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JOURNAL OF CLIMATEVOLUME 28

The Role of Ocean–Atmosphere Coupling in the Zonal-Mean Atmospheric
Response to Arctic Sea Ice Loss
CLARA DESER, ROBERT A. TOMAS, AND LANTAO SUN
National Center for Atmospheric Research,* Boulder, Colorado(Manuscript received 30 April 2014, in final form 24 November 2014)
ABSTRACTThe role of ocean–atmosphere coupling in the zonal-mean climate response to projected late twenty-firstcentury Arctic sea ice loss is investigated using Community Climate System Model version 4 (CCSM4) at
18 spatial resolution. Parallel experiments with different ocean model configurations (full-depth, slab, and nointeractive ocean) allow the roles of dynamical and thermodynamic ocean feedbacks to be isolated. In theabsence of ocean coupling, the atmospheric response to Arctic sea ice loss is confined to north of 308N,
consisting of a weakening and equatorward shift of the westerlies accompanied by lower troposphericwarming and enhanced precipitation at high latitudes. With ocean feedbacks, the response expands to cover
the whole globe and exhibits a high degree of equatorial symmetry: the entire troposphere warms, the globalhydrological cycle strengthens, and the intertropical convergence zones shift equatorward. Ocean dynamics
are fundamental to producing this equatorially symmetric pattern of response to Arctic sea ice loss. Finally,
the absence of a poleward shift of the wintertime Northern Hemisphere westerlies in CCSM4’s response to
greenhouse gas radiative forcing is shown to result from the competing effects of Arctic sea ice loss andgreenhouse warming on the meridional temperature gradient in middle latitudes.

1. Introduction
Perennial Arctic sea ice is projected to disappear by
the mid-to-late twenty-first century in response toanthropogenically driven increases in greenhouse gas(GHG) concentrations (Stroeve et al. 2012; Stocker
et al. 2013). The anticipated loss of Arctic sea ice is expected to impact climate at northern high and middle
latitudes through a variety of mechanisms (e.g., Serreze
and Barry 2011). The most robust impacts include
thermodynamically driven warming and moistening of
the polar atmosphere and adjacent high-latitude continents, and an associated weakening of the zonal-meanwesterlies (e.g., Deser et al. 2010; Liu et al. 2012; Screen
et al. 2013; Peings and Magnusdottir 2014). Less certain
are impacts related to regional atmospheric circulation
changes, such as cooling over portions of Eurasia and* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Dr. Clara Deser, 1850 Table
Mesa Drive, Climate and Global Dynamics Division, NCAR,
Boulder CO 80307.
E-mail: cdeser@ucar.edu
DOI: 10.1175/JCLI-D-14-00325.1
Ó 2015 American Meteorological Society

North America induced by high pressure systems (Liuet al. 2012; Screen et al. 2013) and an increase in extreme
weather events associated with enhanced jet stream
meanders (e.g., Francis and Vavrus 2012; Screen andSimmonds 2013; Barnes 2013).
The role of ocean–atmosphere coupling in the climate
response to projected Arctic sea ice loss has received
little attention. Two early modeling studies, Rind et al.
(1995, hereafter R95) and Chiang and Bitz (2005,
hereafter CB05), considered local thermodynamic air–sea interaction by coupling a 50-m ‘‘slab’’ ocean mixed
layer model to an atmospheric general circulation model(AGCM). Study R95 prescribed global sea ice coverproduced by the Goddard Institute for Space Sciences(GISS) model under present-day CO2 levels as
a boundary condition to the same model under doubled
CO2, thereby isolating the role of GHG-induced sea ice
loss. CB05 specified an idealized pattern of sea ice expansion over the Northern Hemisphere (NH) relevant
for understanding the Last Glacial Maximum (LGM) to
the Community Climate Model version 3 (CCM3). Bothstudies found that local thermodynamic air–sea couplingallowed the atmospheric response to propagate into thetropics. In CB05, this tropical response took the form ofa shift in the intertropical convergence zone (ITCZ)

15 MARCH 2015

DESER ET AL.

away from the hemisphere with enhanced ice cover. R95
reported that the tropical troposphere warms andmoistens in response to sea ice loss, but did not commenton the structure of the temperature and precipitation
changes. We note that both studies used relatively
coarse-resolution AGCMs by today’s standards.
A more complete treatment of oceanic feedbacks was
employed by Scinnoca et al. (2009) and Graversen andWang (2009) through surface albedo modifications
within a fully coupled modeling framework (e.g., atmosphere, full-depth ocean, sea ice, and land modelcomponents). Specifically, Scinocca et al. (2009) investigated the transient response of the Canadian Middle Atmosphere Model (CMAM) to a sudden reduction
in sea ice albedo, while Graversen and Wang (2009)
examined the equilibrium response of the Community
Climate System Model version 3 (CCSM3) to a suppression of all surface albedo feedbacks (sea ice, snow
cover, and terrestrial vegetation) in a doubled CO2 experiment. Scinocca et al. (2009) focused on the stratospheric circulation and ozone responses in the Arctic,
finding a strengthening of the polar vortex and a decrease in ozone concentration in spring in response to
sea ice loss. Graversen and Wang (2009) emphasized therole of surface albedo feedbacks in polar amplification,
finding that about one-third of the surface temperature
response in the Arctic and approximately 15% of‘‘Arctic amplification’’ (the ratio between Arctic andglobal temperature responses to doubled CO2) resultsfrom these feedbacks. Neither study investigated the
response of the tropospheric circulation in detail or
isolated the role of ocean feedbacks (e.g., by comparison
to atmosphere-only model experiments). Furthermore,
as we shall show, the approaches of Scinocca et al. (2009)
and Graversen and Wang (2009) consider only the effects of Arctic sea ice loss during the sunlit portion of theyear (e.g., via shortwave radiative changes associatedwith lowered sea ice albedo). However, winter sea ice
loss, although less pronounced than in other seasons,has a disproportionate impact on the annual mean net
surface heat budget due to the added contribution of
the turbulent energy fluxes which maximize in the coldseason (Deser et al. 2010; Peings and Magnusdottir2014). Thus, the full effect of year-round Arctic sea
ice loss on the coupled climate system remains to beaddressed.
Here we investigate the role of dynamic and thermodynamic ocean–atmosphere coupling in the global
climate response to projected late twenty-first-century
Arctic sea ice loss using Community Climate System
Model version 4 (CCSM4) at 18 spatial resolution. Our
experiments incorporate a realistic seasonal cycle of sea
ice loss, enabling a more complete assessment of its role2169

in future climate change than in previous studies. To
explicitly isolate the contribution of ocean feedbacks,
we conduct companion experiments using the atmospheric model component of CCSM4 with prescribedsea ice and SST. Additional simulations with the slab
ocean version of CCSM4 provide further insight into theroles of dynamic versus thermodynamic ocean couplingin the response to Arctic sea ice loss. We focus ouranalysis on the vertical and latitudinal structures of thezonal-mean temperature and zonal wind responses.
Additional results are provided for precipitation, SST,
and northward energy transport responses.
The rest of this paper is organized as follows. Section 2
describes the model and design of the various experiments. Results are presented in section 3, starting with
the net surface energy flux response (section 3a) followedby the zonal-mean temperature and zonal wind responses
as a function of height and latitude (section 3b) in boththe coupled and uncoupled Arctic sea ice loss experiments. Section 3c provides an assessment of the relativeroles of Arctic sea ice loss and GHG increase in driving
future changes in zonal winds over the extratropicalNorthern Hemisphere. Additional aspects of the response
to Arctic sea ice loss are presented in the remainderof section 3, including the global hydrological cycle(section 3d), tropical precipitation and SST patterns(section 3e), and northward energy transport (section
3f). The paper concludes with a summary (section 4) anddiscussion (section 5) of the key results.

2. Model and experimental designa. CCSM4
CCSM4 is a fully coupled ocean–atmosphere–land–cryosphere general circulation model, with an atmospheric component using a finite volume dynamical coreat a horizontal resolution of 0.908 latitude and 1.258
longitude and 26 vertical levels and an oceanic resolution of 1.148 in longitude and variable in latitude (0.288 at
the equator increasing to 0.668 at approximately
608N) and 60 vertical levels (20 in the upper 200 m).
The dynamic–thermodynamic sea ice model includes a
subgrid-scale ice thickness distribution, energy-conserving
thermodynamics, and elastic–viscous–plastic dynamics(Holland et al. 2012). Details of the model components
and their coupling may be found in Gent et al. (2011)
and references therein. CCSM4 simulates well the observed late twentieth-century distributions of Arctic sea
ice concentration and thickness, as well as trends overthe past few decades (Kay et al. 2011; Jahn et al. 2012;
Wettstein and Deser 2014). Other aspects of the model’s
climatology and variability are presented in the CCSM42170

JOURNAL OF CLIMATEVOLUME 28

TABLE 1. Details of the model experiments. For reference, DICE_coupled 5 ICE_coupled_21 2 ICE_coupled_20; DICE_coupled_albedo 5
ICE_coupled_albedo_21 2 ICE_coupled_20; DICE_atm 5 ICE_atm_21 2 ICE_atm_20; and DRCP8.5 5 RCP8.5 2 Historical.
Name

Model configuration

Radiative forcing

Arctic sea ice target/ forcing

Years of simulation

ICE_coupled_20
ICE_coupled_21
ICE_coupled_albedo_21
ICE_atm_20
ICE_atm_21
Historical
RCP8.5

CCSM4
CCSM4
CCSM4
CAM4
CAM4
CCSM4
CCSM4

Year 2000
Year 2000
Year 2000
Year 2000
Year 2000
Variable
Variable

1980–99/longwave
2080–99/longwave
2080–99/modified albedo
2080–99/prescribed
2080–99/prescribed
1980–99
2080–99

360
360
100
260
260
6 runs
6 runsspecial issue collection of the Journal of Climate (2012).
This version of CCSM4 contributed to the Coupled Model
Intercomparison Project phase 5 (CMIP5; Taylor et al.
2012). We also make use of the slab-ocean model (SOM)
configuration of CCSM4, here termed CCSM4_som. In
this configuration, the full-depth ocean model with active
dynamics is replaced with a slab mixed layer ocean modelwith spatially and seasonally varying mixed layer depths;
all other model components (including sea ice) are identical to those in CCSM4 (Bitz et al. 2012). The mean effects of ocean heat transport on the SST are represented
by specifying a climatological monthly Qflux to the
CCSM4_som. Details of the Qflux and mixed layer depth
specification are provided in section 2b.

b. Coupled model experiments with constrained sea ice
In the coupled model experiments, we artificially control the seasonal cycle of Arctic sea ice concentration andthickness to approximate those in the ensemble mean of
the six CCSM4 historical runs averaged over the period
1980–99, and those in the ensemble mean of the six
CCSM4 RCP8.5 runs averaged over the period 2080–99.
These constrained sea ice coupled model experiments aredenoted ICE_coupled_20 and ICE_coupled_21 for thelate twentieth- and late twenty-first-century sea ice states,
respectively. In both experiments, radiative forcing conditions are kept fixed at the year 2000 in order to isolate
the response to sea ice loss obtained upon differencing
the two experiments. Both coupled experiments startfrom the 1 January 1990 state from an arbitrarily chosen
CCSM4 historical run. To achieve the desired seasonal
cycles of sea ice concentration and thickness for the latetwentieth and twenty-first centuries, we specify an additional longwave radiative flux (LRF) to the ice model at
each grid box and time step. We emphasize that this flux isprescribed only to the ice model and therefore only where
and when there is sea ice. The LRF is seasonally dependent but for simplicity does not vary spatially, and isdetermined using an iterative procedure (see the appendix for details). In a given grid cell, we apply an annualmean globally equivalent value of 0.10 W m22 upward
LRF to simulate late twentieth-century sea ice

conditions, and 0.43 W m22 downward LRF to simulate
the late twenty-first-century sea ice conditions, withlarger (smaller) values during months of thicker (thinner) ice cover. (Note that in order to simulate the latetwentieth-century sea ice, a small upward LRF is needed
to balance the additional ice melt that would have occurred if the historical run were run forward and thecommitted warming realized.) The LRF difference between the two runs is equivalent to an annual-mean
globally averaged value of 0.54 W m22, which is 8% of
the total radiative forcing in the representative concentration pathway 8.5 (RCP8.5) scenario between 2000
and 2100 (6.7 W m22). The local monthly values of theapplied LRF in each experiment are given in the table in
the appendix. Note that by applying the LRF needed tokeep the sea ice near a fixed concentration throughout
the experiments, we effectively limit the amplitude ofinternally generated sea ice variability (not shown). The
ICE_coupled_20 and ICE_coupled_21 experiments areeach run for 360 years. Our analyses are based on averages over years 101–360 to avoid the initial transientadjustment of the Atlantic meridional overturning circulation (AMOC) to the sudden loss of Arctic sea ice in
ICE_coupled_21 (not shown).
In the rest of this paper, we shall refer to the difference between ICE_coupled_21 and ICE_coupled_20 as
DICE_coupled. Similarly, we refer to the difference
between the period 2080–99 in the RCP8.5 runs and theperiod 1980–99 in the historical runs as DRCP8.5. The
former isolates the coupled response of CCSM4 to
GHG-induced Arctic sea ice loss, while the latter includes all feedbacks involved in CCSM4’s response to
GHG forcing. The statistical significance of the responses is assessed using a two-sided Student’s t test. Allof the experiments are summarized in Table 1.
We shall also make use of a similar set of constrained
sea ice experiments with CCSM4_som, which are identical in design to those described above for CCSM4
except for the ocean model configuration. These experiments are denoted ICE_som_20 and ICE_som_21
for the late twentieth- and late twenty-first-century sea
ice states, respectively. Spatially and seasonally varying

15 MARCH 2015

DESER ET AL.

mixed layer depths in both experiments are taken fromthe monthly mean climatology of the ICE_coupled_20
experiment. Each CCSM4_som experiment is run for
300 years under radiative conditions fixed at the year
2000, and initialized from the same 1 January 1990 state as
the CCSM4_coupled runs. The same LRF values are applied to the sea ice model in ICE_som_20 (ICE_som_21)
as in ICE_coupled_20 (ICE_coupled_21). The same Qflux
is used in both CCSM4_som runs and is based on the climatological monthly SST distribution in ICE_coupled_20(note that the Qflux varies over the course of the seasonal
cycle but is fixed from year to year). Thus, any changes in
SSTs between ICE_som_20 and ICE_som_21 are due tothermodynamic processes alone. The difference between ICE_som_21 and ICE_som_20 is referred to as
DICE_som, and is based on averages over years 41–300
to avoid the transient adjustment to the sudden loss ofArctic sea ice in ICE_som_21 (Table 1).

c. Coupled model experiments with modified sea ice
albedoIn addition to the CCSM4 experiments with constrained Arctic sea ice, we have conducted a modified
sea ice–albedo experiment with CCSM4 following theapproach of Scinocca et al. (2009) and Graversen andWang (2009). In particular, we lowered the sea ice albedo in the model’s shortwave radiation formulation(Briegleb and Light 2007) to achieve a nearly ice-free
September similar to that in the CCSM4 RCP8.5 simulation at the end of the twenty-first century (L. Landrum
2014, personal communication). Like our constrained
sea ice experiments, the radiative forcing conditions forthis experiment (hereafter referred to as ICE_coupled_
albedo_21) are kept fixed at the year 2000 so as to isolate
the effect of the sea ice loss. Similarly, ICE_coupled_
albedo_21 is initialized with the 1 January 1990 statefrom a CCSM4 historical run. The modified sea ice albedo experiment was run for 100 years (note that the sea
ice fully responds within a year or two of the albedo
change). The difference between ICE_coupled_albedo_
21 and ICE_coupled_20 is denoted DICE_coupled_
albedo (Table 1). We limit our discussion of the resultsfrom DICE_coupled_albedo to the seasonal cycle of thesimulated sea ice loss and corresponding net surface
heat flux response; the length of the modified ice albedo
run is sufficient for this purpose.

d. Atmospheric model experiments with prescribedsea ice
We have conducted a companion set of experimentswith CAM4, the atmospheric model component of
CCSM4, in which repeating seasonal cycles of ice concentration and thickness taken from CCSM4 are2171

prescribed (see Table 1). In ICE_atm_20, the prescribed
repeating sea ice seasonal cycle (in both hemispheres) isformed by averaging over the period 1980–99; in ICE_
atm_21, it is formed by averaging over the period 2080–
99 in the Arctic only (Antarctic sea ice is kept fixed at
1980–99 values). The sea ice conditions for 1980–99(2080–99) are taken from the average of a six-member
ensemble of historical (RCP8.5) runs with CCSM4. A
similar approach was used in Deser et al. (2010) basedon CCSM3. In ICE_atm_21, we also specify the accompanying 2080–99 SST conditions from CCSM4 atgrid cells where the ice concentration in the late twentyfirst century is lower than that in the late twentiethcentury. This approach accounts for the local ocean
warming that is largely a by-product of the ice loss (seeScreen et al. 2013, 2015). Elsewhere, SSTs are set to the
1980–99 CCSM4 values in both experiments. Each experiment is run for 260 yr, and our results are based on
the average of years 1–260. The difference between ICE_
atm_21 and ICE_atm_20, denoted DICE_atm, isolates
the direct atmospheric response to GHG-induced Arctic
sea ice loss in the absence of nonlocal oceanic feedbacks.

3. Resultsa. Arctic SIC and net surface heat flux response
In response to RCP8.5 radiative forcing, CCSM4 showsa year-round reduction in Arctic sea ice extent (SIE;
defined as the area with at least 15% fractional ice
cover), manifest as a poleward contraction of the ice
cover in the late twenty-first century compared to thelate twentieth century (Fig. 1). In March, the month ofgreatest SIE, the ice loss occurs primarily within themarginal seas of the Pacific and Atlantic; in September,the month of least extensive ice, the reduction is confined to the central Arctic Ocean. By the late twenty-first
century, the Arctic is projected to become essentially icefree in late summer and early autumn (August–October)according to CCSM4 (Fig. 1).
The seasonal cycles of SIE in ICE_coupled_20 and
ICE_coupled_21 show good agreement with theirCCSM4 targets, except for a slight underestimate of thelate twenty-first-century values in winter (Fig. 1). Correspondingly, the seasonal cycle of SIE loss (twenty-firstminus twentieth century) in DICE_coupled is within 5%of that in DRCP8.5 during April–November, and
20%–25% less than DRCP8.5 during December–March(Fig. 2a). In contrast, DICE_coupled_albedo underestimates the SIE loss in DRCP8.5 by 65%–70% in
December–March, 50% in November and April, and 10%
in late summer (August–September). The maximum iceloss in DICE_coupled (27 3 106 km2 or 80% of the latetwentieth-century mean) occurs in October–November,

2172

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FIG. 1. Sea ice concentration (%) distributions in (top) March and (middle) September from the late (left)twentieth-century and (center) late twenty-first-century coupled experiments and (right) their difference. (bottom)
Monthly Arctic sea ice extent (106 km2) during the late twentieth century (solid lines) and late twenty-first century(dashed lines) from the historical and RCP8.5 CCSM4 experiments (red) and the Arctic sea ice coupled experiments(blue).

and the minimum ice loss occurs in February–April(22.5 3 106 km2 corresponding to a 20% reduction fromthe late twentieth century).
The local SST increase associated with Arctic sea ice
loss, determined by averaging SSTs for all grid cells

experiencing SIC loss in the late twenty-first century
compared to the late twentieth century, shows maximum values in July–September (;28C in DRCP8.5 and;1.758C in DICE_coupled) and minimum values inJanuary–April (;0.88C in DRCP8.5 and ;0.48C in

15 MARCH 2015

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FIG. 2. (a) Late twenty-first-century minus late twentiethcentury monthly Arctic sea ice extent (106 km2; note inverted scale)from historical and RCP8.5 CCSM4 experiments (red), the coupledexperiments (DICE_coupled; blue), and the albedo experiment(DICE_coupled_albedo; orange). The months March–June arerepeated for clarity. (b) As in (a), but for SST (8 C) averaged overall grid boxes in which the sea ice concentration is reduced in thelate twenty-first century relative to the late twentieth century.
(c) Net surface heat flux response (sum of the turbulent andlongwave radiative flux components; W m22) to Arctic sea ice loss
in DICE_coupled (blue), DICE_atm (green), and DICE_coupled_
albedo (orange). See text for details.

DICE_coupled). The local Arctic Ocean SST increases in
DICE_coupled_albedo are smaller than those in ICE_
coupled (;20% less in summer and ;50% less in winter),likely due to the underestimated sea ice loss (Fig. 2b).

2173

Arctic sea ice loss results in a net upward flux of energy from the ocean to the atmosphere as cold sea ice isreplaced by (warmer) open water, with much greater
heat content (Fig. 2c). This net upward surface heat flux
is primarily in the form of turbulent sensible and latentenergy fluxes, with a smaller contribution from longwave radiative fluxes [not shown, but see similar resultsin Deser et al. (2010)]. Note that the surface shortwave
radiative flux does not affect the atmosphere directly,
although it plays an important role in promoting sea ice
melt by warming the upper ocean mixed layer. The
largest net upward surface heat flux response occurs in
December–January in both DICE_coupled and DICE_
atm, despite the fact that the greatest ice loss occurs in
October–November (Fig. 2c). As discussed in Deseret al. (2010) (see also Screen and Simmonds 2010), this
delay results from the fact that the climatological air–sea
temperature difference over the Arctic Ocean is greatest
in winter, leveraging the efficiency of the turbulent energy fluxes.
The seasonal cycle of the net surface heat flux response is similar in DICE_coupled and DICE_atm, withmaximum values ;50–60 W m22 in December–January
and minimum values of ;5 W m22 in June–July (Fig. 3c).
The wintertime heat flux response is ;(10%–15%) lower
in the coupled experiment than the uncoupled experiment due mainly to the smaller magnitude of the ice loss
and local SST warming. In contrast, the maximum net
surface heat flux response in DICE_coupled_albedo is
only 25 W m22, occurring in November–December. Averaged over the year, the net upward surface heat flux responsein the Arctic is 25.3 W m22 in DICE_coupled, 27.9 W m22 in
DICE_atm, and 14.4 W m22 in DICE_coupled_albedo: thus,
DICE_coupled_albedo underestimates the heat flux response in DICE_coupled and DICE_atm by 52% and57%, respectively. This shows that the underestimate of
winter ice loss in DICE_coupled_albedo has a disproportionate effect on the net annual surface heat flux
response over the Arctic Ocean, and demonstrates theadvantage of the longwave radiation–based approachfor modifying sea ice compared to the traditional ice–
albedo method.
In summary, the net upward surface heat flux response
to late twenty-first-century Arctic sea ice loss maximizes
in winter despite that the ice loss itself peaks in autumn.
The shortwave effect of Arctic sea ice loss, through thepositive ice–albedo feedback mechanism, accounts for
only about half of the net surface heat flux response in
winter and in the annual mean. This is due to the factthat processes other than the ice–albedo feedback
mechanism contribute to the winter ice loss. This result
has strong implications for the design of coupled experiments aimed at testing the role of sea ice loss in the2174

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FIG. 3. Annual zonally averaged (a)–(c) air temperature (8C) and (d)–(f) zonal wind (m s21) responses (color shading; color bars atbottom of each column) to Arctic sea ice loss in (top)–(bottom) DICE_coupled, DICE_atm, and their difference. Stippling indicates thatthe response is statistically significant at the 95% confidence level. Contours indicate the climatological temperature (contour interval of108C) and zonal wind (contour interval of 5 m s21, zero contour thickened) distributions from the control runs.

climate system. Next, we investigate the global temperature and zonal wind responses to Arctic sea ice loss,
which are initiated by the anomalous upward surface
heat fluxes in regions of ice melt.

b. Zonal-mean temperature and zonal windresponses to Arctic sea ice loss
The zonally averaged annual-mean temperature andzonal wind responses as a function of height and latitude
from the coupled and uncoupled experiments are shownin Fig. 3, superimposed upon the climatological distributions from the corresponding control (e.g., late twentiethcentury) runs. The thermal response in DICE_coupled is

global in extent and exhibits remarkable symmetry
about the equator, even though the forcing is confined tothe surface of the Arctic Ocean (Fig. 3a). The entire
troposphere warms by a few tenths of 8C, with relative
maxima in the tropical upper troposphere (0.58C) andnear the surface at both poles (0.58C at 608–808S and 68C
at 808–908N). In addition to tropospheric warming, theextratropical lower stratosphere in both hemispheres
cools slightly. This pattern bears a strong resemblance tothe fully coupled climate response to increased GHG(DRCP8.5) as discussed below, albeit with reduced
amplitude (;10% in most areas outside of the Arctic;
Table 2).

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DESER ET AL.

TABLE 2. Selected regional zonal-mean responses to twenty-first-century Arctic sea ice loss in DICE_coupled DICE_atm and DRCP8.5:(top) annual mean and (bottom) DJF. SATArctic: 2-m air temperature (8C) averaged over 658–908N; PArctic: precipitation (mm day21)
averaged over 658–908N; U65N: 700-hPa zonal wind (m s21) at 658N; SATGlobal: globally averaged 2-m air temperature (8C); [T]Global: 1000–
300-hPa global temperature average (8C); P15n-15s: precipitation (mm day21) averaged over 158S–158N; and [TU]tropics: upper tropical
tropospheric temperature (500–100 hPa, 158S–158N) average (8C). Percentages are with respect to the values in DRCP8.5.
ANN

SATArctic

PArctic

U65n

SATGlobal[T]Global

P15n-15s[TU]tropics

DICE_coupled

5.76
74%
5.44
69%
7.83

0.14
41%
0.12
35%
0.34

20.68
—
20.57
—
0.11

0.64
17%
0.35
10%
3.66

0.36
9%
0.07
2%
3.95

0.04
15%
0.00
0%
0.27

0.36
8%
0.02
0%
4.74

DJF

SATArctic

PArctic

U65n

SATGlobal[T]Global

P15n-15s[TU]tropics

DICE_coupled

9.67
88%
9.14
83%
11.01

0.19
56%
0.16
47%
0.34

21.35
—
20.99
—
20.42

0.89
24%
0.62
17%
3.75

0.40
10%
0.11
3%
3.90

0.04
14%
0.01
3%
0.29

0.36
8%
0.02
0%
4.74

DICE_atm
DRCP8.5

DICE_atm
DRCP8.5

Unlike the coupled run, the thermal response in
DICE_atm is mainly confined to the extratropicalNorthern Hemisphere (Fig. 3b). Further, the Arctic
warming is considerably shallower in DICE_atm compared to DICE_coupled (the 0.58C contour reaches 700
versus 400 hPa), and does not extend as far south (the0.58C contour reaches 508 versus 408N). However, themagnitude of Arctic surface warming (;5.88 versus
5.48C) is similar between the two runs (Table 2). The
role of air–sea interaction in the response to projected
Arctic sea ice loss is obtained by subtracting DICE_atm
from DICE_coupled (Fig. 3c). Ocean–atmosphere
feedback imparts a high degree of equatorial symmetry
to the thermal response; notably, the magnitude andvertical structure of the temperature increase at highlatitudes is comparable between the hemispheres.
Consistent with the thermal response, the zonallyaveraged zonal wind response to Arctic sea ice loss in
DICE_coupled exhibits a weakening of the westerlies on
their poleward flank (458–758N), with maximum amplitudes of ;1 m s21 at 608N and 350 hPa, and a more modest
strengthening of the westerlies in the core of the jet at
upper levels and on their equatorward side at lower
levels (308–408N; Fig. 3d). This statistically significant
pattern resembles the negative phase of the northern
annular mode (NAM). In addition to the northern extratropics, the tropical lower stratosphere shows anequatorially symmetric and statistically significant response of strengthened westerlies on the equatorward
side of the jet maxima, with peak values ;0.5 m s21 inboth hemispheres. The extratropical Southern Hemisphere shows a weak but statistically significant tropospheric zonal wind response, with positive values near
the pole and negative values at middle latitudes indicative of a weakening of the jet.

Like DICE_coupled, DICE_atm shows a significant
negative NAM response but the amplitude is ;30%
weaker in the middle and upper troposphere, consistent
with the difference in vertical extent of the troposphericwarming (Fig. 3e). Elsewhere, the uncoupled zonal windresponse is weak, but interestingly of opposite sign tothat in the coupled run. The band of enhanced westerlies;(408–508S) is significant throughout the depth of thetroposphere and lower stratosphere. Subtracting thecoupled and uncoupled responses reveals a high degreeof equatorial symmetry throughout the troposphere andlower stratosphere, in keeping with the structure of thethermal response induced by ocean feedbacks (Fig. 3f).
However, the configuration of the zonal wind response
relative to the zonal wind climatology is different in thetwo hemispheres, due in part to the presence of two
distinct jets in the SH and only one in the NH in theannual mean.
The zonally averaged temperature and zonal windresponses in December–February (DJF), the season of
maximum Arctic Ocean heat flux response, are shown in
Fig. 4. The global structure of the response is similar
between boreal winter and the annual mean, but theamplitudes are larger especially in the NH extratropics.
For example, the warming of the Arctic planetary
boundary layer and free troposphere in DICE_coupled
is approximately 70% greater and extends ;108 latitude
farther south in DJF compared to the annual mean
(Fig. 4a and Table 2). Similarly, DICE_atm shows
stronger near-surface Arctic warming in winter than in
the annual mean (Fig. 3b). In the Antarctic, the maindifference between the DICE_coupled temperature responses in DJF and the annual mean is the lack ofsurface-intensified warming in the band 608–808S, consistent with the absence of sea ice in this region in austral

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FIG. 4. As in Fig. 3, but for December–February averages. Note the different U(z) color scale compared to Fig. 3.

summer (Fig. 4b). Subtracting the coupled and uncoupled thermal responses in DJF reveals a similar
global structure as that in the annual mean, with ;30%
larger magnitudes in the free troposphere at high latitudes (Fig. 4c).
Zonal wind in DICE_coupled shows a significant
negative NAM response that is approximately twice asstrong in DJF than in the annual mean, with maximum
easterly (westerly) wind anomalies ;2 m s21 at 608N(;0.75 m s21 at 358N) in the upper troposphere (Fig. 4d;note the different color scale compared to Fig. 3d). In
the SH, the DJF zonal wind response in DICE_coupled
resembles the negative phase of the southern annularmode (SAM), with negative anomalies on the poleward
side of the jet and positive anomalies on the equatorward flank; however, only the negative anomalies are
significant. Thus, relative to the climatological jet position, the extratropical zonal wind responses in DJF in
DICE_coupled are analogous between the two hemispheres. The DJF extratropical zonal wind responses in

DICE_atm are similar in structure but weaker in magnitude compared to those in DICE_coupled, and only
the NH anomalies are significant (Fig. 4e). The main
impact of atmosphere–ocean coupling in DJF is to significantly enhance the strength of the easterly wind response at northern high latitudes by ;35% (Fig. 4f andTable 2).

c. Role of Arctic sea ice loss in GHG-forced
temperature and zonal wind responses
We can estimate the role of Arctic sea ice loss in
CCSM4’s response to RCP8.5 radiative forcing at theend of the twenty-first century by comparing DRPC8.5
and DICE_coupled. It is worth clarifying that although it
takes approximately a century for the AMOC to adjust
to the sudden reduction of Arctic sea ice in ICE_coupled_
21, the atmosphere adjusts much more rapidly (within
20 yr; not shown). Furthermore, the gradual loss of Arctic
sea ice over the course of the twenty-first century in the
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FIG. 5. Annual zonally averaged (a) air temperature (8C) and (c) zonal wind (m s21) responses (color shading) in
DRCP8.5; (b),(d) as in (a),(c), but after removing the effects of Arctic sea ice loss (obtained by subtracting DICE_
coupled from DRCP8.5). Stippling indicates that the response is statistically significant at the 95% confidence level.
Contours as in Fig. 3.

quasi-equilibrium with the sea ice in any given decade.
These considerations justify the comparison of the atmospheric responses in DRPC8.5 and DICE_coupled.
The thermal response DRPC8.5 shows the canonicalstructure of tropospheric warming and lower stratospheric cooling, with amplified temperature increases
in the tropical upper troposphere. In addition to thisequatorially symmetric pattern, DRPC8.5 exhibits stronger tropospheric warming in the extratropics of the NH
compared to the SH (Fig. 5a). The accompanying zonal
wind response is largely symmetric with respect to theequator at low latitudes, but shows substantial differences
between the hemispheres at high latitudes (Fig. 5c). In
particular, the NH lacks any appreciable troposphericzonal wind signal poleward of ;508N, whereas the SHshows strong positive zonal wind anomalies on the poleward flank of the midlatitude jet and negative anomalies
to the south. Subtracting DICE_coupled from DRPC8.5
reveals that Arctic sea ice loss is responsible for most of
the deviation from equatorial symmetry in the temperature and zonal wind responses to GHG forcing (Figs. 5b,d).
In particular, the enhanced warming of the extratropical
troposphere in the NH compared to the SH is eliminatedafter the effects of Arctic sea ice loss are removed.
In addition, the zonal wind responses in the two hemispheres are brought into better alignment, with consistent poleward shifts of the midlatitude westerly jetsin both hemispheres after accounting for the effects ofArctic sea ice loss (Fig. 5d). The only remaining hemispheric asymmetry in the zonal wind response in Fig. 5doccurs near the poles, with a significant easterly response in the SH and negligible response in the NH.
The seasonal evolution of the 700-hPa zonal mean
zonal wind response in DRPC8.5 is shown in Fig. 6a.
While the SH shows a consistent poleward shift of themidlatitude westerlies throughout the year in response
to RCP8.5 radiative forcing, the analogous poleward
shift in the NH is only present and significant during thewarm half of the year. After subtracting the effects ofArctic sea ice loss (e.g., DRPC8.5 2 DICE_coupled), theNH shows a continuous and significant poleward shift of
the westerlies throughout the year, bringing the twohemispheres into better alignment (Fig. 6b). This striking result demonstrates that the lack of a poleward shiftof the wintertime midlatitude eddy-driven jet in response to RCP8.5 radiative forcing is due to the opposing effects of Arctic sea ice loss and GHG increase in
CCSM4. Similar results are found for averages over the
Pacific and Atlantic basins separately (not shown).

d. Zonal-mean atmospheric condensational heating
and precipitation responses to Arctic sea ice loss
A striking aspect of the coupled climate response to
Arctic sea ice loss is the global extent of the troposphericwarming, with relative maxima at upper levels in thetropics and in the lower troposphere at high latitudes,

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FIG. 6. (a) Monthly zonally averaged 700-hPa zonal wind (m s21) response (color shading) in DRCP8.5; (b) as in(a), but after removing the effects of Arctic sea ice loss (obtained by subtracting DICE_coupled from DRCP8.5).
Stippling indicates that the response is statistically significant at the 95% confidence level. Contours indicate theclimatological values from the CCSM4 historical run (contour interval is 5 m s21, zero contour is thickened, andnegative values are dashed). The months May–August have been repeated for clarity.

a pattern that resembles the GHG-forced response (recall
Fig. 5a). This warming pattern is accompanied by an intensification of the global atmospheric hydrological cycle
as shown in Fig. 7. In particular, atmospheric condensational heating in DICE_coupled increases in the upper
troposphere and decreases in the lower troposphere,
indicative of an upward and poleward shift of the climatological heating maxima in both hemispheres (Fig. 7a).
Embedded within this large-scale pattern is an intensification of the two ITCZ heating maxima, especiallyon their equatorward sides near 58N and 58S. The Arctic
planetary boundary layer also shows an increase in condensational heating. The global structure of the condensational heating response to Arctic sea ice loss bears
a striking resemblance to that in DRPC8.5, with ;15% of
the amplitude, reinforcing the notion that Arctic sea ice
loss leads to a ‘‘mini’’ global warming pattern when ocean
feedbacks are included (Fig. 7b; note different color scale).
In contrast, without ocean feedbacks, the condensational
heating response to Arctic sea ice loss is primarily confined to the Arctic planetary boundary layer (not shown).
Consistent with the atmospheric condensational heating
response, DICE_coupled shows a global increase inprecipitation, with the largest increases in the Arctic(;0.2 mm day21), and more modest increases in the deep
tropics (;0.05–0.10 mm day21) and middle latitudes ofboth hemispheres (;0.05 mm day21) (blue curve in Fig. 7c,left y-axis scale). Most of the precipitation increase poleward of ;708N is due to the direct atmospheric response to
sea ice loss (DICE_atm; green curve in Fig. 7c, left y-axis
scale), while the nonlocal precipitation enhancement is due
to ocean–atmosphere coupling. Thus, air–sea feedbacks

impart a high degree of equatorial symmetry to the globalscale precipitation response (dotted blue curve in Fig. 7c,left y-axis scale), a structure that resembles the fully coupled response to GHG forcing (DRPC8.5) with ;15% of
the amplitude (red curve in Fig. 7c, right y-axis scale).

e. Spatial patterns of the tropical precipitation and
SST responses to Arctic sea ice loss
Insight into the response of tropical precipitation to
Arctic sea ice loss may be gained by examining its spatial
pattern in the context of the underlying SST response.
Figure 8 shows the simulated climatological rainfall
distribution in the tropics and its response to Arctic sea
ice loss in DICE_coupled. In response to Arctic sea ice
loss, the climatological ITCZs in the Pacific shift equatorward and the South Pacific convergence zone (SPCZ)
shifts northeastward (cf. Figs. 8a,b). The Atlantic ITCZ
also shifts toward the equator, while the Indian Ocean
ITCZ, which is located south of the equator in the annual
mean, shows a slight strengthening. The equatorward
displacements of the Pacific ITCZs in DICE_coupled canbe understood in the context of the underlying SST response shown in Fig. 8c. Tropical SSTs increase by 0.28–
0.38C, with maximum warming along the equator in the
Pacific sector. Thus, the Pacific ITCZs shift equatorward
in response to the altered local meridional SST gradient.
A similar relationship between rainfall and SST anomalies is found for DRPC8.5 (not shown). The resemblanceof the Pacific ITCZ response patterns in DICE_coupled(Fig. 8b) and DRPC8.5 (Fig. 8d) is noteworthy, although
the magnitude of the response to Arctic sea ice loss is only
;15% of that associated with GHG changes.

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FIG. 7. Annual zonal mean condensational heating rate response (K day21) in (a) DICE_
coupled and (b) DRCP8.5. Contours show the control (late twentieth century) climatology andshading denotes the response. (c) Annual zonal mean precipitation responses (mm day21) in
DICE_coupled (solid blue curve), DICE_atm (solid green), and their difference (dotted blue),and in DRCP8.5 (red). The left y axis is for the blue and green curves, and the right y axis is forthe red curve.

The seasonal evolution of the zonal mean tropicalprecipitation responses in DICE_coupled and DRPC8.5
are shown in Fig. 9 (similar results are found for averages across the Pacific; not shown). Regardless ofwhether the climatological zonal-mean ITCZ lies in the

Northern or Southern Hemisphere, the largest rainfall
increase occurs on its equatorward flank in both experiments. The maximum tropical precipitation response in
DICE_coupled occurs during boreal winter (December–
April), consistent with the seasonality of the Arctic

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FIG. 8. (a) Annual precipitation climatology from the twentieth-century ICE_coupled_20 run. (b) Annual precipitation response(mm day21) and (c) SST response (8C) in DICE_coupled. (d) Annual precipitation response in DRCP8.5. Note the different color scales in(b) and (d).

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FIG. 9. Monthly zonally averaged tropical precipitation responses (color shading; mm day21) in (a) DICE_coupledand (b) DRCP8.5. Contours show the climatological precipitation, with a contour interval of 2 mm day21 and the
4 mm day21 contour is thickened.

surface heat flux response, while the rainfall response in
DRPC8.5 shows a smaller seasonal dependence. Unlike
precipitation, the SST response always peaks at theequator independent of the time of year (not shown).

f. Northward energy transport: Response to Arctic sea
ice loss and the role of ocean dynamics
Meridional energy transport plays a fundamental role
governing the response of the climate system to an imposed heat source. Here we examine the changes inannual mean northward energy transport (NET)resulting from Arctic sea ice loss in DICE_coupled and
DICE_atm. The atmospheric component of the NET is
obtained from the meridional integral of the difference
between the zonal mean net top-of-atmosphere radiation and surface energy flux at each latitude. The oceaniccomponent is the vertical and meridional integral of the
net surface ocean heat fluxes. Figure 10 shows that NET
in both the atmosphere and ocean diminish in response
to Arctic sea ice loss, as expected due to the decrease in
meridional temperature gradient between the Arctic
and lower latitudes (see also Hwang et al. 2011). In theatmosphere, the reduction in NET is confined to highlatitudes (408–808N) with the largest decrease (20.20
PW) near the Arctic Circle (678N) in both DICE_atm
and DICE_coupled. Thus, the atmosphere diverges excess energy associated with sea ice loss out of the polar
cap and converges it into middle latitudes (408–678N).
The reduction in oceanic NET (in DICE_coupled) occurs over a much broader range of latitudes, extending
from approximately 458S to 708N with the largest decrease (20.25 PW) around 388N. Thus, the ocean converges the excess energy associated with Arctic sea ice
loss into the tropics, farther south than where theatmosphere deposits it. The diminished oceanic NET is
associated with a reduction in the strength of the Atlantic meridional overturning circulation (not shown).
We hypothesize that the dynamical ocean response to
Arctic sea ice loss, by converging excess heat into thetropics, causes the equatorially symmetric warming of thetropical oceans which in turn leads to the mini globalwarming response pattern documented above. To explicitly test this assertion, we compare the NET responses
in DICE_coupled and DICE_som (Fig. 11a). Except fora slight difference in magnitude owing to a small underestimate of the Arctic sea ice loss in DICE_som
compared to DICE_coupled (not shown), the latitudinal
profiles of the total NET responses are very similar in thetwo sets of experiments. However, the transport changes
in DICE_som are necessarily accomplished entirely by
the atmosphere, whereas in DICE_coupled they are due
to both the atmosphere and the ocean.
That the atmosphere has to accomplish all of the energy transport in the coupled slab-ocean model setting

FIG. 10. Annual northward energy transport (PW) response in
DICE_coupled (blue, atmosphere; black, ocean) and in DICE_atm(green, atmosphere).

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FIG. 11. (a) Annual northward energy transport (PW) response
in DICE_coupled (sum of the ocean plus atmosphere; gray curve)
and DICE_som (atmosphere; black curve). (b) Annual precipitation response (mm day21) in DICE_som.

has profound consequences for the global atmospheric
response to Arctic sea ice loss. In particular, the tropicalprecipitation response in DICE_som is nearly orthogonal to that in DICE_coupled, with increases in rainfall tothe north of the equator (maximum values ; 0.35 mm day21
at 108N) and decreases to the south of the equator (minimum values ; 20.25 mm day21 at 108S; Fig. 11b). In addition, the magnitude of the tropical precipitation response
is approximately 3 times larger in DICE_som than in
DICE_coupled. We note that the northward shift ofprecipitation in the tropics (e.g., toward the warmedNH) in DICE_som is energetically consistent with therequired reduction in (atmospheric) NET in response to
Arctic sea ice loss [e.g., see the arguments put forth inFrierson et al. (2013)], and in agreement with previous
studies investigating the response of coupled atmosphere–slab ocean models to extratropical heating anomalies (e.g.,Kang et al. 2008; Frierson and Hwang 2012) including
Arctic sea ice (CB05). Other aspects of the response in
DICE_som will be reported in a future study.

4. Summary
We have investigated the role of ocean–atmosphereinteraction in the zonal-mean climate response to latetwenty-first-century Arctic sea ice loss using the fully

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coupled CCSM4 model at 18 spatial resolution. To explicitly isolate the contribution of ocean feedbacks,
we conducted companion experiments with the atmospheric model component (CAM4) under prescribedsea ice and SST conditions. Additional simulations withthe slab ocean version of CCSM4 provided further insight into the roles of dynamic versus thermodynamic
ocean coupling in the response to Arctic sea ice loss. Our
coupled experiments incorporate a realistic seasonal
cycle and spatial pattern of sea ice loss (realistic in thesense that they mimic the ice loss in the CCSM4 RCP8.5
simulation) through the use of a novel longwave radiative nudging technique, enabling a more complete assessment of the role of Arctic sea ice loss in futureanthropogenic climate change than previous studies.
Our key results may be summarized as follows.
In the absence of ocean coupling, the atmospheric
response to Arctic sea ice loss is confined to the northern
extratropics, consisting of a weakening and equatorward
shift of the westerlies accompanied by lower tropospheric warming and enhanced precipitation at highlatitudes, similar to previous atmosphere-only modelingstudies (e.g., Deser et al. 2010; Peings and Magnusdottir2014). With ocean feedbacks, the response expands tocover the whole globe and exhibits a high degree of
equatorial symmetry: the entire troposphere warms, theglobal hydrological cycle strengthens and the ITCZs
shift equatorward. This pattern resembles the full response to RCP8.5 radiative forcing with approximately
10%–15% of the amplitude (e.g., a mini global warming). Ocean dynamics are fundamental to producing thisequatorially symmetric pattern of response to Arctic sea
ice loss: without ocean dynamics, the response takes onan antisymmetric structure. Ocean feedbacks also
strengthen the extratropical NH zonal wind response by;30% in conjunction with enhanced warming of thefree troposphere at high latitudes. Finally, the lack ofa poleward shift of the wintertime Northern Hemisphere westerlies in CCSM4 under RCP8.5 radiative
forcing results from the competing effects of Arctic sea
ice loss and greenhouse warming. The magnitudes of thecoupled ocean–atmosphere response to Arctic sea ice
loss reported here are likely to be conservative due tothe 20%–25% underestimate of the winter ice loss in our
experiments compared to those in the RCP8.5 scenario.

5. Discussion
The results presented above highlight a number ofimportant issues. The first relates to the seasonal timingof Arctic sea ice loss versus the response of the net
surface energy flux. Although the areal extent of latetwenty-first-century Arctic sea ice loss is greatest in late

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fall–early winter (October–November), the net (upward)
surface heat flux response of the Arctic Ocean peaks in
midwinter (December–February) in both the uncoupled
and coupled experiments. This is due to the contributionof the turbulent energy fluxes, which maximize in winterwhen the climatological air–sea temperature differences
over the Arctic Ocean are greatest. This result has implications for the timing of the atmospheric circulation
response, which is forced by the surface energy fluxes
rather than by the sea ice directly.
A second related issue concerns the seasonal cycle ofthe sea ice loss itself. Traditionally, coupled modelingstudies have modified the sea ice albedo as a means of
reducing ice cover. However, this approach does little tolower sea ice in winter, although it achieves the desired
effect in summer. Because of the greater sensitivity ofthe surface heat flux response to sea ice anomalies in
winter compared to other times of year as discussed above,
the ice–albedo approach will greatly underestimate the net
heat flux forcing associated with Arctic sea ice loss. In
CCSM4, the surface heat flux response to projected latetwenty-first-century Arctic sea ice loss is underestimatedby approximately 80% in winter (;50% in the annualmean) in the modified ice–albedo experiments compared to the longwave-forced simulations (which havea better representation of the full seasonal cycle of projected sea ice loss). Thus, to adequately assess the role ofArctic sea ice loss in the coupled ocean–atmosphere system, an alternative to the traditional ice albedo methodology is needed. In this study, we have introduced a newtechnique to obtain realistic year-round ice loss by adding
a seasonally dependent downward longwave radiative flux
to the sea ice model. Although this technique does notconserve energy, water in the ocean and ice models isconserved. This would not be true if the ice distribution
were prescribed or nudged directly, potentially resulting
in detrimental effects on the climate response.
A third issue relates to the role of ocean coupling in
the global climate response to projected Arctic sea ice
loss. Modeling studies on the climate impacts of sea ice
loss have typically ignored air–sea feedbacks, favoring
instead the use of atmospheric models (e.g., Singarayer
et al. 2006; Seierstad and Bader 2009; Deser et al. 2010;
Liu et al. 2012; Peings and Magnusdottir 2014; and manyothers), and occasionally incorporating the accompanying warming of Arctic Ocean SSTs (e.g., Screen et al.
2013, 2015). This study shows that ocean–atmosphere
coupling modifies the response to projected Arctic sea
ice loss in two important ways: 1) the extratropical NH
zonal wind response strengthens by ;30% in conjunction with enhanced tropospheric warming at high latitudes, and 2) the temperature, wind, and precipitation
responses extend into the tropics and SH, and exhibit

2183

a high degree of symmetry about the equator reminiscent of the response to GHG forcing.
In the fully coupled response to Arctic sea ice loss, theenhanced warming of the free troposphere at high latitudes can be traced to an increase in condensational
heating that in turn results from greater poleward
transport of water vapor compared to the uncoupled
response (not shown). This mechanism is consistent withthe results of Hwang and Frierson (2010) for the GHG
forcing case, and underscores the notion that the coupled ocean–atmosphere response to Arctic sea ice loss
resembles a miniature version of the response to GHG
forcing. Indeed, we interpret the similarity between thepatterns of the global coupled response to Arctic sea ice
loss and GHG forcing to result ultimately from the radiative effects associated with their common increase inatmospheric moisture content.
In addition to the global nature of the atmospheric
response to Arctic sea ice loss when ocean feedbacks areactive, an important finding of this study is the role ofArctic sea ice loss in causing the asymmetry between thenorthern and southern annular mode responses to GHG
forcing. The extratropical NH exhibits a notable lack ofa coherent tropospheric zonal wind response to RCP8.5
radiative forcing during the cold season, whereas the SHshows a significant poleward shift of the midlatitude
westerlies year-round. Our results demonstrate that
Arctic sea ice loss (itself driven by the radiative forcing)is largely responsible for this hemispheric asymmetry.
When the effects of Arctic sea ice loss are accounted for,the NH westerlies respond in a similar fashion as the SH
westerlies, with a continuous and significant poleward
shift throughout the year. This result offers a simple
explanation for the hemispheric asymmetries in theannular mode response to GHG forcing, and confirms
the empirical study of Cattiaux and Cassou (2013), whofound a strong (inverse) relationship between the magnitudes of projected Arctic sea ice loss and NAM response in the CMIP phases 3 and 5 archives. It also
agrees with the results of Sigmond and Scinocca (2010)
based on simplified dynamical atmospheric model experiments forced with GHG increases in the absence ofsea ice changes.
The tropical precipitation and SST responses to Arctic
sea ice loss in our fully coupled experiments merit additional discussion, given that they differ from those ofearlier studies (e.g., CB05). Rather than shifting toward
the Arctic (e.g., toward the hemisphere with the anomalous atmospheric heating), the ITCZs intensify on theirequatorward flanks in association with an increase intropical SSTs and maximum warming along the equator,
especially in the Pacific. Thus, the local control of the
SST warming pattern seems to play a larger role in the

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tropical rainfall response than the remote control of the
Arctic heating. Dynamical ocean processes produce theequatorial SST anomaly maximum, with air–sea energy
fluxes acting to damp it (not shown). Coupled modelexperiments employing a thermodynamic slab ocean
mixed layer necessarily lack this dynamically induced
mechanism of tropical response to Arctic sea ice loss.
Indeed, CB05 found that in their coupled atmosphereslab ocean model simulations, the ITCZ shifts southward in response to increased Northern Hemisphere sea
ice cover. Our slab-ocean model experiments confirm
the results of CB05 (in our case, a northward shift of the
ITCZ in response to diminished Arctic sea ice). Thus,
our study highlights the transformative role of ocean
dynamics in the global response to Arctic sea ice loss.
The distinction between the structures of the tropicalresponse in coupled models with a slab ocean versus
a dynamical ocean is also evident for radiative forcing in
the middle latitudes. For example, idealized coupled slab
ocean model experiments show a meridional shift of the
ITCZ toward the hemisphere in which an extratropicalheat source has been imposed (Kang et al. 2008, 2009).
On the other hand, coupled dynamical ocean models
show an equatorially symmetric response of the ITCZ toaerosol forcing that peaks in the NH midlatitudes, similarin structure (but of opposite sign) to that produced by
GHG forcing (Xie et al. 2013). The general issue of therole of ocean dynamics in the sensitivity of tropical SST
and rainfall response patterns to mid- and high-latitude
forcing warrants further investigation.
This study has focused on the equilibrium response of
the coupled ocean–atmosphere system to Arctic sea ice
loss. The mechanisms and pathways of the transientadjustment process, including the roles of the MOC andwind–evaporation–SST feedback (Liu and Xie 1994;
Vimont 2010) in communicating the signal from the
Arctic to the global oceans, are left to future work.
Acknowledgments. This work was supported by a grantfrom the Office of Polar Programs at the National Science
Foundation. We thank Laura Landrum (NCAR) for
conducting the modified sea ice albedo experiment. We
appreciate the constructive comments and suggestions
from the three reviewers and editor John Walsh.

APPENDIX
Formulation of the Coupled Model Experiments with
Constrained Sea Ice
The following description is for the case of deriving the
longwave radiative flux (LRF) for a late twenty-firstcentury coupled sea ice experiment. Similar methodology

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FIG. A1. Scatterplot of the longwave radiative forcing (LRF) ineach of the four initial experiments vs the difference in area average sea ice concentration, late twenty-first-century RCP8.5 minus LRF experiments (red circles) and the linear least squares fit to
the data (blue line). The black circle indicates the area average sea
ice concentration from the last 30 yr of a 100-yr simulation madeusing monthly LRF magnitude derived from the monthly
y-intercept values. This illustrative example is from a late twentyfirst-century sea ice simulation using the Whole Atmosphere
Community Climate Model (WACCM), conducted after the experiments discussed in this study. Using the WACCM simulation allows us
to illustrate all steps and refinements that went into the final methodology, which provided the closest match to the target sea ice.

was used for the late twentieth-century coupled sea ice
control, with negative rather than positive values of
LRF. Our objective was to produce a file containing
monthly and spatially varying fields of longwave radiation to force the sea ice model. New code was written forthe sea ice model to read the appropriate months fromthis file and linearly interpolate the data to model timeas the simulation was running. A monthly varying mask
was first constructed to specify the spatial distribution of
the long LRF. The mask was obtained by taking thedifference between the monthly sea ice concentrations
from the late twenty-first-century RCP8.5 ensemble
mean minus the late twentieth-century historical ensemble mean. If there was a decrease exceeding a threshold of
210% in a grid cell, LRF was added to that cell; otherwise, no forcing was used at that location. Next, an ensemble of four experiments was performed using themonthly varying mask with time invariant LRF of 0, 10,20, and 30 W m22. Each simulation was then run for 30
years. The area averaged sea ice concentration in these15 MARCH 2015

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TABLE A1. LRF (W m22) by month for the twenty-first- and twentieth-century coupled ice simulations (ICE_coupled_20 and ICE_
coupled_21, respectively) obtained by following the procedure described in the appendix.

Twenty-first century
Twentieth century

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

184
223

114
225

82
224

58
221

54
212

49
27

61
25

48
24

53
24

113
25

159
28

104
214

simulations came into equilibrium after approximately
10 years and the last 20 years were analyzed. The differences, calculated by subtracting the monthly area
averaged sea ice concentration from the late twentyfirst-century RCP8.5 (i.e., the target ice) minus themonthly area averages from each ensemble member
were plotted as a function of the four values of LRF used
for each ensemble member. An example for the monthof September is shown in Fig. A1 (red circle) along withthe linear least squares fit line (blue line). Note that thedata in Fig. A1 display a mostly linear relationship andthis was also the case during the months from early summer through early winter. During midwinter through latespring, there was more spread in the data (not shown). Thisseasonal difference appears to be consistent with ourability to match the sea ice loss (Fig. 3a). The y intercept in
the scatterplots provides an estimate of the monthly forcing that should result in zero difference between thelate twenty-first-century RCP8.5 and LRF experiments(35 W m22 in Fig. A1). An adjustment was applied to they-intercept numbers to create the forcing file because thesea ice model code linearly interpolates in time between
the 12 values in the forcing file and the monthly average ofthese interpolated values is not the same as the monthly yintercepts. This procedure is identical to the adjustment
procedure used to produce SST boundary forcing for
AMIP simulations and is described in Taylor et al. (2000).
After running a simulation for approximately 100 yr
using the LRF derived as described in the preceding paragraphs, the sea ice concentration can be evaluated and an
adjustment made. The area averaged sea ice concentrationfrom years 70–100 from such a simulation is indicated by
the black marker in Fig. A1. The adjustment makes use of
the error, defined as the difference between the sea ice
concentration in the late twenty-first-century RCP8.5 experiments minus the century-long LRF experiment, andthe slope of the least squares fit line, using the formula
LRFadjusted 5 LRF 2 (slope 3 error). In Fig. A1, LRF 5
35.0 W m22, the slope is 1.8 W m22 (%)21 and the error is
22.0%, yielding LRFadjusted 5 36.6 W m22. We developed
this adjustment procedure while producing our twentiethcentury coupled control, and after completing our twentyfirst-century coupled simulation. We did not go back andadjust our twenty-first-century simulation using thismethodology, however, because the monthly sea ice
concentrations without the adjustment were felt to be

sufficiently close for our purposes and the additional timeand expense was not warranted. LRF values for thetwenty-first- and twentieth-century coupled ice experiments are listed in Table A1.
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