ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

1

Less surface sea ice melt in the CESM2 improves Arctic sea ice simulation with minimal2

non-polar climate impacts

3
4
5

Jennifer E. Kay1,2, Patricia DeRepentigny1,3, Marika M. Holland4, David A. Bailey4, Alice K.

6DuVivier4, Ed Blanchard-Wrigglesworth5, Clara Deser4, Alexandra Jahn1,3, Hansi Singh6, Madison7

M. Smith5, Melinda A. Webster7, Jim Edwards4, Sun-Seon Lee8,9, Keith B. Rodgers8,9, and Nan

8

Rosenbloom4

9
10

1

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

11

2

Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder,

12

CO

13

3

Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO

14

4

National Center for Atmospheric Research, Boulder, CO

15

5

University of Washington, Seattle, WA

16

6

University of Victoria, British Columbia, Canada

17

7

University of Alaska Fairbanks, AK

18

8Center for Climate Physics, Institute for Basic Science, Busan, South Korea

19

9

Pusan National University, Busan, South Korea

20
21

Corresponding author: Jennifer E. Kay (jennifer.e.kay@colorado.edu)

22

Revised for Journal of Advances in Modeling Earth Systems

23

November 22, 2021

24
25

Publication Units: 6603 words (~13 PU, each PU is 500 words), 15 Figures. Total PU = 28.

26
27
28

Key Points:
•

29
30

transition to an ice-free Arctic Ocean
•

31
32
33

Decreasing surface melt decreases late-summer Arctic sea ice cover biases and delays
Internal variability limits value of sea ice trends and sea ice sensitivity as metrics toconstrain model performance under similar global warming•

Increasing sea ice thickness and area has negligible impacts on non-polar climate andclimate change

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

34

Abstract35
36This study isolates the influence of sea ice mean state on pre-industrial climate and transient37

1850-2100 climate change within a fully coupled global model: The Community Earth System

38

Model version 2 (CESM2). The CESM2 sea ice model physics is modified to increase surface

39

albedo, reduce surface sea ice melt, and increase Arctic sea ice thickness and late summer cover.

40

Importantly, increased Arctic sea ice in the modified model reduces a present-day late-summer

41

ice cover bias. Of interest to coupled model development, this bias reduction is realized without

42

degrading the global simulation including top-of-atmosphere energy imbalance, surface

43

temperature, surface precipitation, and major modes of climate variability. The influence of these44

sea ice physics changes on transient 1850-2100 climate change is compared within a large initial45

condition ensemble framework. Despite similar global warming, the modified model with thicker

46

Arctic sea ice than CESM2 has a delayed and more realistic transition to a seasonally ice free47

Arctic Ocean. Differences in transient climate change between the modified model and CESM2

48

are challenging to detect due to large internally generated climate variability. In particular, two

49

common sea ice benchmarks - sea ice sensitivity and sea ice trends - are of limited value for50

comparing models with similar global warming. More broadly, these results show the importance51

of a reasonable Arctic sea ice mean state when simulating the transition to an ice-free Arctic

52

Ocean in a warming world. Additionally, this work highlights the importance of large initial53

condition ensembles for credible model-to-model and observation-model comparisons.

54
55

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

56

Plain Language Summary

57
58

Satellite observations available from 1979 to present show dramatic Arctic sea ice loss. As a59

result, projecting when the Arctic Ocean may become ice free and the resulting impacts is of60

broad interest to those living in the Arctic and beyond. Climate models are the main tool for making

61such future projections. Yet, projecting sea ice loss is hard because it is affected by multiple

62

factors that are often impossible to disentangle including physical processes, unpredictable63

climate variability, and differences in climate drivers. Unique to this work, we analyze the influence64of the sea ice surface melt while also controlling for all other confounding factors such as the65amount of global warming and unpredictable climate variability. Our work demonstrates that under66

similar global warming, surface melt affects the timing of an ice-free Arctic Ocean. Specifically,67

simulations with less surface melt and more sea ice transition to an ice-free Arctic Ocean later.

68

From the perspective of model development and transient climate change, we also found sea ice

69

amounts and the timing towards an ice-free Arctic have negligible influence on warming,

70

precipitation, and sea level pressure outside of the polar regions.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

71

1. Motivation and Study Goals

72

Satellite-observed Arctic Ocean sea ice cover decreases over the last few decades are a73

visible manifestation of human-caused climate change. Earth system models cannot reproduce

74

this observed ice loss with natural forcing alone (e.g., Kirchmeier-Young et al. 2017, Kay et al.

752011). While models can reproduce the sign of observed multi-decadal Arctic sea ice area trends,

76

these same models exhibit differing Arctic sea ice loss rates and timing (Swart et al. 2015, Notz,

77

D & SIMIP Community 2020). Why do Arctic sea ice loss rates differ between model simulations?

78

Given similar global warming, two factors are important to consider. First, mean state matters:

79

models with thicker Arctic sea ice tend to exhibit less ice area loss but more ice volume loss than80

models with thinner sea ice (e.g., Massonnet et al. 2018, Holland et al. 2010, Bitz 2008). Second,81

internally generated climate variability influences differences in Arctic sea ice loss timing and82

trends (e.g., Notz, D & SIMIP Community 2020, England et al. 2019, Jahn et al. 2016, Swart et83

al. 2015, Notz 2015, Wettstein and Deser 2014, Kay et al. 2011). In fact, recent work emphasizes

84

that internal variability dominates over emissions scenario in affecting projected sea ice loss over85

the upcoming 2-3 decades, including the timing of the first ice-free Arctic Ocean in late summer

86(e.g., Bonan et al., 2021, DeRepentigny et al. 2020, Jahn 2018, Sigmond et al. 2018).

87
88

Sea ice mean state influences transient sea ice response to climate forcing. Indeed, mean89

sea ice thickness has well-known foundational influences on vertical sea ice thermodynamics

90(Bitz and Roe 2004, Holland et al. 2006). The two dominant feedbacks internal to sea ice – the91

positive sea ice albedo feedback and the negative ice-thickness growth feedback – strengthen

92

when sea ice thins. Sea ice loss in models with a wide range of complexities show the importance93

of sea ice mean thickness to thermodynamic sea ice growth and loss. In addition, mean sea ice

94

thickness affects sea ice variability and predictability. When sea ice thins, ice area variability

95

increases, ice thickness variability decreases, and predictor relationships change in location,

96

nature, and magnitude (e.g., Holland et al. 2019, Mioduszewski et al. 2018, Swart et al. 2015,

97

Holland and Stroeve 2011, Blanchard Wrigglesworth et al. 2011, Kay et al. 2011).

98
99

While the importance of sea ice mean state is uncontroversial, the potential to constrain

100

the mean state and reduce projection uncertainty remains unclear. Recent work by Massonnet et101

al. (2018) used regression to quantify the relationship between Arctic sea ice mean state and102transient loss rates in a multi-model ensemble (Coupled Model Intercomparison Project version

103

5 (CMIP5), Taylor et al. 2012). While the relationships between mean state and linear changes in

104

March sea ice volume and September sea ice area were weak, they were statistically significant.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

105

The study arrived at two important conclusions. First, given the importance of mean state and in

106

particular sea ice thickness mean state, models with a biased mean sea ice thickness should be

107

questioned and potentially not used for future projections. Second, it is currently not possible to108

observationally constrain the sea ice thickness mean state due to the lack of long-term and109

reliable observations. This second conclusion is especially striking, is consistent with a recent

110community analysis that questioned the accuracy of sea ice thickness observations (e.g., Notz,

111

D. & SIMIP Community 2020), and leaves many open questions: 1) How reliable is reliable112

enough? 2) How long of an observational record is needed? 3) Does tuning to observed sea ice

113

extent/area help constrain sea ice thickness? Model tuning is necessary (e.g., Mauritsen et al.

1142012), and best accomplished when constrained by available observations, especially when the115

mean state influences transient response as may be the case for sea ice.

116
117

Even if the sea ice mean state can be observationally constrained, internally generated

118

climate variability obscures the influence of the mean state on the transient sea ice response.

119

Having many realizations that show the same response increases confidence that a signal results

120

from a sea ice thickness difference and not from internally generated climate variability. As a121

result, large initial-condition ensembles are needed to quantify the influence of mean sea ice state122

on sea ice projections. While such ensembles are becoming more standard practice and more123

broadly available with CMIP-class models (e.g., Deser et al. 2020), sensitivity tests using large

124

ensembles as a control are rare. In particular, a targeted experiment that isolates the influence of125

sea ice mean state on climate change and variability in a CMIP-class model with a large ensemble

126

has not been done.

127
128
129

In this study, we build on previous work by isolating the influence of the sea ice mean state
on climate. We focus on two research questions:1301) Does sea ice mean state influence the rate and timing of transient anthropogenically131

forced sea ice change? In particular, does thicker Arctic sea ice lead to slower sea ice

132

loss and a later transition to seasonally ice-free conditions in transient projections for133

the 21st century?

1342) What is the impact of sea ice mean state on key global climate variables (surface

135

temperature, precipitation, and sea level pressure)? Specifically, can we detect the136

influence of sea ice mean state on pre-industrial climate and 1850-2100 transient137

climate change and variability in both polar and non-polar regions?

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

138

To answer our research questions, we modify the sea ice model within an earth system139

model to increase surface albedo, reduce surface melt, and increase the mean state sea ice. We

140then quantify the influence of the sea ice mean state differences on mean and transient climate

141

change using a large initial condition ensemble as a control. Working within this numerical142

simulation framework, we can isolate differences in transient projections that arise from sea ice

143mean state alone. While we present results from both poles, we focus more on the Arctic where144

the parameter changes have a larger impact and reduce a model bias. We find that with thicker

145

sea ice, the transition to an ice-free Arctic Ocean is delayed. In addition, the impacts of sea ice

146tuning on non-polar climate are small. While our results rely on one model, our analysis provides

147

guidance for future modeling development efforts, especially those that hope to optimize their148

simulation of transient Arctic sea ice loss.

149
150

2. Methods

151

2.1 Model simulations and comparison strategies

152

We use a well-documented state-of-the-art global climate model: the Community Earth

153

System Model version 2 (CESM2) with the Community Atmosphere Model version 6 (CAM6)

154(Danabasoglu et al. 2020). CESM2-CAM6, hereafter shortened to simply CESM2, is an attractive

155

model to use for two reasons. First, comprehensive simulations exist for CESM2 as a part of the156

Coupled Model Intercomparison Project version 6 (CMIP6) (Eyring et al. 2016) and a recently

157

released large initial-condition ensemble, hereafter referred to as the CESM2-LE (Rodgers et al.

1582021). Second, CESM2 has a mean state Arctic sea ice bias. When compared to present-day

159

observations, CESM2 has insufficient late summer Arctic sea ice cover, a bias that has been160

attributed to the sea ice being too thin (Danabasoglu et al. 2020 Figure 17g, DuVivier et al. 2020).

161

The consequences of this CESM2 thin Arctic sea ice bias for transient sea ice change have been

162

documented in DeRepentigny et al. (2020). For example, the 11 CESM2 CMIP6 transient163

historical simulations have ice-free late summer conditions in the Arctic as early as 2010, which164

is inconsistent with satellite observations even when accounting for internal variability.

165
166

Inspired to remedy the CESM2 thin Arctic sea ice bias and assess its impact on the global

167

climate system, we created CESM2-lessmelt. CESM2-lessmelt is identical to CESM2 except for168

two parameter modifications made within the thermodynamics of the sea ice model. The sea ice

169

model in CESM2 (CICE 5.1.2; Hunke et al. 2015) uses a multiple-scattering Delta-Eddington

170

radiative transfer parameterization which relies on the specification of inherent optical properties

171(Briegleb and Light 2007). These optical properties can be adjusted to change the albedo of snow-

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

172

covered sea ice. In CESM2-lessmelt, we increased the albedo of snow on sea ice by increasing173

the r_snw parameter from 1.25 to 1.5 standard deviations. This r_snw parameter change

174

decreases the dry snow grain radius from 187.5 µm to 125 µm. In addition, we changed the dt_mlt

175

parameter such that the melt onset temperature increases by 0.5 °C from -1.5 °C to -1.0 °C. This176

melt onset temperature determines when the snow grain radius starts to grow from a dry snow

177

value to a melting snow value. Both CESM2-lessmelt parameter changes were implemented to178

increase snow albedo, reduce sea ice melt, and increase the mean state sea ice thickness. Both179

parameter changes were made globally and thus affect sea ice in both hemispheres. Finally, both180

parameter changes are within the observational uncertainty provided by in situ observations from

181

Surface Heat Budget of the Arctic Ocean (SHEBA).

182
183

In this work, we compare simulations with constant pre-industrial control climate

184

conditions. For CESM2, we use the multi-century CMIP6 1850 pre-industrial control run. For

185

CESM2-lessmelt, we ran a 550-year-long CESM2-lessmelt 1850 pre-industrial control run. The

186

CESM2-lessmelt control was branched from year 881 of the CESM2 CMIP6 control. As a sanity

187

check, we assessed global metrics of energy conservation and climate stability in the CESM2-

188

lessmelt control and compared it to the CESM2 control during overlapping years. The global mean

189

surface temperature is 0.16 K lower in CESM2-lessmelt (288.18 K) than in CESM2 (288.34 K).

190

The top-of-model energy imbalance in both models is small: -0.02 Wm-2 for CESM2-lessmelt and

191

0.07 Wm-2 for CESM2. Correspondingly, ocean temperature drift is smaller in CESM2-lessmelt

192

than in CESM2. Overall, both models exhibit small drift in their global mean surface temperature193

and top-of-model energy imbalance. Thus, both CESM2 model versions meet basic energy

194

conservation and stability criteria for global coupled modeling science.

195
196

In addition to pre-industrial control comparisons, we also compare simulations of 1850-

197

2100 transient climate change under the same CMIP6 forcing. For CESM2, we use the first 50

198

ensemble members of the CESM2-LE. As described in Rodgers et al. (2021), members 1-50

199

share the same transient CMIP6 forcing: historical (1850-2014) and the SSP3-7.0 future scenario

200(2015-2100) (O’Neill et al. 2016). For CESM2-lessmelt, we ran a 4-member mini ensemble using201

the same historical and SSP3-7.0 CMIP6 forcing as CESM2-LE members 1-50. The first CESM2-

202

lessmelt ensemble member started at year 1181 of the CESM2-lessmelt 1850 pre-industrial

203

control run and was run from 1850 to 2100. Three additional CESM2-lessmelt ensemble members

204

were run from 1920 to 2100 with initial conditions from the first CESM2-lessmelt ensemble

205

member perturbed by round-off (10-14 K) differences in air temperature.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

206

As all transient ensemble members analyzed in this work share the same forcing, we

207

assume each ensemble member is an equally likely estimate of the transient climate response.

208

This “equally likely” assumption is justified in the Supporting Information (Text S1, Figures S1-

209

S5). This assumption enables us to statistically quantify differences between CESM2-LE and210

CESM2-lessmelt. Given the differences in ensemble size, we use bootstrapping to statistically211

assess when the 4 CESM2-lessmelt ensemble members are distinct from the first 50 members

212of the CESM2-LE. Bootstrapping, or randomly resampling to generate statistics, requires no213

distribution assumptions.

214
215

Finally, it is important to note a feature of all transient model simulations analyzed here.

216

Namely, the CMIP6 historical forcing includes a stark increase in the inter-annual variability of217biomass burning emissions during the satellite era of wildfire monitoring 1997-2014 (Fasullo et al.

218

2021, DeRepentigny et al. 2021). This discontinuity leads to excessive surface warming in the219

northern hemisphere extratropics (Fasullo et al. 2021). It also contributes to 1997-2010 Arctic sea

220

ice loss followed by a 2010-2025 Arctic sea ice recovery (DeRepentigny et al. 2021). While

221

several CMIP6 models show impacts from this discontinuity, the CESM2 has a particularly

222

pronounced response. In this work, we use this discontinuity as an opportunity to assess the223

influence of sea ice mean state on the sea ice response to a short-term radiative forcing.

224
225

3. Results

226

3.1. Pre-industrial sea ice in CESM2 and CESM2-lessmelt

227

Comparison of pre-industrial sea ice volume and cover monthly mean values show

228

CESM2-lessmelt has more sea ice than CESM2 in both hemispheres (Figure 1). In the Arctic, sea

229

ice volume in CESM2-lessmelt exceeds that in CESM2 during all months (Figure 1a). In contrast,

230

Arctic sea ice cover differences have a distinct seasonal cycle with large late summer differences231

and small winter differences (Figure 1b). In the Antarctic, CESM2-lessmelt has larger sea ice

232

cover and volume than CESM2 in all months (Figure 1c,d). Monthly mean volume differences233

between CESM2 and CESM2-lessmelt are larger in the Arctic (30% greater in CESM2-lessmelt)

234

than in the Antarctic (8% greater in CESM2-lessmelt). Larger sea ice changes in the Arctic than235

in the Antarctic are unsurprising because CESM2 and CESM2-lessmelt differ in their surface melt.

236

Unlike in the Arctic, surface melt in the Antarctic is negligible. Almost all Antarctic sea ice melts

237

from below.

238

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

239

Spatially, the largest sea ice cover differences occur at the summer sea ice edge

240

where/when the sea ice can expand/contract without influence of land barriers and ocean

241

circulation. At the late summer seasonal minimum, the CESM2-lessmelt sea ice edge expands242

equatorward at the sea ice margin in both hemispheres (Figure 2). Yet, this late summer

243

expansion in CESM2-lessmelt is not zonally uniform. In the Arctic, the largest late summer ice

244

concentration increases in CESM2-lessmelt occur north of Russia in the East Siberian Sea

245(Figure 2b). In contrast, only modest late summer sea ice expansion happens in the North Atlantic.

246

In the Antarctic, the largest magnitude late summer sea ice concentration expansion equatorward247

occurs off the coast east of the Weddell Sea (Figure 2d). Changes in late-summer Antarctic sea

248

ice concentration are otherwise small, likely due to the lack of sea ice at the seasonal minimum.

249

At the seasonal maximum in late winter, Arctic concentrations differences are small due to the250

land barriers and the ocean heat convergence that controls the sea ice edge (Figure 3a-b; Bitz et251

al. 2005). In the Antarctic, the late winter sea ice edge has a zonally non-uniform response with252

some regions exhibiting sea ice concentration increases and others exhibiting sea ice

253

concentration decreases (Figure 3c-d). In particular, there is slightly less sea ice cover in CESM2-

254

lessmelt than in CESM2 in the Ross Sea. Non-zonally asymmetric sea ice differences255

demonstrate the importance of both thermodynamic and dynamic responses to the sea ice

256

parameter changes made in CESM2-lessmelt.

257
258

Sea ice thickness comparisons are also of interest, especially in the Arctic where thicker

259

late winter sea ice can lead to less late summer sea ice loss. Unlike the concentration differences

260

that manifested at the sea ice edge, sea ice thickness differences at the late winter seasonal

261

maximum occur throughout the sea ice pack (Figure 4). Late-winter sea ice thicknesses are at

262

least 0.5 m greater in CESM2-lessmelt than in CESM2 throughout the central Arctic basin (Figure2634b). Antarctic sea ice thickness differences are much smaller, generally less than 0.25 meters

264(Figure 4d). The largest differences in the Antarctic occur off the west side of the Antarctic265

Peninsula in the Bellingshausen Sea.

266
267

To quantify processes underlying the mean state differences between the two CESM2

268

model variants, we next compare their sea ice mass tendencies. In addition to analyzing the total

269

sea ice mass tendency, we also decompose this total tendency into contributions from dynamic

270

and thermodynamic processes. Dynamic mass tendencies result from advection of ice into or out

271

of a grid cell. Thermodynamic mass tendencies result from the sum of basal ice growth, ice growth272

in supercooled open water, transformation of snow to sea ice, surface melting, lateral melting,ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

273

basal melting and evaporation/sublimation. See DuVivier et al. (2020), Singh et al (2021), and274

Bailey (2020) for more information about these diagnostics including their application to evaluate

275

CESM2 sea ice. Consistent with a balanced mean state and negligible model drift, the annual276

mean tendency terms differences are small (not shown). Yet, substantial differences in the sea

277

ice mass tendency terms occur during both the growth season and the melt season in both

278

hemispheres in response to the parameter changes made in CESM2-lessmelt.

279
280

Arctic sea ice mass tendency diagnostics show CESM2 and CESM2-lessmelt differences281

result from both thermodynamics and dynamics (Figure 5). During the melt season, CESM2-

282

lessmelt has less Arctic thermodynamic sea ice mass loss than CESM2. This thermodynamic sea

283

ice mass loss difference is consistent with a higher snow albedo in CESM2-lessmelt than in

284

CESM2. CESM2-lessmelt also has less thermodynamic Arctic sea ice mass gain than CESM2

285

during the growth season due to the negative ice-thickness growth feedback (Bitz and Roe, 2004).

286

These opposing seasonal influences on thermodynamic tendency terms are consistent with287

thicker Arctic sea ice in CESM2-lessmelt than in CESM2. Dynamical sea ice tendency terms

288

dominate at the sea ice edge and during the growth season, and result primarily from the same289

ice velocity transporting thicker ice. With its thicker sea ice, CESM2-lessmelt has more ice export290

out of and more ice transport within the Arctic basin than CESM2. When more ice is moved into291

a region where sea ice can melt, thermodynamic mass tendencies and dynamic mass tendencies

292

compensate.

293
294

We next evaluate sea ice mass tendencies for CESM2 and CESM2-lessmelt in the295

Antarctic (Figure 6). Positive dynamic mass tendencies increase sea ice away from the Antarctic

296

coast in all seasons. This dynamically driven sea ice mass increases result from wind-driven

297

transport of sea ice away from the Antarctic coast. During the growth season, thermodynamically-298

driven sea ice mass gains occur near the coast, which in turn increases dynamically-driven sea

299

ice mass gains away from the coast. When compared to CESM2, CESM2-lessmelt has more

300

dynamical mass gain associated with this wind-driven sea ice advection in all seasons. As in the301

Arctic, these CESM2 – CESM-lessmelt differences result primarily from sea ice thickness

302

changes with a similar sea ice velocity field. During the melt season, sea ice mass loss due to303

thermodynamics is less in CESM2-lessmelt than in CESM2. Yet, the growth season in the304

Antarctic differs from that in the Arctic. Unlike in the Arctic, the Antarctic has little multi-year ice

305

and thus minimal ice-thickness growth feedback. Also unlike the Arctic, the Antarctic gains mass

306

through snow-ice formation.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

307

3.2. Influence of sea ice tuning on pre-industrial global climate

308

Overall, CESM2-lessmelt and CESM2 have statistically significant differences in surface309

air temperature, precipitation, and sea level pressure at both poles (Figure 7). In contrast, impacts

310

on non-polar climate are small and not statistically significant. Where CESM2-lessmelt has more311

sea ice than CESM2, the Arctic and Antarctic surface are both cooler in CESM2-lessmelt than in

312

CESM2, especially in non-summer seasons. Demonstrating the importance of sea ice to polar

313

surface temperatures, Ross Sea air temperatures increased in CESM2-lessmelt when compared314

to CESM2, consistent with sea ice concentration and thickness decreases from CESM2-lessmelt

315

to CESM2 in this region (Figure 3d, Figure 4d). Generally speaking, precipitation differences316

between CESM2 and CESM2-lessmelt followed surface temperature differences. The relatively

317

cooler CESM2-lessmelt atmosphere converges less moisture and has less precipitation,318

especially in Fall in the Arctic. Despite this precipitation reduction, CESM2-lessmelt has 10% more319

snow on Arctic sea ice in spring than CESM2, which is in better agreement with observations

320(Webster et al., 2021). More snow on sea ice in CESM2-lessmelt than in CESM2 results from a321

CESM2-lessmelt having a larger sea ice platform to collect snow than CESM2, especially during322

the peak snowfall season (Fall). Overall, polar sea level pressure differences are generally small

323

and not statistically significant. One notable exception are statistically significant sea level324

pressure differences between CESM2 and CESM2-lessmelt during Arctic Fall, including the well-325

known atmospheric circulation response to boundary layer thermal forcing (e.g., Deser et al.

3262010). Here, boundary layer cooling in CESM2-lessmelt leads to a local high SLP response in327

autumn (baroclinic vertical structure).

328
329

In addition to mean climate state, we also assessed climate variability differences arising330

from the different sea ice mean states in CESM2 and CESM2-lessmelt. In brief, climate variability

331

differences between the multi-century CESM2 and CESM2-lessmelt pre-industrial control runs332

are small and not statistically significant. Major modes of climate variability, such as those plotted

333

in the Climate Variability Diagnostics Package (Phillips et al 2020), are unchanged between

334

CESM2 and CESM2-lessmelt pre-industrial control runs. Similarly, differences in inter-annual

335

seasonal surface temperature, sea level pressure, and precipitation standard deviations are small336

and not statistically significant (Figure S6).

337
338

3.3. Transient (1850-2100) sea ice evolution in CESM2 and CESM2-lessmelt

339

Present-day (1979-2014) monthly hemispheric mean differences (Figure 8) resemble

340

corresponding pre-industrial control differences (Figure 1). In the Arctic, CESM2-lessmelt has

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

341

more present-day sea ice volume than CESM2 in every month (Figure 8a). Moreover, CESM2-

342

lessmelt also has more present-day Arctic sea ice cover than CESM2 in all months, with the343largest differences during the melt season and especially in late summer (Figure 8b-c). Overall,344

the Arctic sea ice mean state is closer to observations in CESM2-lessmelt than in CESM2. Of

345

particular note, additional present-day late summer Arctic sea ice cover brings CESM2-lessmelt

346

closer to observations than CESM2. While present-day hemispheric multi-decadal Arctic sea ice

347

volume observations are not available (Massonnet et al. 2018), reductions in late-summer sea ice

348

cover biases may suggest CESM2-lessmelt has a more realistic sea ice volume than CESM2.

349

Like in the Arctic, present-day Antarctic sea ice differences between CESM2 and CESM2-

350

lessmelt are also qualitatively similar to the pre-industrial control (Figure S7). But unlike in the351

Arctic, both CESM2 variants have substantial Antarctic mean state biases without consistent bias352

reduction from CESM2 to CESM2-lessmelt. Given similar Antarctic sea ice biases, relatively

353

modest Antarctic mean state sea ice changes, and the inability of CESM2 and CESM2-lessmelt

354

to reproduce observed Antarctic sea trends (Figure S8), we focus on the Arctic for the remainder355

of the transient sea ice comparisons.

356
357

Arctic maps reveal that the sea ice in CESM2 and CESM2-lessmelt evolves differently

358

from the present-day into the 21st century (Figure 9). While both CESM2 and CESM2-lessmelt

359

have their greatest present-day (1979-2014) late winter sea ice thicknesses and late summer sea

360

ice concentrations north of Greenland and the Canadian Archipelago, CESM2-lessmelt has more361

sea ice throughout much of the Arctic Ocean than CESM2 (Figure 9a-d). Notably, September

362

Arctic sea ice concentrations are substantially greater in CESM2-lessmelt than in CESM2 (Figure3639c). Equally important, the present-day March sea ice is 0.5+ meters thicker in CESM2-lessmelt

364

than in CESM2 over most of the central Arctic Ocean (Figure 9d). By 2030-2049, Arctic sea ice

365

differences between CESM2-lessmelt and CESM2 remain for late-summer September

366

concentration but are small for late winter March thickness (Figure 9e-f). Large 2030-2049 late367

summer ice cover differences occur because despite starting the melt season with similar March368

sea ice thickness distributions, less melt occurs in CESM2-lessmelt than in CESM2. This369

difference in 2030-2049 summer melt is consistent with higher albedo in CESM2-lessmelt than in370

CESM2. By 2050-2069, CESM2 and CESM2-lessmelt have similar small September sea ice

371

concentrations (Figure 9g) . Consistent with a transition to a seasonally ice-free Arctic, March sea

372

ice thicknesses are also similar in 2050-2069 over much of the Arctic Ocean (Figure 9h). In fact,373

the only regions where 2050-2069 differences between CESM2-lessmelt and CESM2 persist are

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

374

along the coast of Northern Greenland and the far North Eastern portions of the Canadian375

archipelago.

376
377

While ensemble means provide the most robust assessment of the differences in CESM2

378

and CESM2-lessmelt, ensemble mean values are not physically realized quantities, mute internal379

variability, and thus should not be compared as equals with observed timeseries and trends.

380

Instead, each individual CESM2-LE or CESM2-lessmelt ensemble member’s time evolution

381

should be treated as equally likely and the observations should be treated as the single real world382

ensemble member. Consistent with time-averaged ensemble mean comparisons (Figure 8),383

September Arctic sea ice extent in all four CESM2-lessmelt ensemble members (Figure 10b) is a384

better match to 1979-2020 observations than any of the 50 CESM2-LE ensemble members

385(Figure 10a). Up until ice-free conditions are reached, CESM2-lessmelt ensemble members have386

more September sea ice extent than almost all of the CESM2-LE ensemble members. Unlike sea

387

ice amount, 20-year linear trends in September Arctic sea ice in CESM2-LE, CESM2-lessmelt,388

and observations largely overlap (Figure 10c). In other words, CESM2-lessmelt and CESM2-LE

389

trends are both consistent with observed trends. Due to ensemble size differences, the spread in390

CESM2-lessmelt trends is smaller than the spread in CESM2-LE trends. Thus, even though

391

CESM2-lessmelt trends are more negative than observed trends with end dates of 2001-2006,

392

this may simply be the consequence of ensemble size differences. As introduced in section 2.1

393

and in DeRepentigny et al. (2021), the individual ensemble members show sea ice loss

394

accelerates around the turn of the 21st century and then the sea ice recovers in the early 21st395

century due to the prescribed biomass burning emissions in CMIP6 forcing.

396
397

Continuing with the equally likely framework in mind, we next assess common metrics

398

used for sea ice model evaluation: sea ice sensitivity and the timing of a seasonally ice-free Arctic

399(Figure 11). These metrics illustrate the challenge of large internally driven variability for

400

differentiating between CESM2-lessmelt and CESM2-LE and comparing them to our single

401

observed reality. For September 1979-2014 Arctic sea ice extent trends, there is substantial

402

spread across the CESM2-LE members (Figure 11a). Despite this large CESM2-LE spread, the403

observations and the CESM2-lessmelt ensemble members are on the smaller trend side of the404

distribution. Notably, the observations and one CESM2-lessmelt ensemble member are outside405

of the CESM2-LE spread. Similarly, the sea ice sensitivity per global mean warming appears

406

larger in CESM2-lessmelt with three out of four ensemble members outside of the spread of the407

CESM2-LE (Figure 11b). Given the single observed reality and the 4 CESM2-lessmelt members,

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

408

the spread in CESM2-LE sea ice trends and global mean warming is large and humbling.

409

Assuming any individual ensemble member is equally likely, the large spread in these metrics

410

provide limited value as a model comparison metric for evaluating CESM2-LE and CESM2-

411

lessmelt because the CESM2-lessmelt ensemble is so small (4 members).

412
413

Internal variability also has a strong imprint on the timing of a first seasonally ice-free Arctic

414

Ocean. Indeed, the CESM2-LE exhibits a 38 year spread in this metric with years ranging from415

2007 to 2045 (Figure 11c). While the spread in the CESM2-lessmelt first ice-free Arctic year is416

small (2041 to 2057), the 4 CESM2-lessmelt first ice-free years barely overlap with the 50 CESM2-

417

LE first ice-free years. Bootstrapping the CESM2-LE ice-free dates shows the two distributions

418

are statistically different at the 95% confidence level. In other words, the thicker and more

419

extensive Arctic sea ice in CESM2-lessmelt delays the timing of an ice free Arctic when compared420

to CESM2-LE. While the delay of the first ice-free Arctic is statistically significant, the large

421

internally generated variability still limits its predictability by decades. The spread in ice-free years422

in the first 50 members of the CESM2-LE is made especially large and early by the accelerated423

sea ice decline associated with the CMIP6 biomass burning emissions (DeRepentigny et al.

4242021).

425
426

We next use ensemble means to quantify forced response differences between CESM-

427

LE and CESM2-lessmelt (Figure 12). To make consistent forced response comparisons, we428

bootstrap the 50 CESM2-LE members to generate statistics that are consistent with ensembles

429with only four members. With these bootstrapped values, we can statistically assess when430

CESM2-lessmelt and CESM2-LE differ while accounting for differences in ensemble size. For

431

example, if the CESM2-lessmelt ensemble mean lies outside of the 95% confidence limits of432

sample statistics generated randomly by selecting 4 members of the CESM2-LE many times (here433

1,000 times), the forced response differences are statistically significant. Comparing the434

ensemble means consistent with four ensemble members, we find that CESM2-lessmelt has more

435

September sea ice extent (Figure 12a) and more March sea ice volume (Figure 12b).

436Interestingly, twenty-year trends in September sea ice extent and March sea ice volume are437

statistically indistinguishable in CESM2-lessmelt and CESM2-LE with the exception of three438

periods (Figure 12c-d). The first exception is for the period with trend end dates ~2010 during the439

biomass burning forcing discontinuity. During this time period, the CESM2-lessmelt has less440

negative sea ice extent trends and more negative sea ice volume trends than the CESM2-LE.

441

This first exception is consistent with the thicker sea ice in CESM2-lessmelt being more resilientESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

442

to ice cover changes but more sensitive to ice volume changes due to a weaker thickness-ice

443

growth feedback. The second time period when there are trend differences occurs in the 2060s

444

and 2070s. This exception occurs because CESM2-lessmelt still has ice to lose while CESM2-LE445

is ice-free already in September (Figure 11). As a result, CESM2-lessmelt has more negative

446

September sea ice extent trends than CESM2-LE during the 2060s and 2070s. Similar trend447

differences associated with timing differences to an ice-free Arctic are seen in October and448

August, but shifted later in the 21st century (not shown). The last time period is for trend end dates

449

around 1970 when the volume trends in CESM2-lessmelt are larger than those in CESM2-LE.

450
451

We finish comparing the 1850-2100 transient sea ice evolution by contrasting interannual452

sea ice variability in CESM2-lessmelt and CESM2-LE. As was done for means, we bootstrap the453

CESM2-LE to create variability estimates consistent with an ensemble with only 4 members.

454

Consistent with previous work (Goosse et al. 2009, Mioduszewski et al. 2019), we find Arctic sea

455

ice cover variability strongly depends on the mean sea ice thickness in CESM2-LE and CESM2-

456

lessmelt (Figure S9). Overall, September sea ice extent interannual variability is smaller in

457

CESM2-lessmelt than in CESM2-LE until the middle of the 21st century. Smaller September sea

458

ice variability in CESM2-lessmelt is especially seen during the turn of the century forced sea ice

459

decline (20 year trends ending ~2010). After the 2040s, CESM2-lessmelt has more year-to-year

460

September sea ice extent variability than CESM2-LE because CESM2-lessmelt transitions to a461

seasonally ice-free Arctic later than CESM2-LE.

462
463

3.4. Influence of sea ice mean state on transient climate change464

We next assess the impact of the differing CESM2-LE and CESM2-lessmelt 1850-2100

465

sea ice evolution on transient climate change more broadly. In the end, we focus on surface

466

warming for two reasons. First, climate impacts often scale with surface warming. As a result,467

assessing where/when warming differences occur provides a foundation for assessing if the468

CESM2-LE and CESM2-lessmelt sea ice evolution differences impact climate change and469

variability more broadly. Second, we investigated other climate variables such as precipitation470

and sea level pressure and found that differences in the transient climate response in CESM-LE

471

and CESM2-lessmelt were small and not statistically significant (e.g., Figure S10). One exception472

was smaller 21st century winter Arctic precipitation increases in CESM2-lessmelt than in CESM2-

473

LE. This exception is consistent with Clausius–Clapeyron relation, namely a reduced water vapor

474

increase associated with less warming in CESM2-lessmelt than in CESM2-LE.

475

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

476

When plotted as anomalies, the 1850-2100 evolution of the global mean surface

477

temperature anomaly in CESM2-LE and CESM2-lessmelt are indistinguishable (Figure 13a). Both

478

CESM2 model variants are consistent with the observed global air surface temperature anomaly

479

evolution (Hansen et al. 2010, Rohde and Hausfather 2020). When plotted as absolute values,

480

the global mean surface temperature is lower in CESM2-lessmelt than CESM2-LE (Figure 13b).

481

This absolute temperature difference between the two CESM2 variants remains constant over the482

entire 1850-2100 period. Given the challenges of observing the absolute global mean temperature483

and the spread due to internal variability, it is unclear if CESM2-LE or CESM2-lessmelt provides484

a more realistic representation of global mean temperature. Moreover, the spatial pattern of

485

seasonal warming in CESM2-lessmelt and CESM2 is statistically indistinguishable aside from two

486

notable and sizable exceptions in the Arctic (Figure 14). First, CESM2-lessmelt warms more than487

CESM2-LE along the sea ice edge during Fall, particularly in the Pacific sector. This larger

488

warming occurs because CESM2-lessmelt has more sea ice to lose in these regions than489

CESM2-LE (Figure 2b). Second, CESM2-LE warms more than CESM-lessmelt in the central

490

Arctic Ocean during winter. This difference arises because CESM2-LE has thinner sea ice than491

CESM2-lessmelt. Finally, the Atlantic Meridional Overturning Circulation (AMOC) weakens

492

slightly (<10%) more in the CESM2-lessmelt members than in the CESM2-LE leading to small493

differences in the North Atlantic warming hole.

494
495

While the total zonal mean warming over the period 1920-1939 to 2080-2099 is496

remarkably similar in CESM2 and CESM2-lessmelt, when that warming happens differs between

497

the two model variants in the Arctic. Indeed, comparisons of zonal mean warming rates in CESM2

498

and CESM2-lessmelt show differences in the Arctic warming rates in all seasons except summer

499(Figure 15). In particular, CESM2-LE has large non-summer surface Arctic warming rates earlier

500

than CESM2-lessmelt. These larger early warming rates in CESM2-LE results from an earlier

501

transition towards an ice-free Arctic Ocean in CESM2-LE than in CESM2-lessmelt.

502
503

4. Summary and Discussion

504This study assesses the influence of sea ice mean state on simulated climate change and505

variability in a state-of-the-art global coupled climate model. Novel and new here, a large 50-506

member large ensemble is leveraged as a control for assessing the new small 4-member

507

ensemble with more mean state sea ice, especially in the Arctic. As large initial condition508

ensembles are generally run after model releases, we address a question that is unanswerable

509

during model development: Do differences in the sea ice mean state alter the ensemble spreadESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

510

of realized transient climate change? Our results re-enforce that a realistic Arctic mean state is511

critical to simulating a realistic transition to an ice-free Arctic Ocean. Specifically, simulations with512

the same global warming but more Arctic sea ice have a later transition to a late summer ice-free

513

Arctic over the 21st century. These results demonstrate starting with a reasonable mean state is514

important for trusting model-projected timing towards an ice-free Arctic Ocean in a warming world.

515

Important for climate projections and model development more generally, the sea ice differences516

examined here had negligible impacts outside the polar regions. It is important to emphasize that517

the magnitude of the sea ice influence on polar and non-polar climate is similar to recent inter-518

model comparison studies (e.g., Screen et al. 2018, Smith et al. 2021). Yet, the context here is519

different. Specifically, the differences between CESM2 and CESM2-lessmelt outside of the Arctic520

are small in the context of model development/climate impacts, especially for transient climate

521

change with multiple ensemble members. The model configuration presented here (CESM2-

522

lessmelt) is a viable model for coupled model experimentation. We anticipate and advise the use523

of CESM2-lessmelt for prediction and hypothesis-driven experiments focused on the Arctic.

524
525

Interestingly, many commonly used metrics to benchmark sea ice simulations provide

526

limited value in this study. Assuming any individual ensemble member is equally likely, many527

metrics struggle to differentiate between the thicker (CESM2-lessmelt) and thinner (CESM2) sea

528

ice model variants examined here. For example, this study reinforces previous work showing a529

two decade uncertainty in the timing of an ice-free Arctic due to internally generated variability

530(Jahn et al. 2016, Notz 2015). Here, we find an almost four decade uncertainty in the timing of an531

ice-free Arctic in the first 50 members of the CESM2-LE due to the confluence of CMIP6 biomass

532

burning forcing and thin CESM2 Arctic sea ice. In addition, sea ice sensitivity (i.e., sea ice change

533

scaled by global warming) exhibits large spread in the first 50 CESM2-LE members and thus

534

provides limited value as an observational constraint or a robust model comparison metric to535

CESM2-lessmelt. Finally, linear 20-year sea ice area trends were similar between CESM2 and536

CESM2-lessmelt ensemble members. That said, CESM2-lessmelt is consistent the observed

537

trend while CESM2-LE is not when trends longer than 20 years are considered (e.g., 1979-2014538

following Notz, D. and the SIMIP Community (2020)). The fact that many commonly used metrics

539

provide limited differentiation in this study is sobering and merits emphasis. Internal variability is540

large and must be measured and accounted for when comparing model ensemble size, as was541

done here. Of course, these findings are not entirely surprising given similar global warming in

542

CESM2 and CESM2-lessmelt. In other words, global warming cannot be used as constraint on543

simulated sea ice trends or sensitivity in this study. In fact, the mean state differences probed

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

544

here were not large enough to cause Arctic sea ice trend differences for the same amount of545

global warming. As a result, this work does not refute previous work showing that global warming

546(e.g., Mahlstein and Knutti 2012, Roach et al. 2020, Notz, D. & SIMIP Community 2020, Horvat

5472021) can constrain sea ice change, and can help illustrate when models have the right Arctic548

sea ice trends for the wrong reasons (e.g., Rosenblum and Eisenman 2017). In summary, the549

similarity between CESM2 and CESM2-lessmelt found here provides further evidence that global

550

warming exerts strong controls on Arctic sea ice trends.

551
552

We end by discussing lessons learned for simulation of sea ice in a global coupled climate

553

modeling framework. We began this study by reducing sea ice surface melt in a pre-industrial

554

control simulation in search of a stable model configuration with more Arctic Ocean sea ice volume

555

and late summer Arctic Ocean sea ice cover. The parameter modifications implemented in

556

CESM2-lessmelt were specifically targeted to reduce summer melt in the Arctic where surface

557

melt dominates. Unlike the Arctic, Antarctic sea ice melt is dominated by bottom melt. Thus, we558

anticipated and found relatively small differences in the Antarctic sea ice mean state as a result559

of our parameter modifications. After obtaining a stable multi-century control run, we then ran

560

transient 1850-2100 simulations with no additional changes. What emerged in the transient561

simulations was influenced both by the mean state and by feedbacks in CESM2, and was a562

surprise to us. Indeed, our success in obtaining more realistic transition to an ice-free Arctic state563

with CESM2-lessmelt suggests that sea ice thickness and late summer cover are important

564

targets for sea ice in coupled model development. In contrast, attention to and tuning of Arctic565

sea ice area alone is generally insufficient. That said, sea ice area expansion is important to566

monitor and model development should focus on parameters and physics that lead to credible567

sea ice area distributions. The North Atlantic is especially important to monitor as when sea ice

568

expands to completely cover the ocean there, it can shut down North Atlantic deep water569

formation, and derail global coupled earth system model development as discussed in

570

Danabasoglu et al. (2020).

571
572
573
574
575
576
577

Acknowledgments and DataESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

578
579

J.E.K. was supported by National Science Foundation (NSF) CAREER 1554659. P. D. was

580

supported by the Natural Sciences and Engineering Council of Canada (NSERC), the Fond de581

recherche du Québec – Nature et Technologies (FRQNT) and the Canadian Meteorological and582

Oceanographic Society (CMOS) through Ph.D. scholarships and by NSF CAREER 1554659, and583

NSF CAREER 1847398. M.M.H., D.A.B., A.D., C.D., J.E., and N.R. acknowledge support by the584

National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the585

NSF under Cooperative Agreement 1852977. M. M. H. was additionally supported by NSF-OPP

586

1724748. A.J. was supported by NSF CAREER 1847398. M.M.S. was supported by NSF OPP-

587

1724467 and OPP-172474. M.A.W. was provided by NASA's New (Early Career) Investigator

588

Program in Earth Science Award 80NSSC20K0658. S.-S.L. and K.B.R. were supported by IBS-

589

R028-D1. The CESM project and NCAR are supported primarily by NSF. All authors thank the590

two anonymous reviewers for their constructive and helpful reviews. Computing and data storage

591

resources for developing CESM2, CESM2-lessmelt, and CESM2-LE were provided by the

592Computational and Information Systems Laboratory (CISL) at NCAR, including the Cheyenne593

supercomputer (doi:10.5065/D6RX99HX). The CESM2-LE simulations were run on the IBS/ICCP

594

supercomputer “Aleph”, a 1.43 petaflop high-performance Cray XC50-LC Skylake computing

595

system with 18,720 processor cores, with 9.59 petabytes of disc storage, and 43 petabytes of

596

tape

597

https://www.cesm.ucar.edu/projects/community-projects/LENS2/. The CESM2-lessmelt data are

598

available

599/glade/campaign/cgd/ppc/cesm2_tuned_albedo. For more information on using Globus on NCAR

600

systems, please refer to https://www2.cisl.ucar.edu/resources/storage-and-file-systems/globus-

601

file-transfers602

archive
via

storage.
GlobusCESM2-LE
access

data
to

are
NCAR

available
GLADE

here:
at:

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

603

References

604

Bailey, D. A., Holland, M. M., DuVivier, A. K., Hunke, E. C., & Turner, A. K. (2020), Impact of a605

new sea ice thermodynamic formulation in the CESM2 sea ice component. Journal Of

606

Advances In Modeling Earth Systems, 12, e2020MS002154. doi:10.1029/2020MS002154

607

Bitz, C. M, Holland, M. M., Hunke, E. & Moritz, R. E. (2005), Maintenance of the sea-ice edge, J.

608
609

Climate, 18, 2903-2921.
Bitz, C. M. (2008), Some aspects of uncertainty in predicting sea ice thinning, in Arctic Sea Ice

610

Decline: observations, projections, mechanisms, and implications, AGU Geophysical

611

Monograph Series, vol, edited by E. deWeaver, C. M. Bitz, and B. Tremblay, pp. 63-76 ,612

American Geophysical Union.

613
614

Bitz, C. M. & Roe, G. H. (2004), A Mechanism for the High Rate of Sea-Ice Thinning in the Arctic
Ocean, J. Climate, 17, 3622--31

615

Blanchard-Wrigglesworth E., Bitz, C.M. & Holland, M.M. (2011). Influence of initial conditions and616

climate forcing on predicting Arctic sea ice, Geophysical Research Letters, 38, L18503,

617

doi: 10.1029/2011GL048807

618
619
620

Bonan, D. B., Lehner, F. & Holland, M. (2021), Partitioning uncertainty in projections of Arctic sea
ice, Environmental Research Letters, DOI: 10.1088/1748-9326/abe0ec
Briegleb, B. P., & Light, B. (2007), A delta-Eddington multiple scattering parameterization for

621

solar radiation in the sea ice component of the Community Climate System Model. NCAR

622

Tech. Note TN-4721STR, 100 pp.

623

Danabasoglu, G., Lamarque, J.-F., Bachmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J.,

624

Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large,

625

W. G., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J.,

626

Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van627

Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-

628

Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Long, M. C., Mickelson, S., Moore, J.

629

K., Nienhouse, E., Polvani, L., Rasch, P. J., & Strand, W. G. (2020), The Community Earth

630

System Model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12,631

e2019MS001916. https://doi.org/10.1029/2019MS001916

632

DeRepentigny, P., Jahn., A., Holland, M. M., Fasullo, J., Lamarque, J.-F., Hannay, C., Mills, M.

633

J., Bailey, D. Tilmes, S., & Barrett, A. (2021), Enhanced early 21st century Arctic sea ice

634

loss due to CMIP6 biomass burning emissions, Nature Climate Change, under review,

635

Available here: https://bit.ly/3Ahkjj1

636

DeRepentigny, P., Jahn, A., Holland, M. M., & Smith, A. (2020), Arctic sea ice in the two

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

637

Community Earth System Model Version 2 (CESM2) configurations during the 20th and638

21st centuries. Journal of Geophysical Research: Oceans, 125, e2020JC016133.

639

https://doi.org/10.1029/2020JC016133

640

Deser, C., Lehner, F., Rodgers, K.B. et al. (2020), Insights from Earth system model initial-

641

condition large ensembles and future prospects. Nat. Clim. Chang. 10, 277–286,642

https://doi.org/10.1038/s41558-020-0731-2

643

Deser, C., R. Knutti, , S. Solomon, & Phillips, A. S. (2012), Communication of the role of644

natural variability in future North American climate. Nat. Climate Change, 2, 775–779,

645

doi:10.1038/nclimate1562.

646

Deser, C., R. Tomas, M. Alexander, & Lawrence, D. (2010), The seasonal atmospheric647

response to projected Arctic sea ice loss in the late 21st century. J. Climate, 23, 333-351,

648

10.1175/2009JCLI3053.1.

649

DuVivier, A., Holland, M., Kay, J. E., S. Tilmes, A. Gettelman, & Bailey, D. (2020), Arctic and650

Antarctic sea ice state in the Community Earth System Model Version 2, Journal of651

Geophysical Research—Oceans, http://dx.doi.org/10.1029/2019JC015934

652

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E.

653(2016), Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6)

654

experimental

655

https://doi.org/10.5194/gmd-9-1937-2016.

656
657

designandorganization,

Geosci.

Model

Dev.,

9,

1937–1958,

England, M., Jahn, A., & Polvani, L. (2019), Nonuniform Contribution of Internal Variability to
Recent Arctic Sea Ice Loss, J. Climate, DOI: 10.1175/JCLI-D-18-0864.1

658

Fasullo, J. T., Lamarque, J-F, Hannay, C., Rosenbloom, N., Tilmes, S., DeRepentigny, P., Jahn,

659

A., & Deser, C. (2021). Spurious Late Historical-Era Warming in CESM2 and Other CMIP6

660

Climate Simulations Driven by Prescribed Biomass Burning Emissions, Nature Climate

661

Change, under review, Available here: https://bit.ly/360yXNv

662

Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, & Windnagel, A. K. (2017), Sea Ice Index,

663

Version 3. [1979-2020]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data

664

Center. doi: https://doi.org/10.7265/N5K072F8.

665

Goosse, H., Arzel., O., Bitz, C. M., de Montety, A., & Vancoppenolle, M. (2009) Increased

666

variability of the Arctic summer ice extent in a warmer climate, Geophys. Res. Lett., 36,

667

L23702, https://doi.org/10.1029/2009GL040546, 2009

668
669
670

Hansen, J., Ruedy, R., Sata, M, & Lo, K. (2010), Global surface temperature change, Rev.
Geophys., 48, RG4004, doi:10.1029/2010RG000345
Holland, M.M., L. Landrum, D. Bailey, & Vavrus, S.J. (2019), Changing seasonal predictability

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

671

of Arctic summer sea ice area in a warming climate. J. Climate, doi:10.1175/JCLI-D-19-

672

0034.1

673

Holland, M.M., D.A. Bailey, & Vavrus, S. (2011), Inherent sea ice predictability in the rapidly

674

changing Arctic environment of the Community Climate System Model, version 3, Climate

675

Dynamics, 36, 1239-1253, doi:10.1007/s00382-010-0792-4.

676

Holland, M.M., & Stroeve, J. (2011), Changing seasonal sea ice predictor relationships in a677changing Arctic climate. Geophys. Res. Lett., 38, L18501, doi:10.1029/2011GL049303.

678

Holland, M. M., M. C. Serreze, & Stroeve, J. (2010), The sea ice mass budget of the Arctic and679

its future change as simulated by coupled climate models, Climate Dynamics, 34, 185–

680

200, doi:10.1007/s00382-008-0493-4.

681
682

Holland, M.M., C.M. Bitz, & Tremblay, B. (2006), Future abrupt reductions in the Summer Arctic
sea ice, Geophys. Res. Lett., 33, L23503, doi:10.1029/2006GL028024.

683

Horvat, C. (2021), Marginal ice zone fraction benchmarks sea ice and climate model skill, Nat

684

Communications, 12, 2221 (2021). https://doi.org/10.1038/s41467-021-22004-7

685

Hunke E. C., W. H. Lipscomb, A. K. Turner, N. Jeffery, & Elliott, S. (2015), CICE: The Los Alamos

686

Sea Ice Model. Documentation and Software User’s Manual. Version 5.1. T-3 Fluid

687

Dynamics Group, Los Alamos National Laboratory, Tech. Rep. LA-CC-06-012.

688

Jahn, A. (2018), Reduced probability of ice-free summers for 1.5 °C compared to 2 °C warming.

689
690

Nature Climate Change, 8: 409-413. DOI: 10.1038/s41558-018-0127-8
Jahn, A., Kay, J. E., Holland, M. M., & Hall, D. M. (2016). How predictable is the timing of a691

summer ice-free Arctic?. Geophysical Research Letters, 43(17): 9113-9120. DOI:

692

10.1002/2016GL070067

693

Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J., Bates, S.,

694

Danabasoglu, G., Edwards, J., Holland, M. Kushner, P., Lamarque, J.-F., Lawrence, D.,

695

Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., & Vertenstein,696

M. (2015), The Community Earth System Model (CESM) Large Ensemble Project: A

697

Community Resource for Studying Climate Change in the Presence of Internal Climate

698

Variability,699

doi:10.1175/BAMS-D-13-00255.1.

700

BulletinoftheAmericanMeteorologicalSociety,96,

1333–1349.

Kay, J. E., Holland, M. M., & Jahn, A. (2011), Inter-annual to multi-decadal Arctic sea ice extent

701

trends in a warming world. Geophysical Research Letters, 38, L15708.

702

https://doi.org/10.1029/2011GL048008

703

Kirchmeier-Young, M. C., F. W. Zwiers, & Gillett, N. P. (2017), Attribution of extreme events704

in Arctic sea ice extent, J. Clim., 30, 553–571 doi:10.1175/JCLI-D-16-0412.1.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

705
706
707

Latif, M., T. Martin, & Park, W. (2013), Southern Ocean sector centennial climate variability andrecent decadal trends, J. Clim., 26(19), 7767–7782.
Mahlstein, I., & Knutti, R. (2012), September Arctic sea ice predicted to disappear near 2°C

708

global warming above present, J. Geophys. Res., 117, D06104,

709

doi:10.1029/2011JD016709.

710

Massonnet, F., Vancoppenolle, M., Goosse, H., Docquier, D., Fichefet, T., & Blanchard-

711

Wrigglesworth, E. (2018), Arctic sea-ice change tied to its mean state through712

thermodynamic

713

https://doi.org/10.1038/s41558-018-0204-z

714

processes.

NatureClimateChange,8(7),599–603.

Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M., Haak, H.,

715

Jungclaus, J., Klocke, D., Matei, D., Mikolajweicz, U. Notz, D., Pincus, R., Schmidt, H., &716

L. Tomassini (2012),Tuning the climate of a global model. Journal of Advances in

717

Modeling Earth Systems, 4, M00A01. https://doi.org/10.1029/2012MS000154

718

Mioduszewski, J., S. Vavrus, M. Wang, M. Holland, & Landrum, L. (2019), Past and future719

interannual variability in Arctic sea ice in coupled climate model. Cryosphere, 13, 113–

720

124, https://doi.org/10.5194/tc-13-113-2019.

721

Notz, D. (2015), How well must climate models agree with observations? Philosophical722

Transactions723

https://doi.org/10.1098/rsta.2014.0164

724

of

the

Royal

SocietyA,

373(2052),

20140164.

O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R.,

725

Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B.

726

M. (2016), The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6,

727

Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016.

728

Phillips, A. S., C. Deser, J. Fasullo, D. P. Schneider & Simpson, I. R. (2020), Assessing Climate

729

Variability and Change in Model Large Ensembles: A User’s Guide to the “Climate

730

Variability Diagnostics Package for Large Ensembles”, doi:10.5065/h7c7-f961

731

Roach, L. A., Dörr, J., Holmes, C. R., Massonnet, F., Blockley, E. W., Notz, D., & Bitz, C.

732

M. (2020), Antarctic sea ice in CMIP6. Geophysical Research Letters, 47,733

e2019GL086729. https://doi.org/10.1029/2019GL086729

734

Rodgers, K., Lee, S-S., Rosenbloom, N., Timmerman, A., Danabasoglu, G., Deser, C., Edwards,

735

J., Kim, J-E., Simpson, I., Stein, K, Stuecker, M., F., Yamaguchi, R., Bodai, T., Chang, E-

736

S., Huang, L., Kim, W. M., Lamarque, J.-F., Lombardozzi, D., Wieder, W. R., & S. G.

737

Yeager (2021), Ubiquity of human-induced changes in climate variability, Earth Syst.

738

Dynam. Discuss, accepted, https://doi.org/10.5194/esd-2021-50.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

739

Rohde, R. A. and Hausfather, Z., 2020. The Berkeley Earth Land/Ocean Temperature Record,

740

Earth Syst. Sci. Data, 12, 3469-3479, https://doi.org/10.5194/essd-12-3469-2020.

741
742
743

Rosenblum, E. & Eisenman, I. (2017), Sea ice trends in climate models only accurate in runs
with biased global warming. Journal of Climate, 30(16), pp.6265-6278.
Sigmond, M., Fyfe, J. C. & N. C. Swart (2018), Ice-free Arctic projections under the Paris

744

Agreement, Nat. Clim. Change, https://doi.org/10.1038/s41558-018-

745

0124-y (2018)

746
747

Notz, D. & SIMIP Community (2020), Arctic Sea Ice in CMIP6, Geophysical Research Letters,
47(10), e2019GL086749. doi:10.1029/2019GL086749

748

Singh, H. K. A., Landrum, L., Holland, M. M., Bailey, D. A., & DuVivier, A. K. (2021), An overview

749

of Antarctic sea ice in the Community Earth System Model version 2, part I: Analysis of750

the seasonal cycle in the context of sea ice thermodynamics and coupled atmosphere-751

ocean-ice processes. Journal Of Advances In Modeling Earth Systems, 13,752

e2020MS002143. doi:10.1029/2020MS002143

753

Smith, D. M., R. Eade, M. Andrews, H. Ayres, A. Clark, S. Chripko, C. Deser, N. J. Dunstone, J.

754

Garcia-Serrano, G. Gastineau, L. S. Graff, S. C. Hardiman, B. He, L. Hermanson, T. Jung,

755

J. Knight, X. Levine, G. Magnusdottir, E. Manzini, D. Matei, M. Mori, R. Msadek, P. Ortega,

756

Y. Peings, A. A. Scaife1, J. A. Screen, M. Seabrook, T. Semmler, M. Sigmond, J. Streffing,

757

L. Sun, and A. Walsh (2021), Robust but weak winter atmospheric circulation response to758

future Arctic sea ice loss. Nat. Comm., in press.

759

Swart, N. C., Fyfe, J. C., Hawkins, E., Kay, J. E., & Jahn, A. (2015), Influence of internal

760

variability761

doi:10.1038/nclimate2483.

762
763

on

Arcticsea-icetrends,NatureClimateChange,5,86-89,

Taylor, K. E., R. J. Stouffer, & Meehl, G. A. (2012), The CMIP5 experiment design, Bull. Am.
Meteorol. Soc., 93, 485–498, doi:10.1175/BAMS-D-11-00094.1.

764

Webster, M. A., DuVivier, A. K., Holland, M. M., & Bailey, D. A. (2021), Snow on Arctic sea ice in765

a warming climate as simulated in CESM. Journal Of Geophysical Research: Oceans,

766

126, e2020JC016308. doi:10.1029/2020JC016308

767

Wettstein, J. J. & Deser, C. (2014), Internal variability in projections of twenty-first century Arctic768

sea ice loss: Role of the large-scale atmospheric circulation. J. Climate, 27, 527-550, doi:769

10.1175/JCLI-D-12-00839.1.

770

Yeager, S., Karspeck, A., & Danabasoglu, G. (2015), Predicted slowdown in the rate of Atlantic

771

sea ice loss. Geophysical Research Letters, 42, 10704-10713.

772

doi:10.1002/2015GL065364

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

773

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

774
775
776

777
778
779
780
781
782
783

FiguresFigure 1. Seasonal cycle in CESM2 and CESM2-lessmelt 1850 preindustrial control runs: a) Arctic
sea ice volume, b) Arctic sea ice area and extent, c) Antarctic sea ice volume, d) Antarctic sea ice
area and extent. Values are overlapping 200-year averages (years 911-1110 of the CESM2
CMIP6 1850 pre-industrial control run).

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

784
785
786
787
788
789
790

!"#$%&'()*+,,-+*.

/"#012+3+45+#6$%&'()*+,,-+*.#)#$%&'(75"#$%&'()*+,,-+*.

8"#012+3+45+#6$%&'()*+,,-+*.#)#$%&'(7Figure 2. Late summer sea ice concentration in preindustrial control runs: a) September Arctic
CESM2-lessmelt, b) Difference September Arctic (CESM2-lessmelt - CESM2), c) February
Antarctic CESM2-lessmelt, d) Difference February Antarctic (CESM2-lessmelt - CESM2). Values
are overlapping 200-year averages as in Figure 1. Note: Nonlinear color scale used to emphasize
low ice concentrations.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

791
792
793
794
795
796

!"#$%&'()*+,,-+*.

/"#012+3+45+#6$%&'()*+,,-+*.#)#$%&'(75"#$%&'()*+,,-+*.

8"#012+3+45+#6$%&'()*+,,-+*.#)#$%&'(7Figure 3. Late winter sea ice concentration in preindustrial control runs: a) CESM2-tuned ice
March Arctic, b) Difference March Arctic, c) CESM2-lessmelt September Antarctic, d) Difference
September Antarctic. Values are overlapping 200-year averages as in Figure 1. Note: Nonlinear
color scale used to emphasize low ice concentrations.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

797
798
799
800
801
802

!"#$%&'()*+,,-+*.

/"#012+3+45+#6$%&'()*+,,-+*.#)#$%&'(75"#$%&'()*+,,-+*.

8"#012+3+45+#6$%&'()*+,,-+*.#)#$%&'(7Figure 4. Late winter sea ice thickness in preindustrial control runs: a) CESM2 March Arctic, b)
CESM2-lessmelt March Arctic, c) CESM2 September Antarctic, d) CESM2-lessmelt September
Antarctic. Values are overlapping 200-year averages as in Figure 1. Note: Nonlinear color scale
used to emphasize thin ice categories.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

7;*%=.8<"$='&,>=

3(.4(.#<&:%D("(.#(&/?@&=AB&4+<AC1
7*"8*"&<(2'D*%*"&*(/?@(=AB(8$<AC57;*%=.8<"$='&-

2<"$='&-!"#$%&#'&(9%.:#;(,*$-."(/!)33!,5(,*$(6&*()$--(7*"8*"&'*-,>=

2<"$='&-!"#$%&#'&()*+#(,*$-."(/01234)5(,*$(6&*()$--(7*"8*"&'*-

810
811
812
813
814
815
816

3(.4(.#<&/?@&=AB&4+<AC1:<.+=%#,
37("=-4<.+=%#,

Figure 5. Arctic sea ice mass tendency terms for the melt season [AMJJAS] (left), and thegrowth season [ONDJFM] (right). For each season, the top row is tendency due to dynamics(sidmassdyn), the middle row is tendency due to thermodynamics (sidmassth), and the bottom
row is their sum. All differences are CESM2-lessmelt minus CESM2. Values are overlapping 200year averages as in Figure 1.

7*"8*"&<(/?@(=AB(8$<AC5

803
804
805
806
807
808
809

!"#$%#&5"-6$7&*(+,-.&/89:0;'1&*(+&2#(&'+,,&3(.4(.#%(,

*>=

*>=

37("=-4<.+=%#,:<.+=%#,!"#$%#&'()$&*(+,-.&/!'00!*1&*(+&2#(&'+,,&3(.4(.#%(,

Figure 6. Antarctic sea ice mass tendency terms for the melt season [ONDJFM] (left), and thegrowth season [AMJJAS] (right). For each season, the top row is tendency due to dynamics(sidmassdyn), the middle row is tendency due to thermodynamics (sidmassth), and the bottom
row is their sum. All differences are CESM2-lessmelt minus CESM2. Values are overlapping 200year averages as in Figure 1.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

<=<&80;(#(4/(99=&80;(#(4/(!>?&80;(#(4/(!6.&+)7-.#(/0*0120134&+5-!"#$%&'()*%&+,-

89:&80;(#(4/(

817
818
819
820
821
822
823

Figure 7. Global maps of pre-industrial control differences (CESM2-lessmelt minus CESM2) byseason. Top row shows surface temperature (K). Middle row shows total precipitation (%difference). Bottom row shows sea level pressure (mb). Grey stippling shows regions that are
not statistically different at the 95% confidence level using a 2-sided t-test. Values areoverlapping 200-year averages as in Figure 1.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

a.

b.

c.

824
825
826
827
828

Figure 8. Present-day (1979-2014) ensemble mean seasonal cycle in CESM2-LE and CESM2lessmelt: a) Arctic sea ice volume, b) Arctic sea ice extent, c) Arctic sea ice area. Observations
are from NSIDC sea ice index with pole filling (Fetterer et al. 2017).

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

829
830
831
832
833
834
835

a.

c.

e.

g.

b.

d.

f.

h.

Figure 9. Ensemble mean Arctic sea ice maps: a) Present-day (1979-2014) CESM2-lessmelt
September concentration, b) as in a) but for March thickness, c-d) as in a-b) but for the
CESM2-lessmelt minus CESM2-LE difference, e-f) as in c-d) but for 2030-2049, g-h) as in c-d)
but for 2050-2069

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

836
837
838
839
840
841
842

843
844
845
846
847
848
849
850
851
852
853

Figure 10. Arctic September sea ice extent transient evolution: a) CESM2-LE 1850-2100
timeseries, b) CESM2-lessmelt 1850-2100 timeseries, c) 20-year trends in CESM2-LE, CESM2lessmelt, and observations with end years of 1999-2049. Observations are from NSIDC sea ice
index (Fetterer et al. 2017) with area pole-filling.

Figure 11. Arctic sea ice comparison metrics: a) September sea ice trends, b) sea ice sensitivity
defined as the change in September sea ice extent per degree of change in global meansurface temperature (dSIE/dGMST), and c) year of first seasonally ice-free Arctic Ocean. Sea
ice extent trend and sensitivity calculations follow protocol and years (1979-2014) used forevaluation of CMIP6 by SIMIP Community (2020). In a) and b), the observations are shown as a
red dashed line and the CESM2 Large Ensemble is shown as a box indicating the interquartile
range, a line inside the box indicating the median, and whiskers to show the minimum andmaximum across all ensemble members. In c), a seasonally ice-free Arctic Ocean occurs when
the September sea ice extent first falls below 1 million sq. km.

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

854
855
856
857
858
859
860

Figure 12. CESM2-LE and CESM2-lessmelt Arctic sea ice: a) September extent ensemble mean
1850-2100 timeseries, b) March volume ensemble mean 1850-2100 timeseries, c) September
extent ensemble mean 20-year trends, d) March volume ensemble mean trends. Grey shadingshows 95% confidence intervals on trends calculated by bootstrapping CESM2-LE ensemblemeans with 4 members 1,000 times.
a.

861
862
863
864
865
866

b.

Figure 13. Transient evolution global annual mean surface air temperature in CESM2-LE andCESM2-lessmelt: a) anomaly from 1951-1980, b) absolute value. Observations are from
GISTEMP (Hansen et al. 2010) and BEST (Rohde and Hausfather, 2020).

ESSOAr | https://doi.org/10.1002/essoar.10507477.2 | CC_BY_NC_ND_4.0 | First posted online: Mon, 22 Nov 2021 10:46:18 | This content has not been peer reviewed.

c. MAM CESM-lessmelt

e. JJA CESM-lessmelt

g. SON CESM-lessmelt
Surf. Temp. Change (deg C)

a. DJF CESM-lessmelt

b. DJF Differenced. MAM Difference

f. JJA Difference

8
6
4
2
0
-2
-4
-6
-8

h. SON Difference
Surf. Temp. Change
Difference (deg C)

3

1
0
-1
-2
-3

Figure 14. Ensemble mean surface temperature change (2080-2099 minus 1920-1939) byseason: a) DJF CESM2-lessmelt, b) DJF Difference (CESM2-lessmelt minus CESM2-LE), c-d) as ina-b) but for MAM, e-f) as in a-b) but for JJA, c-d) as in g-h) but for SON. Stippling on difference
maps indicates where differences between CESM2-lessmelt and CESM2-LE ensemble means are*not* statistically significant at the 95% confidence level.
a. DJF CESM-lessmelt

c. MAM CESM-lessmelt

e. JJA CESM-lessmelt

g. SON CESM-lessmelt

b. DJF Differenced. MAM Difference

f. JJA Difference

h. SON Difference
Difference (deg C/20 yrs)

Warming Rate (deg C/20 yrs.)

867
868
869
870
871
872
873

2

874
875
876
877
878

Figure 15. Zonal ensemble mean surface warming rate (deg C/20 years) by season: a) DJF
CESM2-lessmelt, b) DJF difference (CESM2-lessmelt minus CESM2-LE), c-d) as in a-b) but for
MAM, e-f) as in a-b) but for JJA, c-d) as in g-h) but for SON. Warming rates are calculated using
20 years and the start year plotted on the vertical axis.