1

Separating the influences of low-latitude warming and sea-ice loss on

2

Northern Hemisphere climate change

3

Stephanie Hay ∗4

University of Exeter, Exeter, UK

5Paul J. Kushner

6

University of Toronto, Toronto, ON, Canada

7

Russell Blackport

8

Environment and Climate Change Canada, Victoria, BC, Canada

9

Kelly E. McCusker

10

Rhodium Group11

Thomas Oudar

12

CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

13

Lantao Sun

14

Colorado State University, Fort Collins, Co.

15Mark England

16

University of Santa Cruz, Santa Cruz, Ca.

Generated using v4.3.2 of the AMS LATEX template1

17

Clara Deser

18

NCAR, Boulder, Co.

19James A. Screen

20

University of Exeter, Exeter, UK

21

Lorenzo M. Polvani

22

Columbia University, New York, NY

23

24∗ Corresponding author address: Department of Mathematics, University of Exeter, Exeter, UK

E-mail: shay@physics.utoronto.ca
2

ABSTRACT
25

Analyzing a multi-model ensemble of coupled climate model simulations

26

forced with Arctic sea-ice loss using a two-parameter pattern-scaling tech-

27

nique to remove the cross-coupling between low- and high-latitude responses,

28

the sensitivity to high-latitude sea-ice loss is isolated and contrasted to the29

sensitivity to low-latitude warming. In spite of some differences in experi-30

mental design, the Northern Hemisphere near-surface atmospheric sensitivity

31

to sea-ice loss is found to be robust across models in the cold season; how-32

ever, a larger inter-model spread is found at the surface in boreal summer, and33

in the free tropospheric circulation. In contrast, the sensitivity to low-latitude

34

warming is most robust in the free troposphere and in the warm season, with35

more inter-model spread in the surface ocean and surface heat flux over the

36

Northern Hemisphere. The robust signals associated with sea-ice loss include

37

upward turbulent and longwave heat fluxes where sea-ice is lost, warming

38

and freshening of the Arctic ocean, warming of the eastern North Pacific rel-39

ative to the western North Pacific with upward turbulent heat fluxes in the40

Kuroshio extension, and salinification of the shallow shelf seas of the Arc-41

tic Ocean alongside freshening in the subpolar North Atlantic. In contrast,

42

the robust signals associated with low-latitude warming include intensified

43

ocean warming and upward latent heat fluxes near the western boundary cur-44

rents, freshening of the Pacific Ocean, salinification of the North Atlantic, and45

downward sensible and longwave fluxes over the ocean.

346

1. Introduction

47

Arctic sea-ice loss observed by satellites over the last 40 years is one of the most obvious man-48

ifestations of greenhouse warming, and many record low extents have occurred in recent years.

49

The question as to how a shrinking ice cover and corresponding increase in open ocean affects

50

the atmosphere, and the mid-latitude climate in particular, remains a topic of debate [e.g., Co-51

hen et al. (2014); Vihma (2014); Barnes and Screen (2015); Overland et al. (2015); Cohen et al.

52(2020); Blackport et al. (2019); Blackport and Screen (2020)]. It is difficult to disentangle the53

forced response to sea-ice loss from other concurrent forcings, from general greenhouse warming,54

and from natural variability. Because there is only a relatively short observational period, and the55

response to sea-ice loss has a low signal-to-noise ratio (Screen et al. 2014; Ogawa et al. 2018;

56

Liang et al. 2020), a popular method, and the one we focus on here, for linking Arctic sea-ice

57

loss and climate responses is through dedicated modelling experiments with sufficient sampling to58

separate the signal from climate noise.

59

Earlier efforts to isolate the forced response to sea-ice loss used atmospheric general circulation

60

models forced with either high or low sea-ice cover and diagnosed the response as the difference

61

between the resulting atmospheric patterns (Deser et al. 2010; Screen et al. 2012; Peings and62

Magnusdottir 2014; Sun et al. 2015). A robust warming of the lower troposphere as in Arctic

63

amplification is found, but there tends to be a model dependence (Screen et al. 2012), and robust

64

signals generally do not extend far beyond the high latitudes. Recently, studies have examined this65

problem using observed sea-ice loss from a multi-model AGCM approach (Ogawa et al. 2018;

66

Liang et al. 2020), and largely confirmed the relatively weak role that Arctic sea-ice loss plays in67

the mid-latitudes. However, there is also some evidence that sea-ice driven variability is too weak4

68

in models (Mori et al. 2019a), a matter that remains up for debate (Mori et al. 2019b; Screen and69

Blackport 2019b; Zappa et al. 2021).

70

Several studies have sought to study the forced coupled climate response to sea-ice loss for in-71

dividual models (Deser et al. 2015, 2016; Petrie et al. 2015; Blackport and Kushner 2016, 2017;72

Smith et al. 2017; England et al. 2020a,b) and others have compared that response to the forced

73

response to an increase in greenhouse gases under constant sea-ice area (Oudar et al. 2017; Mc-74

Cusker et al. 2017; Sun et al. 2018). The inclusion of a fully dynamical ocean is shown to be75

crucial to obtaining the pattern of “mini-global warming” response to sea-ice loss (Deser et al.

76

2015; Tomas et al. 2016), whereby the zonal-mean temperature response to sea-ice loss resembles77

the response to greenhouse warming with amplified warming over both poles and in the tropical78

upper troposphere. Screen et al. (2018) showed that there were common features amongst these79

different coupled climate model simulations when scaling the atmospheric response per unit sea-80

ice loss and an indirect method of obtaining the response to sea-ice loss in CMIP5 simulations

81

confirmed a generally robust response to sea-ice loss in wintertime across a larger pool of models

82(Screen and Blackport 2019a). However, the warming of lower latitudes found in coupled simula-

83

tions of sea-ice loss will induce its own response locally and remotely, which makes interpretation

84of the source of the mid-latitude response in these simulations difficult.

85

One way we can address this potential issue is to take advantage of the similarity in zonal

86

mean temperature responses to sea-ice loss and greenhouse warming, the aforementioned “mini-

87

global warming” pattern, which motivates the use of two-parameter pattern scaling Blackport

88

and Kushner (2017) to further decompose the coupled response to sea-ice loss to obtain pattern-

89

scaled sensitivities to sea-ice loss (SIL) and to low-latitude warming (LLW). Hay et al. (2018)90

was the first multi-model comparison to apply this concept as a tool to improve comparison of91

uncoordinated experiments of the forced model response to sea-ice loss by combining greenhouse5

92

gas forcing simulations with sea-ice loss simulations and then comparing and contrasting their93

resulting sensitivities. Hay et al. (2018) focused on the near-surface atmospheric response in94

two models and found that the sensitivity to SIL was more robust than the sensitivity to LLW in

95

wintertime.

96

While the use of coupled models has indicated that the ocean’s circulation is crucial to reduce97

the uncertainty in the atmospheric response to Arctic sea-ice loss, comparatively little research has

98

focused on the ocean’s response itself [e.g Tomas et al. (2016); Sévellec et al. (2017); Wang et al.

99(2018); Liu and Fedorov (2019); Liu et al. (2019)]. The Atlantic Meridional Overturning Circu-

100

lation (AMOC) is found to weaken (Sévellec et al. 2017; Liu and Fedorov 2019; Liu et al. 2019),

101

and associated northward heat transport is reduced (Tomas et al. 2016; England et al. 2020b).

102

We make use of the pattern-scaling methodology and preexisting simulations to examine the103inter-model and inter-method differences in the Northern Hemispheric atmospheric and oceanic

104

sensitivities to Arctic SIL and to LLW in five different coupled climate models: CESM1 (Black-105

port and Kushner 2017), WACCM4 (England et al. 2020b), CanESM2 (McCusker et al. 2017),

106

CNRM-CM5 (Oudar et al. 2017), and GFDL-CM3 (Sun et al. 2018). This paper presents a gener-

107

alization of the two model analysis of Hay et al. (2018), and presents both the most comprehensive

108

assessment of the atmospheric sensitivity to SIL within the coupled climate response and for the

109

first time includes a multi-model intercomparison of the sensitivities of the surface ocean to Arctic

110

SIL.

111

This paper is organized as follows: in Section 2, we present an overview of the experiments

112

and review the two-parameter pattern scaling technique of Blackport and Kushner (2017), which

113

yields the pattern-scaled sensitivities that we use to characterize the models. In Section 3, the114

atmospheric and oceanic sensitivities are presented. A robust sensitivity to SIL is found in the115

atmosphere, while a robust sensitivity to LLW is found in the ocean. Section 4 features a more6

116

in-depth discussion of some aspects of the results, including the role of SIL in Eurasian cooling117

and how SIL and LLW can induce negative feedbacks on one another, each within the context of118

the existing literature. Finally, the results are summarized in Section 5.

119

2. Data and Methods120

Due to the uncoordinated nature of these experiments, a given forced response in this ensemble

121of opportunity will depend not only on the model but also on the experimental protocol used. We

122seek to characterize the robustness of the forced responses across model-experiments (MEs), de-123

fined as the combination of a particular model and a particular experimental protocol used to drive124

it (e.g. a radiative perturbation or a sea-ice perturbation, a transient versus equilibrated/timeslice

125

simulation). A forced response is the difference between a control ME and a perturbation ME.

126

Ambiguity is introduced in the mid-latitude forced response to sea-ice loss because the lower-

127

latitude warming that occurs in a fully coupled experiment can in turn impact the mid-latitude128

response. To better separate the tropically driven component of the component driven by sea-ice

129

loss, we use two-parameter pattern-scaling (Blackport and Kushner 2017). This yields pattern-

130

scaled sensitivities to low- and high-latitude drivers that are obscured by the cross-coupling in the131

coupled model responses. By cross-coupling, we mean, for example, that warming at low latitudes

132

in the model in response to sea-ice loss can induce a response in the extratropics.

133

a. Models and Simulations

134

The MEs, i.e. the coupled models and experimental protocols, used in this study are presented

135

in Table 1. For ease of reference to the previous literature, the nomenclature used in the original

136

publications is included in the table (second column). Five models are listed (first column, and137

described below) with their approaches to isolating the response to sea-ice loss from greenhouse7

138

warming (fourth and fifth column), which are also discussed in Screen et al. (2018); Sun et al.

139(2020). Another key distinction between approaches is whether transient or equilibrated solutions

140

are examined (sixth column). Each of the MEs provides a forced response that is dominated by141

sea-ice loss or by the combined effects of both warming and sea-ice loss, and some additionally142

provide a forced response dominated by warming with very little sea-ice loss.

143

1. CESM1: The configuration of the Community Earth System Model version 1 (CESM1) used

144

for the CESM Large Ensemble (LENS) (Kay et al. (2015), and references therein), with a145

nominal resolution of 1◦ in both the atmosphere and the ocean, is used here. The forced

146

sea-ice loss response is obtained from the difference between multi-centennial means of sim-

147

ulations outlined in Blackport and Kushner (2017). These consist of a control simulation

148

with constant year-2000 forcing (branched from member “101” of the LENS simulations at

149

nominal year “0”) and a reduced sea ice simulation branched at year 301 from the control

150

simulation, in which the albedo of the sea ice is permanently reduced globally. To obtain the151

forced response to external RCP8.5 forcing, dominated by greenhouse warming, we choose152

two 20-year mean epochs from the 40-member ensemble mean of the CESM LENS sim-

153

ulations in which sea ice close matches that found in the the control and perturbed albedo

154

experiments.

155

2. WACCM4: The Whole Atmosphere Community Climate Model, version 4, provides im-

156

proved stratospheric representation relative to the atmospheric component of CESM1, but

157

uses previous versions of atmospheric physical parameterizations and a coarser nominal hor-

158

izontal resolution of 2◦ . The ocean model is similar to CESM1 LENS and the simulations

159

are carried out at a nominal 1◦ ocean resolution. The CONTROL and ARCTIC simulations

160

used here are described in England et al. (2020b). These are analogous to the simulations8

161

performed with CESM1 in that they consist of long simulations of which one has a high162

sea-ice area and one has a reduced sea-ice area, all else equal, however they differ in length,163

the control climate state, and in the sea-ice loss protocol. The longwave forcing method of

164

Deser et al. (2015) is used to emulate the sea-ice extent in the Arctic to the mid-20th century

165

in CONTROL, and the end of the 21st century for ARCTIC. For each, the last 250 years of

166

a 350-year simulation are retained for analysis. A small ensemble of three CMIP5 RCP8.5

167

simulations are used to describe projected greenhouse gas forcing in this model (Marsh et al.

1682013).The 15-year epochs chosen from the CMIP5 RCP8.5 WACCM simulations are those169

from which the sea-ice targets for CONTROL and ARCTIC are taken.

170

3. CanESM2: These simulations, outlined in McCusker et al. (2017), use the second generation

171of the Canadian Earth System Model at spectral horizontal resolution T63. These MEs con-172

sists of four simulations in which the Arctic sea-ice mass is nudged grid-cell-by-grid-cell to173

match that from equilibrated preindustrial CO2 (IPI ) or 2×CO2 (I2X ) sea-ice distributions of174

the free-running CanESM2 model. For each of these sea-ice distributions, the radiative forc-

175

ing is set to either preindustrial (PI) or 2×CO2 (2X), and the model is integrated for 201 years

176

for the two control simulations, i.e., those in which the target sea-ice distribution is matched177

to the radiative forcing, and 301 years for the two perturbed simulations, where the target

178

sea-ice distribution is mismatched with the radiative forcing. In each case, the last 200 years

179

are retained for analysis. The greenhouse warming-forced response is given by the difference

180

between the two controls, C2X I2X and CPI IPI , while the forced response to sea-ice loss can be

181

isolated at both cooler and warmer background climates by using the other two simulations.

182

Additionally, the forced response to warming with very little ice loss at high or low ice cover183

can be explicitly determined for this ME, unlike for the CESM1 and WACCM4 MEs.
9

184

4. CNRM-CM5: These simulations are outlined in Oudar et al. (2017). The atmosphere and185

ocean components of this version of the model have a horizontal resolution of 1.4◦ and 1◦ ,186

respectively. These MEs consist of four simulations that are analogous to those performed

187

with CanESM2: they utilize a non-solar flux correction to the ocean model to control the188

seasonal cycle of Arctic sea ice to match that from the late 20th century or the late 21st189

century resulting from historical or RCP8.5 radiative forcing, while the radiative forcing itself

190

is set to either late 20th century or late 21st century. Each integration is 200 years in length191

and the last 100 years are retained for analysis. Again, this experimental design allows us to192

also explicitly assess the forced response to greenhouse warming with very little sea-ice loss.

193

5. GFDL-CM3: Version 3 of the Geophysical Fluid Dynamics Laboratory Coupled Model has

194

an atmospheric resolution of approximately 200km, while the ocean component has a lati-

195

tudinal resolution of 1◦ and enhanced longitudinal resolution reaching 1/3◦ near the equator.

196

The experiments outlined here are those of Sun et al. (2018): five ensemble members of an

197

integration representing 1970 to 2090 in which ice is unconstrained and radiative forcing is

198

given by historical and RCP8.5 forcing provides the forced response to greenhouse warm-

199

ing. Another experiment of five ensemble members is also carried out whereby ice is con-200

strained using a volume-nudging method similar to the mass-nudging method of McCusker

201

et al. (2017) to match its mean distribution from 1970-1990. This experiment is integrated

202

from 1990 to 2090 to provide the transient response to RCP8.5 radiative forcing with very

203

little sea-ice loss. The difference between the ensemble means then gives the transient forced

204

response to sea-ice loss.

205

The way the forced responses are diagnosed from the MEs are shown in Table 2, retaining

206

the nomenclature from the original publications. We separately group the simulations that are
10

207

run to equilibrium with unchanging sea ice and radiative forcing and those that are integrated

208

with transient forcing. We plot annual mean Arctic sea-ice area versus low-latitude sea surface

209

temperature (area-weighted average over 0 to 40◦ N) in Figs. 1a and b. The transient simulations

210

in Fig. 1b show 15-year running means as well as the periods chosen in determining the forced

211

response. Each response that we diagnose is then shown in Fig. 1c on a plot of Arctic sea-ice loss

212

and low-latitude warming, where the responses are colour-coordinated to distinguish between the213

three types of forced responses: warming under near-constant sea-ice area, for which 2.0-3.0◦ C of214

low-latitude warming is obtained with less than one million km2 of Arctic sea-ice loss; sea-ice loss

215

under near-constant low-latitude warming, for which less than 0.5◦ C of low-latitude warming is216

obtained with 3.0-8.0 million km2 of sea-ice loss; and full greenhouse warming, for which about

217

1◦ C of low-latitude warming is obtained per 2 million km2 of Arctic sea-ice loss.

218

b. Pattern Scaling

219

There is no consistent framework upon which these MEs are designed and as a result there220

are differing amounts and patterns of sea-ice loss and ocean, atmospheric, and land warming.

221

For example, we show, for each season, the sea-ice fraction in each ME’s low sea-ice simulation

222

relative to the 15% sea-ice extent contour in its control simulation in Fig. 2. This figure highlights

223

the disparate patterns and magnitudes of sea-ice loss across the MEs considered in this study,

224

motivating the need to normalize the forced responses before a comparison can be made. The study

225

by McCusker et al. (2017) demonstrated the separability and linear additivity of the atmospheric226

response to sea-ice loss and the rest of the greenhouse warming response. At the same time,227

the study of Blackport and Kushner (2017) introduced two-parameter pattern scaling to separate

228

the sensitivity to LLW from that of sea-ice loss. We use the same method here to address the229

inconsistencies in the MEs and their forced responses arising from differences in experimental
11

230

design. This linear decomposition removes the signal of LLW from the pattern-scaled sensitivity

231

to SIL, thus yielding the direct sensitivity to SIL while accounting for the cross-coupling from

232

LLW. It yields also a direct sensitivity to LLW by removing the signal of SIL. Besides attempting to233

consistently scale for the amount of Arctic sea-ice loss, pattern scaling also seeks to simultaneously

234

scale for differences in tropical warming responses that might arise from radiative feedbacks (e.g.

235

in the case of the CESM1 albedo-reduction ME due to the additional effects of Antarctic sea-ice

236

loss on the tropics (England et al. 2020a).)

237

The method of Blackport and Kushner (2017), has previously been used in Hay et al. (2018)238

to compare near-surface atmospheric sensitivities in two models, and in Feldl et al. (2020) to239

understand the locally and remotely driven components of the high-latitude lapse-rate feedback.

240

Classically, pattern scaling simply states that the pattern of forced response to greenhouse warming241

in climate models is proportional to the global mean temperature (Santer et al. 1990; Tebaldi and242

Arblaster 2014; Bichet et al. 2015) and is independent of the details of the forcing. From the243

empirical observation that the zonal mean temperature pattern response to sea-ice loss resembles

244

that from greenhouse forcing (the “mini-global warming” of Deser et al. (2015)), Blackport and245

Kushner (2017) generalized pattern scaling to multiple scaling variables. As in previous studies

246(Hay et al. 2018; Feldl et al. 2020) we use low-latitude (0-40◦ N) sea surface temperature (Tl ) and247

Arctic sea-ice area (I) as the scaling variables. The forced response, δ Zm , of some variable Z, for248

ME response m, i.e., the difference between two MEs in given model, can be written as the sum

249

of two patterns, one that is proportional to SIL and another that is proportional to LLW:

δ Zm =∂Z∂Z
δ Im +
δ Tl,m .
∂ I Tl∂ Tl I(1)

250

The partial derivative terms represent the pattern-scaled sensitivities, and these can be determined251

provided at least two ME responses (i.e, m ≥ 2) are available that feature linearly independent
12

252

responses in LLW and SIL. Therefore, for a given set of MEs and a given season, we choose two

253

forced responses and invert (1), solving for the sensitivities ∂∂ZI

Tl

and ∂∂TZ . These sensitivities are
l

I

254

interpreted as an estimate of how Z would change if SIL hypothetically occurred with low-latitude255

temperatures held fixed and if LLW hypothetically occurred with sea-ice held fixed. They are256

further interpreted as characterizing the forced model response, independent of the experimental

257

protocols in the original MEs.

258

A related method of classical pattern scaling using linear least-squares regression to determine259

the scaling relationship is less susceptible to slight non-linearities (Mitchell 2003) and offers the260

advantage of the calculation of a residual term. However, this method is more computationally ex-261

pensive (Herger et al. 2015), there are not many statistically significant differences in the methods

262

found across CMIP5 models, and the regression method is more susceptible to differences in forc-

263

ing scenario (Lynch et al. 2016). Analogous to the linear least-squares method used in classical264

pattern scaling, an alternative approach to the one we use here may be constructed using multiple

265

linear regression instead.

266

Pattern scaling as outlined here relies on the additivity of internal and external climate forcings.

267

Within studies of detection and attribution of climate change, it is generally implicitly assumed

268

that climate responses to external forcings (e.g., CO2 , anthropogenic aerosols, ozone, volcanic

269

eruptions, land use changes) can be linearly added to obtain the total climate response to the sum

270of the forcings (Stott et al. 2010). This assumption has been tested in a number of studies and it has

271

been found that temperature is generally additive under all scenarios, but additivity breaks down272

in other fields such as precipitation under some forcing scenarios (Shiogama et al. 2013; Marvel273

et al. 2015). Other studies have found significant non-linearities in some seasons and regions

274(Deng et al. 2020). On the the other hand, the additivity of the climate response to internal forcing

275

agents, such as sea ice area and low-latitude temperature, as we have chosen to do here, has been
13

276

studied less extensively, although there is evidence to support it (McCusker et al. 2017; Oudar et al.

2772017). We can expect to obtain the best results from this method as long as we remain within in a278

linear regime and have sufficient sampling. It is important to note that the sensitivities as defined279

here do not necessarily imply causality; instead that is what we get from the model responses. The

280

pattern-scaled sensitivities are a way to further decompose the forced responses to reveal what is281

shared amongst the MEs, irrespective of their differences in experimental construction and sea-ice

282

loss protocols.

283

The two-parameter method is demonstrated in Fig. 3 for the decomposition of the forced zonal-

284

mean temperature response in boreal winter in CNRM-CM5, using the nomenclature of Oudar285

et al. (2017). Five forced response patterns for this ME are shown: in Fig. 3a, “ICE+GHG”,

286

which is the response to greenhouse warming; Fig. 3b, “GHG Effect 21”, the response to green-

287

house warming at fixed late 20th century ice cover; Fig. 3d, “ICE Effect 20”, the response to288

sea-ice loss under late 20th century greenhouse gas (GHG) concentrations; Fig. 3f, “GHG Effect28920”, the response to greenhouse warming at late 21st century ice cover; and Fig. 3h, “ICE Effect29021”, the response to sea-ice loss under late 21st century GHG concentrations. Fig. 3a shows the291

usual pattern of projected greenhouse warming with an overall warming of the troposphere at all

292

Northern Hemisphere latitudes with amplified warming in the Arctic lower troposphere and in the293

tropical upper-troposphere, alongside cooling in the stratosphere. In Figs. 3b and f, when GHG

294

forcing is applied with sea ice held fixed, the pattern closely resembles that in Fig. 3a but it lacks

295

surface Arctic amplification and instead shows Arctic warming with a weak maximum in the mid-296

troposphere. Interestingly, in Fig. 3f, there is a hint that greenhouse gas radiative forcing under297

prescribed high ice conditions gives rise to surface cooling, something that was found after the298

pattern-scaling decomposition for CESM1 in Hay et al. (2018). This weak but significant cooling299

response to GHG forcing with fixed ice cover may arise as an artifact of the sea-ice nudging. In
14

300

Figs. 3d and h, when ice loss alone is imposed, an Arctic amplification pattern is seen, alongside301

a small amount of warming in the tropical mid-troposphere, particularly in “ICE Effect 21”.

302

After the pattern-scaling decomposition we obtain the pattern-scaled sensitivities of this ME
zon

303

to LLW, ∂∂TTlI

, for each background climate in Figs. 3c and g and the pattern-scaled sensitivi-

zon

304

ties to SIL, ∂ T∂ I

Tl

, for each background climate in Figs. 3e and i. We note the similarities of305

the sensitivities between background climates. Pattern-scaling has removed the mid-tropospheric306

tropical warming seen in Fig. 3h from the modelled sea-ice loss response pattern, and the Arctic

307

amplification is slightly shallower, suggesting that some Arctic mid-tropospheric warming scales308with LLW. The sensitivity to LLW comprises all the tropical warming as well as warming of the309

mid-troposphere in the Arctic, with cooling found both at the surface and in the stratosphere. Be-

310

cause the stratospheric response to climate change is dominated by direct CO2 radiative forcing,311

a process that is not at play in simulations forced by sea-ice loss even with low-latitude warming,

312

caution is required in interpreting sensititivies there. The amount of warming that occurs near313

the Arctic surface in Fig. 3b or Fig. 3h is greater than that in Fig. 3a, thus it appears that in cer-314

tain models a modest negative feedback near the Arctic surface arises, i.e. a minimum in surface

315

warming or even a cooling, which scales with LLW.

316

3. Results

317

a. Patterns of Atmospheric Sensitivity

318

We first extend the analysis of Hay et al. (2018), which included the same pattern-scaling de-319

composition for two of the MEs considered here, CESM1 and CanESM2, to include more MEs

320

and zonal-mean climate variables as well as the near-surface variables investigated in that study.
15

321

The left-hand column of Fig. 4 presents the mean boreal winter (DJF) map of the sensitivity to

322

Arctic SIL, per two million km2 of ice loss, for surface temperature, sea level pressure, precipita-

323

tion, and 850hPa zonal wind for all MEs, while Fig. 5 shows the same for zonal-mean zonal wind324

and and zonal-mean temperature. The right-hand column of Fig. 4 and 5 shows the sensitivity to

325

LLW, per degree of warming. Scaling per two million km2 of ice loss and per degree of warming

326

arises from the observation that these models show roughly one degree of tropical warming per

327

two million km2 of sea-ice loss in response to greenhouse warming (Fig. 1). To obtain Figs. 4

328

and 5, we perform the pattern-scaling decomposition for each ME using the bolded and italicized

329

combinations in Table 2. This results in patterns for each of the sensitivities and for each of the330

MEs. These patterns are then regridded onto a 1◦ by 1◦ global grid, and averaged across the set

331

of MEs. The hatched areas in Fig. 4 and 5 represent those regions where at least four of the five332

models agree on the sign of the pattern-scaled sensitivity, and the number in the bottom right-hand333

corner indicates the median spatial correlation of all MEs with each of the rest of the set for the

334

Northern Hemisphere extratropics (defined here to include all regions north of 30◦ N). The number

335

between panels indicates the spatial correlation between the mean sensitivity to SIL and to LLW.

336

A more negative number indicates that the sensitivity to SIL acts as a negative feedback on the337

sensitivity to LLW.

338

For comparison of how the pattern-scaled sensitivities differ from their forced response counter-339

parts, Fig. 6 shows the same variables shown in Fig. 4 and 5 response to sea-ice loss in the upper

340

row, response to greenhouse warming with fixed sea ice (for the models that provide this) in the341

middle row, and response to greenhouse warming with freely evolving sea ice in the lower row.

342

The surface temperature sensitivity to Arctic SIL in DJF, shown in Fig. 4a, consists of a strong343

warming directly over the Arctic Ocean and Hudson Bay of more than two degrees per million km2344

of ice loss, as well as a more modest warming over adjacent high-latitude land areas and over the
16

345

mid-latitudes of North America. A weak cooling over eastern Eurasia is seen in the multi-model

346

mean, but it is not robust, save for a region of Eastern China and Japan. Compared to the forced347

response shown in Fig. 6, pattern scaling implies that much of the mid-latitude warming seen in348

the sea-ice loss experiments can be attributed to the back-effect of LLW and not directly to SIL.

349

The surface temperature sensitivity to LLW (Fig. 4b) correlates negatively with that the sensi-

350

tivity to SIL (r = -0.57) and consists of a robust warming everywhere south of the Arctic circle,

351

excluding the subpolar North Atlantic. There is enhanced warming over Siberia and the Rocky

352

mountains. The temperature sensitivity over the Arctic is not robust, with three of the five models

353

showing some cooling, as was seen in the zonal-mean temperature sensitivity to LLW in CNRM-

354

CM5 in Fig. 3c. We note that there is little consistency over the subpolar North Atlantic in either355

of the two pattern-scaled sensitivities. This region also does not show a robust sign in the modelled356

responses (i.e., before we scale the patterns) to greenhouse warming, warming without ice loss, or357

sea-ice loss (Figs 6a, g, and m). This lack of consistency appears to at least partially be related to358inter-model differences in the location of the North Atlantic ‘warming hole’, differences that are

359

reflected in the ocean response below.

360

To obtain a more complete picture of the robustness across MEs, Fig. 7 presents each of the361inter-ME spatial correlations for all the seasons and the annual mean. The result is a distribution362

of inter-ME correlations for the sensitivities to SIL and LLW in blue and orange, respectively. The

363

black bars represent the median of the correlations. A cluster of dots near r = 1.0 implies that364

all MEs agree on the pattern-scaled sensitivity, a large spread in dots implies that some MEs are365

highly correlated with one another while some are not (or are anti-correlated), and a clustering of366

dots near a lower r implies that the pattern-scaled sensitivities are not robust.

367

The spatial correlations in Fig. 7a show that the extratropical surface temperature sensitivities

368

to both SIL and LLW are robust throughout the year except in boreal summer, when correlations
17

369

between sensitivities to SIL are at a minimum. We expect that the sensitivity to SIL will be370

most robust in DJF (Hay et al. 2018; Screen and Blackport 2019a) due to the greatest turbulent

371

heat fluxes in response to sea-ice loss occurring then, despite having the largest ice losses in372

late summer and early autumn (Deser et al. 2010). Consequently, sea-ice-loss forcing in JJA is373

weaker than in DJF and there is less consistency in forced model responses and pattern-scaled374

sensitivities. We note that the larger differences in sea-ice area shown in Fig. 2 in JJA, with one

375

ME nearly completely ice-free, may also be a confounding factor in the sensitivity comparison in376

this season.

377

The sea level pressure sensitivities are shown in Figs. 4c and d, and the forced responses in Figs.

378

6b and h. The pattern-scaled sensitivities are found to have opposite sign across most regions,379with a correlation of r = -0.61, suggesting that sea-ice loss response acts as a negative feedback380

on the sea level pressure response arising from the tropics. A robust sensitivity to SIL emerges381with a deepening of the Aleutian Low and lowered pressure from the Canadian Basin to Hudson

382

Bay, alongside strengthening of the Siberian High. The latter pattern in forced atmosphere-only

383

modelling studies has been associated with sea-ice loss in the Barents and Kara Seas (Mori et al.

3842014), but whether the same dynamical mechanism is at play in coupled modelling studies remains

385

an open question. There is less consistency in the latitudinal extent of the Aleutian Low, which386

contributes to some of the spread in correlations in Fig. 7b. Differences in the forced Aleutian387

Low response were attributed to differences in tropical precipitation between fully coupled and388

slab ocean sea-ice loss responses in the same model Deser et al. (2016), which may indeed be the389case here, as the precipitation sensitivity to SIL can be seen in left-hand column Fig. 8 and we390

note little inter-ME agreement in the tropics.

391

The Siberian High sensitivity to LLW is a weakening with increasing temperature, and this392

weakening directly opposes the strengthening of the Siberian High associated with the warm18

393

Arctic-cold-Eurasia (WACE) pattern (Mori et al. 2014). This regional tug-of-war suggests that394

changes to the Siberian High associated with the WACE pattern would be difficult to observe in a

395

warming world in the presence of both tropical and Arctic warming. Apart from this signal, the396

entirety of the Western Hemisphere’s sensitivity pattern is not robust to LLW. The Aleutian Low

397

either deepens or weakens depending on the ME in question, as was seen in Hay et al. (2018), and398

this is the reason for the negative pattern correlations seen in DJF in Fig. 7b. The pattern-scaled399

sensitivities in the Atlantic sector are not particularly robust to either forcing.

400

There is a large spread in the inter-ME correlations of LLW pattern-scaled sensitivities in DJF,

401

and to a lesser extent in the other seasons, and the largest spread in correlations between the402

SIL sensitivities in in JJA. We find, as in the surface temperature sensitivity, that the most robust403

sensitivity to LLW occurs in JJA, which is the same season for which we find the least robust404

sensitivity to SIL, as indicated by the median in correlations.

405

The precipitation pattern-scaled sensitivities are shown in Figs. 4e and f and in Fig. 8, and the406

forced responses are shown in Figs. 6c, i, and o. Although the sensitivities are highly structured,

407

pattern-scaling helps to normalize and remove some of the noise from the responses over the408

Arctic Ocean. There are regions of consistently signed sensitivities to both SIL and to LLW.

409

The global precipitation sensitivities for individual models (Fig. 8) shows the consistency in SIL

410(left-hand column) sensitivities is confined to the higher-latitude Northern extratropics, while the411

consistency in LLW (right-hand column) sensitivities covers the mid-latitudes and tropics of each412

hemisphere, in the annual mean. In Fig. 4e and in each individual model in Fig. 8, an increase413

in precipitation over the Arctic Ocean is seen as a result of increased evaporation from the ocean

414

surface under SIL (Bintanja and Selten 2014). Additionally, consistent with the strengthening415

of the Aleutian Low and the inter-ME spread in that pattern-scaled sensitivity, an increase in416

precipitation on the western coast of North America is seen. However, its exact latitudinal position
19

417

is dependent on the ME. In Fig. 4f, the sensitivity to LLW shows a robust increase of precipitation

418

over much of the mid-latitudes, including Western Europe, northern Eurasia, and both coasts of

419

North America, and a drying over the subtropical eastern Atlantic and Mediterranean. We find420

median spatial correlations are lower overall than they were for surface temperature and sea level421

pressure, ranging from r = 0.1 to r = 0.5 for each collection of sensitivities. The spatial correlations

422

across MEs are generally greater for the sensitivity to LLW than to SIL (Fig. 7c).

423

The sensitivity to SIL of the 850hPa zonal wind over the Pacific Ocean (Fig. 4g) is largely con-424

sistent with the sea level pressure sensitivity. This includes a general weakening on the poleward425

flanks of the climatological maximum jet, indicating a southward shift in the Pacific storm track,426

as expected in response to a decrease in the equator to pole temperature gradient via the thermal

427

wind relationship and under a reduction in baroclinicity. A similar pattern is seen in the Atlantic,

428

and though it is not as robust in the pattern-scaled sensitivity or in the forced response (Fig. 6d),429

resembles that found in Peings et al. (2019) for simulations dominated by the effects of Arctic

430

amplification rather than upper tropospheric warming. In most seasons we see a large spread in431

correlations, and, once again the median value of correlation is lowest in JJA. Looking at the sen-432

sitivity of zonal-mean zonal wind in a latitude-height cross-section (Fig. 5c), a robust weakening433

and equatorward intensification is seen, consistent with the dominant pattern in U850. This is in434

agreement with other studies (Deser et al. 2015; Blackport and Kushner 2017).

435

The midlatitude U850 and trospospheric zonal-mean zonal wind sensitivity to LLW (Figs. 4h436

and 5d) is, on average, of opposite sign (r = -0.71 and r = -0.36, respectively) to the SIL sensitivity,437

so that LLW results in poleward intensification of the jet, as expected for an increase in the equator438

to pole temperature gradient aloft, and in good agreement with the idea of a “tug-of-war” between

439high and low latitude forcing (Harvey et al. 2014; Barnes and Screen 2015; Peings et al. 2019).

440

The competing influences of high- and low-latitude forcing are seen in the forced responses in
20

441

Fig. 6e and k. The mean pattern-scaled sensitivities reveal better inter-model agreement in the442

sensitivity to LLW, particularly at low levels, in the mid-latitudes. This sensitivity is not robust443

in DJF at higher altitudes in the zonal mean, due to differences in the Atlantic and Pacific basins.

444

The most robust part of the sensitivity to LLW is the strengthening of the subtropical jet.

445

Lastly, we examine the mean pattern-scaled sensitivities of zonal-mean temperature in Figs. 5a,446

b. The sensitivity to SIL is similar to Arctic amplification and is robustly seen in all models. The

447

latitudinal and vertical extent of the warming is from 45◦ N to the pole and from the surface up to448

400hPa and is significantly reduced compared to the forced response in Fig. 6f. A weak but robust

449

cooling in the mid troposphere and warming above 200 hPa in the mid-latitudes is also found.

450

The sensitivity to LLW is a robust warming throughout the troposphere, intensified at the tropical

451

upper troposphere. In the Arctic lower troposphere we note again that the MEs disagree on the452

sign of the sensitivity. Stratospheric cooling of the Arctic is robustly associated with LLW, while453

the stratospheric sensitivity to SIL is not robust, in agreement with the indirect method of Screen

454

and Blackport (2019a). Inter-ME spatial correlations in zonal-mean temperature are all closely

455

clustered above r = 0.8 (Fig. 7f) except for the SIL sensitivities in JJA that have a large spread in456

correlations.

457

Overall, in DJF (and to a lesser extent in MAM, SON, and ANN), the pattern-scaled atmospheric

458

sensitivities to SIL are more consistent between MEs than to LLW, for the near-surface climate

459

variables (surface temperature, sea level pressure, and 850hPa zonal wind), as evidenced by the460

median of the correlations shown by the black bars in Fig. 7. This reverses in JJA when the461

sensitivity to LLW is more robust in all variables. Therefore, the conclusions from Hay et al.

462(2018) generally hold in this larger pool of models and sea-ice loss protocols.

463

Before carrying out an analysis of the ocean’s surface sensitivity, we decompose surface heat

464

flux responses over the ocean (Fig. 9). The sign of the flux is defined to be positive upwards in to
21

465

the atmosphere. The sensible heat flux sensitivity to SIL (Fig. 9a) is positive where sea ice is lost in

466

the Arctic, and negative equatorward of the climatological sea-ice margins. Robustness is confined467

to the Arctic Ocean itself, as well as a weak but robust positive heat flux south of the Kuroshio

468

Extension. The former arises when sea ice is lost, exposing the ocean to the atmosphere and469

driving a greater transfer of heat from the ocean to atmosphere. Equatorward of the sea-ice margin,

470

atmospheric warming from SIL leads to anomalous warming of the ocean from downward heat

471

fluxes (Deser et al. 2010). On the other hand, the latter result of upward sensible heat south of the472

Kuroshio is curious because the surface ocean (see Fig. 10 below) and overlying atmosphere (Fig.

473

4a) both exhibit cooling in the sensitivity to SIL in the same region. Inter-ME agreement in DJF is474

not as good as for some of the other surface variables, with the median r = 0.48. Here, the details in475

the differences of the patterns of sea-ice loss (Fig. 2) may be driving the lower pattern correlation.

476

Over the ocean, the sensible heat flux sensitivity to LLW (Fig. 9b) is downward everywhere,477

suggesting that the atmospheric warming of tropical origin consistently drives anomalous ocean

478

warming throughout the Northern Hemisphere. While the inter-ME median r is similar, the sign of479

the sensitivity is robust nearly everywhere. The sensitivities are moderately negatively correlated

480with each other.

481

We find a similar pattern in the latent heat flux sensitivity to SIL (Fig. 9c), with an upward482

flux where sea-ice is lost and downward flux equatorward of the sea-ice loss region, but inter-ME

483

agreement is somewhat weaker, with median r = 0.36. Regions of agreement are confined to higher

484

latitudes in the sensitivity to SIL. On the other hand, the sensitivity to LLW (Fig. 9d) is robustly

485

positive over all regions but the Arctic Ocean and the subpolar North Atlantic.

486

The longwave flux sensitivity to SIL (Fig. 9e) exhibits Arctic amplification, while the sensitivity

487

to LLW (Fig. 9f) is a robust but weak negative signal that is likely associated with poleward heat488

and moisture transport (Lee et al. 2019).
22

489

Overall, in contrast to the atmospheric variables presented in Figs. 4 and 5, we find that, for490

surface fluxes, sensitivities to SIL are overall less robust than sensitivities to LLW. We can infer

491

that the heat flux patterns are more strongly controlled by the pattern of sea-ice loss, whereas the492nature of the atmosphere depends less on the details of the sea-ice loss. As in the atmosphere,

493

there is a generally a negative correlation between the mean heat flux sensitivities to SIL and to

494

LLW.

495

b. The ocean’s sensitivity

496

The pattern-scaling decomposition (Hay et al. 2018), as well as other studies comparing the497

forced atmospheric response to sea-ice loss in coupled models (Screen et al. 2018; Screen and

498Blackport 2019a), indicate that coupling to the ocean is important for the generating the sea-499

ice loss response. To elucidate the role of the ocean, we decompose the forced surface ocean

500

response using pattern scaling, as we did for the atmosphere. We present the results for sea surface

501

temperature (SST) in Fig. 10 and for sea surface salinity (SSS) in Fig. 11. Due to the greater502

inconsistency in the pattern-scaled sensitivities on regional scales compared to the atmosphere, we

503

present not only the multi-ME mean as above, but also the individual MEs.

504

McCusker et al. (2017) demonstrated, with the CanESM2 MEs we use here, that there is sep-505

arability and additivity of atmospheric responses to a doubling of CO2 without sea-ice loss and506

to Arctic sea-ice loss under constant background CO2 . We have confirmed additivity in a second

507

model by using the analogous CNRM-CM5 simulations for the atmospheric response. For exam-

508

ple, McCusker et al. (2017) found that 98% (90%) of the surface temperature (sea level pressure)509

variability is explained by the sum of the sea-ice loss and CO2 forced responses, and in CNRM-

510

CM5 we find the percentage of variability explained to be 99% for both surface temperature and511

sea level pressure. We applied the same analysis to the surface ocean and find that 79% of the
23

512

SST variability and 92% of the SSS variability is explained by the sum of the forced responses in

513

CanESM2, while in CNRM-CM5 the percentage of variability explained is 92% and 87%, respec-514

tively. These results justify the applicability of the linear pattern-scaling framework to our surface

515

ocean analysis.

516

The sensitivity of annual mean SST to SIL for individual MEs and for the multi-ME mean, as

517

shown in the left hand column of Fig. 10 and in Fig. 10k, respectively, indicate that the ocean’s

518

surface warms robustly where sea ice is lost and solar radiation is absorbed by the ocean surface,519

as well as where turbulent fluxes are in to the ocean (Figs. 9a, c). The pattern-scaled sensitivity

520

in the Pacific presents as warming along the west coast of North America alongside a cooling that521

extends out across the western Pacific that resembles the positive phase of the PDO. This pattern522

is consistent with what is seen in the surface temperature sensitivity (Fig. 4a), but opposite that523

seen in the sensible and latent heat flux sensitivities (Figs. 9a, c), as previously noted. This robust524

pattern, found in all MEs to varying degrees, if driven by similar processes that drive variability

525

in the PDO, results as a complex interplay of both local and remote atmospheric and oceanic

526

processes (Newman et al. 2016). The decreased turbulent heat fluxes over the anomalously cool527

water shown in Fig. 9a and c act damp SST changes by maintaining the SST gradient, increasing528

low-level baroclinicity and deepening the Aleutian Low, which in turn reinforces the SST gradient529

by surface ocean divergence via a positive feedback (Zhang and Delworth 2015; Luo et al. 2020).

530

All MEs show at least some cooling in the North Atlantic that scales with SIL, although the531

location is not robust and is likely influenced by the mean states of the models, including biases

532

and regions of deep ocean convection. One possible cause of subpolar cooling relative to the global

533

mean is AMOC weakening (Drijfhout et al. 2012; Rahmstorf et al. 2015), and indeed all models

534

considered here also simulate a weakening of the overturning circulation as a response to sea-ice

535

loss (not shown), in agreement with Sévellec et al. (2017); Liu and Fedorov (2019); Liu et al.
24

536

(2019). However, there are other physical processes that we can not rule out, such as high-latitude

537

ocean heat transport, that have also been identified as important for driving the warming hole (Keil538

et al. 2020).

539

The second column of Fig. 10 shows each ME’s sensitivity to LLW, and the multi-ME mean is540

shown in Fig. 10l. As in the atmosphere, there is disagreement in the sign of the sensitivity in the541

Arctic, but otherwise there is a robust warming in most locations. The North Atlantic subpolar542

gyre region exhibits a “warming hole” in all models but CanESM2. The warming in the mean

543

sensitivity is intensified in both the Gulf Stream and the Kuroshio current relative to other regions,544

but we note considerable variability between MEs (e.g. CanESM2 does not exhibit intensified

545

warming of the Kuroshio region, and CNRM-CM5 does not exhibit intensified warming of the546

Gulf Stream.)547

We calculate inter-ME spatial correlations for the Northern Hemisphere oceans north of 30◦ N,

548

as in Section 3a (Fig. 12a), but we also subdivide that area into three ocean basins. Annual mean

549

correlations calculated over the North Atlantic and the North Pacific, for 30◦ N–60◦ N, and the550

Arctic Ocean, north of 60◦ N, are shown in Fig. 12c. There is little seasonal variation in the inter-

551

ME correlations (Fig. 12a), especially compared to atmospheric surface temperatures (Fig. 7a) due

552

to slower timescales of evolution in the ocean. The exception to small inter-seasonal variability is a553

peak in correlations amongst the SIL sensitivity patterns in JJA and SON in the Atlantic compared554

to the other seasons (not shown). We reason that this is because the greatest forcing by sea-ice

555

loss on the ocean coincides with the seasons in which sea-ice loss is greatest and is driven by the556

shortwave flux, unlike for the atmosphere whose response lags by at least a season and is driven

557

by turbulent heat fluxes.

558

The median of inter-ME correlations for the Northern Hemisphere sensitivity to SIL are r =559

0.40, while in the Pacific r ≈ 0.75 year-round. In the Atlantic, the most robust season is JJA
25

560

where the median r = 0.7, while the colder seasons have lower correlations of r = 0.25. The

561

median of inter-ME correlations in the sensitivity to LLW is greater in all seasons for the Northern

562

Hemisphere. In the Atlantic and Pacific basins, the dots are clustered into two groups, where563

the lower correlations are the result of the CanESM2 sensitivity in the Pacific, and the GFDL-

564

CM3 sensitivity in the Atlantic, as noted above. In contrast to the atmospheric sensitivities, but in

565

agreement with the surface flux variables, the pattern-scaled sensitivities to LLW are on average566

more robust than the SIL sensitivities. In the Arctic, we see a considerable spread in correlations,567

which might arise because the sensitivities are more closely tied to the details of ice-loss here,

568

which are in turn dependent on the ME (Fig. 2). This region is directly affected by the sea-ice loss

569

protocol, particularly in the MEs that introduce perturbations into the ocean either directly (e.g.,

570

CNRM-CM5) or indirectly through nudging the sea ice (WACCM4, CanESM2, GFDL-CM3).

571

The SSS sensitivities (Fig. 11) show that the sensitivity to LLW is more consistent across MEs

572(Fig. 11k) than the sensitivity to Arctic SIL (Fig. 11l). The agreement in the sensitivity to SIL is573

mainly confined to the Arctic, where there is a robust freshening of the central Arctic ocean and574

salinification around the continental shelves, which may be indicating increased brine rejection575

as a consequence of more seasonal sea-ice formation along the shelves. Outside the Arctic, we

576

note a freshening in the extension of the Gulf Stream. On the other hand, the sensitivity to LLW

577

indicates a clear and consistent freshening in the Pacific and Arctic Oceans that extends into the578

subpolar North Atlantic, and salinification of the rest of the Atlantic and the Mediterranean. This579

pattern reflects both observed and projected changes in salinity, which can be understood through580

the intensification of the hydrological cycle and manifests as an amplification of the mean pattern581

of salinity (Durack and Wijffels 2010; Skliris et al. 2014). Freshening of the Arctic Ocean in the582

sensitivity to LLW occurs in absence of increased precipitation (see Fig. 4f), suggesting that it may

583

be explained locally through reduced evaporation (see Fig. 9d), or remotely via ocean currents that
26

584

carry relatively fresh Pacific Water through the Bering Strait, where they remain near the surface

585(Aagaard and Carmack 1989; Serreze et al. 2006), or even through increased precipitation over586

land, which scales with LLW (Fig. 4f) and increases river runoff into the Arctic Ocean (Nummelin587

et al. 2016). We do believe that we can rule out that this is an artifact of the sea-ice loss protocol

588

because all protocols are consistent in finding that the surface ocean under greenhouse warming

589

will be fresher than under sea-ice loss alone.

590

Correlations over the Northern hemisphere as a whole (Fig. 12b) show minimal seasonality and591

reflect the relative robustness of the oceans LLW sensitivities compared to the SIL sensitivities.

592

The median correlations in the Pacific are small for both sensitivities, so while the sign is consistent593

in the sensitivity to LLW, this reveals that the details of the pattern of freshening are not particularly594

robust. In the Arctic, despite a consistent sign in the sensitivity, as for SST, the low correlations

595

amongst the pattern-scaled sensitivities to SIL reflects the differing patterns of sea-ice loss and596

sea-ice loss protocols. The lower inter-ME for the ocean sensitivities compared to the atmosphere597

could also be due to the longer adjustment time scales of the ocean and the difference in length of598

the various model integrations (Table 1).

599

4. Discussion

600

Some aspects of the results warrant a more in-depth discussion. Firstly, whether cooling of

601

Eurasia is a forced response to Arctic sea-ice loss in model experiments remains a topic of debate

602(Cohen et al. 2014; McCusker et al. 2016; Sun et al. 2016; Blackport et al. 2019; Mori et al.

603

2019a; Cohen et al. 2020; Labe et al. 2020), with these results suggesting it may play a minor

604

role in the sensitivity to SIL in some models, as seen in Fig. 4a. However, this cooling is only

605

weakly present in some of the coupled models’ forced responses to Arctic sea-ice loss (see Fig.

6066a) and emerges more clearly only after we remove the back-effect of LLW. In particular, Figs. 4
27

607

c, d suggest that forced LLW acts to weaken the Siberian High in opposition to the forced response

608

to SIL, which strengthens the Siberian High and cools by advection. Deser et al. (2016) found that609

coupling reduced Eurasian cooling compared to equivalent experiments run with an atmosphere-

610

only model, a result also found comparing the atmosphere-only runs in England et al. (2018)611

with the coupled runs of England et al. (2020b,a). However, the Siberian High was found to be

612

stronger in the coupled runs, and so the reduced cooling is not a dynamical response to LLW found613

in the coupled runs. Rather, it is likely related to the background mini global warming (which614

scales with LLW), or the stronger Arctic warming from the coupling advected over the continent.

615

Beyond regional circulation changes, broader hemispheric warming coherent with LLW appears,

616

in the models, to overwhelm any Eurasian cooling induced by SIL. Thus, if the models represent617

the global warming process accurately, such cooling is not likely to be observed as a long-term

618

mean climate response under greenhouse warming. While these results on their own do not rule619

out the possibility that sea-ice loss could enhance cold extremes, only that we should not expect620

cooling on average, other modelling studies have pointed to the reduced risk of cold extremes

621

in response to sea-ice loss because of reduced subseasonal variability (Screen et al. 2014, 2015;

622

McCusker et al. 2016; Blackport and Kushner 2017; Collow et al. 2019). An additional note on this623

topic is that the model with the strongest cooling sensitivity in Eurasia is WACCM4 (not shown),

624

suggesting the potential importance of stratospheric dynamics in the development of that pattern625

of response, as has been previously found (Zhang et al. 2018). Lastly, recent work (He et al. 2020;

626

Labe et al. 2020) has pointed out that the depth of Arctic amplification, which is not captured

627

by our surface-parameter based pattern scaling approach, may be crucial to simulating Eurasian628

cooling. However, these studies do not suggest that the depth of Arctic warming is necessarily

629

linked to sea-ice loss, and we can not rule out that the Arctic upper tropospheric warming arises

630

from low-latitude warming.
28

631

Eurasian cooling from SIL is an example of the mid-latitude “tug-of-war”-type responses where632

sea-ice loss acts as a negative feedback on the response to lower latitude warming (Harvey et al.

633

2014; Barnes and Polvani 2015; McCusker et al. 2017). Many of the mean pattern-scaled sen-634

sitivities were found to be negatively correlated with one another, such as the sea level pressure

635

over Eurasia as just described, and similarly for the opposite lower tropospheric winds in the west

636

Pacific and in the zonal-mean zonal winds. For example, all MEs indicate a strengthening of the637

Siberian High in the sensitivity to SIL and a weakening of U850 winds in the Pacific (a weaken-

638

ing/equatorward shift of the Pacific Storm track), a weakening of the Siberian High and a poleward639

shift of the Pacific storm track in the sensitivity to LLW. Other negative feedbacks are found only640

in certain models, such as the finding that the sensitivity of the Aleutian Low in CESM1 strength-641

ens with SIL and weakens with LLW. While we do find overall that pattern-scaled sensitivities

642

tend to be negatively correlated, we note that not all aspects of the sensitivities are robust in the643

multi-ME mean.

644

Lastly, the curious cooling of atmospheric surface temperature directly above the Arctic Ocean

645

that scales with LLW is found in three of the five MEs considered here: CESM1, WACCM4, and646

CNRM-CM5. For the former two MEs, this counterintuitive result suggests that, per unit sea-ice

647

area loss, there is more warming in the dedicated sea-ice loss experiment than in the transient

648

RCP8.5 simulations. Because the sea-ice distribution in WACCM4 is matched to that from the649

RCP8.5 simulation, it cannot be attributed to large differences in sea-ice area (although small dif-

650

ferences could be a factor), however discrepancies in sea-ice thickness may arise since it is not

651

explicitly controlled for (England et al. 2020b). In the CMIP5 models, Feldl et al. (2020) found652

a negative lapse-rate feedback associated with remote changes rather than the positive lapse-rate653

feedback from local ones (e.g., sea-ice loss), consistent with a negative lapse-rate feedback in the654

sensitivity to LLW using the same pattern-scaling methodology and CESM1 simulations we use
29

655

here. The two models that have Arctic warming that scales with LLW use similar nudging proto-656

cols to lose sea ice and better match both thickness and area. It is therefore uncertain whether this657

Arctic cooling signal is a real physical response, an artifact of the ice loss protocol, or an artifact

658

of the pattern-scaling decomposition. All of these possibilities warrant further investigation with,659

for example, an approach involving application of multiple ice-loss protocols, such as nudging and660

ghost forcing, in a single model to better elucidate mechanisms.

661

5. Conclusion

662

We have explored available simulations from five different coupled models designed to isolate663

the forced coupled climate response to sea-ice loss as well as those that simulate the response to664

greenhouse warming. First, we have decomposed the forced responses into pattern-scaled sen-665

sitivities that scale with Arctic SIL and with LLW separately. Our results largely confirm the666

conclusions of Hay et al. (2018), which focused on just two of the model experiments used here.

667

We find that the near-surface atmospheric sensitivity to SIL is surprisingly robust despite differ-668

ences in sea-ice loss protocols, background climate, and model physics. It consists of warming

669

directly over the Arctic Ocean and over the high-latitude land masses that extends up to 400hPa, a670

dipole in sea level pressure with lower pressure over North America and high pressure over Eura-

671

sia, a weakening and equatorward shift of the storm tracks and jet that is more robust in the Pacific672

than in the Atlantic sector, and increased precipitation over the newly open Arctic and along the673

west coast of North America. The extratropical atmospheric sensitivity to SIL is generally more674

robust than the sensitivity to LLW in the cold season and near to the surface. However, in the

675

warm season, the sensitivity to SIL is not robust amongst MEs, whereas the sensitivity to LLW is.

676

For the first time, we have applied pattern-scaling to the forced surface ocean response (SST and

677

SSS) and while there are greater inter-ME differences, we find a more robust sensitivity to LLW
30

678

than to SIL in virtually all seasons and ocean basins, in general agreement with the sensitivities679

of surface heat fluxes. For both the surface ocean and the surface fluxes, the precise pattern of

680

sea-ice loss appears to be important in driving differences in the sensitivity to SIL, which is not681

the case for the atmospheric sensitivities. The sensitivity to LLW exhibits warming of the North

682

Pacific and Atlantic intensified along the western boundary currents as well as freshening of the683

Pacific and Arctic Oceans (including the subpolar North Atlantic) and salinification of the Atlantic

684

Ocean.

685

Taken together, the implications from the results presented here for the climate response to SIL

686

provide a partially cohesive picture. The atmospheric sensitivity is generally quite robust, but it

687

is difficult with the simulations at hand to determine whether some of the discrepancies that we688

are seeing are the result of differences in model physical parameterizations, of sensitivity to the689

mean-state (Smith et al. 2017), of including a mix of transient and equilibrated simulations, or690

of including Arctic-only and global sea-ice loss (with the latter being the case for the CESM1

691

low albedo simulation). While Sun et al. (2020) have shown that the sea-ice loss protocol is692

unlikely to play a major role in the atmosphere or in the SST responses (although albedo forcing

693

will likely result in a weaker response in DJF), whether the sea-ice loss protocol is playing an

694

important role in other aspects of the ocean response remains an open question. A downside to695

the pattern scaling approach we have taken is that it is not particularly helpful in understanding696

the underlying physics of the features it reveals and we are left to speculate. Fortunately, the697

Polar Amplification Model Intercomparison Project [PAMIP Smith et al. (2019)] has the potential

698

to include coordinated coupled modelling experiments with consistent sea-ice loss protocols and699

target sea-ice distributions, which should help elucidate some of the questions left open in this

700

work.

31

701

Acknowledgments. This work is supported by a grant from the U.S. Department of Energy Grant

702

DE-SC0019407. LMP is supported, in part, by a grant from the US National Science Foundation

703

to Columbia University.

704

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925

LIST OF TABLES

926

Table 1.

927
928
929
930
931
932
933
934

935
936
937
938
939
940
941
942
943
944

Table 2.

Summary of individual simulations and MEs used in this study, including
the nomenclature from the original publication, how many ensemble members and years of simulation we have, the radiative forcing and targeted ice
area/volume, and whether the integrations are performed as transient or timeslice experiments. The symbols shown in the model column are the same as
those used in Fig. 1. Subscripts on the simulations names indicate the publications: 1 Blackport and Kushner (2017), 2 Kay et al. (2015), 3 England et al.
(2020b), 4 Marsh et al. (2013), 5 McCusker et al. (2017), 6 Oudar et al. (2017),
7 Sun et al. (2018). . . . . . . . . . . . . . . . .

.

44

A summary of the response pattern names used in this paper and how they arecalculated, including the names of the simulations as they are in the original
publication, as well as the time periods chosen here. The right two columns
show the amount of annual mean low-latitude warming in ◦ C and Arctic seaice loss in million km2 between the differenced simulations. These are thevalues that are used to obtain the matrices used to calculate the sensitivities.
The bolded responses are those that represent the response to Arctic sea-ice
loss in isolation in a cool background climate, whereas the italicized responses
are those to both radiative forcing and sea-ice loss. The symbols shown in themodel column are the same as those used in Fig. 1. . . . . . . . .

.

45

43

945

TABLE 1. Summary of individual simulations and MEs used in this study, including the nomenclature from946

the original publication, how many ensemble members and years of simulation we have, the radiative forcing

947

and targeted ice area/volume, and whether the integrations are performed as transient or time-slice experiments.

948

The symbols shown in the model column are the same as those used in Fig. 1. Subscripts on the simulations

949

names indicate the publications: 1 Blackport and Kushner (2017), 2 Kay et al. (2015), 3 England et al. (2020b),950

4 Marsh et al. (2013), 5 McCusker et al. (2017), 6 Oudar et al. (2017), 7 Sun et al. (2018)

Model

Simulation Name

# Members and Years simulated

Radiative forcing

Ice target

Transient?

CESM1Control1

1 x 735 years

Y2000

unconstrainedno

Low Albedo1

1 x 540 years

Y2000

unconstrainednoLarge Ensemble2

40 x 180 years

Historical/RCP8.5

unconstrained

yes

WACCM4

CONTROL3

1 x 351 years

1955-1969

1955-1969

no◦

ARCTIC3

1 x 351 years

1955-1969

2085-2099

noCMIP543 x 145 years

Historical/RCP8.5

unconstrainedyes

CPI IPI 5

1 x 201 years

1xCO2

1xCO2

no

C2X I2X 5

1 x 201 years

2xCO2

2xCO2

no

C2X IPI 5

1 x 300 years

2xCO2

1xCO2

no

CPI I2X 5

1 x 300 years

1xCO2

2xCO2

no

CTL2061 x 200 years

late 20thlate 20thno

CTL2161 x 200 years

late 21stlate 21stnoICE2161 x 200 years

late 21stlate 20thnoICE2061 x 200 years

late 20thlate 21stno

GFDL-CM3

RCP8.57

5 x 120 years

RCP8.5

unconstrained

yes.

ICE19907

5 x 100 years

RCP8.51990’syes

CanESM2/

CNRM-CM5



44

951

TABLE 2. A summary of the response pattern names used in this paper and how they are calculated, including952

the names of the simulations as they are in the original publication, as well as the time periods chosen here.

953

The right two columns show the amount of annual mean low-latitude warming in ◦ C and Arctic sea-ice loss in954

million km2 between the differenced simulations. These are the values that are used to obtain the matrices used955

to calculate the sensitivities. The bolded responses are those that represent the response to Arctic sea-ice loss in

956

isolation in a cool background climate, whereas the italicized responses are those to both radiative forcing and957

sea-ice loss. The symbols shown in the model column are the same as those used in Fig. 1.

Model

Responseδ T (◦ C)

δ I(106 km2 )CESM1

Large Ensemble = LENS(2057:2066) - LENS(2027:2036)

1.16

2.27



Albedo = Low Albedo(501:840) - Control(301:735)

0.35

2.30

WACCM4

WACCM-CMIP5 = CMIP5(2085:2099) - CMIP5(1955:1969) ,

1.68

2.33

◦

WACCM-ARCTIC = ARCTIC(101:350) - CONTROL(101:350)

0.35

4.45

CanESM2

Full = C2X I2X (2:201), CPI IPI (2:201)

2.27

4.43

ICEcold = CPI I2X (101:300) - CPI IPI (2:201)

0.16

4.07

ICEwarm = C2X I2X (2:201)- C2X IPI (101:300)

0.28

4.07

CO2lo = C2X I2X (2:201) - CPI I2X (101:300)

2.12

0.36

CO2hi = C2X IPI (101:300)- CPI IPI (2:201)

2.00

0.36

ICE+GHG = CTL21(101:200) - CTL20(101:200)

2.64

7.10

ICE Effect 21 = ICE20(101:200) - CTL20(101:200)

0.12

6.55

ICE Effect 20 = CTL21(101:200) - ICE21(101:200)

0.34

6.39

GHG Effect 20 = ICE21(101:200) - CTL20(101:200)

2.30

0.7

GHG Effect 21 = CTL21(101:200) - ICE20(101:200)

2.52

0.55

GFDL-CM3∆RCP8.5 = RCP8.5(2070:2090) - RCP8.5(1990:2010)

2.04

6.02

.

∆ICE1990 = ICE1990(2070:2090) - ICE1990(1990:2010)

01.83

0.2

∆ICE = [RCP8.5(2070:2090) - RCP8.5(1990:2010)] - [ICE1990(2070:2090) - ICE1990(1990:2010)]

0.21

5.82

/

CNRM-CM5



45

958

LIST OF FIGURES

959

Fig. 1.

960
961
962
963
964
965
966
967
968
969

970

Fig. 2.

971
972
973
974

975

Fig. 3.

976
977
978

In (a) is shown the amount of annual mean Arctic sea-ice and Northern Hemisphere lowlatitude (0-40◦ N) SST for each of the time slice or equilibrated simulations, as indicated inTable 1. In (b) is shown the same thing for the transient experiments. For these, we also show
the evolution in time for each 15-year epoch of each simulation, while the time periods chosen for analysis (see Table 2) are outlined in black. The models are color- and shape-coded
according to the legend, and the colors of the lines connecting the various simulations indicate what response (ice loss alone, warming alone, or both ice loss and warming together)obtained by differencing those specific simulations. In (c) is shown the amount of Arctic
sea-ice loss and low-latitude warming in these various responses. The shape of the symbol
corresponds to the model and the color of the symbol groups the responses according to thetype of response. . . . . . . . . . . . . . . . . . . . .

.

In the upper row is shown the 15% contour of Arctic sea-ice concentration in the control
simulation (thick red contour) and the sea-ice fraction in the simulation with low sea-ice
area (filled contours) in DJF (a), MAM (b), JJA (c), and SON (d) for the CESM1 MEs.
The next rows as in the first but for the analogous WACCM4, CanESM2, CNRM-CM5, and
GFDL-CM3 MEs, respectively. . . . . . . . . . . . . . . . . .

48

. 49

An example of the pattern-scaling decomposition for zonal-mean temperature using all of
the CNRM-CM5 ME simulations in DJF: in (a) the ICE+GHG response is shown; in (b),(d), (f), and (h) “GHG Effect 21”, “ICE Effect 20”, “GHG Effect 20”, and “ICE Effect 21”
responses, δ Tmzon are shown, respectively. In (c) and (e) the patterns of sensitivity to LLW,
∂ T zon∂ T zon2∂ Tl I , per degree of warming, and SIL, ∂ I T , per 2 million km of sea-ice loss, utilizing979

lthe response patterns in (a) and (b) or (d) are shown, and (g) and (i) are as in (c) and (e) but
for the calculation utilizing the response patterns in (a) and (f) or (h). . . . . . . .

980
981

982

Fig. 4.

983. 50

In the left-hand column is shown the DJF multi-ME mean of the pattern of sensitivity to
SIL, ∂∂ZI , per 2 million km2 of Arctic sea-ice loss, for (a) surface temperature, (c) sea
Tl

984
985
986
987
988
989

990

Fig. 5.

991

992

Fig. 6.

993
994
995

996
997
998
999
1000
1001

Fig. 7.

level pressure, (e) precipitation, and (g) 850 hPa zonal wind. (b), (d), (f), and (h) as in (a),(c), (e), and (g) but for the pattern of sensitivity to LLW, ∂∂TZ , per degree of warming. In
l I
each panel, the hatched areas represent locations where at least 4 of the 5 MEs agree on thesign. The bolded number in the lower right hand corner of each panel represents the medianpattern correlation across the different MEs, while the number between panels represents
the pattern correlation between the mean sensitivity patterns. . . . . . . . . .

. 51

As in Fig. 4 but for DJF zonal-mean temperature in (a) and (b), and for zonal-mean zonal
wind in (c) and (d). . . . . . . . . . . . . . . . . . . . .

. 52

In (a) through (f) is the DJF modelled response to sea-ice loss in DJF for surface temperature,
sea level pressure, precipitation, 850 hPa zonal wind, zonal-mean zonal wind, and zonalmean temperature, and in (g) through (l) is the reponse to warming alone in, and in (m)through (r) is the response to greenhouse warming. . . . . . . . . . . .
In (a) the pattern correlations over the Northern Hemisphere extratropics are shown (definedas all regions north of 30◦ N). Each dot represents the inter-ME correlation between individual sensitivity patterns of surface temperature obtained through pattern-scaling for eachseason (DJF, MAM, JJA, SON, as well as in the annual mean, ANN). The blue dots represent
correlations between the SIL sensitivity patterns and the orange dots represent correlations
between the LLW sensitivity patterns. (b) through (f) as in (a) but for sea level pressure,

46

.

53

precipitation, 850 hPa zonal wind, zonal-mean zonal wind, and zonal-mean temperature.
The black bars represent the median of the correlations. . . . . . . . . . .

.

54

In (a) is the sensitivity of annual mean precipitation to SIL in CESM1 and in (b) is thesensitivity to LLW in CESM1. (c) – (j) as in (a) and (b) but for CanESM2, CNRM-CM5,
GFDL-CM3, and WACCM4. In (k) is the multi-ME mean sensitivity to SIL and in (l) is themulti-ME mean sensitivity to LLW. Hatching represents where at least 4 of the 5 MEs agree
on the sign of the sensitivity. . . . . . . . . . . . . . . . . .

.

55

As in Fig. 4 but for DJF sensible heat flux in (a) and (b), for latent heat flux in (c) and (d),for surface net shortwave flux in (g) and (h), and for surface net longwave flux in (i) and (j).
A flux from the ocean to the atmosphere is defined as positive. . . . . . . . .

.

56

1016

Fig. 10. The left-hand column shows the annual mean sensitivity of sea surface temperature to Arctic
SIL shown for each of the ME’s: (a) CESM1, (c) WACCM4, (e) CanESM2, (g) CNRMCM5, (i) GFDL-CM3, and for the multi-ME mean in (k). The hatched regions represent
where at least 4 out of 5 MEs agree on the sign. (b), (d), (f), (h), (j), and (l) represent thesame but for the sensitivity to LLW. . . . . . . . . . . . . . . .

.

57

1017

Fig. 11. As in Fig. 10 but for sea surface salinity.

.

58

1002
1003

1004

Fig. 8.

1005
1006
1007
1008

1009
1010
1011

1012
1013
1014
1015

1018
1019
1020
1021

Fig. 9.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Fig. 12. In (a) and (b) as in Fig. 7 but for annual mean SST and SSS, respectively. In (c) inter-ME
annual mean spatial correlations are shown for the Atlantic,Pacific, and Arctic basins. (d)as in (c) but for SSS. Blue markers are correlations between SIL sensitivity patterns, whileorange makers are correlations between LLW sensitivity patterns. . . . . . . . .

47

. 59

1022

F IG . 1. In (a) is shown the amount of annual mean Arctic sea-ice and Northern Hemisphere low-latitude (0-102340◦ N) SST for each of the time slice or equilibrated simulations, as indicated in Table 1. In (b) is shown the same1024

thing for the transient experiments. For these, we also show the evolution in time for each 15-year epoch of each1025

simulation, while the time periods chosen for analysis (see Table 2) are outlined in black. The models are color-1026

and shape-coded according to the legend, and the colors of the lines connecting the various simulations indicate

1027

what response (ice loss alone, warming alone, or both ice loss and warming together) obtained by differencing

1028

those specific simulations. In (c) is shown the amount of Arctic sea-ice loss and low-latitude warming in these1029

various responses. The shape of the symbol corresponds to the model and the color of the symbol groups the1030

responses according to the type of response.

48

1031

F IG . 2. In the upper row is shown the 15% contour of Arctic sea-ice concentration in the control simulation

1032(thick red contour) and the sea-ice fraction in the simulation with low sea-ice area (filled contours) in DJF (a),1033

MAM (b), JJA (c), and SON (d) for the CESM1 MEs. The next rows as in the first but for the analogous

1034

WACCM4, CanESM2, CNRM-CM5, and GFDL-CM3 MEs, respectively.

49

1035

F IG . 3. An example of the pattern-scaling decomposition for zonal-mean temperature using all of the CNRM-

1036

CM5 ME simulations in DJF: in (a) the ICE+GHG response is shown; in (b), (d), (f), and (h) “GHG Effect 21”,1037

“ICE Effect 20”, “GHG Effect 20”, and “ICE Effect 21” responses, δ Tmzon are shown, respectively. In (c) and1038(e) the patterns of sensitivity to LLW, ∂∂TT

zon
l

zon

I

, per degree of warming, and SIL, ∂ T∂ I

Tl

, per 2 million km2 of

1039

sea-ice loss, utilizing the response patterns in (a) and (b) or (d) are shown, and (g) and (i) are as in (c) and (e)1040

but for the calculation utilizing the response patterns in (a) and (f) or (h).

50

1041

F IG . 4. In the left-hand column is shown the DJF multi-ME mean of the pattern of sensitivity to SIL, ∂∂ZI

Tl

,

1042

per 2 million km2 of Arctic sea-ice loss, for (a) surface temperature, (c) sea level pressure, (e) precipitation,1043

and (g) 850 hPa zonal wind. (b), (d), (f), and (h) as in (a), (c), (e), and (g) but for the pattern of sensitivity to1044

LLW, ∂∂TZ , per degree of warming. In each panel, the hatched areas represent locations where at least 4 of the
l

I

1045

5 MEs agree on the sign. The bolded number in the lower right hand corner of each panel represents the median1046

pattern correlation across the different MEs, while the number between panels represents the pattern correlation

1047

between the mean sensitivity patterns.

51

1048

1049

F IG . 5. As in Fig. 4 but for DJF zonal-mean temperature in (a) and (b), and for zonal-mean zonal wind in (c)and (d).

52

1050

F IG . 6. In (a) through (f) is the DJF modelled response to sea-ice loss in DJF for surface temperature, sea

1051

level pressure, precipitation, 850 hPa zonal wind, zonal-mean zonal wind, and zonal-mean temperature, and in

1052(g) through (l) is the reponse to warming alone in, and in (m) through (r) is the response to greenhouse warming.

53

1053

F IG . 7. In (a) the pattern correlations over the Northern Hemisphere extratropics are shown (defined as all1054

regions north of 30◦ N). Each dot represents the inter-ME correlation between individual sensitivity patterns of1055

surface temperature obtained through pattern-scaling for each season (DJF, MAM, JJA, SON, as well as in the1056

annual mean, ANN). The blue dots represent correlations between the SIL sensitivity patterns and the orange

1057

dots represent correlations between the LLW sensitivity patterns. (b) through (f) as in (a) but for sea level1058

pressure, precipitation, 850 hPa zonal wind, zonal-mean zonal wind, and zonal-mean temperature. The black1059

bars represent the median of the correlations.

54

1060

F IG . 8. In (a) is the sensitivity of annual mean precipitation to SIL in CESM1 and in (b) is the sensitivity to1061

LLW in CESM1. (c) – (j) as in (a) and (b) but for CanESM2, CNRM-CM5, GFDL-CM3, and WACCM4. In (k)1062

is the multi-ME mean sensitivity to SIL and in (l) is the multi-ME mean sensitivity to LLW. Hatching represents

1063

where at least 4 of the 5 MEs agree on the sign of the sensitivity.

55

1064

F IG . 9. As in Fig. 4 but for DJF sensible heat flux in (a) and (b), for latent heat flux in (c) and (d), for surface1065

net shortwave flux in (g) and (h), and for surface net longwave flux in (i) and (j). A flux from the ocean to the1066

atmosphere is defined as positive.

56

1067

F IG . 10. The left-hand column shows the annual mean sensitivity of sea surface temperature to Arctic SIL

1068

shown for each of the ME’s: (a) CESM1, (c) WACCM4, (e) CanESM2, (g) CNRM-CM5, (i) GFDL-CM3, and1069

for the multi-ME mean in (k). The hatched regions represent where at least 4 out of 5 MEs agree on the sign.

1070(b), (d), (f), (h), (j), and (l) represent the same but for the
57 sensitivity to LLW.

F IG . 11. As in Fig. 10 but for sea surface salinity.

58

1071

F IG . 12. In (a) and (b) as in Fig. 7 but for annual mean SST and SSS, respectively. In (c) inter-ME annual

1072

mean spatial correlations are shown for the Atlantic,Pacific, and Arctic basins. (d) as in (c) but for SSS. Blue

1073

markers are correlations between SIL sensitivity patterns, while orange makers are correlations between LLW

1074

sensitivity patterns.

59