RESEARCH LETTER
10.1029/2019GL085788
Key Points:• Two ice‐constraining approaches incoupled models (albedo andnudging) to study the climate
responses of future ice loss areevaluated• Given the same seasonal cycle of ice
loss, ice albedo reduction andice‐ﬂux nudging generate very
similar global climate responses• Ice albedo reduction underestimates
projected winter sea ice loss and thus
its impacts, with implications for
coordinated model experiments

Supporting Information:• Supporting Information S1
Correspondence to:
L. Sun,lantao.sun@colostate.edu

Citation:Sun, L., Deser, C., Tomas, R. A., &Alexander, M. (2020). Global coupledclimate response to polar sea ice loss:
Evaluating the effectiveness of different
ice‐constraining approaches.
Geophysical Research Letters, 47,e2019GL085788. https://doi.org/
10.1029/2019GL085788
Received 10 OCT 2019
Accepted 15 JAN 2020
Accepted article online 21 JAN 2020

Global Coupled Climate Response to Polar Sea Ice Loss:
Evaluating the Effectiveness of Different Ice‐Constraining
Approaches
Lantao Sun1

, Clara Deser2, Robert A. Tomas2, and Michael Alexander3

1

Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA, 2National Center for
Atmospheric Research, Boulder, CO, USA, 3NOAA Earth System Research Laboratory Physical Science Division, Boulder,
CO, USA

Abstract Coupled ocean‐atmosphere models have been utilized to investigate the global climate
response to polar sea ice loss using different approaches to constrain ice concentration and thickness. The
goal of this study is to compare two commonly used methods within a single model framework: ice albedo
reduction, which is energy conserving, and ice‐ﬂux nudging, which is not energy conserving. The two
approaches generate virtually identical equilibrium global climate responses to the same seasonal cycle ofsea ice loss. However, while ice‐ﬂux nudging is able to control the sea ice state year‐round, albedo reduction
is most effective during summer and lessens the effects of climate change in winter due to theunderestimation of sea ice loss. These evaluations have implications for the Polar Ampliﬁcation Model
Intercomparison Project (PAMIP), which proposes a set of coordinated coupled model experiments but
without a deﬁned protocol on how to constrain sea ice.

Plain Language Summary What does polar ampliﬁcation mean for global climate? This
question has been addressed by using different arbitrary methods to isolate melting of Arctic and/or
Antarctic sea ice in coupled ocean‐atmosphere modeling experiments. However, these experiments
sometimes obtained different answers. It is still unclear whether the divergent results arise from models'
structural differences or from the different methods used to melt sea ice. In this study, two commonly usedapproaches (albedo reduction and nudging) are applied to a single climate model to generate the same
seasonal cycle of ice loss and polar ampliﬁcation. Nearly identical responses are found, including slowing
down the thermohaline circulation in the Atlantic Ocean, weakened westerly winds over high latitudes,
increased precipitation over the west coast of North America associated with a deepened Aleutian Low, andtropical upper tropospheric warming. However, ice albedo reduction is found to be ineffective in winter,
unlike nudging, which can control sea ice year‐round. We therefore recommend nudging to be adopted for
the Polar Ampliﬁcation Model Intercomparison Project (PAMIP) coupled experiments.

1. Introduction©2020. American Geophysical Union.
All Rights Reserved.

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The warming Arctic and associated melting of the ice cap are among the most prominent features of anthropogenic climate change. Understanding the global consequences of observed and projected Arctic sea ice
loss is an area of active research with many outstanding questions. Empirical studies suggest a connection
between Arctic sea ice loss and cold air outbreaks over Eurasia (e.g., Cohen et al., 2013; Francis & Vavrus,
2012; Kug et al., 2015; Overland et al., 2011), although the direction of causality is under debate(Blackport et al., 2019; Blackport & Screen, 2019; Collow et al., 2018; Kretschmer et al., 2016; McCusker
et al., 2016; Mori et al., 2014; Mori et al., 2019; Ogawa et al., 2018; Peings, 2019; Perlwitz et al., 2015; Sunet al., 2016). Modeling studies targeting future sea ice loss (i.e., isolating the sea ice change seen in coupledmodel experiments with increasing greenhouse gases) have shown the importance of ocean‐atmosphere
coupling, not only for magnifying the extratropical atmospheric response but also for communicating the
response into the tropics and Southern Hemisphere (Deser et al., 2015, 2016; Blackport & Kushner, 2016,2017, 2018; Tomas et al., 2016; Oudar et al., 2017; Cvijanovic et al., 2017; Smith et al., 2017; Sun et al.,2018; Wang et al., 2018; Liu & Fedorov, 2019). While models agree on the general characteristics of the atmospheric and oceanic responses to Arctic sea ice loss, such as a weakening of the westerlies on the poleward
ﬂank of the jet stream and a reduction in the strength of the Atlantic meridional overturning circulation

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Geophysical Research Letters10.1029/2019GL085788(AMOC), regional climate responses are less consistent (Hay et al., 2018; Screen et al., 2018). The extent towhich these discrepancies arise from structural differences across models (i.e., model physics and resolution)or from differences in experimental design (location of sea ice loss and methodology) has not been
documented. Thus, a coordinated experimental protocol is needed to properly compare model simulations(Smith et al., 2019).
In the aforementioned coupled modeling studies, sea ice has been manipulated in different ways by modifying the surface energy budget. The two most common approaches are (1) albedo reduction, which includes
ice and/or snow parameter adjustments (Bitz et al., 2006; Blackport & Kushner, 2016, 2017; Blackport &Screen, 2019; Cvijanovic et al., 2017; Graversen & Wang, 2009; Liu et al., 2019; Liu & Fedorov, 2019;
Scinocca et al., 2009; Winton, 2008); and (2) “ghost ﬂux” (Deser et al., 2015; Oudar et al., 2017) or similar“ice‐ﬂux” (McCusker et al., 2017; Sun et al., 2018) nudging schemes, in which an additional surface heat ﬂux
term is added to the sea ice module (the term “ghost ﬂux” refers to the fact that this additional energy is invisible to other components of the model: Deser et al., 2015; Screen et al., 2018). Albedo reduction conservesenergy but is only effective during the sun‐lit portion of the year. By contrast, ghost ﬂux and ice‐ﬂux nudging
do not conserve energy but are effective in adjusting sea ice year‐round. Other methods have also been used
to melt sea ice, such as sea surface temperature (SST) relaxation or abrupt CO2 quadrupling at high latitudes(Stuecker et al., 2018; Yoshimori et al., 2018). However, these approaches introduce additional forcing
perturbations besides sea ice loss and thus target the response to high‐latitude warming in general insteadof sea ice loss in particular.
It is still unclear the extent to which the coupled climate response to sea ice loss is sensitive to these different
approaches, since there has been no attempt to compare different approaches within a single modeling framework. Ghost ﬂux and ice‐ﬂux nudging, for instance, have been criticized for not conserving energy. While
this ﬂux is only prescribed to the ice model (Deser et al., 2015; Sun et al., 2018), the artiﬁcial ﬂux could exert
an additional inﬂuence on the ocean and/or atmosphere, thus making it difﬁcult to determine whether the
response originates solely from sea ice change (Cvijanovic et al., 2017). However, consistent extratropicalatmospheric responses to future Arctic sea ice loss have been found for both ghost ﬂux and albedo reduction
methodologies (Blackport & Kushner, 2016, 2017; Deser et al., 2015; Screen et al., 2018), implying energy
conservation might not be that important. Nevertheless, one cannot rule out the possibility that there could
still be some sensitivity to the method of sea ice melting, especially in remote regions such as the tropics. For
example, the albedo reduction method (Liu & Fedorov, 2019) and the ghost/ice‐ﬂux nudging method(Deser et al., 2015; Tomas et al., 2016; Wang et al., 2018; Sun et al., 2018) yield somewhat different patternsof tropical SST and precipitation responses. Without further investigations, it is not obvious whether thedifferent responses are due to the ice‐constraining approach or other factors (e.g., model physics, resolution,and sea ice forcing).
A coordinated set of coupled ocean‐atmosphere model experiments has been proposed as part of the Polar
Ampliﬁcation Model Intercomparison Project (PAMIP) to investigate the global climate response to Arctic
and Antarctic sea ice loss on decadal and centennial timescales (Smith et al., 2019). In these experiments,
polar sea ice will be artiﬁcially decreased without altering greenhouse gas (GHG) concentrations so as to differentiate its effect on the global climate system from other aspects of anthropogenic climate change.
However, there is as yet no deﬁned protocol on how to constrain sea ice in models, and it is still unclear
which approach(es) should be used in PAMIP.
The goal of this study is to compare two approaches for constraining sea ice using a single coupled model. To
that end, we present a pair of 360‐year climate model experiments with approximately the same Arctic andAntarctic sea ice loss but achieved through albedo reduction and ice‐ﬂux nudging, respectively. We examine
the sensitivity of the global atmospheric and oceanic responses to these two methods and evaluate the effectiveness of the different ice‐constraining approaches for achieving the desired sea ice state. Lastly, based on
the ﬁndings, we provide recommendations on experimental protocol for PAMIP.

2. Model Experiment Design
We build upon an existing set of constrained sea ice simulations (Deser et al., 2015) with the fully
coupled Community Climate System Model Version 4 (CCSM4; Gent et al., 2011). All experiments are
conducted for 360 years with the ﬁrst 100 years discarded as spin‐up, and the radiative forcings are
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Geophysical Research Letters10.1029/2019GL085788

ﬁxed at the year 2000. In the control experiment (hereafter “CONTROL”), Arctic and Antarctic sea ice
concentrations and thicknesses approximate late 20th century (1980–1999) conditions simulated by aﬁve‐member ensemble of CCSM4 historical simulations (There is slight constraining on the Arctic sea
ice; see Deser et al., 2015. Whether there is ice constraining or not in the control run should not substantially affect our conclusion, because the climate response is calculated by subtracting the same climatology). In the albedo experiment (hereafter “ALBEDO”), ice and snow albedo values are lowered to achievea September Arctic sea ice extent that is close to the 2080–2099 climatology of a ﬁve‐member ensemble of
CCSM4 Representative Concentration Pathway 8.5 (RCP8.5) simulations. This is done by justifying thetuning parameters for snow (r_snw = −10.0; rsnw_melt_in = 3,000) and ice (r_ice = −4.0), similar toBlackport and Kushner (2016). It is important to note that the sea ice extent in ALBEDO greatly underestimates the sea ice loss in RCP8.5 during the winter months, a point we shall return to in section 3.
Finally, in the ghost ﬂux simulation (denoted “GHOST_F”), an additional seasonally varying downward
energy ﬂux is added to the sea ice module in the Arctic to achieve a seasonal cycle of ice concentrationand thickness that is close to the 2080–2099 climatology of the ﬁve‐member ensemble of CCSM4
RCP8.5 simulations.
We also conduct a new 360‐year simulation (denoted “NUDGE”) in which we use ice ﬂux nudging to achieve
approximately the same seasonal cycle of sea ice concentration and thickness as in ALBEDO. In this way, we
can directly compare the global climate responses in the two experiments, since their sea ice loss is nearly
identical. The ice ﬂux nudging scheme developed for CCSM4 does not directly nudge the sea ice concentration as in Smith et al. (2017). Instead, it constrains ice volume by modifying the surface energy ﬂux within
the ice model (Knutson, 2003; McCusker et al., 2017; Sun et al., 2018). At each time step, a time‐varying ﬂux
is calculated by
Qflux ¼

Lice Dice ðCice hice −Cice t hice t Þ
;
τice

(1)

where Lice is the latent heat of fusion (3.34 × 105 J kg−1), Dice is the density of ice (905 kg m−3), Cice is the sea
ice concentration (%), hice is the sea ice thickness (m), Cice_t is the target sea ice concentration, hice_t is thetarget sea ice thickness, and τice is the relaxation times. This ﬂux is applied to the bottom of the ice, thereby
adjusting the ice concentration and thickness toward the target values. While energy is not conserved, thewater mass budget is conserved, which is desirable because the freshwater ﬂux is important for the
AMOC response (Liu et al., 2019).
A crucial factor for the ice ﬂux nudging scheme is the selection of nudging timescale. When a value of 60days is used as recommended by PAMIP (Smith et al., 2019), the sea ice area in both hemispheres is overestimated compared to the target ALBEDO simulation (supporting information Figure S1). To improveupon this, three additional simulations were run with nudging timescales of 1, 5, and 10 days. For both
the Arctic and Antarctic, the overestimation of sea ice area lessens when the nudging timescale decreases(Figure S2) and eventually changes to an underestimation for 1‐day nudging. In addition, there is slightdownward drift in sea ice area over the 360‐year simulation, especially in the Antarctic, and this drift
appears for both the 1‐ and 10‐day nudging timescales. This is possibly due to the feedback of extratropical
SST warming as a result of sea ice loss. Based on these sensitivity tests, we chose 10 days for the damping
timescale. Similar results are found when the sensitivity tests are evaluated using on sea ice volume insteadof sea ice area (not shown).
The September and March sea ice concentrations and sea ice area for the CONTROL, ALBEDO, and
NUDGE simulations are shown in Figure 1, and the corresponding sea ice thickness distributions are shownin Figure S3. There is a very close match between ALBEDO and NUDGE sea ice conditions when averaged
over years 101–360: Their annual sea ice area difference is less than 0.1 million km2, which is less than 5% of
the polar sea ice loss relative to CONTROL.
The climate response to polar sea ice loss in each experiment is deﬁned relative to the control simulation:

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ΔALBEDO ¼ ALBEDO–CONTROL;(2)

ΔNUDGE ¼ NUDGE–CONTROL;(3)

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Geophysical Research Letters10.1029/2019GL085788

Figure 1. (a) March and September Arctic and Antarctic sea ice concentrations (%) in the CCSM4 CONTROL, ALBEDO, and NUDGE experiments. (b) Monthly
62Arctic (left) and Antarctic (right) sea ice area (10 km ) in the CONTROL (solid black line), ALBEDO (dashed black line), and NUDGE experiments (nudging
timescale of 10 days; dashed blue line). The months March–June are repeated for clarity.

ΔGHOST F ¼ GHOST F–CONTROL:(4)

The March and September polar sea ice loss in ΔALBEDO, ΔNUDGE, and their difference are shown in
Figure S4.
The statistical signiﬁcance is evaluated using a two‐sided Student's t test, and the number of degrees of freedom is calculated by taking into account the serial correlation (Zwiers & von Storch, 1995).

3. Results
The annual‐mean global SST responses in ΔALBEDO (left), ΔNUDGE (middle), and their difference (right)
are shown in Figures 2a–2c. Overall, these two experiments generate very similar responses, with a patterncorrelation above 0.99 and root‐mean‐square (RMS) difference of 0.04 °C. SSTs increase everywhere
except directly east of northern Japan and along the northern ﬂank of the Gulf Stream, with maximum
warming (1–2 °C) in regions of sea ice loss. The tropical oceans warm by 0.4–0.6 °C, with a maximum
along the equator especially in central and eastern Paciﬁc. The magnitudes of AMOC weakening are also
comparable (Figure S5), and the structures of the AMOC response are almost identical for two
approaches (Figure S6).
The precipitation response is also very similar between the two experiments (Figures 2d and 2e; pattern correlation of 0.88 and RMS difference of 0.06 mm day−1). Diminished sea ice enhances precipitation locally,
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Geophysical Research Letters10.1029/2019GL085788−1

Figure 2. Annual (a, b, c) SST (°C), (d, e, f) precipitation (mm day ), and (g, h, i) sea‐level pressure (hPa) responses in (left) ΔALBEDO, (middle) ΔNUDGE, and−1
their difference (right). The red contour lines in the middle panel show the climatological precipitation (6, 8, and 10 mm day isopleths) for context. Stippling
denotes the 95% conﬁdence level based on a two‐sided Student's t test.

with slightly larger magnitude in the Antarctic compared to the Arctic. In the tropical Paciﬁc, there are two
zonally oriented bands of enhanced precipitation on either side of the equator across much of the basin,
ﬂanked on their poleward sides by negative precipitation anomalies. This implies an equatorward shift
and intensiﬁcation of the climatological ITCZ (contours). Precipitation also increases near the equator
over the Atlantic. There are some differences in the small‐scale features and magnitudes of the tropicalprecipitation response in ΔALBEDO and ΔNUDGE, but these are mostly statistically insigniﬁcant(Figure 2f). When zonally averaged, the responses are nearly identical at each latitude (not shown).
The sea‐level pressure responses are also very similar in the two experiments (Figures 2g and 2h; pattern correlation of 0.97 and RMS difference of 0.08 hPa), with negative anomalies over eastern North America andpositive anomalies over northern Eurasia, which is a robust feature across models (Hay et al., 2018; Screen
et al., 2018). There is also a pronounced deepening of the Aleutian Low, and the associated counterclockwise
circulation transports warm moist air on its eastern side leading to an increase in precipitation along thewestern United States and Canada (Figures 2d and 2e). This circulation change is largely due to teleconnections driven by tropical SST warming (Tomas et al., 2016) but may also partly result from the direct sea ice
forcing (e.g., Deser et al., 2016; Sun et al., 2015) and extratropical SST warming (Blackport & Kushner, 2018).
Sea‐level pressure increases over Antarctica and decreases in the Southern Hemisphere midlatitudes, inagreement with other similar studies (e.g., Ayres & Screen, 2019; England et al., 2018).
Figure 3 shows the annual zonal‐mean temperature and zonal wind responses in ΔALBEDO (left),
ΔNUDGE (middle), and their difference (right) as a function of pressure and latitude, superimposed on
the climatology (contours). Again, the features are broadly similar in the two experiments, and thedifferences are small and statistically insigniﬁcant. The temperature response is nearly symmetric about
the equator, with strong polar ampliﬁcation near the surface and in the tropical upper troposphere. It
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Geophysical Research Letters10.1029/2019GL085788−1

Figure 3. Annual zonal‐mean (a, b, c) air temperature (°C) and (d, e, f) zonal wind (m s ) responses in (left) ΔALBEDO, (middle) ΔNUDGE, and their difference−1(right) as a function of pressure and latitude. The black contour lines denote the climatological values with an interval of 10°C for temperature and 10 m s forzonal wind. Stippling denotes the 95% conﬁdence level based on a two‐sided Student's t test.

resembles the overall response to an increase in GHG, but with much‐reduced amplitude: hence, the term“mini‐global warming” (Deser et al., 2015). The extratropical stratosphere cools slightly (similar to Sun
et al., 2018) with nearly the same magnitude in both ΔALBEDO and ΔNUDGE. Consistent with thetemperature responses and thermal wind considerations, the zonal wind is reduced in the latitude bands
45–80°N and 40–70°S, with maxima at 60°N/S near 300 hPa, and increased in the subtropical lower
stratosphere in both hemispheres.
We also examine the seasonality by averaging the responses over extended boreal winter (October–March)and summer (April–September) seasons, as shown in Figures S7–S10. Deepening of the Aleutian Low andincreased precipitation over the western coast of North America occur mainly in the boreal winter.
Weakening of the westerly winds near 60°N and 60°S is largest in the corresponding winter season of eachhemisphere, consistent with seasonality of the polar warming. But more importantly, the differences
between ΔALBEDO and ΔNUDGE are small and mostly statistically insigniﬁcant in either season.
As mentioned above, our ALBEDO simulations (and those of Blackport & Kushner, 2016, 2017) underestimate winter sea ice loss relative to RCP8.5. This is highlighted in Figure 4a, which compares the monthly sea
ice area loss in ΔGHOST_F (red bars), in which the sea ice is nudged to the 2080–2099 state of the CCSM4
RCP8.5 simulations, with ΔALBEDO (blue bars) and ΔNUDGE (orange bars). The seasonal cycles of ice
area loss are very similar in ΔALBEDO and ΔNUDGE, by design. However, they underestimate ice area loss
in RCP8.5 in all months except September. In boreal winter (e.g., November, December, and January), theloss of Arctic ice is underestimated by as much as ~50%. While matching September sea ice as done here
is arbitrary and one may choose larger albedo reduction to better match other seasons, the ineffectivenessof constraining sea ice in winter appears to be a common issue in previous studies using thealbedo approach.
The underestimation of sea ice loss in ΔALBEDO compared to RCP8.5 can substantially affect the magnitudes of the local and remote responses. Figure 4b shows the monthly Arctic surface energy ﬂux response(sum of sensible, latent, and longwave ﬂuxes averaged over climatological March sea ice extent in
CONTROL), where positive values denote upward ﬂux anomalies. The energy entering the atmosphere issubstantially underestimated in ΔALBEDO and ΔNUDGE compared to ΔGHOST_F, especially in winterwhen the peak ﬂux response occurs. As a result, warming of the Arctic lower troposphere is substantially lessin ΔALBEDO and ΔNUDGE compared to ΔGHOST_F (not shown, but see Deser et al., 2015 for
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Geophysical Research Letters10.1029/2019GL085788

temperature response in ΔGHOST_F). It is therefore not surprising thatthe decrease in zonal‐mean zonal wind at 700 hPa during winter(October–March) over the latitude band 50–70°N is substantially smaller
in ΔALBEDO and ΔNUDGE compared to ΔGHOST_F (Figure 4c). The
magnitude of AMOC weakening is also underestimated in ΔALBEDO
experiments (Figure S5).

4. Implications for Coordinated Experiments
in PAMIP

62

Although PAMIP involves a coordinated set of coupled modeling experiments to investigate the response to sea ice loss, there are no formal
recommendations on a common experimental protocol to be used for constraining sea ice. To ﬁll this void, we have compared two methodologies
using long (360 year) simulations with CCSM4, taking care to ensure thatthe sea ice loss in both hemispheres is nearly identical in the two
approaches. Our results reveal that the two techniques result in virtually
the same global atmospheric and oceanic responses, including (1) polar
ampliﬁcation and locally enhanced precipitation, (2) weakening of the
AMOC, (3) equatorward displacement of the Paciﬁc ITCZ, (4) deepeningof the Aleutian Low and increased precipitation over the western UnitedStates and Canada, (5) “mini‐global warming” signature in tropospherictemperatures, and (6) zonal wind deceleration near 60°N/S. All of thesefeatures are consistent with most previous coupled modeling studies on
this topic (Deser et al., 2015, 2016; Tomas et al., 2016; Blackport &Kushner, 2016, 2017; Oudar et al., 2017; Wang et al., 2017; Sun et al.,2018; Hay et al., 2018; Blackport & Screen, 2019). Note that our ﬁndings
are distinct from those of Cvijanovic et al. (2017) who found increased
geopotential height over the North Paciﬁc and decreased precipitation
over the west coast of the United States in response to Arctic sea ice loss.
They speculated that the disparity between their results and previous studies is due to the method used to constrain sea ice. Our results suggest thatthe disparity is more likely due to the inadequacy of the slab‐ocean model
conﬁguration used in Cvijanovic et al. (2017) rather than the ice‐
constraining methodology.

Figure 4. (a) Monthly Arctic sea ice area anomaly (10 km ; note inverted
scale) in ΔGHOST_F (red), ΔALBEDO (blue), and ΔNUDGE (orange)
experiments. The months March–June are repeated for clarity. Error bars
denote +/− one standard deviation. (b) Net surface heat ﬂux response (sumof the turbulent and longwave radiative ﬂux components, positive upward;−2
W m ) to polar sea ice loss in ΔGHOST_F (red), ΔALBEDO (blue), andΔNUDGE (orange). In (a) and (b), the months March–June are repeated forclarity. (c) The zonal‐mean zonal wind response at 700 hPa averaged over−1
October–March (m s ) in ΔGHOST_F (red), ΔALBEDO (blue), andΔNUDGE (orange). Note that the seasonal cycle of Arctic sea ice area
anomaly in ΔGhost_F is very similar to the ice loss in 2080–2099 relative to
1980–1999 in CCSM4 under historical and RCP8.5 radiative forcing.

Despite the consistency in response patterns, the ALBEDO experimenthas been found to underestimate winter sea ice loss, and thus its local
and remote impacts. This leads us to make some suggestions based on
the effectiveness of the ice‐constraining approaches to be used in the
PAMIP coupled experiments. We acknowledge that all sea ice‐
constraining approaches are idealized, since sea ice loss is just one component of the response to increased GHG and is always tied to other changes.
As a result, all sea ice‐constraining approaches are imperfect (Smith et al.,2019). For example, in the ice‐ﬂux nudging experiments, sea ice has too
little variability at interannual timescales compared to the albedo method(Figure S11) as does the associated interannual SST variability(Figure S12). In addition, the ice‐ﬂux nudging appears to have difﬁculty
matching the sea ice in winter when the target sea ice becomes very thin (e.g., in the highly sensitive modelsuch as Geophysical Fluid Dynamics Laboratory Coupled Model Version 3; Sun et al., 2018). Nevertheless,for the purpose of model intercomparison, a recommended experimental protocol will beneﬁt the PAMIP
community and enable better coordination among the model institutes performing the experiments.
Based on our ﬁndings, we recommend using the ghost ﬂux or ice‐ﬂux nudging approaches in the PAMIP
coupled experiments. These approaches are effective at constraining polar sea ice year‐round and do not

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appear to produce spurious responses. They can be compared directly to the atmosphere‐only modelexperiments to isolate the role of ocean‐atmosphere coupling (e.g., Deser et al., 2015, 2016) and can be
more closely compared with other PAMIP coupled experiments because the ice forcing is approximately
identical. We also emphasize that to properly constrain polar sea ice, both concentration and thickness
need to be considered, and the global freshwater water budget needs to be conserved. These are vitalfor understanding the ocean response (e.g., AMOC and associated SST and sea surface salinity) to pastand future sea ice loss.
If the model infrastructure is too complex to adopt ice nudging, albedo reduction can also provide useful
information. However, one must bear in mind that this approach underestimates winter sea ice loss, andthus its local and remote effects especially in winter (which are driven by the concurrent winter ice loss:
Sun et al., 2015; Blackport & Screen, 2019). A further consequence is that comparison with other PAMIP
experiments will be more qualitative in nature. However, the pattern scaling approach developed byBlackport and Kushner (2017) and Hay et al. (2018) might be able to account for some of the sea ice differences, but with potential limitations due to its assumption of linearity.
Acknowledgments
We appreciate the constructive
suggestions of the two anonymous
reviewers. We also appreciate valuable
discussions with Dave Bailey andMarika Holland (NCAR) in developing
the CCSM4 ice‐ﬂux nudging technique,and suggestions from Jim Hurrell(CSU) and Matt Newman (CIRES/NOAA ESRL) and comments fromparticipants at the PAMIP workshop in
June 2019. LS is supported by Jim
Hurrell's presidential chair startup
funding at CSU. This project is partly
supported by the NOAA Climate
Program Ofﬁce Earth System Modelingprogram. We would like toacknowledge high‐performancecomputing support from Cheyenne
provided by NCAR's Computationaland Information Systems Laboratory,
sponsored by the National Science
Foundation. NCAR is sponsored by the
National Science Foundation. Modeldata are available at https://zenodo.
org/record/3479888#.XZ_PP‐dKhBw.

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