Clim Dyn
DOI 10.1007/s00382-017-3893-5

Tropically driven and externally forced patterns of Antarctic sea
ice change: reconciling observed and modeled trends
David P. Schneider1 · Clara Deser1

Received: 7 February 2017 / Accepted: 28 August 2017
© Springer-Verlag GmbH Germany 2017

Abstract Recent work suggests that natural variability
has played a significant role in the increase of Antarctic sea
ice extent during 1979–2013. The ice extent has responded
strongly to atmospheric circulation changes, including a
deepened Amundsen Sea Low (ASL), which in part has
been driven by tropical variability. Nonetheless, this increase
has occurred in the context of externally forced climatechange, and it has been difficult to reconcile observed andmodeled Antarctic sea ice trends. To understand observedmodel disparities, this work defines the internally driven
and radiatively forced patterns of Antarctic sea ice change
and exposes potential model biases using results from two
sets of historical experiments of a coupled climate modelcompared with observations. One ensemble is constrained
only by external factors such as greenhouse gases and stratospheric ozone, while the other explicitly accounts for the
influence of tropical variability by specifying observed SST
anomalies in the eastern tropical Pacific. The latter experiment reproduces the deepening of the ASL, which drives anincrease in regional ice extent due to enhanced ice motion
and sea surface cooling. However, the overall sea ice trend
in every ensemble member of both experiments is characterized by ice loss and is dominated by the forced pattern, as
given by the ensemble-mean of the first experiment. This
pervasive ice loss is associated with a strong warming of theocean mixed layer, suggesting that the ocean model does not
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00382-017-3893-5) contains supplementarymaterial, which is available to authorized users.
* David P. Schneider
dschneid@ucar.edu
1

Climate and Global Dynamics Laboratory, National Center
for Atmospheric Research, Boulder, CO, USA

locally store or export anomalous heat efficiently enough tomaintain a surface environment conducive to sea ice expansion. The pervasive upper-ocean warming, not seen in observations, likely reflects ocean mean-state biases.
Keywords Antarctica · Southern Ocean · Sea ice ·Models · Climate variability and change

1 Introduction
The modest increase in Antarctic sea ice extent (SIE), apparent since the late 1990s and continuing through at least 2015
(Parkinson and DiGirolamo 2016), has been an enigma ina warming world. The observed increase generally contradicts expectations from climate model experiments (e.g.
Turner et al. 2013; Hobbs et al. 2015; Jones et al. 2016).
Recent work towards resolving this paradox points to (a)the large interannual-to-interdecadal climate variabilityof Antarctica and the Southern Ocean (Turner et al. 2016;
Jones et al. 2016; Fan et al. 2014; Polvani and Smith 2013)that may mask any forced changes; (b) the specific role oftropical teleconnections in this variability, notably a shift
in the phase of the Pacific Decadal Oscillation (PDO, also
referred to as the Interdecadal Pacific Oscillation or IPO) in
1998–1999, which has led to a deepened ASL (Meehl et al.
2016; Purich et al. 2016; Raphael et al. 2016); (c) the uncertain response of the Southern Ocean to the stronger westerly
winds associated with polar stratospheric ozone depletion,
which depends on the timescale and model examined (Ferreira et al. 2015; Kostov et al. 2016); and (d) the role playedby processes not well observed nor routinely represented inmodels, such as freshening of the Southern Ocean from themelting of ice shelves and glacial ice (e.g. Bintanja et al.
2013; Swart and Fyfe 2013). Some authors have questioned

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D. P. Schneider, C. Deser

the reality of the trend in the observational record of Antarctic sea ice extent (Eisenman et al. 2014), while others havefound that additional indicators, including sea surface temperatures (SSTs) and near-surface winds, have changed in a
way that is consistent with the hemispheric and regional SIE
trends (Fan et al. 2014; Armour and Bitz 2015). The strong
covariance of SIE with SST and near-surface winds on interannual-to-interdecadal timescales suggests that the increase
in SIE is part of a large-scale, systematic pattern. As such,
explanations for the increase in Antarctic SIE should also
account for the trends in SSTs and winds (Fan et al. 2014;
Armour and Bitz 2015). Many explanations for the trend in
SIE were reviewed in Armour and Bitz (2015) and Hobbs
et al. (2016), and both studies concluded that large-scaleatmospheric circulation changes are the most likely causeof the observed Antarctic sea ice trends.
Recent trends in the large-scale atmospheric circulation
over the Southern Ocean were interpreted by Schneider
et al. (2015), who used a series of atmospheric modelingexperiments to show that the spatial and seasonal aspectsof zonal wind trends could be explained by a combinationof stratospheric ozone depletion, tropical teleconnections,and intrinsic variability. Specifically, wind anomalies in thesouth Pacific during the non-summer months can be reproduced in atmospheric model experiments forced only with
time-varying, observed tropical SST anomalies. Zonal-meanwesterly wind trends in austral summer, reflected in the positive trend of the Southern Annular Mode (SAM), are best
explained if stratospheric ozone depletion is included in themodel simulation. In turn, these wind trends have impacted
Southern Ocean SST trends. The sequence of Southern
Ocean SST anomalies during 1979–2014 can be empirically modeled as a response to circulation trends associatedwith the El Niño-Southern Oscillation (ENSO) and the SAM(Yeo and Kim 2015).
Given the previous interpretations of the wind and SST
trends, a logical extension of the conclusion of Armour andBitz (2015) and Hobbs et al. (2016) is that atmospheric circulation trends, driven by a combination of tropical teleconnections and external forcing including stratospheric ozone,
have caused the observed Antarctic sea ice trends. We notethat this argument dates back in various forms to at least Liu
et al. (2004). Part of the problem in accepting this hypothesisis that the sea ice trends that are congruent with common
indices of the atmospheric circulation are weaker than theactual sea ice trends and do not fully explain the spatial pattern (e.g. Liu et al. 2004; Matear et al. 2015; Simpkins et al.
2013; Hobbs et al. 2016). This is perhaps due to the complexity of atmosphere-ocean-sea ice interactions that cannot
be captured in simple regression statistics. In addition, SAM
and ENSO-related anomalies do not account for all of theatmospheric circulation variability that may be important to
sea ice trends (Matear et al. 2015). As empirical regression

13

models may have limited value in such a complex system,
coupled climate models may be better suited to understanding the response of sea ice to atmospheric forcing. However,to date, there have been relatively few studies with coupledmodels of the linkages between atmospheric circulation
changes induced by tropical variability and Antarctic sea ice
trends, and the work on the sea ice response to stratosphericozone depletion has been inconclusive (e.g. Ferreira et al.
2015; Sigmond and Fyfe 2014; Kostov et al. 2016).
Some of the first coupled modeling studies to directly
address the role of tropical teleconnections in Antarctic
sea ice trends include Meehl et al. (2016) and Purich et al.
(2016). Meehl et al. (2016) showed that Coupled Model
Intercomparison Project, phase 5 (CMIP5) models consistently give positive Antarctic sea ice extent anomalies during the negative phase of the IPO, and that the few modelruns which happen to show the negative phase IPO during 1999–2014 also show a positive Antarctic sea ice trend
during that period, along with a deeper ASL. In contrast,
during the full 1979–present period of the satellite sea ice
record, the vast majority of model simulations show sea
ice loss (e.g. Jones et al. 2016). This is consistent with theidea that natural variability, including that associated with
the IPO, should dominate trends over the 15-year period of
1999–2014, while forced changes should be more evident in
trends over the longer period of 1979–2014.
Purich et al. (2016) presented results from coupled modelexperiments in which observed three-dimensional ocean
temperature and salinity anomalies were assimilated by
the ocean model over various regions, including the global
ocean as well just the equatorial Pacific. All of their data
assimilation experiments showed ice loss over most regionsof the Southern Ocean during 1979–2013. However, if theensemble mean of the historical experiment is subtracted
from the experiments with data assimilation, then the residual fields—which represent the influence of internal variability—show sea ice trend patterns that are more similar
to the observed, especially in the global and tropical Pacificassimilation experiments. While suggestive of a role forthe tropical Pacific in recent Antarctic sea ice trends, theirresults also indicate that the externally forced change may
be too dominant in these experiments, since it had to be
subtracted from the model results to obtain patterns similar
to the observed.
The argument that observed Antarctic sea ice trends
are consistent with natural variability has been made inpart on the basis of large multi-model ensembles likethe CMIP5 ensemble (e.g. Polvani and Smith 2013).
Generally, the observed sea ice extent trend of approximately + 0.20 × 106 km2/decade over the ~35 year record
falls within the spread of ~35 year trends in pre-industrial
control runs (Jones et al. 2016). However, if the spread ofsimulated historical trends during 1979–2005 is used inTropically driven and externally forced patterns of Antarctic sea ice change: reconciling…

place of the pre-industrial trends, then the observed trendlies on the outer edge of the ensemble spread (Jones et al.
2016). Moreover, if information about the spatial and seasonal patterns of sea ice trends is used in the comparison ofobservations and models, then the observed trends in summer and autumn lie outside of the models’ internal variability, and they are different from the models’ forced response(Hobbs et al. 2015). The importance of using the historical
trends, rather than the preindustrial trends, is that the simulated historical trends include the impacts of greenhousegases and other anthropogenic forcings. Even if the observed
Antarctic sea ice trend is consistent with natural variability,
it has occurred in the context of externally forced climatechange, and models should be able to account for naturaland external factors simultaneously.
One problem with using the spread of multi-model
ensembles such CMIP5 to study the role of internal variability is that the differences among simulated trends canarise from different model physics, in addition to internalvariability. There are considerable differences in the climatology of Antarctica and the Southern Ocean among
CMIP5 models, and there is evidence that these differences
influence the simulated responses to external forcing. For
instance, the present-day September SIE maximum ranges
from about 4 to 23 × 106 km2 among CMIP5 models (Zunzet al. 2013) and this range has a significant bearing on thesimulated changes in twenty first century Antarctic sea ice,
temperature and precipitation (Agosta et al. 2015; Bracegirdle et al. 2015). The wide range in sea ice climatology may
relate to a similarly broad range in mean SST, which can belargely accounted for by differences in shortwave cloud forcing (Schneider and Reusch 2016). The response of clouds
themselves to increasing greenhouse gases depends on theirmean state and has large impacts on the resulting warmingat the surface (Trenberth and Fasullo 2010; Gettleman et al.
2013; Tan et al. 2016), and by extension, on the amount of
sea ice loss.
To overcome the often ambiguous meaning of differences
within multi-model ensembles like CMIP5, we make useof a large, initial condition ensemble of a single model, theCommunity Earth System Model (CESM Large Ensemble,
Kay et al. 2014). Each ensemble member is subject to thesame forcing scenario, but starts from a slightly different
atmospheric initial state. In this paper, the ensemble-meanof this “Large Ensemble” experiment is used to define themodel’s response to external forcing, while the ensemble
spread is used to illustrate the role of internally generated
variability on the simulated trends. As shown below, theobserved Antarctic SIE trend during 1979–2013 lies outside
the model spread. Observed tropical climate trends during
this time period were unusually pronounced by some measures (e.g. England et al. 2014) and have contributed to theslowdown in global surface temperature trends (Kosaka andXie 2013) as well as to atmospheric circulation trends over
the Southern Ocean (Schneider et al. 2015). The chance that
free-running historical climate model experiments simulate
a sequence and magnitude of tropical variability like theobserved is low.
The largest impacts of tropical variability, as well as
the largest trends in Antarctic SIE, have occurred in the
Pacific Sector of the Southern Ocean, which includes theRoss, Amundsen, and Bellingshausen Seas (e.g. Hobbs
et al. 2016). The Ross Sea is the only region of the Southern
Ocean where significant trends in SIE have occurred yearround (Hobbs et al. 2016). Figure 1 shows the timeseries ofmonthly sea ice extent anomalies in the Ross Sea, together
with SST and wind anomalies across the Pacific Sector, andwind anomalies from the central equatorial Pacific. All timeseries exhibit a similar evolution of anomalies. The periods
1998–2002 and 2007–2014 for example are marked by positive sea ice extent anomalies, negative SST anomalies, westerly wind anomalies in the south Pacific and easterly wind
anomalies in the central equatorial Pacific. Opposite-signed
anomalies are present during 1991–1996 and 2002–2016. As
noted above, the south Pacific wind anomalies were interpreted by Schneider et al. (2015) and linked with tropicalteleconnections. The influence of large-scale atmosphericcirculation variability in the Pacific Sector on Ross Sea SIE
anomalies is discussed in Turner et al. (2016).
Given the potential importance of the evolution of tropical variability over the past several decades to Antarctic
sea ice trends, it is perhaps not surprising that free-running
coupled model experiments do not simulate the observed
Antarctic sea ice trend. In addition to the 30-member Large
Ensemble, we use a second initial condition ensemble of 10
members, in which the observed sequence of SST anomalies
in the eastern tropical Pacific is specified using a nudging
technique. The external forcings applied are the same as for
the Large Ensemble. Trends in a given ensemble memberof this experiment reflect a combination of the radiatively
forced response, the response to tropical variability, andadditional, internally-generated variability arising fromatmosphere–ocean interaction or from intrinsic atmosphericand oceanic variability. The intention of this paper is to separate out these components in order to understand which
one drives the overall trends in the model and in nature. As
shown below, the response of sea ice to tropical variability
is not enough to account for the gap between modeled andobserved trends. The forced response of sea ice tends todominate the modeled trends and is largely responsible forthe observed-model mismatch. This leads us to suspect thatthe model’s forced response is biased, and thus the final goalof this paper is to discuss some of the reasons for this bias.
The remainder of this paper is organized as follows. In
Sect. 2, the model and the experiments are discussed in moredetail, and the methods for defining the externally forced

13

D. P. Schneider, C. Deser

(a)

(b)

(c)

Table 1  Summary of the CESM1 experiments discussed in thispaper
Experiment

Time-varying forcing

No. of runs

Large ensemble (LENS)
Tropical Pacific pacemaker(PACE)All radiative forcings
30
All radiative forcings + 10
Obs. SST anomalies (15°N–15°S;
80°–180°W)

Further details are given in Sect. 2.1 of the main text

physical processes that give rise to the sea ice trends, including anomalous ice motion, changes due to thermodynamics,
and changes in the surrounding oceanic environment due
to anomalous horizontal Ekman advection and air-sea heat
fluxes. Section 4 discusses the implications of the results,
especially in regards to possible model biases and provides
summary and conclusion statements.

2 Materials and methods
2.1 Model and experiments(d)Fig. 1  Observed, monthly mean timeseries of anomalies in a Sea ice
extent in the Ross Sea (160°E–130°W), b SST in the Pacific Sector(50°–70°S, 150°E–90°W), c zonal wind at 850 hPa in the Pacific Sector (40°–60°S, 70°–160°W) and d zonal wind at 850 hPa in the central equatorial Pacific (10°N–10°S, 160°E–160°W). For visual clarity,
all timeseries are smoothed with an 8-month running mean and thecommon base period is 1981–2010. Note that the y-axis is reversed inb to highlight the similarity with the other timeseries

and tropically forced responses are explained. Section 3
gives the major results of the experiments, showing theexternally forced and tropically forced patterns of trends in
Antarctic sea ice and related variables in comparison withthe observed patterns. The additional role of unforced internal variability, as given by the differences among individual ensemble members, is also highlighted. We discuss the13This study uses the fully coupled Community Earth System
Model (CESM), version 1.1., configured with interactive
atmosphere, ocean, land, and sea ice component models(e.g. Hurrell et al. 2013). The atmosphere and land are on
a 0.9° latitude × 1.2° longitude horizontal grid and the sea
ice and ocean are on a nominal 1° grid. This is a very stableand extensively evaluated configuration of the CESM, which
has been used in a variety of climate science applications,
including the experiments of the Coupled Model Intercomparison Project, Phase 5 (CMIP5, Taylor et al. 2012).
Results from two major CESM 1.1 experiments are analyzed (Table 1). The first experiment is the CESM Large
Ensemble, or “LENS”, a 30-member initial atmosphericcondition ensemble designed for studying the impactsof climate change in the presence of internal variability.
Details of this experiment are given in Kay et al. (2014).
Most importantly, each ensemble member is forced in an
identical way, except for an initial atmospheric temperature
perturbation applied on the first day of the simulation. Dueto the chaotic nature of the system, this small perturbation
generates spread among the ensemble members. The second
experiment is also an initial condition ensemble with random temperature perturbations applied at the start of eachrun. In addition, it follows the protocol of Kosaka and Xie(2013) in which the observed sequence of SST anomalies
in the eastern tropical Pacific is specified using a nudging
technique. In this experiment, SST anomalies in the eastern equatorial Pacific (15°N–15°S; 80°–180°W) from theTropically driven and externally forced patterns of Antarctic sea ice change: reconciling…

Extended Reconstruction SST version 3b dataset (ERSST
v3b; Smith et al. 2008) are converted to sensible heat fluxes
and applied to the ocean component model. A buffer zoneof linearly tapering anomalies extends to 20°N and 20°S,
and from 180°W to the coast of the Americas. The rest of
the model’s coupled climate system is free to evolve. Ten
ensemble members of this “Pacemaker” experiment (“pacemaker” in the sense that the eastern tropical Pacific sets the‘pace’ or sequencing of internal climate variability) were
performed for the period 1920–2013. Note that our Pacemaker experiment (hereafter denoted “PACE”) differs inseveral key ways from the temperature and salinity constrained pacemaker experiments discussed in Purich et al.
(2016). First, the atmospheric model version used for PACE
is CAM5 instead of CAM4 and the horizontal resolution of
the atmosphere, ocean and land are ~1° instead of ~3.75°.
Second, the most comparable experiment of Purich et al.
(2016) is their Equatorial Pacific Ocean experiment, whichassimilated anomalies over the entire tropical Pacific Ocean,rather than just the eastern equatorial Pacific. Finally, thePurich et al. (2016) experiments assimilated observed threedimensional temperature and salinity anomalies into theocean model, rather than just the SST anomalies.
All LENS and PACE ensemble members share thesame external forcing, using the CMIP5 historical forcing
specification for 1920–2005 and the RCP 8.5 forcing for
2006–2013 (details in Kay et al. 2014). The exception is that
the LENS used stratospheric ozone concentrations from thewhole atmosphere community climate model (WACCM),
an interactive chemistry-climate model (Marsh et al. 2013),
while the Pacemaker ensemble used stratospheric ozone
concentrations from the SPARC dataset (Cionni et al.
2011), a dataset more commonly used in the CMIP5 historical experiments of other models. Schneider et al. (2015)
compared atmospheric model experiments forced by SPARC
and WACCM ozone, and found the lower tropospheric wind
trends over the Southern Ocean for 1979–2011 to be statistically indistinguishable, with only subtle differences.
The small difference in external forcing of the LENS andPacemaker ensembles has minimal influence on the results
discussed here.
For the analysis of sea ice trends, we take advantageof the model’s ability to distinguish between sea ice area
tendencies due to dynamics and tendencies due to thermodynamics. In the Community Ice Code version 4 (CICE4;
Hunke and Lipscomb 2008; Holland et al. 2012), the sea
ice model used in CESM1.1, the dynamic component of themodel determines ice motion resulting from a force balance
between wind stress, water stress, internal ice stress, and sea
surface slope. The thermodynamic component of the model
determines vertical and horizontal melt and growth rates bysolving for the vertical temperature profile arising from thesurface energy balance, vertical heat transfer, and heat supplied by the ocean at the bottom of the ice.
For comparison with the ensemble mean of the CESM1LENS, we computed a multi-model ensemble mean from 29
models contributing to CMIP5. Monthly mean output was
obtained from the first ensemble member of the 1850–2005
historical (twentieth century) experiment. The data wereregridded to a common 1° latitude × 1° longitude grid. See
Table S1 for a list of the models used.
2.2 Observational and atmospheric reanalysis datasets
To characterize the observed trends in sea ice and related
variables, the following datasets are utilized: SST anomalies
from ERSST v3b; sea ice concentration from the Goddard
Merged analysis of passive microwave observations (Penget al. 2013; Meier et al. 2013); oceanic potential temperaturefrom the EN4 objective analysis, version 4.2.0 (Good et al.
2013); and geopotential height and zonal winds from the
ERA-Interim Reanalysis (Dee et al. 2011).
2.3 Methods
The role of external radiative forcing, termed the “forced
response”, is obtained from the ensemble mean of the LENS
experiment (denoted “LENS-EM”). The PACE ensemble mean (denoted “PACE-EM”) includes both the forced
response and the response to observed internal tropicalvariability. The role of the tropics (specifically, the eastern
Equatorial Pacific), called the “tropical response”, is isolated by subtracting LENS-EM from PACE-EM. Individual
ensemble members of PACE reflect a combination of theforced response, the tropical response, and internal variability beyond the eastern Equatorial Pacific. The goal ofthese methods is to separate out these three components, assummarized in Table 2. Nature should be most comparablewith an individual ensemble member, rather than any one
component. The estimate of the tropical response assumes
that the responses to tropical SSTs and external forcings arelinear. This assumption may not strictly hold, and in any
event, it is difficult to purely separate external and tropicalsignals, as the observed tropical SSTs themselves have been
influenced by external forcing. This caveat notwithstanding,
the results here are consistent with the expected responseto recent tropical variability, namely a deepened ASL andlower SSTs and expanded sea ice in some sectors of theSouthern Ocean.
The sea ice extent (SIE) is defined as the total area of gridcells containing an ice fraction of 15% or greater. For observations, we calculate extent from the polar stereographic
grids of the Goddard Merged data, using cell areas provided
by the National Snow and Ice Data Center. For the models,13

D. P. Schneider, C. Deser
Table 2  Summary of how
the role of various drivers isdetermined

Response

How derived

SIE trend(×106 km2/
decade)Radiatively forced response
Tropical response
Tropical response + intrinsic variability

LENS-EM
LENS-EM minus PACE-EM
PACE #4 minus LENS-EM

−0.45
0.09
0.18

Further details are given in Sect. 2.2 of the main text

we calculate SIE from the atmospheric grid, using cell areas
provided by the CESM modeling group.
We also use output from the ocean model to computethe horizontal Ekman heat flux and the air-sea heat fluxes.
The Ekman term is computed on the ocean grid, applying
the surface stress to the horizontal temperature gradients inthe uppermost ocean layer. The surface heat flux term, ­Qnet,is also computed on the ocean grid. Q­ net includes the ocean
heat flux due to ice formation or ice melting, the sensible andlatent turbulent heat fluxes, and the net surface longwave andnet surface shortwave radiative fluxes, all as viewed from theocean’s perspective.
Monthly anomalies in all variables are computed bysubtracting the climatological monthly means for the baseperiod of 1981–2000 from the corresponding month of eachyear. Seasonal averages are formed from the monthly anomalies and referred to by the first letters of the corresponding
month means (e.g. August–September–October is ASO).
Linear least-squares analysis is used to compute trends.
Stippling is used on the plots to indicate areas of statisticalsignificance, which is determined from a two-sided t test
methodology and adjustment for autocorrelation as outlined
by Santer et al. (2000). Unless otherwise noted, the sample
size and the degrees of freedom for indexing the critical t
value are adjusted according to the lag-1 autocorrelation of
the residuals.

3 Results
3.1 Observed and simulated monthly trends
in atmospheric circulation and sea ice
We begin by describing the hemispheric-scale context inwhich the Antarctic sea ice increase has occurred. Observed
850-hPa geopotential height trends during 1979–2013 basedon all months of the year (Fig. 2a) exhibit a wave-train pattern in the Southern Hemisphere (SH), with positive height
trends in the south Pacific Ocean centered near 100°W, negative height trends off the coast of Antarctica centered near
80°W and positive height trends in the south Atlantic Ocean
centered near 60°W. The negative height trends near Antarctica are a signature of a deepened Amundsen Sea Low (ASL,

13

e.g. Raphael et al. 2016). The pattern of negative SST trends
in the eastern Pacific basin of both hemispheres, contrasted
with positive SST trends in the western Pacific, resembles
the negative phase of the PDO (e.g. Trenberth et al. 2014).
SST cooling is largest in the Pacific Sector of the Southern
Ocean. As the highly correlated timeseries in Fig. 1 suggest
and the work of Yeo and Kim (2015) shows, the SST cooling is physically consistent with the overlying atmosphericcirculation trends.
Figure 2b,c depict analogous height and SST trends
from the model experiments. The forced response, given by
LENS-EM (Fig. 2b) shows positive SST trends nearly everywhere, except for a small region in the north Atlantic. The
warming is largest in the polar regions of both hemispheres(note that the model’s SST shown in this figure is the surface
skin temperature, which responds strongly to changes in sea
ice cover, and is not directly comparable to the observed SST
in the ice-covered regions). Over the tropical and mid-latitude oceans, the forced response of SST shows little spatial
structure, with subtle regional patterns including a maximum of warming along the equatorial Pacific, which has
been associated with wind anomalies (Xie et al. 2010). The
most notable geopotential height trend in the forced response
is a weak expression of the positive SAM pattern, marked bynegative trends polewards of 60°S, and positive trends in the
SH midlatitudes. The forced response given by LENS-EM
is similar in many respects to that given by the multi-model
mean of CMIP5 (Fig. S1), suggesting that the CESM1 is not
an outlier with respect to its forced response. In SST, LENSEM and CMIP5-EM show the strongest warming at the poles
and a local maximum of warming in the equatorial Pacific.
Like the LENS-EM, the CMIP5-EM shows a weakly positive SAM pattern in its geopotential height trend, although
the node is shifted somewhat equatorward in CMIP5-EM
compared with LENS-EM.
PACE-EM (Fig. 2c) shows the dramatic effect of specifying the observed tropical Pacific SST variability in addition
to external forcing. Cooling is evident in the eastern Pacificbasin of both hemispheres, while the Southern Ocean nearWest Antarctica warms considerably less than in LENS-EM.
A wave-train pattern in 850-hPA height trends in the SH,
similar in form but weaker in magnitude to the observed, isalso evident in PACE-EM. Differencing the PACE-EM andTropically driven and externally forced patterns of Antarctic sea ice change: reconciling…

(a)

(b)

(c)

(d)

Fig. 2  Trends in SST (colors) and 850 hPa geopotential height(contours, spacing of 2 m/decade, negative trends dashed lines andpositive trends solid lines) over 1979–2013, using all months, for aobservations and reanalysis, b the ensemble mean of the LENS, c theensemble mean of PACE, d the difference of the ensemble means of
LENS and PACE. Stippling indicates that the local SST trend is significant at or above the 90% level

LENS-EM patterns isolates the tropical response (Fig. 2d),which shows cooling over most of the global ocean, especially in the eastern Pacific basin and the Pacific and Atlantic
sectors of the Southern Ocean. The tropical response largely
accounts for the wave-train pattern in SH geopotential height
trends that is evident in PACE-EM.
The corresponding Antarctic sea ice concentration (SIC)
trends are displayed in Fig. 3a–d. In observations (Fig. 3a),
SIC trends are positive in most regions, except for the Bellingshausen Sea near the Antarctic Peninsula and the Amundsen Sea near coastal West Antarctica. By contrast, the forced
response (Fig. 3b) is characterized by sea ice loss nearly
everywhere, especially at the northern ice edge. This pattern is largely mirrored in PACE-EM (Fig. 3c) except in the
Pacific sector, where SIC trends are very weak. The tropicalresponse (Fig. 3d), therefore, is associated with a modest but
statistically significant ice gain in the Pacific sector and isnotable for the lack of negative SIC trends in most regions of
the Southern Ocean. We shall describe the remaining panelsof Fig. 3 below.
To quantify the observed and modeled SIE trends,
Fig. 4 shows the Antarctic SIE trend over 1979–2013
in each ensemble member, the observations, and theensemble means, including the multi-model ensemble
mean, CMIP5-EM. In contrast to the observed trend of+0.20 × 106 km2/decade, the forced response from LENSEM is −0.45 × 106 km2/decade, only slightly more negative
than CMIP5-EM, while PACE-EM is −0.36 × 106 km2/
decade. Differencing LENS-EM and PACE-EM yields thetropical response of + 0.09 × 106 km2/decade. The role ofinternally generated variability is illustrated by the ensemble spreads. In the LENS, the simulated trends range from−0.74 × 106 to −0.12 × 106 km2/decade and in PACE, thesimulated trends range from −0.50 × 106 to −0.27 × 106 km2/
decade. This suggests that internal variability, superimposedon the forced or the forced plus tropical responses, does notaccount for the sign and magnitude of the observed Antarctic
sea ice trend. However, if the forced response is subtracted
out of each ensemble member, then the ensemble spreadsof the residual LENS and Pacemaker ensembles overlap
the observed trend. When the forced response is removed,nine out of ten PACE ensemble members show positive SIE
trends. The largest trend, +0.18 × 106 km2/decade—close to
the observed value—is in ensemble member four (PACE#4); this ensemble member is analyzed further below.
In map view (Fig. 3e), the original PACE #4 shows SIC
trends with a pattern similar to PACE-EM (Fig. 3c), but oflarger magnitude. Removing the forced response results inpositive SIC trends in most regions of the Southern Ocean(Fig. 3f), especially in the Amundsen and eastern Ross seas.

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D. P. Schneider, C. Deser

(a)

(b)

(c)

(d)

this period are part of a longer-term decline in SIE that isevident when the timeseries are extended about a decade
before and after the 1979–2013 observational period. This
long-term, gradual decline in ice cover is consistent with theexternal forcing (mainly, rising greenhouse gas concentrations) applied to the model. The LENS-EM and CMIP5-EM
timeseries behave similarly, suggesting that CESM1 agrees
with the multi-model consensus of the forced response. With
the forced response retained in the PACE experiment, theobserved timeseries falls outside the bounds of the ensemble.
With the forced response removed, however, the observed
timeseries lies within the ensemble (Fig. 5b). The timeseriesof the tropical response (purple line, Fig. 5b) shows a modest positive trend that results from predominantly negative
anomalies before the late 1990s and predominantly positive
anomalies after the late 1990s.
3.2 Seasonal‑mean responses in sea ice
and atmospheric circulation

(e)

(f)

Fig. 3  Trends in sea ice concentration (colors) and 850 hPa geopotential height (contours, spacing of 2 m/decade, negative trends
dashed lines and positive trends solid lines) over 1979–2013, using
all months, for a observations and reanalysis, b the ensemble meanof the LENS, c the ensemble mean of PACE, d the difference of theensemble means of LENS and PACE, e PACE #4, and f the differenceof PACE #4 and the ensemble mean of the LENS experiment. Stippling indicates that the local sea ice concentration trend is significant
at or above the 90% level

This “residual pattern” may be interpreted as the trend arising from a combination of the tropical response (Fig. 3d)plus intrinsic variability beyond the tropical Pacific. In
PACE #4, the intrinsic variability generally acts to reinforce
the pattern associated with the tropical response.
Timeseries of Antarctic SIE from the experiments andobservations (Fig. 5a) emphasize the systematic nature of
the forced response and the data-model mismatch that hasreceived much attention. All ensemble members of both the
LENS and PACE experiments exhibit negative trends during 1979–2013, consistent with Fig. 4. The trends during

13

Having considered the large-scale, all-month trends in sea
ice and related variables, we now turn to seasonal-mean
trend patterns. The seasons are delineated to capture thelate winter sea ice maximum extent (ASO), the rapid retreat
in spring and summer (NDJ), and the gradual ice advancein autumn and winter (FMA and MJJ). In observations(Fig. 6a), ice gain during 1979–2013 occurs in all seasons.
In the winter (ASO and MJJ), the largest gains in SIC are at
the ice edge, while in summer and autumn, the largest gains
are closer to the continent, and are especially pronounced in
the western Ross and eastern Weddell seas. Ice loss occurs
near the Antarctic Peninsula in all seasons, and along coastal
West Antarctica in the summer and autumn. The modeled
SIC trend patterns are quite different from the observed. The
forced response (Fig. 6b) exhibits ice loss in all seasons,
mainly at the ice edge during winter and near the coast in
autumn, with relatively little change in summer. The tropical response (Fig. 6c) shows ice gain in all seasons, with thelargest magnitudes in ASO in the Pacific sector.
In the overlying atmospheric circulation trends, the forced
response (Fig. 6b) has the largest magnitude in summer, inthe form of the positive SAM pattern, with negative height
trends over Antarctica and positive trends over the midlatitudes. The tropical response (Fig. 6c) is associated withnegative height trends in the Pacific sector in all seasons,most spatially extensive during the winter. In ASO, positive
height trends occur in the Eastern Hemisphere, giving shape
to an east–west dipole that broadly resembles the observed
pattern.
Given that the tropical response shows the largest SIC
trends in ASO and the overlying atmospheric circulation
pattern resembles the observed pattern, we focus on ASO
for further analysis. Figure 7 repeats the layout of Fig. 3,

Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling…
Fig. 4  Antarctic sea ice extent
trends, using all months during
1979–2013, as given by theobservations, the LENS, thePACE, and the difference of the
PACE and the LENS ensemblemeans. The 95% confidencebound of he observed trend isgiven by the solid blue line.
Also indicated is the trend in
the CMIP5 ensemble mean for
1979–2005

Fig. 5  Timeseries of anomalies in Antarctic sea ice extent,
using all months, according toa the LENS and PACE and b
the residual of the PACE runs
minus the ensemble mean of
the LENS experiment. The
observed timeseries is shownin both panels, and the CMIP5
ensemble mean is shown in a.
For visual clarity, the originaltimeseries are smoothed witha 10-month running mean andthe common base period is
1981–2010

(a)

(b)

13

D. P. Schneider, C. Deser

(a)

(b)

(c)

Fig. 6  Trends in seasonal-mean ice concentration (colors) and
850 hPa geopotential height (contours, spacing of 2 m/decade, negative trends dashed lines and positive trends solid lines) over 1979–
2013, for a observations and reanalysis, b the ensemble mean of the

LENS, and c the difference of the ensemble means of LENS and
PACE. Stippling indicates that the local sea ice concentration trend issignificant at or above the 90% levelbut for ASO instead of the monthly means. The atmospheric circulation trend in PACE #4 (Fig. 7e) is similar
to the observed (Fig. 7a), both in terms of the east–west
dipole in the sign of the trend, and in terms of the position
and magnitude of the maximum low pressure trend near
56°S, 110°W. Despite the realistic pattern and magnitudeof the atmospheric circulation trend, the SIC trend pattern in PACE #4 does not resemble the observed, with thenotable exception that the dipole in the sign of the trend
is present between the Peninsula region and the eastern
Ross Sea. Subtracting out the forced response (Fig. 7f) has
little influence on the atmospheric circulation trend, but
yields a SIC trend pattern that is comparable in sign andmagnitude to the observations. In the Amundsen and eastern Ross Sea (90°–160°W; blue box in Fig. 7e), where thesimulated positive SIC trends are largest, increasing SIC
is associated with anomalous northward and westward ice
motion. This pattern of ice motion trends is consistent withexpectations from observations, in that freely drifting ice
moves at a small angle to the left of the geostrophic winds,
which are parallel to the geopotential height contours (e.g.
Holland and Kwok 2012; Kimura et al. 2004). In addition,
as discussed by Fan et al. (2014), Raphael et al. (2016)
and others, cold air advection associated with northward
geostrophic wind anomalies in the eastern Ross Sea also
leads to increased SIC and extent.

13

Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling…

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 7  Same layout as Fig. 3, but for ASO and also including thetrends in ice motion shown as arrows. Stippling indicates that the
local sea ice concentration trend is significant at or above the 90%
level

3.3 Dynamic and thermodynamic aspects of the sea ice
response
Next we further examine the physical mechanisms of thesea ice responses, using the dynamic and thermodynamic
sea ice area tendencies given by the CICE model. To provide context to the changes in these terms, Fig. 8 displays
the climatology from the LENS ensemble mean. In general,
thermodynamic and dynamic processes offset each other.
Near the continent, the climatological mean ice motion isdivergent (Fig. 8a) and the dynamic ice area tendency is negative (Fig. 8b). This creates openings for new sea ice to formin the cold polar waters, so the thermodynamic tendency ispositive (Fig. 8c). Near the ice edge, the ocean is too warm

to support thermodynamic ice growth, but the ice motion isconvergent and the dynamic tendency is positive. Within arelatively small band as the winter ice edge is approached,
the ice concentration drops rapidly from 80% to less than
20% (Fig. 8a) and the interannual standard deviation of ice
concentration is large (Fig. 8d). It is changes within this“marginal ice zone” that are most reflected in SIE anomalies.
We now consider the thermodynamic and dynamic
tendencies in the region of the Amundsen and eastern
Ross Sea where the SIC increases the most in the tropicalresponse and in the residual (blue-outlined area in Fig. 7e).
From south to north, the climatological ice concentration
decreases from 100% at 72°S, to 15% (the conventional iceedge) at about 62°S (Fig. 9a). At approximately 66°S, theice motion changes from divergent to convergent, and thethermodynamic tendency changes from positive to negative. The relatively sparse ice cover equatorwards of 66°S is
maintained by dynamics, with the ice moving in a northwardand eastward direction.
Changes in these terms in the LENS and PACE experiments during 1979–2013 are shown in Fig. 9b, using anepoch difference (1999:2013 minus 1979–1998) to enabledirect comparison to the mean state (trend results are similar; not shown). The forced response (black line in Fig. 9b)
is characterized by maximum ice loss at 66°S. This change
implies a small poleward shift in the latitude at which thesea ice is thermodynamically stable. The dynamic changes
are too weak in the forced response to offset the ice lost due
to thermodynamics. In the tropical response (dashed blueline in Fig. 9b), ice gain at 66°S corresponds with weak
thermodynamic ice growth, which is enabled by enhanced
divergent ice motion. Equatorwards of 66°S, the gain in ice
concentration is supported by dynamics, which is reflectedin the increased northward and eastward ice motion. In theresidual pattern (PACE #4—LENS-EM; red line in Fig. 9b),
ice gain is maximized between 66° and 62°S, associatedwith positive tendencies due to thermodynamics between
66° and 64°S and due to dynamics between 64°S and 62°S.
The positive thermodynamic ice tendency around 66°S may
be viewed as a response to the divergent ice motion. Equatorwards of where the ice motion change switches to convergent at 64°S, dynamics help to increase the ice cover. At
about 62°–63°S, the northward component of the ice motion
more than doubles compared to the climatology (the climatology is ~0.4 cm/s, which increases by ~0.5 cm/s), acting
to expand the ice edge.
3.4 Associated changes in SSTs and air‑sea heat fluxes
As discussed by Armour and Bitz (2015) and Fan et al.
(2014), the observed Antarctic sea ice increase should
be viewed in the context of SST trends across the entire
Southern Ocean. To understand the context in which the13

D. P. Schneider, C. Deser

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 8  Seasonal climatology for ASO according the ensemble meanof the LENS experiment of a ice concentration and motion, b ice area
tendency due to dynamics, c ice area tendency due to thermodynam-

ics, d interannual standard deviation of ice concentration, e meridional component of ice motion and f zonal component of ice motion

sea ice changes discussed above occur in the model environment, we compute the trends in horizontal Ekman heat
flux and air-sea heat fluxes and compare these patterns to
the SST trends for the region 30°S to the Antarctic coast.
In the forced response (Fig. 10a), the SST increases everywhere. The Ekman heat flux is predominantly negative,
consistent with the largely westerly wind forcing, which
can be inferred from the geopotential height patternsin Fig. 6b. The surface heat flux pattern largely reflects
changes at the retreating ice edge, as heat is removed fromthe ocean where ice is lost and added to the ocean equatorwards of the ice edge. The relatively weak atmosphericcirculation changes in the forced response are likely not astrong driver of the heat flux trends.

In the tropical response (Fig. 10b), the SST and Ekman
trends are zonally asymmetric and show a broad-scale correspondence to each other. These patterns are consistent withthe east–west dipole in the atmospheric circulation trend
discussed above (e.g., Fig. 6b). The Q­ net term is less influenced by the ice edge than in the forced response, except in
the south Pacific sector, where ice gain is associated with
reduced heat loss. Generally, the combined Ekman and surface heat fluxes help to drive the SST pattern rather thandamp it (that is, the heat flux and SST trends are of the same
sign in most regions).
The SST and Ekman patterns in the residual pattern(Fig. 11b) resemble those in the tropical response, but they
are stronger in magnitude, consistent with the relatively

13Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling…
Fig. 9  Latitudinal transects
integrated over the eastern Ross
Sea and Amundsen Sea region(90°–160°W) of, from top tobottom row, ice concentration,
ice area tendency due to dynamics, ice area tendency due tothermodynamics, meridional ice
motion and zonal ice motion.
Column a shows the mean over
1979–1998 and column b shows
the differences of the means
between 1999 and 2013 and
1979–1998(a)strong atmospheric circulation trends (see Fig. 7f). The­Qnet term is much stronger than in the ensemble means,consistent with the stronger atmospheric circulation
trends and associated changes in wind speed, meridional
heat advection, cloud cover and precipitation. Broadly,
the combined Ekman and surface heat fluxes are of thesame sign as the SST trends. Importantly, the negative
heat flux and SST trends in the south Pacific sector help
to support the ice expansion there, reinforcing the trends(b)associated with enhanced ice motion. Similarly, positive
heat flux trends are co-located with reduced ice extent in
the western Weddell Sea region, reinforcing the anomalous
poleward ice motion. The observed SST trend (Fig. 11a)more closely resembles the residual pattern than it does
the forced response. The model results suggest that both
Ekman and surface air-sea heat fluxes have contributed to
the negative SST trends in Pacific Sector.

13

D. P. Schneider, C. Deser
(a)

(b)

Fig. 10  Trends in the ASO seasonal-mean, from left to right column,
SST, Ekman heat flux, surface heat fluxes, and the sum of the Ekman
and surface heat fluxes during 1979–2013 for a the ensemble meanof the LENS and b the ensemble mean of PACE minus the ensemble

mean of LENS. Also indicated in each panel is the ASO ice edge,
shown as the solid black line. Stippling indicates that the local SST orheat flux trend is significant at or above the 90% level

4 Discussion

reproduces the dominant spatial pattern of atmospheric circulation trends in the mid-to-high-latitude SH, most importantly a deepened ASL. However, despite giving realistic
atmospheric circulation trends over the Southern Ocean,none of the pacemaker ensemble members simulates a positive trend in Antarctic sea ice extent over 1979–2013. By
including the observed sequence of tropical variability andthereby more realistically capturing circulation trends over
the Southern Ocean, the pacemaker simulations reduce thepossibility that data-model discrepancies in sea ice trends
result from an unusual sequence of internal variability.
The ensemble-mean of the LENS experiments provides
a good estimate of the radiatively forced response of sea ice
and related variables, and it is in broad agreement with themulti-model mean from CMIP5. In CESM, as in most models (e.g. Hobbs et al. 2016), the forced sea ice trend differs
sharply in sign and spatial pattern from the observed trends.
In all seasons of the year, the forced response shows sea ice
loss in every sector of the Southern Ocean (e.g. Fig. 6b).
Typically, in comparing observed and modeled trends,
we would expect nature to more closely resemble a single
ensemble member than the forced response. However, in

The above results add to the body of work that highlights
the observed-model discrepancies in recent Antarctic sea
ice trends (e.g. Turner et al. 2013; Zunz et al. 2013; Hobbs
et al. 2015; Jones et al. 2016). Using a large ensemble ofhistorical simulations with the CESM, we have examined
the role of internal variability in explaining this discrepancy.
This expands previous work using multi-model ensembles(e.g. Polvani and Smith 2013; Jones et al. 2016) in which it
is difficult to disentangle the roles of structural uncertainty
and internal variability. Given that all ensemble members of
the LENS show a decrease in Antarctic sea ice extent during
1979–2013, in contrast to the observed increase, it seems
that internal variability alone is an insufficient explanation
for the observed-model mismatch. With a second ensembleof the CESM, we have considered the hypothesis that SST
variability in the tropical Pacific has been a driver of Antarctic sea ice trends (e.g. Meehl et al. 2016; Purich et al. 2016).
This Pacemaker experiment supports this hypothesis byshowing less sea ice loss compared to the LENS, especiallyin the Pacific Sector. In addition, the Pacemaker experiment13

Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling…

(a)

(b)

Fig. 11  a Trend in the ASO seasonal-mean SST according to observations during 1979–2013. b As in Fig. 10, but for the difference of the
PACE #4 and the ensemble mean of the LENS. Stippling indicates that the local SST or heat flux trend is significant at or above the 90% level

the case of Antarctic sea ice, the forced response dominates
the trends in every ensemble member, so that none of theensemble members look like nature. This leads us to suspect that the Antarctic sea ice in the model is too sensitive
to external forcing. Support for this conclusion is provided
by the fact that if the forced response is subtracted out ofeach ensemble member, then the observed sea ice trend falls
within the ensemble spread of both the LENS and pacemaker ensembles.
Removing the forced response and accounting for the
influence of tropical teleconnections are two importantfactors in resolving the observed-model discrepancies in
Antarctic sea ice trends. The impacts of these two steps areillustrated by comparing the forced response (e.g. Fig. 3b)with the tropical response (e.g. Fig. 3d). The latter showsa net increase in Antarctic sea ice extent with the greatest
gains in ice concentration in the Pacific sector, underlying
a deepened ASL. The tropical response pattern is similar to
the results of the equatorial Pacific data assimilation experiment of Purich et al. (2016) (see their Fig. 2d). However, an
additional role is played by internal variability associatedneither with external forcing nor tropical teleconnections.
Such intrinsic variability can strongly impact decadal-scaleatmospheric circulation trends as well as the underlying sea

ice trends, as illustrated by sea ice simulations forced bysynoptic-scale atmospheric variability (Matear et al. 2015).
We examine the role of intrinsic variability by removing
the forced response from the individual ensemble membersof the PACE experiment. In one member (PACE #4), thisdoubles the overall sea ice extent trend that can be explained
compared to the ensemble-mean of PACE (e.g. Fig. 4). In
PACE #4, the atmospheric circulation trends and sea ice
trends in austral winter are similar to the observed (e.g.
Fig. 7f, a). In summary, if we hypothesize that the radiatively
forced response is flawed (in the sense that it is too strong, of
the wrong sign, or incorrect spatial pattern) and that internalvariability (including that arising from tropical teleconnections and extratropical atmosphere-ocean-sea ice dynamics) plays an important role, we can then largely explain
the observed-model discrepancy in Antarctic sea ice trends.
The austral winter sea ice grain in the residual pattern(PACE #4 minus LENS ensemble-mean) occurs for reasonsphysically consistent with expectations from observations.
In the model, the sea ice gain in the Amundsen and eastern
Ross Sea is associated with an increase in ice motion driven
by an intensified ASL. In the region of ice divergence (polewards of ~64°S), thermodynamic ice growth replaces the ice
cover exported from the region, leading to a net increase in13

D. P. Schneider, C. Deser

ice concentration. Equatorwards of ~64°S, a strong increasein northward and eastward ice motion helps to expand thesea ice extent. Holland and Kwok (2012) described a similar
scenario for the observed autumn and winter Antarctic sea
ice expansion during 1992–2010, lending observational support to the mechanisms of sea ice expansion in the model.
In the model and in nature, the sea ice expansion is likely
enhanced by feedbacks associated with the decreased SSTs
in the Pacific sector, which in turn are consistent with equatorward Ekman transport and negative surface heat fluxes.
While we have hypothesized that the observed-model
discrepancies in Antarctic sea ice trends arise from shortcomings in the model and not solely from internal variability, we do not claim to have solved the problem. A true
solution would involve improving the CESM and othermodels of its class, and this requires identifying what
aspects of the model are most critical to the response of
sea ice to external climate forcing. A good starting place
is likely the ocean’s thermal structure, as discussed byArmour and Bitz (2015) and Kostov et al. (2016). Compared to observations (Fig. 12a), the annual, zonal-mean
mixed layer depths in CESM 1.1 are too shallow, as can
Fig. 12  a Climatology ofannual-mean, zonal-mean
potential temperature in theSouthern Ocean according to
the EN4 dataset. b Model bias
in annual-mean, zonal-mean
potential temperature in theSouthern Ocean, calculated as
the difference of the observations and the ensemble meanof the LENS experiment. The
black contours are the mean
isotherms from the observed
dataset and the gray contours
are the mean isotherms from the
LENS

(a)

(b)

13

be inferred from the shallower isotherms in CESM 1.1
compared to EN4 (Fig. 12b). This shallow mixed layer bias
is common among CMIP5 models (e.g. Sallée et al. 2013;
Huang et al. 2014). Between ~40°S and ~60°S, the upper~100 m of the ocean is too warm and buoyant, due in partto the excessive cloud radiative forcing over this region(e.g. Schneider and Reusch 2016). This bias, which may
be caused by weak vertical mixing (Huang et al. 2014),
limits the model’s ability to store excess heat from greenhouse warming deep in the mixed layer. In the polar ocean,underlying the seasonal ice cover, the upper ~100 m is
too cold, and the layers between ~100 and ~500 m are too
warm (Fig. 12b). The excessive vertical temperature gradient that these biases imply has an important bearing on themodel’s response to wind forcing. While the above results
show that the model does reasonably well with surface
processes such as Ekman transport and air–sea heat fluxes,
the vertical temperature gradient may determine how
quickly the surface response to stronger winds transitions
from a cooling driven by surface processes to a warming
driven by upwelling of relatively warm waters at depth(Kostov et al. 2016). In CESM, this transition happens

Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling…
Fig. 13  Trends in annual-mean,
zonal-mean potential temperature in the Southern Ocean
during 1979–2013 according
to a the EN4 observational
dataset, b the ensemble mean of
the LENS, and c the differenceof PACE #4 and the ensemble
mean of the LENS. The black
contours are the mean isotherms
from the observations for panel
a and the LENS for b, c. Stippling indicates that the local
zonal-mean temperature trend
is significant at or above the
90% level

(a)

(b)

(c)

relatively quickly, within a few years of the imposed wind
forcing (Ferriera et al. 2015; Kostov et al. 2016).
The trends in ocean potential temperature suggest that theocean plays a very important role in the forced response as
given by the ensemble-mean of the LENS (Fig. 13b). The
entire Southern Ocean warms, especially the upper ocean.
This warming pattern is very consistent across ensemble

members, creating an environment that is not conducive
to sea ice expansion. By contrast, the ocean potential temperature trends in the residual are rather weak (Fig. 13c),suggesting that the ocean plays little role in the anomalous
sea ice expansion, consistent with the idea that surface processes are the dominant cause. In observations, the Southern Ocean has warmed most at the base of the mixed layer

13

D. P. Schneider, C. Deserbetween 40°–60°S, and cooled in the polar surface ocean(Fig. 13a). This observed pattern has been interpreted as
the response to stronger westerly winds (Armour and Bitz
2015), with anomalous surface currents transporting coldpolar waters northwards to about 45°S, inducing a divergence of surface waters that drives anomalous upwelling
south of ~55°S. As ocean temperatures increase with depth
in this region (Fig. 12a), these upwelled waters are relatively warm. Counteracting this process, increased sea ice
extent and spring melt has contributed to the freshening of
the surface ocean (Haumann et al. 2016), which increases
buoyancy and reduces the entrainment of warm deepwater
into the mixed layer. Thus, the freshening effect of increased
sea ice is a mechanism that can delay the “slow response”
to stronger winds discussed by Kostov et al. (2016). Northof about 45°S, the wind trends have driven anomalous convergence and enhanced subduction, driving anomalous heat
into the mixed layer.
The observed ocean potential temperature trend may also
bear the signature of the long-term response to greenhousewarming, typically described as the delayed Southern Ocean
warming mechanism (e.g. Armour et al. 2016; Marshall et al.
2014; Manabe et al. 1990), in which the Southern Ocean
poleward of the Antarctic Circumpolar Current warms more
slowly than the rest of the global ocean. This pattern is hinted
at in the CESM’s forced response (Fig. 13b) in that the ocean
warms relatively little polewards of 60°S compared to thewarming in the mid-latitudes. Indeed, the warming pattern
in Fig. 13b resembles the temperature perturbation that is
explicitly attributed to increased atmospheric greenhousegas concentration in Marshall et al. (2014; see their Fig. 4b).
While simulated by models (Marshall et al. 2014; Armour
et al. 2016), the delayed warming pattern does not seem adequate to explain the Southern Ocean surface cooling and sea
ice expansion of the past several decades. To explain thesetrends, it is tempting to invoke internal variability, which,according to the model results, could produce an Antarctic
sea ice extent trend of the same sign and similar magnitude
as the observed trend in the absence of external forcing. Yet
the observed increase has occurred in the context of externally forced climate change, and models are not necessarily
giving the correct Southern Ocean responses (in rate, spatial
pattern, and/or sign) to stronger westerlies (driven by ozone
depletion) nor to increased greenhouse gases. The question
remains: “What is the true forced response, and what role
has it played in the Southern Ocean climate trends of the past
several decades relative to internal variability?” To address
this question, more work is needed to understand the role ofocean dynamics in the observed and modeled sea ice trends.
In summary, our results suggest that the observed-model
discrepancy in Antarctic sea ice trends since 1979 is not
simply a matter of internal variability (e.g. Polvani andSmith 2013) that is masking the forced response. Nor is the13

data-model mismatch solely attributable to the fact that most
historical coupled model simulations do not simulate theobserved sequence of tropical Pacific variability, including
the recent shift in phase of the IPO and PDO (Purich et al.
2016; Meehl et al. 2016). In CESM1, sea ice loss is likely
in historical climate change experiments given the strong
ocean warming in response to external radiative forcing(e.g. Fig. 13b), combined with a relatively fast increase inpolar SSTs (after a ~3-year initial cooling) associated withto stronger westerlies (e.g. Kostov et al. 2016). However, if
we allow for internal variability, as well as uncertainty in the
responses to greenhouse gasses and stronger westerlies, thenwe can explain the disagreement of observed and modeled
trends. Improving the mean state of the Southern Ocean,particularly with respect to temperature biases and mixing
processes, may be the key to achieving more reliable Antarctic sea ice and climate change simulations.
Acknowledgements The authors thank the CESM Climate Variability and Change Working Group (CVCWG, http://www.cesm.ucar.
edu/working_groups/CVC/) and Dr. Tingting Fan for conducting andpost-processing the CESM1 Tropical Pacific Pacemaker simulations.
The CESM Large Ensemble Community Project provided the LENS
data. Both experiments benefited from computing resources on Yellowstone managed by NCAR-CISL and sponsored by the National
Science Foundation. Drs. David Bailey, Marika Holland and Laura
Landrum provided valuable discussions during the course of this work.
D. Schneider was supported through NCAR and by National Science
Foundation Grant 1235231. The figures were produced with the NCAR
Command Language Software Package. NCAR is sponsored by the
National Science Foundation. The authors also thank the individual
climate modeling groups that have produced and made available theCMIP5 model output, as well as the coordination of those experiments
by the World Climate Research Program’s Working Group on Coupled
Modeling, and the software infrastructure for distributing model data
supported by the U.S. Department of Energy’s Program for Climate
Model Diagnosis and Intercomparison. Finally, the authors thank two
anonymous reviewers and the editor for constructive comments that
led to an improved manuscript.

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