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In this study, we use the National Center for Atmospheric Research Community Earth System Model to investigate the contribution of sea ice and land snow to the climate sensitivity in response to increased atmospheric carbon dioxide content. We focus on the overall effect arising from the presence or absence of sea ice and/or land snow. We analyze our results in terms of the radiative forcing and climate feedback parameter. We find that the presence of sea ice and land snow decreases the climate feedback parameter (and thus increases climate sensitivity). Adjusted radiative forcing from added carbon dioxide is comparatively less sensitive to the presence of sea ice or land snow. The effect of sea ice on the climate feedback parameter decreases as sea ice cover diminishes at higher CO2 concentration. However, the influence of both sea ice and land snow on the climate feedback parameter remains substantial under the CO2 concentration range considered here (to eight times preindustrial CO2 content). Approximately, one quarter of the effect of sea ice and land snow on the climate feedback parameter is a consequence of the effect of these components on longwave feedback that is mainly associated with cloud change. Polar warming in response to added CO2 is approximately doubled by the presence of sea ice and land snow. Relative to the case in which sea ice and land snow are absent in the model, in response to increased CO2 concentrations, the presence of sea ice and land snow results in an increase in global mean warming by over 40%.
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Estimating Contributions of Sea Ice and Land Snow
to Climate Feedback
Lei Duan
1
, Long Cao
1
, and Ken Caldeira
2
1
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, China,
2
Department of
Global Ecology, Carnegie Institution, Stanford, CA, USA
Abstract In this study, we use the National Center for Atmospheric Research Community Earth System
Model to investigate the contribution of sea ice and land snow to the climate sensitivity in response to
increased atmospheric carbon dioxide content. We focus on the overall effect arising from the presence or
absence of sea ice and/or land snow. We analyze our results in terms of the radiative forcing and climate
feedback parameter. We nd that the presence of sea ice and land snow decreases the climate feedback
parameter (and thus increases climate sensitivity). Adjusted radiative forcing from added carbon dioxide is
comparatively less sensitive to the presence of sea ice or land snow. The effect of sea ice on the climate
feedback parameter decreases as sea ice cover diminishes at higher CO
2
concentration. However, the
inuence of both sea ice and land snow on the climate feedback parameter remains substantial under the
CO
2
concentration range considered here (to eight times preindustrial CO
2
content). Approximately, one
quarter of the effect of sea ice and land snow on the climate feedback parameter is a consequence of the effect
of these components on longwave feedback that is mainly associated with cloud change. Polar warming
in response to added CO
2
is approximately doubled by the presence of sea ice and land snow. Relative to the
case in which sea ice and land snow are absent in the model, in response to increased CO
2
concentrations,
the presence of sea ice and land snow results in an increase in global mean warming by over 40%.
Plain Language Summary Sea ice and land snow are two crucial components that affect the
climate response to external forcings. Feedbacks between ice/snow and climate change cause amplied
surface warming in high latitudes. In this study, we use a climate model to estimate the contribution of sea
ice and land snow to climate change in response to increased CO
2
concentrations. We compare the climate
response to increased CO
2
between the simulations with sea ice and/or land snow and the simulations
without them. We show that the existence of sea ice and land snow substantially amplies the global
temperature response to increased CO
2
with sea ice having a stronger effect than land snow. Under higher
CO
2
levels, the effect of sea ice diminishes more rapidly than does the effect of land snow. About one
quarter of the total climate feedback from sea ice and land snow is associated with the change in longwave
radiation. Also we show that the effect of sea ice and land snow on the sensitivity of topofatmosphere
net energy ux to the global mean temperature change is approximately additive.
1. Introduction
Observations and modeling studies have recognized that the global warming from increased atmospheric
CO
2
concentrations is amplied in polar regions (e.g., Bekryaev et al., 2010; Bromwich et al., 2013;
Chapman & Walsh, 2007; Manabe et al., 1991; Manabe & Stouffer, 1980; Meehl et al., 2007; Pithan &
Mauritsen, 2014; Serreze & Francis, 2006; Solomon, 2006). Sea ice and land snow are two important
components contributing to substantial warming in middle and high latitudes, primarily from their surface
albedo feedback and the insulation feedback (Caldeira & Cvijanovic, 2014; Cess et al., 1991; Hall, 2004;
Holland et al., 2001; Holland & Bitz, 2003; Screen & Simmonds, 2010; Serreze et al., 2009; Stroeve et al.,
2012; Vavrus, 2007; Winton, 2006; Zhang, 2005). For example, sea ice and land snow retreats expose less
reective land and ocean surface, increasing the absorption of solar insolation. The presence of sea ice
and land snow inhibits the energy exchange between atmosphere and surface. Other factors also contribute
to highlatitude amplication of the temperature change, including the Planck feedback, changes in water
vapor content, atmospheric and oceanic heat transport, and cloud fraction in response to the imposed
forcing and the seaice and land snow decline (e.g., Kay et al., 2012; Mahlstein & Knutti, 2011; Overland
& Wang, 2010; Södergren et al., 2018; Solomon, 2006; Taylor et al., 2013).
DUAN ET AL. 199
RESEARCH ARTICLE
10.1029/2018JD029093
Key Points:
We use NCAR CESM to investigate
the overall contribution of sea ice
and land snow to climate feedback
to increased carbon dioxide content
Sea ice and land snow lead to
substantial increases in polar
warming to increased CO
2
content
with sea ice playing a more
important role
The presence of sea ice and land
snow decreases the climate feedback
parameter with about one quarter of
the effect from longwave feedback
Supporting Information:
Supporting Information S1
Correspondence to:
L. Cao,
longcao@zju.edu.cn
Citation:
Duan, L., Cao, L., & Caldeira, K. (2019).
Estimating contributions of sea ice and
land snow to climate feedback. Journal
of Geophysical Research: Atmospheres,
124, 199208. https://doi.org/10.1029/
2018JD029093
Received 31 MAY 2018
Accepted 14 DEC 2018
Accepted article online 7 JAN 2019
Published online 15 JAN 2019
©2019. American Geophysical Union.
All Rights Reserved.
The presence of sea ice and land snow exerts great impact on local climate, but their effects are not conned
to highlatitude regions. The absence of sea ice and land snow has been shown to produce warming in tro-
pical regions (Cvijanovic & Caldeira, 2015; Vavrus, 2007). At the same time, the cooling effect induced by the
presence of additional high latitude ice could alter the location of the Intertropical Convergence Zone and
inuence the tropical precipitation (Broccoli et al., 2006; Chiang & Bitz, 2005). Previous studies have con-
cluded that changes in sea ice and land snow are likely to affect extreme weather events in the midlatitudes
through their inuence on largescale atmospheric circulation including storm tracks, jet streams, and pla-
netary waves (e.g., Francis et al., 2009; Francis & Vavrus, 2012; Liu et al., 2012; Peings & Magnusdottir,
2014). However, other studies (Barnes et al., 2014; Cohen et al., 2014; Screen & Simmonds, 2013; Wallace
et al., 2014) argued that the robustness of such correspondence remains uncertain.
The relative importance of sea ice and land snow on the climate feedback parameter (Gregory et al., 2004),
which is closely related to the topofatmosphere (TOA) net radiative ux (in the absence of any tempera-
ture change) divided by the equilibrium global mean temperature change, has been quantied in previous
modeling works. For instance, Ingram et al. (1989) found that under a doubling of atmospheric CO
2
, the
absence of sea ice feedback increases the climate feedback parameter from 0.77 to 0.95 W·m
2
·K
1
.
Similarly, Rind et al. (1998) found that under a doubling of atmospheric CO
2
, the climate feedback para-
meter increases from a default value of 0.95 to 1.51 W·m
2
·K
1
if sea ice is not allowed to change with
warming. Using the slabocean conguration of CESM, Caldeira and Cvijanovic (2014) found that under
various CO
2
levels, on average the climate feedback parameter increased by 0.26 W·m
2
·K
1
(ranging from
0.22 to 0.3 W·m
2
·K
1
) in the absence of sea ice feedback. In their simulations, about one third of the
change in the climate feedback parameter caused by the presence of seaice feedbacks is a consequence
of the change in longwave radiation.
The effect of snow on climate feedback was investigated in a number of studies. Vavrus (2007) found that
removing the entire snow cover on land resulted in 0.57 W m
2
of radiative forcing, resulting in 0.84 K of
warming. An earlier model intercomparison project (Cess et al., 1991) of 17 general circulation models
examined the effect of snow retreat under a prescribed 4 K sea surface temperature warming and found that
changes in the cloud distribution and longwave feedback due to snow change contribute to the net snow
feedback in addition to surface albedo change. Qu and Hall (2014) analyzed the shortwave snow albedo feed-
back using results from 25 climate models that participated in the Coupled Model Intercomparison Project
Phase 5. Their results suggested that the removal of the snowalbedo feedback would increase the climate
feedback parameter by about 0.08 W·m
2
·K
1
(ranging from 0.03 to 0.16 W·m
2
·K
1
).
The combined impact of sea ice and land snow on the climate response is also examined in a number of pre-
vious works. For example, Winton (2006) analyzed simulation results from 12 Earth system models with 1%/
year increase in CO
2
and found that the surface albedo feedback associated with sea ice and snow decreases
the climate feedback parameter by about 0.3 W·m
2
·K
1
. Holland et al. (2001) found about 17% and
Graversen and Wang (2009) found that about 15% of the global mean warming induced by a doubling of
atmospheric CO
2
is attributable to sea ice and land snow albedo feedbacks. Taylor et al. (2013) demonstrated
that the surface albedo feedback is the largest contributor to the extratropical warming in both hemispheres,
while Flanner et al. (2011) found that the Northern Hemisphere surface albedo feedback decreases the cli-
mate feedback parameter by 0.33 W·m
2
·K
1
. Crook and Forster (2014) analyzed satellite data and sug-
gested that current climate models underestimate the surface albedo feedback in the Northern
Hemisphere extratropical regions.
Table 1
ModelSimulated Global Mean Changes in Surface Air Temperature for None,”“Ice,”“Snow,and BothSimulations Relative to the Corresponding 1 × CO
2
Case
With the Same Sea Ice and Snow Treatment
1×CO
2
2×CO
2
1×CO
2
4×CO
2
1×CO
2
6×CO
2
1×CO
2
8×CO
2
1×CO
2
Temperature (K) None 290.10 ± 0.01 1.94 ± 0.02 4.22 ± 0.02 5.86 ± 0.03 7.14 ± 0.02
Ice 288.89 ± 0.01 2.51 ± 0.02 5.14 ± 0.02 6.93 ± 0.02 8.35 ± 0.02
Snow 289.13 ± 0.01 2.23 ± 0.02 4.80 ± 0.02 6.63 ± 0.02 8.10 ± 0.02
Both 287.02 ± 0.01 3.17 ± 0.02 6.46 ± 0.03 8.53 ± 0.03 10.15 ± 0.02
Note. All results are calculated from the last 60year simulations of 100year slabocean simulations. Uncertainty bars represent one standard error of the mean.
10.1029/2018JD029093
Journal of Geophysical Research: Atmospheres
DUAN ET AL. 200
In this study, we use the Community Earth System Model (CESM) to per-
form a series of slabocean simulations with different sea ice and land
snow treatments to investigate the contribution of sea ice and land snow
to the climate feedback parameter and its shortwave and longwave com-
ponents. We compare the results from simulations with the presence of
sea ice and land snow separately and jointly to that with no existence of
sea ice and/or land snow. In contrast to previous studies, we analyze the
change in the climate feedback parameter associated with a complete loss
of sea ice and land snow, including changes in the energy ux, cloud, and
water vapor due to the change in sea ice and land snow. We also investi-
gate the contribution of sea ice and land snow at various CO
2
levels.
2. Methods
The model used in this study is the National Center for Atmospheric
Research (NCAR) Community Earth System Model version 1.2
(CESM1.2). In our simulations, the atmosphere is represented by the
Community Atmosphere Model version 4 (Neale et al., 2010). The
Community Atmosphere Model 4 uses a nite volume dynamic core with a hybrid sigmapressure vertical
coordinate. The horizontal resolution for the atmosphere is 1.9° latitude by 2.5° longitude with 26 vertical
levels. The land is represented by the Community Land Model version 4 (Oleson et al., 2010), which adopts
the same horizontal resolution as the Community Atmosphere Model 4 but with 15 vertical layers and up
to 5 snow/ice layers. Land snow in the Community Land Model 4 is represented by a series of state variables,
including mass of water (w
liq
), mass of ice (w
ice
), layer thickness (Δz), and layer temperature (T). Snow can
also exist on land surface without being represented by explicit snow layers when its thickness is less than
aspecied minimumdepth of0.01 m. The ocean component isrepresented by a mixed ocean layerwith depth
varying between 10 and 200 m and a prescribed ocean heat transport (Qux). The slab ocean calculates sea
surface temperature changes by considering the energy imbalance at airsea interface and the prescribed heat
ux at the base of the mixed layer. The current version of the slabocean conguration simulates sea ice
change by including an active sea ice component with dynamic and thermodynamic processes (Hunke
et al., 2010). The sea ice component calculates the ice growth rates, ice package transport, and interactions
between sea ice and external couplers. Using an earlier version of
CESM, the conguration with a slabocean component was found to pro-
duce an equilibrium atmospheric response that is similar to that produced
by the conguration with a full ocean (Bitz et al., 2012).
To assess the overall effect of sea ice and land snow on the climate feed-
back parameter and climate sensitivity, we rst spin up four types of simu-
lations under preindustrial CO
2
level (1 × CO
2
, 284.7 ppm). The CESM
slabocean conguration reaches equilibrium within about 30 years, and
we perform each simulation for 100 years. In these simulations, sea ice
and land snow components are either included or disabled. These simula-
tions are denoted as follows: (1) Both: both sea ice and land snow com-
ponents are included as the default in CESM; (2) None: neither sea ice
nor land snow is included; (3) Ice: the sea ice and snow components
are included, but the land snow component is disabled; (4) Snow: the
land snow component is included, but the sea ice component is disabled.
In this study, we remove the sea ice component by setting the freezing
point of water in the ocean model to absolute zero. Caldeira and
Cvijanovic (2014) showed that results for radiative forcing and climate
feedback parameters were very similar regardless of whether sea ice feed-
backs were disabled by xed sea ice cover and thickness or by setting the
freezing point of water to absolute zero. In the normal conguration
(Both), snow may appear on land or sea ice. We remove the snow
(and other forms of frozen water) on land by setting the freezing point
Figure 1. Modelsimulated global mean surface temperature change for
None,Ice,Snow, and Bothsimulations under various CO
2
levels.
All results are calculated using the last 60year results of 100year slabocean
simulations with uncertainty represented by one standard error.
Figure 2. Modelsimulated changes in zonal distribution of surface air
temperature for None,”“Ice,”“Snow,and Bothsimulations under
4×CO
2
relative to the corresponding 1 × CO
2
case with the same sea ice and
snow treatment. All results are calculated from the last 60year simulations
of 100year slabocean simulations. Ice and snow responses amplify the
temperature response to increased atmospheric CO
2
, especially in polar
regions.
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Journal of Geophysical Research: Atmospheres
DUAN ET AL. 201
of water to absolute zero in the land model including lakes that are
allowed to supercool to melt snow and ice. In the Community Land
Model 4, the glacier land types are represented by a prescribed surface
fraction and surface albedo in each grid cell. Therefore, to change the
freezing points of water in the land model will not affect the state of
glacier. Our modications of the model do not directly change cloud
processes in the atmosphere, which may involve phase changes of water.
Thus, snow and ice may be present in the atmosphere and fall on the
surface. All snow is immediately converted to liquidwater equivalent
when reaching the surface, and the energy consumed to melt snow is
considered to decrease the surface temperature. Thus, energy is conserved
in our simulations.
To examine the contribution of sea ice and land snow to the climate feed-
back parameter under various CO
2
levels, we conduct simulations under
1×CO
2
,2×CO
2
,4×CO
2
,6×CO
2
, and 8 × CO
2
conditions for each
sea ice and land snow treatment described above. All simulations are
run for 100 years. We use the average results for the last 60year simula-
tions to represent climatological means. The TOA radiative forcing and
climate feedback parameter are diagnosed using the Gregorys regression
method with all 100year simulation results (Gregory et al., 2004). To
improve the statistical power of the linear regression, for each combina-
tion of sea ice, land snow, and atmospheric CO
2
treatment, two additional
10year simulations are conducted with slightly altered initial condition as
done before in Caldeira and Cvijanovic (2014).
3. Results and Discussion
3.1. Sea Ice and Land Snow Effects on Temperature Response
The equilibrium global mean surface temperature at 1 × CO
2
concentra-
tion for None,”“Ice,”“Snow,and Bothsimulations are shown in
Table 1. In our simulations, adding sea ice to the Nonesimulation cools
the planet by 1.21 ± 0.02 K. Adding land snow to the Nonesimulation
cools the planet by 0.97 ± 0.02 K. Adding both sea ice and land snow to
the Nonesimulation cools the planet by 3.08 ± 0.02 K. Global mean
temperature responses to 2 × CO
2
,4×CO
2
,6×CO
2
, and 8 × CO
2
forcing
are shown in Table 1 and Figure 1.
The presence of sea ice and land snow contributes to substantially larger
global warming in response to CO
2
increase. In our simulations, the tem-
perature rise with both sea ice and land snow is about 63%, 53%, 46%, and
42% greater than without sea ice or land snow for 2 × CO
2
,4×CO
2
,
6×CO
2
, and 8 × CO
2
forcing, respectively. The temperature rise with
sea ice only is about 30%, 22%, 18%, and 17% greater than without sea
ice or land snow for these forcing levels. With land snow only, the tem-
perature rise is about 15%, 14%, 12%, and 13% greater than without sea
ice or land snow for these forcing levels. The impact of sea ice and land
snow on global temperature is associated with the change in total sea
ice and land snow amount and the seasonal duration under different
CO
2
levels (Figure S1 in the supporting information). Our results indicate that both the sea ice (Ice) and
land snow (Snow) account for the larger surface temperature response with sea ice playing a more
important role.
Feedbacks from the sea ice and land snow components are crucial in shaping the latitudinal warming pat-
tern as well. Figure 2 shows the zonal distribution of change in surface temperature under 4 × CO
2
for dif-
ferent sea ice and land snow treatments. Results at other CO
2
levels and the corresponding normalized zonal
Figure 3. Gregory regressions of topofatmosphere (TOA) (a) net radiative
ux (positive downward), (b) net shortwave, and (c) net longwave radiation
versus global mean surface temperature change for None,”“Ice,”“Snow,
and Bothsimulations under 4 × CO
2
simulations relative to the corre-
sponding 1 × CO
2
case with the same sea ice and snow treatment. The
100year slabocean simulations with two additional 10year simulations
with slightly altered initial condition are used for regression. Results for the
slope (climate feedback parameter) and vertical axis intercepts (adjusted
radiative forcing) of this regression are shown in Table 2. Panels for other
CO
2
levels are shown in supporting information Figure S4. The primary
inuence of ice and snow on the climate feedback parameter is through the
shortwave component.
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Journal of Geophysical Research: Atmospheres
DUAN ET AL. 202
distribution of change in surface temperature can be found in Figures S2 and S3. For 4 × CO
2
simulation,
there is an obvious equatortopole gradient with the surface temperature response where polar regions
warm substantially larger than the tropics. Compared to Nonesimulations, the presence of sea ice and
land snow (Bothsimulations) leads to substantial increases in high latitudes warming. For example,
surface temperature increases by 13.92 and 11.84 K in the Northern and Southern Hemisphere polar
regions, which is about 98% and 102% larger than that in Nonesimulations. In the model, warming in
tropical regions (between 30°S and 30°N) in response to a quadrupling of CO
2
is slightly larger (4.47 K) in
the Bothsimulation than that in the Nonesimulation (3.46 K). Thus, the tropical response is affected
by ice and snow feedbacks, but the tropics are not affected by these feedbacks as strongly as the high
latitude regions (Figure 2). Ocean heat uxes are the same in all of our simulations, and thus the
difference in tropical temperature is due only to the atmospheric communication of remote sea ice and
land snow effect. Use of a dynamic ocean would allow oceanic communication of high latitude ice and
snow effects and therefore would be expected to lead to somewhat different quantitative results.
By comparing only sea ice (Ice) or land snow (Snow) presence simulations with Nonesimulations, it is
found that the sea ice component alone results in almost half of the warming observed in the Both
simulation in Arctic and the Southern Ocean, whereas land snow alone contributes most strongly to warm-
ing in the Northern Hemisphere midlatitudes (Figure 2). Such features are presented at all CO
2
levels
(Figures S2 and S3).
3.2. Sea Ice and Land Snow Contributions to Climate Feedback Parameters
We adopt a linear regression approach based on Gregory et al. (2004) to calculate the TOA radiative forcing
and the climate feedback parameter using the following formula:
N¼ERFCO2 λΔT(1)
where Nis the TOA net radiative ux calculated as the net shortwave minus longwave radiation (all radia-
tive uxes are set downward positive), ERF
CO2
is the effective radiative forcing due to increased CO
2
concen-
tration, λis the climate feedback parameter, and ΔTrepresents the change in global mean surface
temperature. For each of our simulation cases with increased CO
2
, we subtract results from the correspond-
ing 1 × CO
2
simulation with the same sea ice and land snow treatment. Then we apply the Gregory regres-
sion on the TOA net radiative ux versus the global mean surface temperature change. Regression results for
all cases are summarized in Figures 3 and S4, and Tables S1 to S3.
Applying Gregory regression to the Nonesimulation 4 × CO
2
case, we estimate the climate feedback para-
meter λ
None,4 × CO2
to equal 1.48 ± 0.04 W·m
2
·K
1
(Table 2 and Figure 3). The estimated climate feedback
parameter for the Bothsimulation 4 × CO
2
case λ
Both,4 × CO2
is 0.90 ± 0.03 W·m
2
·K
1
. Comparison of
λ
Both,4 × CO2
and λ
None,4 × CO2
indicates that the presence of both sea ice and land snow decreases the climate
feedback parameter in response to CO
2
forcing by 39 ± 9%. The overall contribution of the sea ice and land
snow to the climate feedback parameter (λ
IceSnow
) can be estimated as the difference between climate feed-
back parameters diagnosed from Bothand Nonesimulations:
λIceSnow ¼λBothλNone (2)
As a consequence, we estimate λ
IceSnow
to be 0.58 ± 0.05 W·m
2
·K
1
under 4 × CO
2
case.
Table 2
Estimated TopofAtmosphere Radiative Forcing (Downward Positive) and the Climate Feedback Parameter (λ) for None,Ice,Snow, and BothSimulations at
4×CO
2
Levels Relative to 1 × CO
2
by Applying Gregory Regression on 100Year Simulation Results and Two Additional 10Year Simulations
4×CO
2
1×CO
2
None Ice Snow Both
Radiative forcing (W m
2
) 6.23 ± 0.17 5.79 ± 0.16 6.47 ± 0.16 5.79 ± 0.17
Climate feedback parameter (W·m
2
·K
1
) 1.48 ± 0.04 1.13 ± 0.03 1.35 ± 0.04 0.90 ± 0.03
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DUAN ET AL. 203
The climate feedback parameters derived from the corresponding Ice
and Snowsimulations under 4 × CO
2
are 1.13 ± 0.03 and
1.35 ± 0.04 W·m
2
·K
1
, respectively. Using the same approach as for
Bothand Nonesimulations, we estimate the contribution of sea ice
(λ
Ice
) and land snow (λ
Snow
)tobe0.35 ± 0.04
and 0.13 ± 0.04 W·m
2
·K
1
, respectively. Our results thus indicate a
stronger sea ice effect than land snow. Figure 4 shows the estimated con-
tribution of sea ice and land snow to the climate feedback parameter
under various CO
2
levels. It is clear that while the contribution of sea
ice to the climate feedback parameter is larger than that from land snow,
it reduces as sea ice area diminishes at higher CO
2
levels (Figures 4 and 5).
At the same time, land area covered by snow reduces from
40.43 ± 0.11 × 10
12
m
2
under 1 × CO
2
to 21.22 ± 0.14 × 10
12
m
2
under
8×CO
2
, maintaining a relatively unchanged land snow contribution to
the climate feedback parameter due largely to the relatively small climate
inuence of land snow over land ice (see Figure S5). Under 8 × CO
2
case,
the sea ice and land snow show roughly equivalent contributions to the
climate feedback parameter of 0.23 ± 0.04 and
0.13 ± 0.04 W·m
2
·K
1
, respectively. Contributions from sea ice and
land snow to the climate feedback parameter can be understood in terms of radiative forcing changes from
the change in sea ice and land snow area and the sensitivity of these areas to the global mean temperature
change (see Caldeira and Cvijanovic, 2014, and supporting information).
By comparing red and gray bars in Figure 4, we notice that the contribution of individual sea ice and land
snow component to climate feedback parameter is approximately additive. That is, the combined effect of
sea ice and land snow on climate feedback parameter is approximately equal to the linear sum of the indi-
vidual effect from the existence of sea ice or land snow. Our results are consistent with previous studies in
which the total climate feedback can be decomposed into linear combinations of independent feedback pro-
cesses (Colman, 2003; Soden & Held, 2006; Winton, 2006).
Gregory regressions can be performed independently on TOA net shortwave and longwave radiation (posi-
tive downward) to evaluate their individual contributions to the total climate feedback parameter. For
Nonesimulation 4 × CO
2
case, we estimate the shortwave component feedback (λ
None,SW
)tobe
0.38 ± 0.03 W·m
2
·K
1
and longwave component feedback (λ
None,LW
) to be 1.87 ± 0.02 W·m
2
·K
1
(Figure 3 and Tables S2 and S3). Opposite signs suggest that the shortwave
feedback tends to amplify the climate change while longwave tends to
damp the climate change. The corresponding shortwave and longwave
feedback for the Bothsimulation 4 × CO
2
case is 0.84 ± 0.02 and
1.74 ± 0.01 W·m
2
·K
1
, respectively.
Applying equation (2) on the shortwave and longwave feedbacks between
the Noneand Bothsimulation 4 × CO
2
cases, we estimate the contri-
bution of TOA net longwave feedback parameter due to the presence of
sea ice and land snow to be 0.13 ± 0.02 W·m
2
·K
1
, ~28% of the corre-
sponding shortwave contribution (0.46 ± 0.04 W·m
2
·K
1
). We further
decompose the TOA net shortwave and longwave feedbacks into clear
sky and cloudysky components. As shown in Table S4, the contribution
of the shortwave component between Noneand Bothcases is mainly
associated with the surface albedo change and thus the clearsky short-
wave feedback, while the contribution of the longwave component is
mainly due to the cloud cover change that affects the cloudysky
longwave feedback.
When considering all CO
2
levels, a decrease trend in the shortwave contri-
bution due to the presence of sea ice and land snow is simulated, whereas
the longwave contribution is less sensitive to the CO
2
change (Figure S6).
Figure 5. Modelsimulated sea ice area and land snow area as a function of
global mean surface temperature for None,”“Ice,”“Snow,and Both
simulations at various CO
2
levels. All results are calculated from the last 60
year simulations of 100year slabocean simulations. The seaice and land
snow areas are calculated by multiplying the seaice and landsnow fraction
in each grid cell with areas of that grid and then integrating over the globe.
Figure 4. Contributions of the sea ice and/or land snow components to the
climate feedback parameter for Both,”“Ice,and Snowsimulations
relative to the Nonesimulations. The Ice+Snowbar represents the
height of Icebar stacked on top of the Snowbar. The comparison
between “‘Bothand Ice + Snowshows that the effects of ice and snow on
the climate feedback parameter are approximately additive.
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DUAN ET AL. 204
The contribution of longwave feedback to the climate feedback parameter
increases to ~45% of the shortwave contribution under 8 × CO
2
case.
Thus, our results conrm the importance of longwave feedback arising
from the sea ice and land snow change as reported in Caldeira and
Cvijanovic (2014) that examined sea ice effect only and emphasize the
necessity to consider the longwave feedback in the study of the climate
response to sea ice and land snow change.
3.3. Change of the Climate Feedback Parameter at Different
CO
2
Levels
Taking into account the results under various CO
2
levels, we nd that the
climate feedback parameter decreases at higher CO
2
levels for the None
simulation (λ
None
, Figure 6), implying a more sensitive global mean tem-
perature response to CO
2
induced forcing. Decrease in λ
None
is mainly
associated with enhanced water vapor feedback. More water vapor is
added into the atmosphere for per degree global warming under higher
CO
2
levels (Figure S7b). Since more water vapor in the atmosphere traps
additional outgoing longwave radiation, it contributes to a smaller TOA
longwave feedback parameter and a smaller climate feedback parameter for Nonesimulations
(Tables S1 and S3). The importance of water vapor feedback in determining the longwave feedback and
the climate feedback parameter has been recognized in many previous modeling studies (e.g., Raval et al.,
1994; Huang et al., 2007; Jonko et al., 2013; Meraner et al., 2013). Also, the cloud fraction change per degree
of the global mean temperature change differs at different CO
2
levels (Figure S7c and Table S4), contributing
to changes in the climate feedback parameter.
In contrast to Nonesimulations, λ
Both
increases under higher CO
2
levels, suggesting a less sensitive cli-
mate response (Figure 6). As shown in Figure 6, the decrease in λ
Both
is primarily a result of diminishing
sea ice feedback at higher CO
2
levels. Values of the λ
Ice
and λ
Snow
from different sea ice and land snow treat-
ments approach each other at higher CO
2
concentrations mainly because the strength of the sea ice feedback
decreases due to the loss of ice cover under higher temperature.
4. Discussion and Conclusion
Using the NCAR CESM, we investigate the overall contribution arising from the presence or absence of sea
ice and/or land snow to the climate sensitivity in response to a series of carbon dioxide increase scenarios.
Feedbacks from the sea ice and land snow components are found to increase the global warming by over
40% relative to simulations where both of their effects are disabled. Sea ice and land snow are also crucial
in determining the substantially larger warming in high latitudes. Feedbacks from the sea ice component
only are found to increase the global warming by over 17%. We nd that the sea ice component accounts
for nearly half of the temperature increase in the Arctic and Southern Ocean. When considering only land
snow feedbacks, global warming increases by over 12%. The primary effects of land snow are found mainly
in the Northern Hemisphere midlatitudes. Here we have focused on the climate effect of sea ice and land
snow, but it should be borne in mind that the loss of ice or snow will also have substantial impacts on other
components of the natural system.
Using the NCAR CESM, we estimated an averaged contribution of sea ice and land snow components to the
climate feedback parameter as 0.6 W·m
2
·K
1
considering the full set of feedbacks at various CO
2
levels,
larger than the estimates made in previous studies that considered only the surface albedo feedback (e.g.,
Graversen & Wang, 2009; Holland et al., 2001; Winton, 2006). We also nd that approximately one quarter
of the warming effect from sea ice and land snow is associated with the longwave feedback.
Furthermore, we evaluate the contribution of sea ice and land snow to the climate feedback parameter using
the Gregory regression and equilibrium state sea ice and land snow results from Ice,”“Snow,and None
simulations, respectively. Our derived sea ice effect is stronger than that from the land snow but diminishes
at higher CO
2
levels due to decreases in sea ice cover. Under 8 × CO
2
case, the effect of sea ice and land snow
components becomes similar. Contributions from sea ice and land snow components to the climate feedback
Figure 6. Gregory regression results of the climate feedback parameter (λ)
versus radiative forcing for None,”“Ice,”“Snow,and Bothsimulations
at various CO
2
levels.
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DUAN ET AL. 205
parameter are roughly additive, consistent with previous studies that decompose the total climate feedback
as the linear sum of independent processes (e.g., Andrews & Webb, 2018; Colman, 2003; Hansen et al., 1984;
Jonko et al., 2013; Soden & held, 2006; Wetherald & Manabe, 1988).
We used a single climate model to examine the sea ice and land snow effects on the climate sensitivity.
Results from the Coupled Model Intercomparison Project have shown that climate feedback parameters
are highly model dependent and vary signicantly among models (e.g., Andrews et al., 2012; Winton,
2006). Using the Coupled Model Intercomparison Project Phase 5 ensemble results, Massonnet et al.
(2012) showed that there is a large spread of the sea ice responses among different models. In our simula-
tions, a slabocean conguration with specied ocean heat transport is used to represent the ocean response,
thus excluding the interaction of climate with deep water and oceanic heat transport. These interactions are
found to affect the global warming pattern as well as the atmospheric feedback (Boer & Yu, 2003; Mahlstein
& Knutti, 2011; Polyakov et al., 2005). In addition, the sea ice and land snow feedbacks can be quite sensitive
to ocean processes that are not represented in the slabocean mode (Johns et al., 2006). Therefore, caution
should be exercised when interpreting quantitative conclusions presented in this paper.
Key qualitative conclusions reached in this study are as follows: (1) Sea ice has a stronger inuence on the
climate feedback parameter than does land snow, but both of them substantially decrease the climate feed-
back parameter, particularly at high latitudes. (2) Sea ice and snow feedbacks involve a substantial long-
wave component. (3) The sum of the effects of sea ice and land snow taken individually on the climate
feedback parameter is approximately equal to the effect of sea ice and land snow taken jointly on the cli-
mate feedback parameter, as would be expected of a quasilinear system. (4) The inuence of sea ice on
the climate feedback parameter diminishes with increasing atmospheric CO
2
more rapidly than does the
inuence of land snow on the climate feedback parameter. It would be useful to test if effects of sea ice
and land snow on climate sensitivity are similar in other models.
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Acknowledgments
This work is supported by the National
Key Basic Research Program of China
(2015CB953601), the National Natural
Science Foundation of China (41422503
and 41675063), and the Fundamental
Research Funds for the Central
Universities. We also received support
from the Department of Global
Ecology, Carnegie Institution for
Science. The model simulations were
performed out at the HighPerformance
Computing Facility MAZAMA
funded by the Department of Global
Ecology, Carnegie Institution for
Science. CESM and les needed to run
it are available from NCAR. The data
set used to produce gures and tables in
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... 5) increases mean annual temperatures by up to 5 °C in North America and Eurasia (Varvus 2007) and 0.84-0.97 °C globally (Vavrus 2007;Duan et al. 2019). For our sample, removing the 1972-2008 mean value for snow cover for the 215 cells that have a mean greater than zero raises their average temperature by about 0.70 °C. ...
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... The issue of CO2 effects on global warming of 371 the land and ocean is established. This situation is contrasted with the studies by Duan et al. (2019), which allows validating the vulnerability of sea ice and land snow when exposed to the effects of increased atmospheric CO2. ...
... Reducing surface albedo causes greater absorption of solar radiation that warms the surface and drives additional sea ice and snow melt. Studies estimate that the sea ice-snow albedo feedback is responsible for 30-60% of the total CO 2 -induced Arctic warming (Dickinson et al., 1987;Hall 2004;Taylor et al., 2013;Boeke and Taylor 2018;Duan et al., 2019) and is the largest local Arctic feedback (Taylor et al., 2013;Yoshimori et al., 2014;Goosse et al., 2018). Multi-centennial climate simulations show that Arctic warming slows after most of the sea ice melts, further highlighting the importance of sea ice (Bintanja and van der Linden 2013;Dai et al., 2019). ...
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•The Arctic is continuing to warm faster than any other region on Earth, but key uncertainties remain in our knowledge of the Arctic carbon cycle. •We review the most current knowledge pertaining to estimates of arctic greenhouse gas components and discuss uncertainties associated with these measurements and models. •While the Arctic Ocean is consistently estimated as a carbon sink, we have yet to reach an agreement on either the magnitude or the sign of the arctic terrestrial carbon budget. •Much of the uncertainty in the arctic carbon budget is related to the extent of the amount of carbon released as permafrost thaws, the magnitude of shoulder season and winter ecosystem respiration, and the impact of rising temperature and atmospheric [CO2] on plant growth. •We cannot count on the Arctic to store as much carbon as it has in the past, and evidence indicates it will likely store much less.
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