. D14 did this analysis in a mostly statistical way 531

and found that the ENSO dynamics of many of the CMIP3 and CMIP5 models show similarities with 532

the Heat Flux El Niño and have quite different thermocline response compared with observations. It 533

would be very interesting to fully understand the ENSO dynamics in the presence of weak atmospheric 534

feedbacks in a physical way, but this is beyond the scope of this paper. 535

The open question of Lloyd et al. (2011), why there is such a huge difference in feedback strength 536

between ECHAM5 AMIP-type run and ECHAM5 coupled to an OGCM, can be answered with the 537

results of this study: It is the cold bias that evolves during coupling due to the combination of too 538

strong equatorial mean zonal wind (Fig. 9d), which causes more ocean upwelling, and weaker net heat 539

flux along the equator (Fig. 9e). The cold bias is a common problem in many CGCM (Davey et al. 540

2002; Guilyardi et al. 2009b; Vannière et al. 2013). Therefore AMIP-type runs are a good test for the 541

general ability of the model to generate realistic atmospheric feedback strengths. Furthermore, the 542

results of this study suggest a detailed and systematic analysis of the influence of the convection 543

parameters on ENSO properties and ENSO feedback strengths. The study of Wengel et al. (2016, 544

submitted to Clim. Dyn.) indicates that e.g. the “convective mass-flux above level of non-buoyancy” 545

parameter strongly influences the equatorial cold bias in the KCM and thus ENSO phase locking via 546

atmospheric feedback strengths. Further it would be very interesting to investigate the influence of the 547

vertical resolutions on the feedback strengths, as in KCM runs with the same mean state in W in Niño4 548

but a different vertical resolution the one with the higher vertical resolution has the stronger 549

atmospheric feedbacks (Fig. 11c). This is in line with the results of Harlaß et al. (2015), that the vertical 550

resolution is important in the tropics, especially near the equator, where the Coriolis Force is low and 551

the radius of deformation becomes large. 552

Further, a more realistic strength of the feedback parameters µ and α improves important ENSO 553

properties in climate models, e.g. the seasonal ENSO phase locking or the asymmetry in the SST 554

anomalies between El Niño and La Niña events. Improving the phase locking would improve seasonal 555

ENSO predictions, as in observations the equatorial Pacific SSTs are strongly damped in boreal spring 556

due to stronger negative feedbacks and become more unstable till the end of the year due to stronger 557

positive feedbacks in the second half of the year to cause an El Niño event (Dommenget and Yu 2016; 558

Wengel et al. 2016, submitted to Clim. Dyn.). And improving the asymmetry between El Niño and La 559

Niña is important for the global warming projections, as the strength of the east-west shift of the 560

Walker Circulation during ENSO events depends on the ability of simulate the spatial asymmetry in 561

SST between El Niño and La Niña events, thus influences how well the eastward or westward shift of 562

the Walker Circulation under global warming is represented in the climate models (Bayr et al. 2014). A 563

zonal shift of the Walker Circulation under global warming can have large socio-economical impacts, 564

as it also shifts the convection regions in the equatorial Pacific. 565

This study supports to use perturbed physics ensemble of one model for investigations additional to a 566

multi model ensemble. Using only one model reduces the possible causes and can give a more clear 567

answer than the multi model ensemble, as seen e.g. in Fig. 11. And it is possible to further narrow 568

down the possible explanations by experiments with individual model components, as we have done 569

here with the AGCM experiments. 570

There are several publications that show an influence of the different model physics or convection 571

schemes on the ENSO atmospheric feedback strength (e.g. Guilyardi et al. 2009a; Lloyd et al. 2009, 572

2011, 2012; Bellenger et al. 2014), but these studies do not detangle the direct effect of different model 573

physics and the indirect effect of the different model physics on the mean state. A recent study of 574

Dommenget (2016) shows that differences in the mean state can dominate the uncertainties in climate 575

models and not biases in the model physics. This we can also see in the CMIP5 ensemble, as we get a 576

remarkable linear relationship between the mean state of Niño4 and the feedback strength with r² > 0.5 577

(Fig. 11d-f). This means that the different mean states account for more than half of the feedback 578

strength differences and biases in the model physics play a smaller role. This can be underlined by the 579

strong similarities between CMIP5 and KCM, as in KCM we could directly attribute the difference in 580

feedback strength to the differences in the mean state in the AGCM experiments. Further, the results 581

shown here suggest a quite simple explanation for the diversity of the simulated ENSO: In many 582

CGCM the Southern Oscillation is not well coupled to the ocean, as the rising branch of the Walker 583

Circulation is too far in the west over the Maritime Continent, where the ocean is not so sensitive to 584

zonal wind changes. 585

The results presented here show that the atmospheric part of the Bjerknes Feedback is maybe too much 586

simplified in the Bjerknes Feedback formulation as it does not fully describe the complexity of the 587

atmospheric feedbacks: In Bjerknes Feedback theory the zonal wind response in the Niño4 region is 588

caused by a decrease in SST gradient due to warming in the eastern Pacific. This reduces (at least in 589

our thinking) the Southern Oscillation to a surface phenomenon and omits the atmospheric circulation 590

of the Walker Circulation in the free atmosphere. We showed in this study that the mean state of the 591

Walker Circulation is as important for the Bjerknes Feedback as the thermocline slope in ocean, as only 592

a mean state of the Walker Circulation close to observations can force the ocean at the right location so 593

that these two oscillations can couple and generate El Niño Southern Oscillation dynamics similar to 594

what we observe. Thus it would be of great benefit to rethink the atmospheric role in the Bjerknes 595

Feedback, to more satisfy the complexity of the atmospheric feedbacks in the Bjerknes Feedback. 596

Acknowledgements 597

We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, the 598

individual modeling groups of the Climate Model Intercomparison Project (CMIP3 and CMIP5), the 599

UK Met Office, ECMWF, NOAA, ISCCP and Woods Hole Oceanographic Institution for providing 600

the data sets. The climate model integrations of the KCM and ECHAM5 were performed at the 601

Computing Centre of Kiel University. This work was supported by the SFB 754 “Climate-602

Biochemistry Interactions in the tropical Ocean”, the European Union’s InterDec project, the ARC 603

Centre of Excellence for Climate System Science (Grant CE110001028), the ARC project ‘‘Beyond the 604

linear dynamics of the El Niño Southern Oscillation’’ (Grant DP120101442). This is a contribution to 605

the Cluster of Excellence “The Future Ocean” at the University of Kiel. 606

References 607

An, S.-I., Y.-G. Ham, J.-S. Kug, F.-F. Jin, and I.-S. Kang, 2005: El Niño–La Niña Asymmetry in the 608

Coupled Model Intercomparison Project Simulations*. J. Clim., 18, 2617–2627. 609

An, S. Il, and F. F. Jin, 2004: Nonlinearity and asymmetry of ENSO. J. Clim., 17, 2399–2412, 610

doi:10.1175/1520-0442(2004)017<2399:NAAOE>2.0.CO;2. 611

Bayr, T., and D. Dommenget, 2013: The Tropospheric Land–Sea Warming Contrast as the Driver of 612

Tropical Sea Level Pressure Changes. J. Clim., 26, 1387–1402, doi:10.1175/JCLI-D-11-00731.1. 613

——, ——, T. Martin, and S. B. Power, 2014: The eastward shift of the Walker Circulation in response 614

to global warming and its relationship to ENSO variability. Clim. Dyn., doi:10.1007/s00382-014-615

2091-y. 616

Bellenger, H., E. Guilyardi, J. Leloup, M. Lengaigne, and J. Vialard, 2014: ENSO representation in 617

climate models: From CMIP3 to CMIP5. Clim. Dyn., 42, 1999–2018, doi:10.1007/s00382-013-618

1783-z. 619

Bjerknes, J., 1969: Atmospheric Teleconnections from the Equatorial Pacific. Mon. Weather Rev., 97, 620

163–172. 621

Burgers, G., and D. B. Stephenson, 1999: The “normality” of El Niño. Geophys. Res. Lett, 26, 1027–622

1030. 623

Davey, M., and Coauthors, 2002: STOIC: A study of coupled model climatology and variability in 624

tropical ocean regions. Clim. Dyn., 18, 403–420, doi:10.1007/s00382-001-0188-6. 625

DiNezio, P. N., and Coauthors, 2012: Mean Climate Controls on the Simulated Response of ENSO to 626

Increasing Greenhouse Gases. J. Clim., 25, 7399–7420, doi:10.1175/JCLI-D-11-00494.1. 627

Dommenget, D., 2010: The slab ocean El Niño. Geophys. Res. Lett., 37, L20701, 628

doi:10.1029/2010GL044888. 629

——, 2016: A simple model perturbed physics study of the simulated climate sensitivity uncertainty 630

and its relation to control climate biases. Clim. Dyn., 46, 427–447, doi:10.1007/s00382-015-2591-631

4. 632

——, and Y. Yu, 2016: The seasonally changing cloud feedbacks contribution to the ENSO seasonal 633

phase-locking. Clim. Dyn., 1–12, doi:10.1007/s00382-016-3034-6. 634

——, T. Bayr, and C. Frauen, 2013: Analysis of the non-linearity in the pattern and time evolution of 635

El Niño southern oscillation. Clim. Dyn., 40, 2825–2847, doi:10.1007/s00382-012-1475-0. 636

——, S. Haase, T. Bayr, and C. Frauen, 2014: Analysis of the Slab Ocean El Nino atmospheric 637

feedbacks in observed and simulated ENSO dynamics. Clim. Dyn., doi:10.1007/s00382-014-2057-638

0. 639

Frauen, C., and D. Dommenget, 2010: El Niño and La Niña amplitude asymmetry caused by 640

atmospheric feedbacks. Geophys. Res. Lett., 37, L18801, doi:10.1029/2010GL044444. 641

Guilyardi, E., and Coauthors, 2004: Representing El Niño in Coupled Ocean–Atmosphere GCMs: The 642

Dominant Role of the Atmospheric Component. J. Clim., 17, 4623–4629, doi:10.1175/JCLI-643

3260.1. 644

Guilyardi, E., and Coauthors, 2009a: Atmosphere Feedbacks during ENSO in a Coupled GCM with a 645

Modified Atmospheric Convection Scheme. J. Clim., 22, 5698–5718, 646

doi:10.1175/2009JCLI2815.1. 647

——, A. Wittenberg, A. Fedorov, M. Collins, C. Wang, A. Capotondi, G. J. van Oldenborgh, and T. 648

Stockdale, 2009b: Understanding El Niño in ocean-atmosphere general circulation models: 649

Progress and challenges. Bull. Am. Meteorol. Soc., 90, 325–340, doi:10.1175/2008BAMS2387.1. 650

Harlaß, J., M. Latif, and W. Park, 2015: Improving climate model simulation of tropical Atlantic sea 651

surface temperature: The importance of enhanced vertical atmosphere model resolution. Geophys. 652

Res. Lett., 42, 2401–2408, doi:10.1002/2015GL063310. 653

Jansen, M. F., D. Dommenget, and N. Keenlyside, 2009: Tropical Atmosphere–Ocean Interactions in a 654

Conceptual Framework. J. Clim., 22, 550–567, doi:10.1175/2008JCLI2243.1. 655

Jin, F. F., S. T. Kim, and L. Bejarano, 2006: A coupled-stability index for ENSO. Geophys. Res. Lett., 656

33, 2–5, doi:10.1029/2006GL027221. 657

Kim, D., J.-S. Kug, I.-S. Kang, F.-F. Jin, and A. T. Wittenberg, 2008: Tropical Pacific impacts of 658

convective momentum transport in the SNU coupled GCM. Clim. Dyn., 31, 213–226, 659

doi:10.1007/s00382-007-0348-4. 660

Kim, S. T., W. Cai, F.-F. Jin, A. Santoso, L. Wu, E. Guilyardi, and S.-I. An, 2014a: Response of El 661

Niño sea surface temperature variability to greenhouse warming. Nat. Clim. Chang., 4, 786–790, 662

doi:10.1038/nclimate2326. 663

——, ——, F. F. Jin, and J. Y. Yu, 2014b: ENSO stability in coupled climate models and its 664

association with mean state. Clim. Dyn., 42, 3313–3321, doi:10.1007/s00382-013-1833-6. 665

Latif, M., and N. S. Keenlyside, 2009: El Niño/Southern Oscillation response to global warming. Proc. 666

Natl. Acad. Sci., 106, 20578–20583. 667

Lloyd, J., E. Guilyardi, H. Weller, and J. Slingo, 2009: The role of atmosphere feedbacks during ENSO 668

in the CMIP3 models. Atmos. Sci. Lett., 10, 170–176, doi:10.1002/asl.227. 669

——, ——, and ——, 2011: The role of atmosphere feedbacks during ENSO in the CMIP3 models. 670

Part II: using AMIP runs to understand the heat flux feedback mechanisms. Clim. Dyn., 37, 1271–671

1292, doi:10.1175/JCLI-D-11-00178.1. 672

——, ——, and ——, 2012: The role of atmosphere feedbacks during ENSO in the CMIP3 models. 673

Part III: The shortwave flux feedback. J. Clim., 25, 4275–4293, doi:10.1175/JCLI-D-11-00178.1. 674

Madec, G., 2008: NEMO ocean engine. Note du Pole modélisation 27, Inst. Pierre-Simon Laplace, 193 675

pp. 676

——, P. Delecluse, M. Imbard, and C. Lévy, 1998: OPA 8.1 Ocean General Circulation Model 677

Manual. Note du Pole modélisation 11, Inst. Pierre-Simon Laplace, 91 pp. 678

Mauritsen, T., and Coauthors, 2012: Tuning the climate of a global model. J. Adv. Model. Earth Syst., 679

4, n/a-n/a, doi:10.1029/2012MS000154. http://doi.wiley.com/10.1029/2012MS000154. 680

Meehl, G. A., and Coauthors, 2007a: Global Climate Projections. Climate Change 2007: The Physical 681

Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the 682

Intergovernmental Panel on Climate Change, Cambridge University Press, 747–846. 683

Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. 684

E. Taylor, 2007b: THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change 685

Research. Bull. Am. Meteorol. Soc., 88, 1383, doi:10.1175/BAMS-88-9-1383. 686

Neelin, J. D., D. S. Battisti, A. C. Hirst, F.-F. Jin, Y. Wakata, T. Yamagata, and S. E. Zebiak, 1998: 687

ENSO theory. J. Geophys. Res., 103, 14261, doi:10.1029/97JC03424. 688

Van Oldenborgh, G. J., S. Philip, and M. Collins, 2005: El Nino in a changing climate: a multi-model 689

study. Ocean Sci., 1, 81–95. 690

Park, W., N. S. Keenlyside, M. Latif, A. Ströh, R. Redler, E. Roeckner, and G. Madec, 2009: Tropical 691

Pacific Climate and Its Response to Global Warming in the Kiel Climate Model. J. Clim., 22, 71–692

92, doi:10.1175/2008JCLI2261.1. 693

Philander, S., 1990: El Niño, La Niña, and the southern oscillation. Academic Press, San Diego, USA, 694

293 pp. 695

Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, 696

and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air 697

temperature since the late nineteenth century. J. Geophys. Res., 108, 4407. 698

Roeckner, E., and Coauthors, 2003: The atmospheric general circulation model ECHAM5. PART I: 699

Model description, Report 349. Max Planck Institute for Meteorology, Hamburg, Germany, 140 700

pp. 701

Rossow, W. B., and R. a Schiffer, 1999: Advances in Understandig Clouds from ISCCP. Bull. Amer. 702

Meteor. Soc., 80, 2261–2287, doi:10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2. 703

Schneider, E. K., 2002: Understanding differences between the equatorial Pacific as simulated by two 704

coupled GCMs. J. Clim., 15, 449–469, doi:10.1175/1520-705

0442(2002)015<0449:UDBTEP>2.0.CO;2. 706

Simmons, A., S. Uppala, D. Dee, and S. Kobayashi, 2007: ERA-Interim: New ECMWF reanalysis 707

products from 1989 onwards. ECMWF Newsl., 110, 25–35. 708

Stocker, T., D. Qin, G. Plattner, M. Tignor, and S. Allen, 2013: IPCC 2013: Climate change 2013: The 709

physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the 710

Intergovernmental Panel on Climate Change. Cambridge University Press, New York, 1535 pp. 711

Sun, D. Z., Y. Yu, and T. Zhang, 2009: Tropical water vapor and cloud feedbacks in climate models: A 712

further assessment using coupled simulations. J. Clim., 22, 1287–1304, 713

doi:10.1175/2008JCLI2267.1. 714

Taylor, K. E., R. J. Stouffer, and G. a. Meehl, 2012: An Overview of CMIP5 and the Experiment 715

Design. Bull. Am. Meteorol. Soc., 93, 485–498, doi:10.1175/BAMS-D-11-00094.1. 716

Trenberth, K. E., 1997: The definition of El Niño. Bull. Am. Meteorol. Soc., 78, 2771–2778. 717

Uppala, S. M., and Coauthors, 2005: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, 2961–3012, 718

doi:10.1256/qj.04.176. 719

Vannière, B., E. Guilyardi, G. Madec, F. J. Doblas-Reyes, and S. Woolnough, 2013: Using seasonal 720

hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on 721

ENSO. Clim. Dyn., 40, 963–981, doi:10.1007/s00382-012-1429-6. 722

Vinukollu, R. K., R. Meynadier, J. Sheffield, and E. F. Wood, 2011: Multi-model, multi-sensor 723

estimates of global evapotranspiration: climatology, uncertainties and trends. Hydrol. Process., 25, 724

3993–4010, doi:10.1002/hyp.8393. 725

Wang, C., and J. Picaut, 2004: Understanding ENSO physics—a review. Earth’s Climate, American 726

Geophysical Union, 21–48. 727

——, C. Deser, and J. Yu, 2012: El Niño and Southern Oscillation (ENSO): A review. Coral Reefs of 728

the Eastern Pacific. 729

Wengel, C., M. Latif, W. Park, J. Harlaß, and T. Bayr, 2016: Controls of seasonal ENSO phase locking 730

in the Kiel Climate Model: The importance of the equatorial cold sea surface temperature bias. 731

Submitt. to Clim. Dyn.,. 732

Xie, P., and P. A. Arkin, 1997: Global Precipitation: A 17-Year Monthly Analysis Based on Gauge 733

Observations, Satellite Estimates, and Numerical Model Outputs. Bull. Am. Meteorol. Soc., 78, 734

2539–2558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2. 735

Yu, B., and F. W. Zwiers, 2010: Changes in equatorial atmospheric zonal circulations in recent 736

decades. Geophys. Res. Lett., 37, L05701. 737

Yu, B., F. W. Zwiers, G. J. Boer, and M. F. Ting, 2012: Structure and variances of equatorial zonal 738

circulation in a multimodel ensemble. Clim. Dyn., doi:10.1007/s00382-012-1372-6. 739

Yu, L., X. Jin, and R. A. Weller, 2008: Multidecade Global Flux Datasets from the Objectively 740

Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, 741

and related surface meteorological variables. Oa-2008-01, 64, doi:10.1007/s00382-011-1115-0. 742

Zhang, T., and D.-Z. Sun, 2013: ENSO Asymmetry in CMIP5 Models. J. Clim., 27, 4070–4093, 743

doi:10.1175/JCLI-D-13-00454.1. 744

745

Tables 746

747

Tab. 1: List of experiments with the KCM. 748

Name of

experiments

Number of

experiments

(and vertical

resolution)

Convection

parameters

SSTs Experiment

length

A Perturbed physics

CGCM

40

(1-28: L19,

29-34: L31,

35:40: L62)

three convection

parameters changed;

see Wengel et al.

(2016) for details

simulated by

OGCM

100 years

B AGCM AMIP-type 3 (L19, L31,

L62)

standard forced with monthly

SST from HadISST

1980-2009

C Perturbed physics

AGCM with

observed SSTs

28 (16 x L19,

6 x L31,

6 x L62)

like in A forced with monthly

SST from HadISST

1980-2009

D AGCM with

simulated SSTs

28 (16 x L19,

6 x L31,

6 x L62)

standard forced with

simulated SSTs

from A

80 years

E AGCM Biased-

Slab-Ocean

1 (L19) standard slab ocean with

simulated SST

climatology from

CNRM-CM3 model

1000 years

749

Figure Captions 750

751

Figure 1: Composite Hoevmoeller diagrams of El Niño and La Niña events of the equatorial Pacific 752

(averaged between 5°S and 5°N), with five month running mean Niño3.4 index > 0.5 | <-0.5 standard 753

deviations as selection criterion according to Trenberth (1997) for observations/reanalysis data in a) 754

equatorial Sea Surface Temperature (SST), in b) zonal wind in 10 m height (U10), in c) vertical wind in 755

500 hPa (W, negative upward), in d) precipitation (precip), in e) total cloud cover, in f) net heat flux 756

(Qnet), in g) net short wave radiation (SW), in h) net long wave radiation (LW), in i) sensible heat flux 757

(SH), in j) latent heat flux (LH); All variables are normalized with mean Niño3.4 SST three months 758

before and after the maximum of the events and are centered in time on the month of the maximum of 759

the ENSO events (lag 0). All heat fluxes are defined as positive downward and note the different 760

colorbar range for each heat flux. The dashed lines mark the Niño3 and Niño4 regions and the 761

maximum of the ENSO events in time. The box marks the region where the ENSO response is 762

strongest in observations. 763

764

Figure 2: Zonal wind feedback (µ) vs. net heat flux feedback (α) in ENSO in a) for individual CMIP5 765

models; in b) for a series of perturbed physics experiments with the Kiel Climate Model (KCM). ERA 766

Interim is shown as the yellow circle, the BCCR CM2.0 model is the dark green circle, ECHAM5 767

Biased-Slab-Ocean run from D10 is the cyan circle and the ECHAM5 AMIP-type runs with three 768

different vertical resolutions are the purple triangles (L19 is the downward pointing triangle, L31 769

sideward, L62 upward). Values shown here are the averages over the boxes as shown in Fig. 1b) and 770

1f) for ERA Interim, i.e. for wind feedback over the Niño4 region, and for heat flux feedback over the 771

Niño3 and Niño4 region in the space domain, and ±3 months before and after the maximum of the 772

ENSO events in time domain. The colors of the numbers indicate the members of the three sub-773

ensembles with WEAK (green), MEDIUM (blue) and STRONG (red) atmospheric feedbacks as used 774

in the following. 775

776

Figure 3: Same as Fig. 1, but here for ECHAM5 AMIP-type ensemble (a-e), for sub-ensembles of 777

KCM runs with STRONG (f-j), MEDIUM (k-o) and WEAK (p-t) atmospheric feedbacks and for 778

ECHAM5 Biased-Slab-Ocean run (u-y). Note that for ECHAM5 Biased-Slab-Ocean run the lag is 779

different (±40 months) as the duration of the Heat flux El Niño is longer and W at 500 hPa was not 780

available. 781

782

Figure 4: Same as Fig. 3, but here for heat fluxes. 783

784

Figure 5: Same as Fig. 3, but here for CMIP5 sub-ensembles with STRONG (a-e), MEDIUM (f-j) and 785

WEAK (k-o) atmospheric feedbacks. 786

787

Figure 6: Same as Fig. 4, but here for CMIP5 sub-ensembles with STRONG (a-e), MEDIUM (f-j) and 788

WEAK (k-o) atmospheric feedbacks. 789

790

Figure 7: ENSO atmospheric feedback strength in perturbed physics experiments with the KCM 791

CGCM (blue bars), perturbed physics experiments with the AGCM (same convection scheme 792

parameters as in CGCM) but forced with observed SSTs (red bars) and AGCM experiments with 793

convection parameters as in AMIP-type run but simulated SSTs from CGCM (green bars) in a) for 794

zonal wind feedback µ and in b) for net heat flux feedback α. The black line indicates the strength of 795

the feedbacks in ERA Interim, the magenta line of the ECHAM5 AMIP-type ensemble as shown in 796

Fig. 2b). The experiments are ordered by their atmospheric feedbacks strength (i.e. µ and α together) 797

798

Figure 8: a) Mean SST relative to area mean tropical Indo-Pacific SST for observations; SST 799

difference KCM minus observations in b) for STRONG, in d) for MEDIUM, in f) for WEAK sub-800

ensemble; SST difference CMIP5 minus observations in c) for STRONG, in e) for MEDIUM, in g) for 801

WEAK sub-ensemble. The black box marks the Niño4 region. 802

803

Figure 9: Equatorial mean state (5°S-5°N) in observations, in KCM sub-ensembles with STRONG, 804

MEDIUM and WEAK feedbacks, in ECHAM5 AMIP-type ensemble and in ECHAM5 Biased-Slab-805

Ocean run; in a) for SST relative to area mean tropical Indo-Pacific SST, in b) for precipitation, in c) 806

for W in 500 hPa, in d) for U10, in e) for Qnet, in f) for total cloud cover. The black dashed-dotted 807

(dashed) line is the mean over all El Niño (La Niña) months in observations. For the ECHAM5 Biased-808

Slab-Ocean run W at 500 hPa is not available. 809

810

Figure 10: Same as Fig. 9, but here for the STRONG, MEDIUM and WEAK sub-ensemble of CMIP5. 811

812

Figure 11: KCM: a) SST bias in Niño4 region (black box in Fig. 8), with SST relative to the area mean 813

of tropical Indo-Pacific SST, on y-axis vs. atmospheric feedbacks on the x-axis (average of µ and α, 814

normalized with value of ERA Interim); b) same as a) but here for mean precipitation in Niño4 region 815

on the y-axis; c) same as a) but here for mean W at 500 hPa in the Niño4 region on the y-axis; The 816

colors of the numbers indicate the vertical resolution: L19 in black, L31 in magenta and L62 in cyan 817

and the r² values are given for all experiments, and in brackets for only L19, only L31, only L62, 818

respectively (only for KCM); in d-f): same as a-c), but here for the CMIP5 models. The yellow circle 819

represents observations, the dark green circle the BCCR CM2.0 model, the cyan circle the ECHAM5 820

Biased-Slab-Ocean run and the magenta triangles the ECHAM5 AMIP-type experiments. 821

822

Figure 12: Mean state of Walker Circulation represented as zonal stream function along the equator, in 823

a) for ERA Interim, in b) for STRONG, in d) for MEDIUM, in e) for WEAK sub-ensemble of CMIP5; 824

positive values indicate a clockwise circulation, the thick black line at the bottom marks the land 825

masses of the Maritime Continent and South America, the vertical dashed line the rising branch of the 826

Walker Circulation in observations; c) mass weighted vertical mean of zonal stream function along the 827

equator in observations and CMIP5 sub-ensembles as seen in a,b,d,e), the dashed-dotted (dashed) lines 828

is the average over all El Niño (La Niña) months; f) longitude of the rising branch of the Walker 829

Circulation (where stream function crosses the zero line) on the x-axis vs. relative SST bias in Niño4 as 830

shown in Fig. 11d on the y-axis. 831

832

Figure 13: Heat flux feedbacks during ENSO events for Qnet, SW, LW, SH, and LH in ERA Interim 833

reanalysis, the ECHAM5 AMIP-type ensemble, the KCM STRONG, MEDIUM and WEAK sub-834

ensembles and the ECHAM5 Biased-Slab-Ocean run in a) for Niño4 region, and in b) for Niño3 835

region; c) same as a) but here for Niño4 heat flux response in OA heat flux data set, ERA40 and 836

STRONG, MEDIUM and WEAK CMIP5 sub-ensembles; d) same c) but here for Niño3 region. The 837

values shown here are the averages over the Hoevmoeller composites ±3 months before and after the 838

ENSO event, as shown e.g. in Fig. 4. 839

840

Figure 14: Composites of SW with Niño3.4 SST as selection criterion and normalized with mean 841

Niño3.4 SST for El Niño, La Niña and the difference La Niña - El Niño , i.e. a measure for the non-842

linearity of the SW feedback; in a-c) for observations (OA Flux), in d-f) for ECHAM5 AMIP-type 843

ensemble, in g-i) for STRONG, j-l) for MEDIUM, m-o) for WEAK sub-ensemble in KCM. 844

845

Figure 15: Same as Fig. 14, but here for the STRONG, MEDIUM and WEAK sub-ensemble of 846

CMIP5. 847

848

Figure 16: a) same as Fig. 11a), but here on the y-axis a measure of the ENSO phase locking in the 849

Niño3.4 region in KCM; b) same as a), but here on the y-axis a measure for the asymmetry between El 850

Niño and La Niña in KCM (skewness of Niño3 - skewness of Niño4); c-d) same as a-b), but here for 851

CMIP5 models. 852

853

Table captions: 854

855

Tab. 1: List of experiments with the KCM. 856

Figures to:

Mean State Dependence of ENSO Atmospheric Feedbacks

in Climate Models

ENSO Composites in observations

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

SST

a)

K/ K

Nin

o3.4

−1.25

−1

−0.75

−0.5

−0.25

0

0.25

0.5

0.75

1

1.25

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

U10

b)m

/s/ K

Nin

o3.4

−2

−1.6

−1.2

−0.8

−0.4

0

0.4

0.8

1.2

1.6

2

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

W at 500 hPa

c)

10−

2 Pa/

s/ K

Nin

o3.4

−3.5

−2.8

−2.1

−1.4

−0.7

0

0.7

1.4

2.1

2.8

3.5

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

LongitudeM

onth

s

Precip

d)

mm

/day

/ KN

ino3

.4

−4

−3.2

−2.4

−1.6

−0.8

0

0.8

1.6

2.4

3.2

4

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

Cloud cover

e)

%/ K

Nin

o3.4

−15

−12

−9

−6

−3

0

3

6

9

12

15

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

Q net

f)

W/m

2 / KN

ino3

.4

−30

−24

−18

−12

−6

0

6

12

18

24

30

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

SW

g)

W/m

2 / KN

ino3

.4

−25

−20

−15

−10

−5

0

5

10

15

20

25

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

LW

h)

W/m

2 / KN

ino3

.4

−10

−8

−6

−4

−2

0

2

4

6

8

10

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

SH

i)

W/m

2 / KN

ino3

.4

−3

−2.4

−1.8

−1.2

−0.6

0

0.6

1.2

1.8

2.4

3

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

LH

j)

W/m

2 / KN

ino3

.4

−15

−12

−9

−6

−3

0

3

6

9

12

15

Figure 1: Composite Hoevemoeller diagrams of El Niño and La Niña events of the equatorial Paci�c (averagedbetween 5oS and 5oN), with �ve month running mean Niño3.4 index > 0.5| < −0.5 standard deviations asselection criterion according to Trenberth (1997) for observations/reanalysis data in a) equatorial Sea SurfaceTemperature (SST), in b) zonal wind in 10 m height (U10), in c) vertical wind in 500 hPa (W, negative upward),in d) precipitation (precip), in e) total cloud cover, in f) net heat �ux (Qnet), in g) net short wave radiation(SW), in h) net long wave radiation (LW), in i) sensible heat �ux (SH), in j) latent heat �ux (LH); All variablesare normalised with mean Niño3.4 SST three months before and after the maximum of the events and arecentered in time on the month of the maximum of the ENSO events (lag 0). All heat �uxes are de�ned aspositive downward and note the di�erent colorbar range for each heat �ux. The dashed lines mark the Niño3 andNiño4 regions and the maximum of the ENSO events in time. The box marks the region where the ENSOresponse is strongest in observations.

1

Zonal wind vs. net heat �ux feedback in

ERA Interim (1.49/−18.11)BCCR−BCM2.0 (0.34/2.22)

1 : ACCESS1−0 (0.77/−12.37)2 : ACCESS1−3 (0.67/−4.17)3 : BCC−CSM1−1 (0.66/−8.71)4 : BNU−ESM (0.90/−10.52)5 : CanESM2 (0.53/−6.87)6 : CMCC−CM (0.93/−11.56)7 : CNRM−CM5 (1.10/−15.06)8 : CSIRO−Mk3−6−0 (0.47/1.90)9 : GFDL−CM3 (0.64/−6.93)

10 : GFDL−ESM2G (0.51/−3.55)11 : GFDL−ESM2M (1.00/−9.88)12 : GISS−E2−R (1.29/−10.35)13 : HadGEM2−CC (0.70/−6.04)14 : HadGEM2−ES (0.75/−4.17)15 : INM−CM4 (0.73/−5.07)16 : IPSL−CM5A−LR (0.44/−1.13)17 : IPSL−CM5A−MR (0.57/−2.18)18 : MIROC5 (0.91/−8.23)19 : MIROC−ESM (0.56/−3.72)20 : MIROC−ESM−CHEM (0.49/−4.20)21 : MPI−ESM−LR (0.39/−5.12)22 : MPI−ESM−MR (0.63/−4.46)23 : MRI−CGCM3 (0.56/−4.86)24 : NorESM1−ME (1.26/−9.51)

0 0.5 1 1.5

−20

−15

−10

−5

0

5

1

2

3

4

5

6

7

8

9

10

11 12

13

1415

1617

18

192021

2223

24

Wind feedback in Nino4 [m/s/KNino3.4

]

Hea

t flu

x fe

edba

ck in

Nin

o3 a

nd N

ino4

[W/m

2 /KN

ino3

.4]

CMIP5r2 = 0.57

a) ERA Interim (1.49/−18.11)BCCR CM2.0 (0.34/2.22)ECHAM5 Biased−Slab (0.38/−0.54)ECHAM5 AMIP L19 (1.63/−16.46)ECHAM5 AMIP L31 (1.63/−17.14)ECHAM5 AMIP L62 (1.55/−21.49)Exp1 (0.36/2.39)Exp2 (0.41/0.85)Exp3 (0.51/−1.02)Exp4 (0.62/−4.29)Exp5 (0.69/−5.98)Exp6 (0.88/−8.92)Exp7 (0.91/−10.14)Exp8 (0.95/−11.09)Exp9 (1.05/−12.33)Exp10 (0.53/−5.76)Exp11 (0.48/−1.63)Exp12 (0.49/−0.62)Exp13 (0.42/−0.70)Exp14 (0.36/−0.21)Exp15 (0.36/−0.62)Exp16 (0.37/−0.50)Exp17 (0.34/−0.17)Exp18 (0.56/−2.47)Exp19 (0.58/−2.60)Exp20 (0.48/−0.96)Exp21 (0.45/−0.46)Exp22 (0.33/0.13)Exp23 (0.67/−7.02)Exp24 (0.45/−0.57)Exp25 (0.74/−10.09)Exp26 (0.61/−5.01)Exp27 (0.45/−2.29)Exp28 (0.59/−5.60)Exp29 (0.59/−6.23)Exp30 (0.94/−11.17)Exp31 (0.88/−9.21)Exp32 (0.72/−7.39)Exp33 (0.35/−2.62)Exp34 (0.69/−7.69)Exp35 (0.54/−2.45)Exp36 (0.82/−8.67)Exp37 (0.72/−7.83)Exp38 (0.64/−6.45)Exp39 (0.48/−2.82)Exp40 (0.43/−3.98)0 0.5 1 1.5

−20

−15

−10

−5

0

5

1

2

3

4

5

678

9

10

11121314151617

1819

202122

23

24

25

26

27

2829

30

31

32

33

34

35

3637

38

3940

Wind feedback in Nino4 [m/s/KNino3.4

]

Hea

t flu

x fe

edba

ck in

Nin

o3 a

nd N

ino4

[W/m

2 /KN

ino3

.4]

KCMr2 = 0.88

b)

Figure 2: Zonal wind feedback (µ) vs. net heat �ux feedback (α) in ENSO in a) for individual CMIP5 models;in b) for a series of perturbed physiks experiments with the Kiel Climate Model (KCM). ERA Interim is shown asthe yellow circle, the BCCR CM2.0 model is the dark green circle, ECHAM5 Biased-Slab-Ocean run from D10 isthe cyan circle and the ECHAM5 AMIP runs with three di�erent vertical resolutions are the purple triangles (L19is the downward pointing triangle, L31 sideward, L62 upward). Values shown here are the averages over the boxesas shown in Fig. 1b) and 1f) for ERA Interim, i.e. for wind feedback over the Niño4 region, and for heat �uxfeedback over the Niño3 and Niño4 region in the space domain, and ±3 months before and after the maximum ofthe ENSO events in time domain. The colors of the numbers indicate the members of the three sub-ensembleswith WEAK (green), MEDIUM (blue) and STRONG (red) atmospheric feedbacks as used in the following.

2

ENSO Composites in KCMSST U10 W at 500 hPa Precip Cloud cover

AMIP-type

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

a)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

b)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

c)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

d)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

e)

STRONG

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

f)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

g)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

h)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

i)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

j)

MEDIUM

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

k)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

l)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

m)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

n)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

o)

WEAK

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

p)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

q)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

r)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

s)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

t)

Biased-Slab

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

u)

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

v)

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

x)

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

y)

K/ KNino3.4

−1.25 −0.625 0 0.625 1.25

m/s/ KNino3.4

−2 −1 0 1 2

10−2 Pa/s/ KNino3.4

−3.5 −1.75 0 1.75 3.5

mm/day/ KNino3.4

−4 −2 0 2 4

%/ KNino3.4

−15 −7.5 0 7.5 15

Figure 3: Same as Fig. 1, but here for ECHAM5 AMIP-type ensemble (a-e), for sub-ensembles of KCM runswith STRONG (f-j), MEDIUM (k-o) and WEAK (p-t) atmospheric feedbacks and for ECHAM5Biased-Slab-Ocean run (u-y). Note that for ECHAM5 Biasd-Slab-Ocean run the lag is di�erent (±40 months) asthe duration of the Heat �ux El Niño is longer and W at 500 hPa was not available.

3

ENSO Composites in KCMQ net SW LW SH LH

AMIP-type

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

a)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

b)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

c)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

d)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

e)

STRONG

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

f)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

g)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

h)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

i)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

j)

MEDIUM

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

k)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

l)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

m)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

n)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

o)

WEAK

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

p)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

q)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

r)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

s)

150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

t)

Biased-Slab

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

u)

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

v)

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

w)

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

x)

120E 150E 180 150W 120W 90W−40

−30

−20

−10

0

10

20

30

40

Longitude

Mon

ths

y)

W/m2/ KNino3.4

−30 −15 0 15 30

W/m2/ KNino3.4

−25 −12.5 0 12.5 25

W/m2/ KNino3.4

−10 −5 0 5 10

W/m2/ KNino3.4

−3 −1.5 0 1.5 3

W/m2/ KNino3.4

−15 −7.5 0 7.5 15

Figure 4: Same as Fig. 4, but here for heat �uxes.

4

ENSO Composites in CMIP5SST U10 W at 500 hPa Precip Cloud cover

STRONG

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

a)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

b)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

c)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

d)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

e)

MEDIUM

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

f)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

g)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

h)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

i)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

j)

WEAK

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

k)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

l)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

m)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

n)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

o)

K/ KNino3.4

−1.25 −0.625 0 0.625 1.25

m/s/ KNino3.4

−2 −1 0 1 2

10−2 Pa/s/ KNino3.4

−3.5 −1.75 0 1.75 3.5

mm/day/ KNino3.4

−4 −2 0 2 4

%/ KNino3.4

−15 −7.5 0 7.5 15

Figure 5: Same as Fig. 3, but here for CMIP5 sub-ensembles with STRONG (a-e), MEDIUM (f-j) and WEAK(k-o) atmospheric feedbacks.

5

ENSO Composites in CMIP5Q net SW LW SH LH

STRONG

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

a)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

b)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

c)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

d)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

e)

MEDIUM

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

f)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

g)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

h)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

i)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

j)

WEAK

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

k)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

l)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

m)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

n)

120E 150E 180 150W 120W 90W−20

−15

−10

−5

0

5

10

15

20

Longitude

Mon

ths

o)

W/m2/ KNino3.4

−30 −15 0 15 30

W/m2/ KNino3.4

−25 −12.5 0 12.5 25

W/m2/ KNino3.4

−10 −5 0 5 10

W/m2/ KNino3.4

−3 −1.5 0 1.5 3

W/m2/ KNino3.4

−15 −7.5 0 7.5 15

Figure 6: Same as Fig. 4, but here for CMIP5 sub-ensembles with STRONG (a-e), MEDIUM (f-j) and WEAK(k-o) atmospheric feedbacks.

6

all 1 22 14 24 20 33 3 39 35 40 19 10 26 28 29 38 5 32 34 37 36 25 6 31 7 30 8 90

0.5

1

1.5

2

a)

Experiment Nr.

Win

d fe

edba

ck in

Nin

o4 [m

/s/K

Nin

o3.4

]

Zonal wind feedback

CGCM: perturbed physics ...AGCM: perturbed physicsAGCM: simulated SSTsERA InterimAGCM: AMIP−type

all 1 22 14 24 20 33 3 39 35 40 19 10 26 28 29 38 5 32 34 37 36 25 6 31 7 30 8 9−25

−20

−15

−10

−5

0

b)

Experiment Nr.

Hea

t flu

x fe

edba

ck in

Nin

o3 a

nd N

ino4

[W/m

2 /KN

ino3

.4]

Net heat flux feedback

Figure 7: ENSO atmospheric feedback strength in perturbed physics experiments with the KCM CGCM (bluebars), perturbed physics experiments with the AGCM (same convection scheme parameters as in CGCM) butforced with observed SSTs (red bars) and AGCM experiments with convection parameters as in AMIP-type runbut simulated SSTs from CGCM (green bars) in a) for zonal wind feedback µ and in b) for net heat �ux feedbackα. The black line indicates the strength of the feedbacks in ERA Interim, the margenta line of the ECHAM5AMIP-type ensemble as shown in Fig. 2b). The experiments are ordered by their atmospheric feedbacks strength(i.e. µ and α together)

7

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

a) Relative mean SST in Obs

K

−6−4.8−3.6−2.4−1.201.22.43.64.86

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

b) KCM: Diff. STRONG − Obs

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

c) CMIP5: Diff. STRONG − Obs

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

d) KCM: Diff. MEDIUM − Obs

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

e) CMIP5: Diff. MEDIUM − Obs

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

f) KCM: Diff. WEAK − Obs

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

g) CMIP5: Diff. WEAK − Obs

K−3 −1.5 0 1.5 3

Figure 8: a) Mean SST relative to area mean tropical Indo-Paci�c SST for observations; SST di�erence KCMminus observations in b) for STRONG, in d) for MEDIUM, in f) for WEAK sub-ensemble; SST di�erenceCMIP5 minus observations in c) for STRONG, in e) for MEDIUM, in g) for WEAK sub-ensemble. The blackbox marks the Niño4 region.

8

KCM: Equatorial mean state

120E 150E 180 150W 120W 90W−4

−3

−2

−1

0

1

2

3

longitude

SS

T [K

]

Rel. equatorial mean SST

a)

ObsObs mean El Nino ...Obs mean La NinaSTRONGMEDIUMWEAKAMIP−typeBiased−Slab

120E 150E 180 150W 120W 90W0

5

10

15Equatorial mean Precip

longitude

Pre

cip

[mm

/day

]

b)

120E 150E 180 150W 120W 90W−12

−10

−8

−6

−4

−2

0

2

4

6Equatorial mean W at 500 hPa

longitude

W a

t 500

hP

a [1

0−2 P

a/s]

c)

120E 150E 180 150W 120W 90W−8

−6

−4

−2

0

2

4Equatorial mean U10

longitude

U10

[m/s

]

d)

120E 150E 180 150W 120W 90W−20

0

20

40

60

80

100

120

140Equatorial mean Q net

longitude

Q n

et [W

/m2 ]

e)

120E 150E 180 150W 120W 90W30

40

50

60

70

80

90

100Equatorial mean Cloud cover

longitude

Clo

ud c

over

[%]

f)

Figure 9: Equatorial mean state (5oS − 5oN) in observations, in KCM sub-ensembles with STRONG,MEDIUM and WEAK feedbacks, in ECHAM5 AMIP-type ensemble and in ECHAM5 Biased-Slab-Ocean run; ina) for SST relative to area mean tropical Indo-Paci�c SST, in b) for precipitation, in c) for W in 500 hPa, in d)for U10, in e) for Qnet, in f) for total cloud cover. The black dashed-dotted (dashed) line is the mean over all ElNiño (La Niña) months in observations. For the ECHAM5 Biased-Slab-Ocean run W at 500 hPa is not available.

9

CMIP5: Equatorial mean state

120E 150E 180 150W 120W 90W−6

−5

−4

−3

−2

−1

0

1

2

3

longitude

SS

T [K

]

Rel. equatorial mean SST

a)

ObsObs mean El Nino ...Obs mean La NinaSTRONGMEDIUMWEAK

120E 150E 180 150W 120W 90W0

2

4

6

8

10

12Equatorial mean Precip

longitude

Pre

cip

[mm

/day

]

b)

120E 150E 180 150W 120W 90W−8

−6

−4

−2

0

2

4Equatorial mean W at 500 hPa

longitude

W a

t 500

hP

a [1

0−2 P

a/s]

c)

120E 150E 180 150W 120W 90W−7

−6

−5

−4

−3

−2

−1

0

1

2

3Equatorial mean U10

longitude

U10

[m/s

]

d)

120E 150E 180 150W 120W 90W−20

0

20

40

60

80

100

120

140Equatorial mean Q net

longitude

Q n

et [W

/m2 ]

e)

120E 150E 180 150W 120W 90W30

40

50

60

70

80

90Equatorial mean Cloud cover

longitude

Clo

ud c

over

[%]

f)

Figure 10: Same as Fig. 9, but here for the STRONG, MEDIUM and WEAK sub-ensemble of CMIP5.

10

Atmospheric feedbacks vs. mean state in Niño4 of

SST precipitation W at 500 hPa

KCM

0 0.2 0.4 0.6 0.8 1

−2

−1.5

−1

−0.5

0

0.5

1

2

3

4

5 67 8

9

10

11121314151617

1819202122

23

24

25

26

27

2829

303132

33

34

35

3637

38

39

40

Atmospheric feedbacks ( µ and α, normalized)

SS

T b

ias

in N

ino4

[K]

r2 = 0.86 (0.91|0.93|0.57)

a)

0 0.2 0.4 0.6 0.8 10

1

2

3

4

5

6

7

8

1

2

3

4

5

6

78

9

10

111213

14151617

18

19

2021

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

3738

3940

Atmospheric feedbacks ( µ and α, normalized)

mea

n pr

ecip

in N

ino4

[mm

/day

]

r2 = 0.73 (0.97|0.83|0.91)

b)

0 0.2 0.4 0.6 0.8 1−0.05

−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

1

2

3

4

5

6

7 89

10

111213

14151617

1819

2021

22

23

24

25

2627

28

29

30

31

32

33

34

35

36

3738

3940

Atmospheric feedbacks ( µ and α, normalized)

mea

n W

at 5

00hP

a in

Nin

o4 [P

a/s]

r2 = 0.67 (0.96|0.91|0.88)

c)

CMIP5

0 0.2 0.4 0.6 0.8 1

−2

−1.5

−1

−0.5

0

0.5

1

2

34

5

6

7

8

9

10

11

12

1314

15

1617

18

192021

22

23

24

Atmospheric feedbacks ( µ and α, normalized)

SS

T b

ias

in N

ino4

[K]

r2 = 0.53

d)

0 0.2 0.4 0.6 0.8 10

1

2

3

4

5

6

7

8

1

2 3

4

5

6

7

8

9

10

11

12

1314

15

16 17

18

1920

21

22

23

24

Atmospheric feedbacks ( µ and α, normalized)

mea

n pr

ecip

in N

ino4

[mm

/day

]

r2 = 0.62

e)

0 0.2 0.4 0.6 0.8 1−0.05

−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

1

2

34

5

6

7

8

9

10

11

12

14

1516 17

18

1920

2122

23

24

Atmospheric feedbacks ( µ and α, normalized)

mea

n W

at 5

00hP

a in

Nin

o4 [P

a/s]

r2 = 0.62

f)

Figure 11: KCM: a) SST bias in Niño4 region (black box in Fig. 8), with SST relative to the area mean oftropical Indo-Paci�c SST, on y-axis vs. atmospheric feedbacks on the x-axis (average of µ and α, normalised withvalue of ERA Interim); b) same as a) but here for mean precipitation in Niño4 region on the y-axis; c) same as a)but here for mean W at 500 hPa in the Niño4 region on the y-axis; The colors of the numbers indicate the vericalresolution: L19 in black, L31 in magenta and L62 in cyan and the r2 values are given for all experiments, and inbrackets for only L19, only L31, only L62, respectively (only for KCM); in d-f): same as a-c), but here for theCMIP5 models. The yellow circle represents observations, the dark green circle the BCCR CM2.0 model, the cyancircle the ECHAM5 Biased-Slab-Ocean run and the margenta triangles the ECHAM5 AMIP-type experiments.

11

120E 150E 180 150W 120W 90W1000925850

700

600

500

400

300

250

200

150

100

Longitude

Pre

ssur

elev

el /

hPa

Observationsa)

120E 150E 180 150W 120W 90W1000925850

700

600

500

400

300

250

200

150

100

Longitude

Pre

ssur

elev

el /

hPa

STRONGb)

120E 150E 180 150W 120W 90W−1

−0.5

0

0.5

1

1.5x 10

11

longitude

kg s

−1

Equatorial mean zonal stream function

c)

ObsSTRONG ...MEDIUMWEAK

120E 150E 180 150W 120W 90W1000925850

700

600

500

400

300

250

200

150

100

Longitude

Pre

ssur

elev

el /

hPa

MEDIUMd)

120E 150E 180 150W 120W 90W1000925850

700

600

500

400

300

250

200

150

100

Longitude

Pre

ssur

elev

el /

hPa

WEAKe)

120 130 140 150 160 170−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1

2

34

5

6

7

8

9

10

11

12

1314

15

1617

18

192021

22

23

24

longitude of raising branch of Walker Circulation [oE]

cold

bia

s in

Nin

o4 [K

]

r2 = 0.52

Walker Circulation vs. cold biasf)

1011 kg s−1−2 −1 0 1 2

Figure 12: Mean state of Walker Circulation represented as zonal stream function along the equator, in a) forERA Interim, in b) for STRONG, in d) for MEDIUM, in e) for WEAK sub-ensemble of CMIP5; positve valuesindicate a clockwise circulation, the thick black line at the bottom marks the land masses of the MaritimeContinent and South America, the vertical dashed line the raising branch of the Walker Circulation inobservations; c) mass weighted vertical mean of zonal stream function along the equator in observations andCMIP5 ensembles as seen in a,b,d,e), the dashed-dotted (dashed) lines is the average over all El Niño (La Niña)months; f) longitude of the raising branch of the Walker Circulation (where stream function crosses the zero line)on the x-axis vs. relative cold bias in Niño4 as shown in Fig. 11d on the y-axis.

12

Heat �ux feedbacks in

Niño4 Niño3

KCM

ERA Interim AMIP STRONG MEDIUM WEAK Biased−Slab

−25

−20

−15

−10

−5

0

5

10

W/m

2 /KN

ino3

.4

a)

Qnet

...

SWLWSHLH

ERA Interim AMIP STRONG MEDIUM WEAK Biased−Slab

−25

−20

−15

−10

−5

0

5

10

W/m

2 /KN

ino3

.4

b)

CMIP5

OA Flux ERA40 STRONG MEDIUM WEAK

−25

−20

−15

−10

−5

0

5

10

W/m

2 /KN

ino3

.4

c)

OA Flux ERA40 STRONG MEDIUM WEAK

−25

−20

−15

−10

−5

0

5

10

W/m

2 /KN

ino3

.4

d)

Figure 13: Heat �ux feedbacks during ENSO events for Qnet, SW,LW,SH, and LH in ERA Interim reanalysis,the ECHAM5 AMIP-type ensemble, the KCM STRONG, MEDIUM and WEAK sub-ensembles and theECHAM5 Biased-Slab-Ocean run in a) for Niño4 region, and in b) for Niño3 region; c) same as a) but here forNiño4 heat �ux response in OA heat �ux data set, ERA40 and STRONG, MEDIUM and WEAK CMIP5sub-ensembles; d) same c) but here for Niño3 region. The values shown here are the averages over theHoevmoeller composites ±3 months before and after the ENSO event, as shown e.g. in Fig. 4.

13

Obs and KCM: Composites of SW for

El Niño La Niña La Niña − El Niño

Obs

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

a)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

b)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

c)

AMIP-type

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

d)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

e)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

f)

STRONG

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

g)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

h)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

i)

MEDIUM

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

j)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

k)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

l)

WEAK

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

m)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

n)

150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

o)

W/m2/ KNino3.4

−40 −20 0 20 40

W/m2/ KNino3.4

−20 −10 0 10 20

Figure 14: Composites of SW with Niño3.4 SST as selection criterion and normalised with mean Niño3.4 SSTfor El Niño, La Niña and the di�erence La Niña − El Niño , i.e. a measure for the non-linearity of the SWfeedback; in a-c) for observations (OA Flux), in d-f) for ECHAM5 AMIP-type ensemble, in g-i) for STRONG, j-l)for MEDIUM, m-o) for WEAK sub-ensemble in KCM.

14

CMIP5: Composites of SW for

El Niño La Niña La Niña − El Niño

STRONG

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

a)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

b)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

c)

MEDIUM

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

d)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

e)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

f)

WEAK

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

g)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

h)

120oE 150oE 180oW 150oW 120oW 90oW

10oS

5oS

0o

5oN

10oN

i)

W/m2/ KNino3.4

−40 −20 0 20 40

W/m2/ KNino3.4

−20 −10 0 10 20

Figure 15: Same as Fig. 14, but here for the STRONG, MEDIUM and WEAK sub-ensemble of CMIP5.

15

Atmospheric feedbacks vs.

phase locking index ENSO asymmetry

KCM

0 0.2 0.4 0.6 0.8 10.8

1

1.2

1.4

1.6

1.8

2

2.2

1

2

3

4 5

6

78

9101112

131415

16

17

1819

20

21

22

23

24

2526

27

28

29

3031

32

33 34

35

36

37

3839

40

Atmospheric feedbacks ( µ and α, normalized)

phas

e lo

ckin

g in

dex

in N

ino3

.4

r2 = 0.29

a)

0 0.2 0.4 0.6 0.8 1−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

12

34

56

7 8

9

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

25

26

27

2829

30

3132

33

34

35

36

3738

39

40

Atmospheric feedbacks ( µ and α, normalized)

ske

wne

ss(N

ino3

) −

ske

wne

ss(N

ino4

)

r2 = 0.63

b)

CMIP5

0 0.2 0.4 0.6 0.8 10.8

1

1.2

1.4

1.6

1.8

2

2.2

1

2

3

4

5

6

7

8

9

10

11

12

1314

15

16 17

18

1920

2122

23

24

Atmospheric feedbacks ( µ and α, normalized)

phas

e lo

ckin

g in

dex

in N

ino3

.4

r2 = 0.46

c)

0 0.2 0.4 0.6 0.8 1−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1

2

3

4

5

67

8

9

10

11

12

13

14

15

1617

18

192021

22

23

24

Atmospheric feedbacks ( µ and α, normalized)

skew

ness

(Nin

o3)

− s

kew

ness

(Nin

o4)

r2 = 0.65

d)

Figure 16: a) same as Fig. 11a) but here on the y-axis a measure of the ENSO phase locking in the Niño3.4region in KCM; b) same as a), but here on the y-axis a measure for the asymmetry between El Niño and La Niñain KCM (skewness of Niño3 − skewness of Niño4); c-d) same as a-b) but here for CMIP5 models.

16