RESEARCH LETTER

Is There a Tropical Response to Recent Observed
Southern Ocean Cooling?

Key Points:

Xiyue Zhang1,210.1029/2020GL091235• T he global impact of recent
observed Southern Ocean surface
cooling is studied using coupledmodel experiments in a Pacemakerframework• Southern Ocean cooling induces
significant cooling of the tropicalSouth Atlantic via increased
cloudiness and strengthened tradewinds• It also slows down the rate ofradiatively induced Antarctic sea ice
loss, although model disagreementwith observed ice expansion remains
Supporting Information:• Supporting Information S1
Correspondence to:
X. Zhang,sallyz@jhu.edu
Citation:Zhang, X., Deser, C., & Sun, L.
(2021). Is there a tropical responseto recent observed Southern Ocean
cooling? Geophysical Research Letters,
48, e2020GL091235. https://doi.
org/10.1029/2020GL091235
Received 11 OCT 2020
Accepted 2 DEC 2020

, Clara Deser1, and Lantao Sun3

1

Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA, 2Department of Earthand Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA, 3Department of Atmospheric Science,
Colorado State University, Fort Collins, CO, USA

Abstract Despite global warming, SSTs in the Southern Ocean (SO) have cooled in recent decades
largely as a result of internal variability. The global impact of this cooling is assessed by nudging evolving
SO SST anomalies to observations in an ensemble of coupled climate model simulations under historicalradiative forcing, and comparing against a control ensemble. The most significant remote response toobserved SO cooling is found in the tropical South Atlantic, where increased clouds and strengthened
trade winds cool the sea surface, partially offsetting the radiatively forced warming trend. The SO
ensemble produces a more realistic tropical South Atlantic SST trend, and exhibits a higher patterncorrelation with observed SST trends in the greater Atlantic basin, compared to the control ensemble.
SO cooling also produces a significant increase in Antarctic sea ice, but not enough to offset radiatively
induced ice loss; thus, the SO ensemble remains biased in its sea ice trends.
Plain Language Summary Understanding how the observed pattern of global sea surface

temperatures (SST) changes come about remains a key objective in climate science. SSTs are expected
to rise as greenhouse gas concentrations increase. However, from 1979 to 2013, SSTs in the Southern
Ocean cooled because of natural climate variability, accompanied by Antarctic sea ice expansion. Yet thiscooling and sea ice expansion are not generally captured by climate models. In this study, we artificiallyincorporate the observed Southern Ocean cooling in a climate model to see how it affects SSTs in otherregions. We found that in response to Southern Ocean cooling, the tropical South Atlantic SST cools andAntarctic sea ice expands, similar to observations. However, in simulations without the Southern Ocean
cooling, the Atlantic SST response look distinctly different, and Antarctic sea ice retreats significantly.
Our study suggests that realistic simulation of internal decadal SO SST variability may be important for
credible decadal SST projections in the tropical South Atlantic.

1. Introduction
Observed global sea surface temperature (SST) trends in recent decades show a distinctive spatial pattern, withwarming in the western Pacific, Indian Ocean, and North Atlantic, and cooling in the eastern Pacific, South
Atlantic, and Southern Ocean (SO, Figure 1a). This pattern is reminiscent of both the Pacific Decadal Oscillation/Interdecadal Pacific Oscillation (PDO/IPO) (Power et al., 1999; Mantua et al., 1997) and the Atlantic
Multidecadal Oscillation (AMO) (Enfield et al., 2001; Kerr, 2000), the dominant modes of internal low frequency variability over the Pacific and Atlantic sectors, respectively. The cooling over the SO has been partiallyattributed to internal variability associated with changes in deep water formation (Cabré et al., 2017; Kostov
et al., 2018; Latif et al., 2013; Zhang et al., 2019), in addition to contributions from ozone depletion (Ferreira
et al., 2014) and melting of the Antarctic ice sheet (Bintanja et al., 2013; Bronselaer et al., 2018).
While the role of the tropics in extratropical climate variability is well established (Alexander et al., 2002;
Deser et al., 2004; Kosaka & Xie, 2013; Newman et al., 2016), the extratropics may also influence the tropics
via coupled air-sea interactions. For example, midlatitude atmospheric variability can effectively provide
stochastic forcing for ENSO via the “seasonal footprinting mechanism” (Alexander et al., 2010; Vimont
et al., 2003) and via the “meridional mode” (Amaya et al., 2019; Zhang et al., 2014); the latter may also playa role in tropical Pacific decadal variability (Liguori & Di Lorenzo, 2019; Sun & Okumura, 2019).
© 2020. American Geophysical Union.
All Rights Reserved.

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At high latitudes, projected sea ice loss in both hemispheres has been shown to impact the tropical Pacificvia dynamical and thermodynamic air-sea interaction processes, although the detailed mechanisms are not
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Figure 1. Monthly SST trends over 1979–2013 for (a) ERSSTv3b, (b) LENS-EM, (c) TPACE-EM, (d) SOPACE-EM, (e) TPACE-internal, and (f) SOPACE-internal.
Stippling indicates local trend that is significant at or above 95% level. Black lines outline the SST nudging domain. (g) Box-and-whisker plot of SST trends
averaged over the tropical South Atlantic (gray box in (f)) for each model ensemble. Green stars show the EM values. Orange lines show the median values.
Gray horizontal line shows the observed value. (h) Same as (g) but for pattern correlations of SST trends with EM r(i, EM) over the broader Atlantic region (graybox in (a)). Green triangles show r(obs, EM). SST, sea surface temperatures.

yet fully understood (Deser et al., 2015; England et al., 2020; Wang et al., 2018). SO SST variability resulting
from open-ocean convection in Weddell Sea has a significant impact on global energy balance redistribution, affecting tropical SSTs and precipitation (Cabré et al., 2017). Idealized studies have explored theeffects of SO SST cooling, which often extends into the tropical southeastern Pacific and Atlantic (Hwanget al., 2017; Kang et al., 2019), reducing the warm SST biases in those regions (Mechoso et al., 2016). Hwang

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et al. (2017) attributed the zonal asymmetry in the tropical South Pacific SST response to wind-evaporation-SST (WES) and shortwave cloud feedbacks, while ocean dynamics play an additional role in coupledmodels (Kang et al., 2019). Here, we show that the same processes contribute to the SST response in thetropical South Atlantic to the observed SO cooling.
Unlike the idealized SO studies cited above, which examined the equilibrium response to large amplitude
perturbations, we investigate the transient response to a much smaller and realistic perturbation, namely
the observed cooling of SO SSTs in recent decades. In particular, we address the following questions. Does
observed SO cooling produce a robust, remote climate response that extends into the tropics? If so, what is
the nature of the response pattern and its underlying mechanisms? To what extent is the observed expansion of Antarctic sea ice controlled by SO SST cooling? To probe these questions, we apply a “pacemaker” experimental protocol (Kosaka & Xie, 2013) by nudging SO SST anomalies in a fully coupled global
climate model to follow the observed evolution over the period 1979–2013. This protocol has previously
been applied to the tropical Pacific to study the origins of the global surface warming hiatus (e.g., Deseret al., 2017; Kosaka & Xie, 2013), as well as teleconnections from ENSO and the tropical lobe of the PDO(i.e., Deser, Simpson, et al., 2017; Schneider & Deser, 2018), and to the North Atlantic for assessing the
global impact of observed Atlantic multidecadal variability (i.e., Ruprich-Robert et al., 2016). To the bestof our knowledge, we are the first to apply the pacemaker protocol to investigate the influence of observed
SO SST evolution on the global coupled climate system. The effects of observed SO cooling are compared with those from observed tropical Pacific SST changes based on the Pacific Pacemaker simulations(TPACE, Deser et al., 2017), as well as the radiatively forced response derived from the Community Earth
System Model version 1 (CESM1) Large Ensemble Project (LENS, Kay et al., 2015); note that all three setsof simulations employ the identical model version for direct comparison. We describe our experimental
design and data in Section 2, followed by results in Section 3. We end with summary and discussion in
Section 4.

2. Model and Experimental Design
We conduct a 20-member ensemble of SO Pacemaker Experiments (SOPACE) with CESM1.1.2 at 1°
horizontal resolution, the same version used for LENS (Kay et al., 2015). The methodology follows Kosaka and Xie (2013) (also detailed in Deser et al., 2017). Briefly, SST anomalies (as opposed to absolute
SSTs to avoid the model’s mean state warm biases in the SO, Wang et al., 2014) at each grid box over the
SO are nudged to the observed evolution of SST anomalies from 1975 to 2016 taken from the NOAA Extended Reconstruction Sea Surface Temperature version 3b (ERSSTv3b) data set (Smith et al., 2008). The
nudging region covers ocean grids south of 40°S, with a linearly tapering buffer zone from 35°S to 40°S.
In regions with LENS climatological sea ice cover, SST is nudged to the melting temperature of −1.8°C.
The nudging time scale is 2 days for the model’s 10-m deep ocean surface layer, which is equivalent to
the 10-days time scale for a 50-m deep mixed layer used in Kosaka and Xie (2013). Outside of the nudging region, the model’s coupled climate system evolves freely. All simulations are subject to historicalradiative forcing before 2005 and the RCP8.5 scenario thereafter, following the CMIP5 protocols (Taylor
et al., 2011). All SOPACE members are initialized from the first member of LENS on 1 January 1975,
with a random initial atmospheric temperature perturbation of  (10 14 ) K to create ensemble spread.

The surface heat flux forcing used to nudge the model’s SST anomalies shows significant spatial andtemporal variations over the SO (not shown). The total energy perturbation in SOPACE is ∼−0.1 PW,
which is much less than that used in idealized experiments cited earlier, for example, −0.8 PW in Kang
et al. (2019).
We also analyze the LENS and a 20-member ensemble of the TPACE, also conducted with CESM1.1.2 (Deser et al., 2017; Schneider & Deser, 2018). TPACE shares the same experimental design as SOPACE, but withnudging in the tropical eastern Pacific (15°N–15°S; 80°–180°W) over the period 1920–2013. LENS consistsof 40 members that extend from 1920 to 2100, and shares the same historical and RCP8.5 forcing with
SOPACE and TPACE, but has no nudging toward observations. The LENS ensemble mean (EM) is used todefine the model’s response to external forcing, and the spread about the EM defines the model’s internalvariability. Observational data sets are described in supporting information.
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3. Results
3.1. SST Trends
We examine trends over the period 1979–2013 when the SO surface cooled, consistent with Schneider andDeser (2018). Figures 1a–1d show the observed and EM SST trends from LENS, TPACE, and SOPACE. Overthe Pacific, observations show a large-scale pattern reminiscent of the negative phase of the PDO and IPO,
with warming in the west and cooling in the east. In the Atlantic, positive SST trends in the subpolar gyreand the tropical North Atlantic resemble the positive phase of the AMO. The Indian Ocean shows near-uniform surface warming. The SO cools overall, except for the Indian sector, with the strongest cooling in the
Pacific sector north of the Amundsen and Bellingshausen Sea (Figure 1a).
The LENS-EM SST trend pattern, which represents the model’s radiatively forced response, differs considerably from observations (Figure 1b). LENS-EM shows warming around 0.1–0.2 K/decade over most of the
global ocean, with the notable exception of the subpolar North Atlantic, which cools as a result of a slowdown of the Atlantic Meridional Overturning Circulation (AMOC, Drijfhout et al., 2012). Enhanced equatorial warming occurs in the Pacific and Atlantic, while muted warming is found in southeast and northeast
subtropical Pacific. Unlike observations, positive SST trends are evident throughout the SO (except along theAntarctic coastline), with enhanced warming in the Atlantic sector. This leads to the interpretation based on
LENS that the recent observed SO cooling is internally generated; however, potential biases in the model’s
forced response and/or the lack of an interactive Antarctic ice sheet in CESM1 may affect this interpretation(e.g., Bronselaer et al., 2018). Unlike LENS-EM, TPACE-EM shows a negative PDO/IPO pattern that bears
a close resemblance to observations (Figure 1c). However, the observed cooling over the SO is generally not
simulated in TPACE-EM, except in the eastern Pacific sector where weak cooling occurs. This indicates thatwhile the tropical Pacific has some influence on the SO, it is not large enough to overwhelm the radiatively
forced response (see also Schneider & Deser, 2018). In the tropical Atlantic, SST trends in TPACE-EM aregenerally of opposite sign compared to observations. Unlike TPACE-EM, SOPACE-EM shows a realistic
pattern of SST trends in the tropical Atlantic, with greater warming in the north compared to the south,
although the amplitude of this dipole is weaker than observed (Figure 1d). More importantly, the pattern of
SST trends in the tropical Atlantic in SOPACE-EM differs from the radiatively forced response (LENS-EM),
indicating a significant influence of SO SSTs in this region. SOPACE-EM also shows greater cooling in thesoutheast subtropical Pacific compared to LENS-EM.
To isolate the nonradiatively forced responses in TPACE-EM and SOPACE-EM, we subtract the LENS-EM
trends. These residual EM trends (termed “TPACE-internal” and “SOPACE-internal," Figures 1e and 1f)
denote the response to the internal component of the imposed regional SST anomalies, as opposed to theexternal radiatively forced responses. In both TPACE-internal and SOPACE-internal, the SST trends arenegative over much of the globe. In TPACE-internal, the negative PDO/IPO pattern is generally preserved,
and we see significant cooling in the tropical North Atlantic, leading to a north-south tropical Atlantic SST
gradient that is opposite to observations. On the other hand, in SOPACE-internal, significant cooling isfound in the tropical South Atlantic with maximum amplitude along the west coast of Africa.
In the North Atlantic, SOPACE-internal shows cooling in the subpolar gyre, colocated with the radiatively
forced “warming hole” in LENS-EM. Because of the connection between AMOC and the subpolar gyre SST(Rahmstorf et al., 2015), this cooling suggests that the observed SO cooling may also contribute to AMOC
slowdown. The AMOC trend of the SOPACE ensemble is significantly lower than that of the LENS ensemble (Figure S2). However, caution is needed in interpreting the deep ocean circulation response in SOPACE
due to the experimental protocol: SOPACE-internal exhibits significant subsurface cooling in the SO down
to ∼300 m depth (not shown), contrary to the observed subsurface warming and positive trend in SO heatuptake (Armour et al., 2016; Tung & Chen, 2018).

3.2. Tropical South Atlantic Response
The most significant remote response in SOPACE-internal is the SST cooling in the tropical South Atlantic (Figure 1f). This cooling extends all the way to the equator, with maximum amplitude in the tropicalsoutheastern Atlantic. As mentioned earlier, this SO-induced cooling of the tropical South Atlantic brings
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the pattern of SST trends over the entire tropical Atlantic in SOPACE-EM into closer alignment with observations. In particular, there is a positive north-south gradient in both observations and SOPACE-EM,
opposite to that in TPACE-EM and distinct from LENS-EM (Figure 1). To explore the robustness of thisfeature, we show the ensemble distribution of SST trends averaged over the tropical South Atlantic (TSA:
10°N–30°S, 20°W–10°E; gray box in Figure 1f) in LENS, TPACE, and SOPACE (Figure 1g). The observed
TSA trend is weakly positive at 0.053 K/decade (horizontal gray line in Figure 1g), neighboring the middle
50% of the SOPACE distribution, but only within the bottom 10% of LENS and bottom 25% of TPACE. In
addition, the TSA trend spread is smaller in SOPACE than in LENS and TPACE. In summary, it is more
likely for SOPACE members to have a TSA trend that is consistent with observations than it is for LENS or
TPACE members, and the distribution of TSA trend in SOPACE is more constrained than those in either
LENS or TPACE.
We have also compared the basin-wide patterns of Atlantic SST trends between observations and the model
ensembles over the domain 20°N–40°S, 70°W–15°E (gray box in Figure 1a). The pattern correlation (r) withobservations is considerably higher for SOPACE (r(obs,EM)SOPACE = 0.63) than for either LENS (r(obs,EM)LENS= 0.20) or TPACE (r(obs,EM)TPACE = −0.28), consistent with visual impression from Figure 1 (green stars
in Figure 1h). This suggests that the internally forced response to observed SO cooling makes a substantial
contribution to the spatial pattern of observed SST trends over the broad Atlantic region (and that the radiatively forced response cannot sufficiently explain the observed SST trend pattern over the Atlantic).
In order to better quantify the spread due to internal variability, we also computed the pattern correlation between each ensemble member i and the EM of each experiment (denoted r(i,EM)). The range of r(i,EM)SOPACE
spans from 0.17 to 0.82, with r(obs,EM)SOPACE lying within the distribution. This indicates that the inclusion ofobserved internal SO cooling results in a more realistic pattern of simulated SST trends over the broad(20°N–40°S) Atlantic domain. Moreover, the observed SST trend pattern in this region resembles the simulated
response to SO cooling. The distribution of r(i,EM)TPACE, on the other hand, does not encompass the negativer(obs,EM)TPACE, further emphasizing the inability of the observed tropical eastern Pacific cooling to produce
the observed pattern of Atlantic SST trends (Figure 1h). While the distribution of r(i,EM)LENS also encompasses the observed pattern correlation, r(obs,EM)LENS is much lower than r(obs,EM)SOPACE. Furthermore, inthe LENS ensemble, the influence of the model’s internal SO SST trends on the Atlantic pattern correlations
cannot be isolated.

3.3. Ocean Mixed-Layer Heat Budget Analysis
What processes contribute to the remote Atlantic SST response in SOPACE? To answer this question, we
analyze the heat budget of the upper ocean mixed layer following Xie et al. (2010),
T c p H s  FSW  FLW  SH  LH  O,(1)twhere ρ is the density of ocean, cp is the specific heat of ocean, H is the ocean mixed-layer depth, and Ts
is the mixed-layer temperature. Hence the left-hand side (LHS) represents the mixed-layer heat storage
term. The right-hand side (RHS) consists of net surface shortwave (FSW) and longwave (FLW) fluxes, sensible(SH) and latent (LH) heat fluxes, and heat flux due to ocean dynamics (O). Our convention is that positive
values on the RHS warm Ts and vice versa. We compute the ensemble-mean trend (1979–2013) of eachterm in Equation 1 for SOPACE-internal in order to isolate the forced response to the internal componenttof observed SO SST trends. Maps of the trends in each quantity (denoted by the superscript t, e.g., FSW) areshown in Figure S4 for the tropical eastern Pacific and Atlantic domain. Heat storage trends are negligible(Figure S4b), indicating that the trends of the RHS terms of Equation 1 are in quasi-equilibrium. A similar
result was shown by Cook et al. (2018) based on ocean reanalysis products. This allows us to compute Ot as
a residual term (Equation 2).
The dependency of latent heat flux on SST allows us to rewrite Equation 1 as a diagnostic equation of the SST
trend Tst , following Jia and Wu (2013) and Hwang et al. (2017) (see derivation in supporting information):

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Figure 2. SST trend decomposition based on Equation 2 using monthly trends during 1979–2013 from SOPACEttttinternal over the tropical eastern Pacific and Atlantic: (a) net Tst, (b) TSW, (c) TLW
, (d) TSH
, (e) TOt , (f) TLH,W
with nearttsurface wind trends overlaid (vectors), (g) TLH,RH, and (h) TLH,ΔT. SST, sea surface temperatures.

t
t
t
t
t
t
Tst  TSW
 TLW
 TSH
 TOt  TLH,w
 TLH,RH
 TLH,ΔT.
(2)The RHS in Equation 2 represents the contributions to SST trend from FSW, FLW, SH, O, and LH. The latentt, near-surface
heat term can be further broken down into contributions from trends in near-surface wind TLH,wtt, and air-sea temperature gradient TLH,ΔT. The sum of the terms on the RHS of Equarelative humidity TLH,RH

tion 2 (Figure 2a) closely approximates the actual Tst (Figure 1f), validating our methodology. The major termst(due to increased cloud liquid water, Figure 2b), TOt
contributing to cooling trends in both basins include TSWt(due to strengthened trade winds, Figure 2f).
(largely due to Ekman advection, Figure 2e), and TLH,w

In the Atlantic, the contribution from FSW is centered at the coastal stratocumulus region between 10°S
and 15°S. A positive trend in liquid water path is expected as the SST cools and strengthens the cloud top
temperature inversion. The equatorial Atlantic cooling is dominated by the WES feedback, where there is astrengthening of the southeasterly trade winds, which enhances evaporative cooling. At 25°S, the strengthened winds cool the SST via Ekman advection (Figure S6). In the Eastern Pacific, FSW plays a more dominant role in cooling the SST than in the Atlantic, due to a weaker near-surface wind response and associated
LH. To summarize, the pattern of the negative Tst is caused by the sum of contributing terms instead of
dominated by a single heat flux trend.

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3.4. Antarctic Sea Ice Trends
Observational evidence suggests that multidecadal SST and wind variability over the SO plays an essential role in governing the pattern of Antarctic sea ice trends (Armour & Bitz, 2015; Blanchard-Wrigglesworth et al., 2020; Fan et al., 2014). Can SOPACE reproduce the observed trends in Antarctic sea ice? The
observed Antarctic sea ice extent (SIE) trend during this period is 0.23 × 106 km2 per decade, which is
drastically different from the radiatively forced SIE trend of −0.36 × 106 km2 per decade in LENS-EM and−0.30 × 106 km2 per decade in TPACE-EM (Figure 3a). In contrast, SOPACE-EM shows an Antarctic SIE
trend of 0.0077 × 106 km2 per decade, a rate that is an order of magnitude slower than in LENS-EM and
TPACE-EM. Furthermore, 40% of SOPACE ensemble members show a positive SIE trend, although none
is as large as observed (the largest trend in SOPACE is 0.031 × 106 km2 per decade; Figure 3a). The significant differences between Antarctic SIE trends in SOPACE-EM, LENS-EM, and TPACE-EM indicate thatobserved SO SST cooling plays an important role in influencing Antarctic SIE trend, though it is not the only
factor, and it may not be independent of SIE.
In addition to differences in the total SIE trend, there are significant differences in the patterns of sea ice
concentration (SIC) trends among observations and the model ensembles. The observed SIC shows positive trends over most of the SO, except for the West Antarctic coastline and north of the Weddell Sea(Figure 3c). LENS-EM shows a nearly zonally symmetric negative SIC trend pattern (Figure 3d), similar tothat in TPACE-EM although with weaker magnitude in the Atlantic and Pacific sectors (Figure 3e; see also
Figure 3g). On the other hand, SIC trends in SOPACE-EM show a mixture of positive and negative values,
with ice gain in the Bellingshausen Sea and ice loss in the Weddell Sea (Figure 3f). Indeed, the imposed SST
cooling in the SO leads to a marked Antarctic-wide expansion of sea ice relative to the radiatively forced
response (Figure 3h).
Although observed SO cooling produces an Antarctic-wide increase in SIC, when combined with theradiatively forced response, the SIC trend pattern in SOPACE-EM differs from observations. For example, SOPACE-EM produces sea ice gain in the Bellingshausen Sea and Drake Passage but ice loss in the
Weddell Sea and Ross Sea, opposite to observations (Figures 3f and 3c). At first glance, this seems to contradict the observational evidence that positive SIC trends are associated with SST cooling, and vice versa.
However, observations also suggest that near-surface winds can drive thermodynamic sea ice changes
via temperature advection. In general, anomalous northerly winds contribute to SST increase and sea
ice loss via warm-air advection, while anomalous southerlies correspond to opposite conditions (Fanet al., 2014). Indeed, north of the Weddell Sea and Antarctic Peninsula, ERA-5 shows northerly near-surface winds that are generally associated with warm-air advection and sea ice loss. In the Ross Sea, southerly winds are colocated with positive SIC trends (Figure 3c). In the Amundsen Sea, southerly wind
trends are associated with negative sea ice trends along the coast, consistent with the finding that ice
advection driven by local winds dominates the sea ice loss there (Holland & Kwok, 2012). The near-surface wind trends in SOPACE-EM display significant pattern differences from observations, especially in
the Amundsen Sea and the Ross Sea (Figure 3f), which partially explain the differences in regional SIC
trends between SOPACE-EM and observations. We note, however, that the highest pattern correlation
between SIC trends in any single member of SOPACE and observations is only 0.093. Furthermore, therange of r(i,EM)SOPACE (0.59–0.91) does not encompass r(obs,EM)SOPACE = −0.11 (Figure 3b). Thus, ourresults suggest that specifying the observed SO SST trends in CESM1 does not guarantee a match between
the observed and simulated response of Antarctic SIC trends, and exposes model biases in both the radiatively forced component as well as patterns of internally generated SIC trends in SOPACE. Processes
such as ocean-ice shelf interaction are absent in CESM1, potentially contributing to biases, e.g., in the
Weddell Sea (Park & Latif, 2019).

4. Summary and Discussion
We have examined the remote impact of observed SO SST cooling during 1979–2013 in CESM1 using a
20-member initial-condition ensemble in which SST anomalies over the SO are nudged to the observed
monthly evolution (SOPACE). The results are compared to the 20-member TPACE, where the same proto-

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Figure 3. (a) Box-and-whisker plot of Antarctic SIE trends for each model ensemble. Green stars show the EM values.
Orange lines show the median values. Gray horizontal line shows the observed value. (b) Same as (a) but for patterncorrelations of SIC trends with EM r(i, EM) over 50°S–80°S. Green triangles show r(obs, EM). Monthly SST (colors),
SIC (contours), and near-surface wind (vectors, m/s/decade) trends over 1979–2013 for (a) ERSSTv3b, (b) LENS-EM, (c)TPACE-EM, (d) SOPACE-EM, (e) TPACE-internal, and (f) SOPACE-internal. Blue (positive) and red (negative) contours
outline regions with SIC trend magnitudes greater than 0.5%/decade. SIE, sea ice extent; EM, ensemble mean; and SIC,
sea ice concentration.

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col was applied to SSTs in the tropical eastern Pacific, as well as the freely evolving 40-member LENS. The
forced response in each experiment is estimated by the EM.
While previous studies focused on the tropical eastern Pacific’s SST response to Southern Ocean cooling,
our results highlight a more significant response in the tropical Atlantic. In this region, the observed SO
cooling leads to a significant reduction in the radiatively forced warming trend, enhancing the north-south
gradient in tropical Atlantic SST trends. The muted tropical Atlantic warming south of the equator compared to the north in SOPACE-EM resembles the observed SST trend pattern, which neither TPACE-EM
nor LENS-EM captures. Indeed, TPACE-EM shows an opposite trend in the tropical Atlantic SST gradient.
Furthermore, it is more likely for SOPACE members to have realistic tropical South Atlantic SST trends thaneither LENS or TPACE members. The SOPACE ensemble also has a realistic depiction of the SO cooling’s
influence on the broader tropical Atlantic SST trend pattern, unlike TPACE. The tropical South Atlantic
response suggests an important role the SO SST plays on decadal time scales, consistent with previous findings (Cabré et al., 2017).
A mixed-layer heat budget analysis of the tropical South Atlantic region shows that increased low-levelcloud amount and enhanced trade winds contribute to reducing SSTs via shortwave radiative flux and the
WES-feedback mechanism, respectively. Ekman advection also contributes to SST cooling along the African coast where strong climatological SST gradients exist. The observed trends of low-level cloud amountare significantly positive over the southeastern Atlantic and Pacific during 1984–2009, consistent with ourfindings based on SOPACE-internal (Seethala et al., 2015). Multilinear regression analysis suggests thatstrengthened inversion stability is the dominant cloud-controlling factor for the observed positive low-cloudamount trends. The processes that contribute to the SO-induced cooling are consistent with the idealizedstudies (Hwang et al., 2017; Kang et al., 2019), although the WES feedback is much weaker in the tropicaleastern Pacific in SOPACE-internal.
Antarctic SIE has increased during 1979–2013, opposite to the radiatively forced decrease in LENS-EM.
While the SIE trend in TPACE-EM is similar to that in LENS-EM, SIE trend in SOPACE-EM is near zero,and 40% of SOPACE ensemble members show positive SIE trends although none as large as observed.
Furthermore, the observed SO cooling leads to Antarctic-wide positive SIC trends in SOPACE-internal, but
because of differences in near-surface wind trends, the spatial pattern of the simulated SIC trends differsfrom observations. The low pattern correlations between observed SIC trends and those in the individual members of SOPACE, and the negative pattern correlation between observed SIC trends and those in
SOPACE-EM (which lies outside the distribution of pattern correlations between individual members of
SOPACE and SOPACE-EM), indicates a likely bias in the amplitude and pattern of the model’s radiatively
forced SIC trends.
Acknowledgments
The authors thank the reviewers fortheir constructive comments, and Nan
Rosenbloom for helping with the pacemaker experimental setup.
X. Zhang is supported by an Advanced
Study Program postdoctoral fellowship from the National Center for
Atmospheric Research and the National
Science Foundation Office of Polar
Programs. This material is based uponwork supported by the National Center
for Atmospheric Research, which is amajor facility sponsored by the National
Science Foundation under cooperative
agreement 1852977. The authors would
like to acknowledge high-performancecomputing support from Cheyenne(https://doi.org/10.5065/D6RX99HX)
provided by NCAR’s Computationaland Information Systems Laboratory,
sponsored by the National Science
Foundation.

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Our results have implications for understanding the future trajectory of Antarctic sea ice and tropical South
Atlantic SSTs. Indeed, SSTs have recently warmed in the SO while Antarctic SIE reached record low values
in 2017 (Meehl et al., 2019; Parkinson, 2019). If this SO SST warming trend continues, the tropical South
Atlantic may experience accelerated warming due to the combined influence from SO SSTs and increasing
greenhouse gas concentrations. Future work will investigate the observed SO warming period from 1950 to1979, and its relevance for the predictability in the coming decades.

Data Availability Statement
Data are available at https://doi.org/10.5065/arn2-5j02.

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