Clouds play an important role in the climate system: (1) cooling Earth
by reflecting incoming sunlight to space and (2) warming Earth by
reducing thermal energy loss to space. Cloud radiative effects are
especially important in polar regions and have the potential to
significantly alter the impact of sea ice decline on the surface radiation
budget. Using CERES (Clouds and the Earth's Radiant Energy System) data and 32 CMIP5 (Coupled Model Intercomparison Project) climate models, we quantify the
influence of polar clouds on the radiative impact of polar sea ice
variability. Our results show that the cloud short-wave cooling effect
strongly influences the impact of sea ice variability on the surface
radiation budget and does so in a counter-intuitive manner over the polar
seas: years with less sea ice and a larger net surface radiative flux show a
more negative cloud radiative effect. Our results indicate that 66±2%
of this change in the net cloud radiative effect is due to the reduction in
surface albedo and that the remaining 34±1 % is due to an increase in cloud
cover and optical thickness. The overall cloud radiative damping effect is
56±2 % over the Antarctic and 47±3 % over the Arctic. Thus,
present-day cloud properties significantly reduce the net radiative impact
of sea ice loss on the Arctic and Antarctic surface radiation budgets. As a
result, climate models must accurately represent present-day polar cloud
properties in order to capture the surface radiation budget impact of polar sea ice loss and thus the surface albedo feedback.
Introduction
Solar radiation is the primary energy source for the Earth system and
provides the energy driving motions in the atmosphere and ocean, the energy
behind water phase changes, and the energy stored in fossil fuels. Only
a fraction (Loeb et al.,
2018) of the solar energy arriving to the top of the Earth atmosphere
(short-wave radiation; SW) is absorbed at the surface. Some of it is
reflected back to space by clouds and by the surface, while some is absorbed
by the atmosphere. In parallel, Earth's surface and atmosphere emit
thermal energy back to space, called outgoing long-wave (LW) radiation,
resulting in a loss of energy (Fig. 1). The balance between these energy
exchanges determines Earth's present and future climate. The change in this
balance is particularly important over the Arctic, where summer sea ice is
retreating at an accelerated rate (Comiso et al.,
2008), surface albedo is rapidly declining and surface temperatures are
rising at a rate double that of the global average (Cohen et al., 2014; Graversen et al., 2008),
impacting sub-polar ecosystems (Cheung et al., 2009; Post et al.,
2013) and possibly mid-latitude climate (Cohen et al., 2014, 2019).
Clouds play an important role in modifying the radiative energy flows that
determine Earth's climate. This is done both by increasing the amount of SW
reflected back to space and by reducing the LW energy loss to space relative
to clear skies (Fig. 1). These cloud effects on Earth's radiation budget can
be gauged using the cloud radiative effect (CRE), defined as the difference
between the actual atmosphere and the same atmosphere without clouds (Charlock and Ramanathan, 1985). The different
spectral components of this effect can be estimated from satellite
observations: the global average SW cloud radiative effect (SWcre) is
negative, since clouds reflect incoming solar radiation back to space,
resulting in a cooling effect. On the other hand, the LW cloud radiative
effect (LWcre) is positive, since clouds reduce the outgoing LW radiation to
space generating a warming effect (Harrison
et al., 1990; Loeb et al., 2018; Ramanathan et al., 1989).
Cloud properties and their radiative effects exhibit significant
uncertainty in the polar regions (e.g. Curry et al., 1996; Kay and Gettelman,
2009; Boeke and Taylor, 2016; Kato et al., 2018). For instance, climate models
struggle to accurately simulate cloud cover, optical depth and cloud phase
(Cesana et al., 2012; Komurcu et al., 2014; Kay et al., 2016). An accurate
representation of polar clouds is necessary because they strongly modulate
radiative energy fluxes at the surface, in the atmosphere and at the TOA (top of the atmosphere),
influencing the evolution of the polar climate systems. In addition, polar
cloud properties interact with other properties of the polar climate systems
(e.g. sea ice) and influence how variability in these properties affects
the surface energy budget (Qu and Hall, 2006; Kay and L'Ecuyer, 2013; Sledd
and L'Ecuyer, 2019). Moreover, Loeb et al. (2019) documented severe
limitations in the representation of surface albedo variations and their
impact on the observed radiation budget variability in reanalysis products,
motivating the evaluation of radiation budget variability over the polar
seas in climate models. In this study, we use the Clouds and the Earth's
Radiant Energy System (CERES) top-of-atmosphere (TOA) and surface (SFC)
radiative flux datasets and 32 Coupled Model Intercomparison Project (CMIP5)
climate models to estimate the relationship between the CRE and the surface
radiation budget in polar regions to improve our understanding of how clouds
modulate the surface radiation budget.
Schematic representation of radiative energy flows in the polar
seas under total-sky conditions (a, c) and clear-sky conditions (b, d) for
two contrasting surface conditions: without sea ice (a, b) and with sea ice (c, d). All fluxes are taken positive downwards.
Methods and dataCERES EBAF Ed4.0 products
Surface and TOA radiative flux quantities are
taken from the NASA CERES Energy Balanced and Filled (EBAF) monthly dataset
(CERES EBAF-TOA_Ed4.0 and CERES EBAF-SFC_Ed4.0), providing monthly, global fluxes on a 1∘×1∘
latitude–longitude grid (Loeb et al., 2018;
Kato et al., 2018). CERES surface LW and SW radiative fluxes are used to
investigate the effect of clouds on the surface radiation budget response to
sea ice variability over the polar seas. CERES SFC EBAF radiative fluxes
have been evaluated through comparisons with 46 buoys and 36 land sites
across the globe, including the available high-quality sites in the Arctic.
Uncertainty estimates for individual surface radiative flux terms in the
polar regions range from 12 to 16 W m-2 (1σ) at the monthly
mean 1∘×1∘ gridded scale (Kato et al., 2018). CERES
EBAF-TOA and SFC radiative fluxes show a much higher reliability than other
sources (e.g. meteorological reanalysis) and represent a key benchmark for
evaluating the Arctic surface radiation budget (Christensen et al., 2016;
Loeb et al., 2019; Duncan et al., 2020).
In addition to radiative fluxes, cloud cover fraction (CCF) and cloud
optical depth (COD) data available from CERES EBAF data are used. Monthly
mean CCF and COD data are derived from instantaneous cloud retrievals using
the Moderate Resolution Imaging Spectroradiometer (MODIS) radiances (Trepte
et al., 2019). Instantaneous retrievals are then spatially and temporally
averaged onto the 1∘×1∘ monthly-mean grid consistent
with CERES EBAF.
Cloud radiative effect
CRE is used as a metric to assess the radiative
impact of clouds on the climate system, defined as the difference in net
irradiance at the TOA between total-sky and clear-sky conditions. Using the
CERES Energy Balanced and Filled (EBAF) Ed4.0 (Loeb et al.,
2018) flux measurements and CMIP5 simulated fluxes, CRE is calculated by
taking the difference between clear-sky and total-sky net irradiance flux at
the TOA. All fluxes are taken as positive downwards.
1SWcre=SWtotal-SWclear 2LWcre=LWtotal-LWclear 3NETcre=SWcre+LWcre
Earth's surface radiative budget
Surface radiative fluxes are taken
from the CERES SFC EBAF Ed4.0 dataset (Kato et al.,
2018). The net SW and LW fluxes at the surface (SWsfc and LWsfc,
respectively) are calculated as the difference between the downwelling
SWdown (LWdown) and upwelling SWup (LWup) as shown
in Eq. (4) (Eq. 5).
4SWsfc=SWdown-SWup 5LWsfc=LWdown-LWup 6NETsfc=SWsfc+LWsfc
Sea ice concentration
Sea ice concentration (SIC) data are from the
National Snow and Ice Data Center (NSIDC; http://nsidc.org/data/G02202, last access: May 2018). This dataset is a climate data record
(CDR) of SIC from passive microwave data and provides a consistent, daily
and monthly time series of SIC from 9 July 1987 through the most recent
processing for both the northern and southern polar regions (Peng
et al., 2013; Meier et al., 2017).
The data are provided on a 25 km × 25 km grid. We used the latest version
(version 3) of the SIC CDR created with a new version of the input product
from Nimbus-7 SMMR (Scanning Multichannel Microwave Radiometer) and DMSP SSM/I-SSMIS Passive Microwave Data (Defense Meteorological Satellite Program Special Sensor Microwave Imager Special Sensor Microwave Imager/Sounder).
Polar seas
We define the polar seas as ocean regions where the monthly
SIC is larger than 10 % for least 1 month during the 2001–2016 period. The extent of the polar seas is shown in Fig. S1.
CMIP5 models
To reconstruct the historical CRE and surface energy budget
and project their future changes, we used an ensemble of simulations
conducted with 32 climate models (models used are shown in Figs. 3 and S3)
contributing to the Coupled Model Intercomparison Project Phase 5 (CMIP5)
(Taylor et al., 2012). These model experiments
provided historical runs (1850–2005) in which all external forcings are
consistent with observations and future runs (2006–2100) using the RCP8.5 (Representative Concentration Pathway)
emission scenarios (Taylor et al., 2012). The
comparison with the satellite data is made over 2001–2016 by merging
historical runs 2001–2005 with RCP8.5 2006–2016.
Estimation of the local variations in radiative flux, cloud cover and
cloud optical depth concurrent with changes in sea ice concentration
This study employs a novel method for quantifying the variations in
radiative fluxes and cloud properties with SIC. This methodology leverages
inter-annual variability of sea ice cover to assess these relationships.
Figure 2 schematically shows the methodology based on the following steps.
We use SW as an example and apply the approach in the same way to other
variables.
ΔSWj values are summarised in a schematised plot (Fig. 2a)
where each cell j in such plot shows the average ΔSWm observed
for all possible combinations of SIC at a grid box between 2 consecutive
observation years (year yi and yi+1 from the time period 2001–2016) displayed
on the x and y axes, respectively. For the sake of clarity in Fig. 2 the x
and y axes report SIC in intervals of 10 %, while in Figs. 5, 6, 7, S5
and S6 the axes are discretised with 2 % bins.
Because of the regular latitude–longitude grid used in the analysis, the
area of the grid cells (am) varies with the latitude. The energy signal
(ΔSWj) is therefore computed as an area-weighted average
(Eq. 7), where M is the number of grid cells that are used to compute
cell j in the schematised plot of Fig. 2a. Figure 2b shows the total area of all
these grid cells as described by Eq. (8).7ΔSWj=∑m=1MamΔSWm∑m=1Mam8Aj=∑m=1Mam
Calculation of the area-weighted average (ΔSWp) of the
energy signal of all N cells with the same fraction X of a change in SIC
(shown with the same colour in Fig. 2a; Eq. 9).ΔSWp=∑j=1NAjΔSWj∑j=1NAj∑j=1NAj is the total area of all grid cells with a
particular SIC change.
ΔSWp is the energy-weighted average of all grid cells with a
particular SIC change.
Schematic representation of the methodology used to quantify the
energy flux sensitivity to changes in sea ice concentration as a linear
regression between the percentage of sea ice concentration and the variation
in energy flux (c) using SW energy flux data and sea ice
concentration defined in (a, b).
The average energy signals (ΔSWp) per class of sea ice
concentration change are reported in a scatterplot (Fig. 2c) and
used to estimate a regression line with zero intercept.
The slope S of this linear regression represents the local SW energy signal
generated by the complete sea ice melting of a 1∘ grid cell. The
weighted root mean square error (WRMSE) of the slope is estimated by
Eq. (10), where p represents one of the NP points in the scatterplot (NP = 6 in Fig. 2c; number of points) and Xp is the relative change in sea ice concentration in
the range ±1 (equivalent to ±100 % of sea ice cover change).
WRMSE=∑p=1NPApΔSWp-SXp2∑p=1NPAp,
where
Ap=∑j=1NAj.
Diagnosis of contributions to SWcre
SWcre at the surface for the year yi (Eq. 11) and year yi+1 (Eq. 12) is
function of surface albedo α, SWdown under clear-sky conditions
(SW↓clr) and SWdown under total-sky conditions
(SW↓tot).
11SWcreyi=1-αyi(SW↓tot,yi-SW↓clr,yi)12SWcreyi+1=1-αyi+1(SW↓tot,yi+1-SW↓clr,yi+1)
Using the first-order Taylor series expansion to Eq. (11) yields
ΔSWcreyi+1-yi≅-Δαyi+1-yiSW↓tot,yi-SW↓clr,yi+1-αyiΔyi+1-yi(SW↓tot-SW↓clr),
where
Δyi+1-yiSW↓tot-SW↓clr≅SW↓tot,yi+1-SW↓clr,yi+1-(SW↓tot,yi-SW↓clr,yi).
Separating the terms yields
ΔSWcreAlb≅-Δαyi+1-yi(SW↓tot,yi-SW↓clr,yi),
where ΔSWcreAlb is the part of SWcre change that is induced by
the change in surface albedo, and
ΔSWcreCloud≅1-αyiΔyi+1-yi(SW↓tot-SW↓clr),
where ΔSWcreCloud is the part of SWcre change that is induced
by the change in cloud cover and cloud optical depth.
ΔSWcreyi+1-yi≅ΔSWcreAlb+ΔSWcreCloud
The above equations are used in Figs. 7 and S5.
Results and discussionsNegative correlation patterns between cloud radiative effect and surface radiation on polar seas
Given the known cloud influence on the surface radiative budget, a positive
correlation between TOA CRE and surface radiative budget is expected (the
amount of absorbed radiation at the surface decreases with a more negative
SWcre and a less positive LWcre). Figure 3 illustrates a positive
correlation between the annual-mean NETcre and NETsfc over much of the
global ocean using the CERES TOA flux data from 2001 to 2016. However, our
analysis reveals the opposite pattern over the polar seas (defined in
Sect. 2.5) where the correlation is negative over the Antarctic and partly
negative over the Arctic (Bering Strait, Hudson Bay, Barents Sea and the
Canadian Archipelago; Fig. 3a, b). Considering the SWcre and LWcre components,
we find that the SWcre (Fig. 3c, d) shows a similar pattern of correlation as
the NETcre (Fig. 3a, b) but with a stronger magnitude, while LWcre generally
shows the opposite correlations (Fig. 3e, f). This suggests that the factors
influencing SWcre are responsible for the sharp contrast in the correlation
found in the polar regions. Indeed, SWsfc and SWcre (Fig. 3g, h) show the
sharpest and most significant contrast between the polar regions and the
rest of the world (Fig. S2 is similar to Fig. 3, but only significant
correlations at the 95 % confidence level are reported in blue and red
colours). Overall, climate models are able to reproduce the spatial pattern
of the observed SW correlation but also show a large inter-model spread in
the spatial extent of the phenomena (Figs. 4 and S3). On the other hand,
several models completely fail to reproduce the correlation. The ACCESS1-3,
MIROC5, CanESM2 and CSIRO-Mk3-6-0 models show a negative correlation over the
Antarctic continent in contrast to an observed positive correlation. Some
models, like IPSL-CM5B-LR, GISS-E2-R and bcc-csm1-1, fail to reproduce the
observed negative correlation over the Antarctic Ocean. This suggests that
these models contain misrepresentations of the relationships of SWcre and
NETsfc, likely resulting from errors in the relationships between sea ice,
surface albedo, cloud cover and thickness, and their influence on surface
radiative fluxes that could severely impact their projections. Moreover,
Fig. 4 demonstrates that simple correlations between NETsfc and the
individual radiation budget terms represent a powerful metric for climate
model evaluation that allows for a quick check for realistic surface radiation
budget variability in polar regions.
Correlation between TOA CRE and surface radiation budget terms over
2001–2016 from CERES measurements for the Northern Hemisphere (a, c, e, g) and
Southern Hemisphere (b, d, f, h) polar sea. Positive correlations shown by the red
colours indicate that years with less NETsfc coincide with years where NETcre
has a stronger cooling effect and vice versa.
Correlation between SWcre and SWsfc shown by 32 CMIP5 Earth system
models and CERES between 2001 and 2016 over the Southern Hemisphere.
Effects of sea ice concentration change
We illustrate that the apparent contradiction over the polar seas between
NETcre and NETsfc found in Fig. 3a, b is caused by the factors contributing to
the SW fluxes. This can be explained by the following. (i) SWcre can change even if cloud
properties are held constant due to the changes in clear-sky radiation
induced by changes in sea ice and surface albedo. When surface albedo is
reduced, the surface absorbs more sunlight at the surface, resulting in a
greater SWtotal. At the same time, SWclear increases, since the lower albedo
allows a larger fraction of the extra downwelling SW at the surface to be
absorbed (see Fig. 1). Therefore, SWcre becomes more negative even in the
absence of cloud changes (a purely surface-related effect). (ii) On the
other hand, the relationship between cloud cover and thickness and sea ice could
lead to cloudier polar seas under melting sea ice
(Abe et al., 2016; Liu et al., 2012)
such that the SWcre decreases (increasing the amount of SW reflected back to
space by clouds; see Fig. 1); thus the cloud cooling effect is enhanced
concurrently with melting sea ice (a purely cloud-related effect). Both of
these factors occur simultaneously.
Annual changes in SW, LW and NET as a function of SIC. Annual changes
in SW (top), LW (middle) and NET (bottom) of radiative down (a),
up (b), sfc = down-up (c) and cre (d) over the Antarctic Ocean as a
function of SIC change between 2 consecutive years, yi+1 and yi, from the 2001 to 2016 time period. The top triangles in (c top) refer to the
increase (growing) in SIC, while the blue colours mean a reduction (cooling)
in SWsfc. The top triangles in (d) refer to the increase in SIC,
while the red colours mean an increase (decreasing the cooling role of
clouds) in SWcre. Each dot in column (e) represents the average of one
parallel to the diagonal in (c) or (d) as described in Sect. 2.7.
Over the Antarctic Ocean, analysis of the year-to-year changes in SWdown
stratified in 2 % SIC bins retrieved from satellite microwave radiometer
measurements (see Sect. 2.7) shows an increase in SWdown with increased
SIC and vice versa (Fig. 5a). This suggests that years with higher SIC also have fewer
and/or thinner clouds (Liu et al., 2012) (Fig. 6), larger
SWdown and also larger upward SW radiation (SWup) (Fig. 5b), due to higher
surface albedo (Fig. S4). Consequently, these years show a more negative
SWsfc (Fig. 5c) and thus are characterised by stronger surface cooling.
Furthermore, the presence of fewer clouds implies a reduction of the cloud cooling effect
(less negative SWcre) as described above in process (ii); this accounts for
(19.42×100)/56.59=34±1 % (Fig. 7d bottom) of the total
change in SWcre, and as described in process (i) the increase in the surface
albedo also makes SWcre less negative and explains (37.17×100)/56.59=66±2 % of the observed change (Fig. 7d bottom). Thus, the
observed negative correlation between SWcre and SWsfc over the polar seas
results from the larger effects of process (i) than (ii). Similar results
are found over the Arctic Ocean with a slightly different sensitivity (Figs. S5, S6). This difference is tied to differences in sun angle and available
sunlight, as Antarctic sea ice is concentrated at lower latitudes than
Arctic sea ice.
Using the regression relationships derived from our composite analysis, we
estimate the magnitude of the cloud effect. For the Antarctic system, we use
the numbers found in Fig. 5e where we find the annual-mean relationship
between NETsfc (in W m-2) and SIC (fraction between 0 and 1) and NETcre
(in W m-2) and SIC (fraction between 0 and 1).
18ΔNETsfc=(-36.61±0.72)ΔSIC19ΔNETcre=(47.03±1.01)ΔSIC
When excluding the CRE, the ΔNETsfc would be equal to (-36.61–47.03)
ΔSIC=-83.64ΔSIC.
We estimate that the existence of clouds and their property variations are
damping the potential increase in the NETsfc within the Antarctic system due
to the surface albedo decrease from sea ice melt by 56 % (47.03/83.64).
The uncertainty is calculated by summing the uncertainties shown in Eqs. (18) and (19) as follows: (0.722+1.012)1/2/83.64=2 %.
Similarly, over the Arctic (Fig. S5), we compute the cloud influence on the
surface net radiative budget that covaries with sea ice loss is 47±3 %, in agreement with the study of Sledd and L'Ecuyer (2019).
Seasonal and annual changes in cloud cover fraction (CCF) and cloud
optical depth (COD) over the Antarctic polar sea region as a function of SIC
change between 2 consecutive years, yi+1 and yi, from the 2001 to 2016
time period. In order to use the same scale, COD has been multiplied by a
factor of 10. The top triangles in the two first columns refer to the increase
(growing) in SIC, while the blue colours mean a reduction in CCF or COD.
Seasonal and annual changes in SWcreAlb, SWcreCloud and SWcre over
the Antarctic polar sea region as a function of SIC change between 2 consecutive years, yi+1 and yi, from the 2001 to 2016 time period. The
analysis is based on the method described in Sect. 2.7 and observations from
satellites data.
Altogether the results suggest that clouds substantially reduce the impact of sea
ice loss on the surface radiation budget and thus the observed sea ice
albedo feedback. This effect in the polar climate system leads to a
substantial reduction (56±2 % over the Antarctic and 47±3 % over the Arctic) of the potential increase in NETsfc in response to
sea ice loss. This magnitude is similar to a previous study (Qu and Hall,
2006) showing that across a climate model ensemble clouds damped the TOA
effect of land surface albedo variations by half. Sledd and L'Ecuyer (2019)
also determined that the cloud damping effect (also referred to as cloud
masking) of the TOA albedo variability results from Arctic sea ice changes
was approximately half. Despite this mechanism, the sharp reduction in
Arctic surface albedo has dominated the recent change in the surface
radiative budget and has led to a significant increase in NETsfc since 2001
in the CERES data (Duncan et al., 2020). These results demonstrate that the
trends in polar surface radiative fluxes are driven by reductions in SIC and
surface albedo and that clouds have partly mitigated the trend (i.e. a
damping effect). Our findings highlight the importance of processes that
control sea ice albedo (i.e. sea ice dynamics, snowfall, melt pond
formation and the deposition of black carbon), as the surface albedo of the
polar seas in regions of seasonal sea ice is crucial for the climate
dynamics.
Sensitivity of the surface energy budget to variability of sea ice concentration
Our results are consistent with other recent studies (Taylor et
al., 2015; Morrison et al., 2018) that demonstrate a CCF response to reduced
sea ice in autumn and winter but not in summer (Fig. 8a) over the Arctic Ocean.
The lack of a summer cloud response to sea ice loss is explained by the
prevailing air–sea temperature gradient, where near-surface air temperatures
are frequently warmer than the surface temperature (Kay and Gettelman, 2009).
Surface temperatures in regions of sea ice melt hover near freezing due to
the phase change, whereas the atmospheric temperatures are not constrained
by the freezing–melting point. Despite reduced sea ice cover, increases in
surface evaporation (latent heat) are limited (Fig. 8m, n), as also suggested
by the small trends in the surface evaporation rate derived from satellite-based
estimates (Boisvert and Stroeve, 2015; Taylor
et al., 2018). We argue that the strong increase of SWcreCloud under
decreased sea ice observed during summer is induced by larger values of COD
(Fig. 8a), which depend on the liquid or ice water content. We also show
that the relationships derived from our observation-driven analysis match
the projected changes in the Arctic and Antarctic surface energy budget in
the median CMIP5 model ensemble (Fig. 8). However, we find a large spread
amongst climate models that indicates considerable uncertainty.
Analysing the seasonal cycle of the sensitivity of the surface energy budget
to SIC variability, we found that SWsfc (SWcre) explains most of the
observed changes in the NETsfc (NETcre) during summer, while LWsfc plays a
minor role (Fig. 8). In contrast, during winter LWsfc (LWcre) explains most
of the observed changes in the NETsfc (NETcre). In general, the median of
the 32 CMIP5 (Taylor et al., 2012) climate models
captures the observed sensitivity of the radiative energy budget and cloud
cover change to SIC, but the spread between climate models is large,
especially for CCF. We have to note here that the numbers reported in
Fig. 8 are for 100 % SIC loss, while the ones reported in the previous
figures (Figs. 5, 6 and 7) are for 100 % SIC gain, explaining the opposite
sign.
Monthly change in different terms of the radiative energy balance,
cloud optical depth (COD) and cloud cover fraction (CCF) extrapolated from
observations for a hypothetical 100 % decrease in SIC over the areas where
SIC change was observed during the period 2001–2016. This estimate came from
the use of a linear interpolation of the change of different parts of the
energy budget, COD and CCF as a function of a change in SIC coming from all
possible combinations of couplets of consecutive years for a given month
from 2001 to 2016, and for all grid cells for which SIC is larger than zero
in 1 of the 2 years (see Sect. 2.7). CERES data are shown by solid
lines (the standard deviation of the slopes are also reported but are too
small to be visible), while CMIP5 models are shown by boxplot, and the box
(in the same colour as observations) represents the first and third quartiles
(whiskers indicate the 99 % confidence interval, and black markers show
outliers). In order to use the same scale, COD has been multiplied by a
factor 10.
Time series of the anomaly with respect to the whole period
1850–2100 of the radiative flux. Mean modelled SWcre, LWcre and NETcre (blue)
and surface SWsfc, LWsfc and NETsfc (orange) anomalies over the 1850–2100
period under the RCP8.5 scenario averaged over the Arctic Ocean. The solid line
shows the median, where the envelope represents the 25th and 75th percentile of
the 32 CMIP5 models. The linear regression (grey solid line and its 68 %
– dark-grey envelope – and 95 % – light-grey envelope – confidence interval)
between the trend in SWdown and trend in sea ice extent (g, h) of the
32 CMIP5 climate models shown by grey dots over 2001–2016. The observed
trends are shown by red colours, where the confidence interval refers to the standard
error of the trend.
Projections and uncertainties of cloud radiative effects on the surface energy budget
Under the RCP8.5 scenario (“business as usual”;
Taylor et al., 2012), CMIP5 models show an
increase in SWsfc over the Arctic Ocean (Fig. 9a), consistent with the
expected decrease in the SIC (Comiso
et al., 2008; Serreze et al., 2007; Stroeve et al., 2007). This increase in
SWsfc occurs despite the large, concurrent and opposing change in SWcre.
Projections of LW flux changes (Fig. 9c) are expected to play a small but
non-negligible role on the total energy budget in summer by slightly increasing
NETsfc (Fig. 9e). In addition, CMIP5 models indicate that by 2100 the
magnitude of the NETcre decrease will be slightly smaller than the increase
in NETsfc (Fig. 9e) over the Arctic Ocean, while the Antarctic polar sea
region shows the opposite (Fig. 9f). This is in line with the estimated
damping effect of clouds coming from CERES over 2001–2016 that is about
47±3 % in the Arctic and 56±2 % in the Antarctic. The
stronger cloud damping effect in the Antarctic region is indicated by the
stronger negative change in NETcre in the Antarctic compared to the Arctic
(Fig. 9e, f).
Large uncertainties remain in the rate of summer sea ice decline and the
timing of the first sea-ice-free Arctic summer
(Arzel et al., 2006; Zhang and Walsh,
2006). The processes responsible for the large inter-model spread between
climate models are still under scrutiny
(Holland
et al., 2017; Simmonds, 2015; Turner et al., 2013). However, recent studies
reaffirm the important role of the sea ice albedo feedback and the
associated increased upper Arctic Ocean heat content (Holland and Lundrum,
2015; Boeke and Taylor, 2018) as well as the contributions from
temperature-related feedbacks (Pithan and Maruitsen, 2014; Stuecker et al.,
2018). Figure 9g, h shows that the annual-mean Arctic and Antarctic sea ice
extent trend from 32 CMIP5 models possesses a large positive correlation
with the simulated trend in the SWdown, in line with previous studies
(Holland and Lundrum, 2015). We note that from the 32 CMIP5 models tested,
only a few show SWdown trends consistent with observed trends in SWdown and
SIC over 2001–2016 (Fig. 9g, h). Understanding the factors responsible for
this disagreement between model-simulated and observed trends in SWdown and
SIC may provide insights into the processes responsible for the
inter-model spread in Arctic climate change projections and are the subject
of future work. We also find that the models with a larger trend in cloud
cover also possess a larger decrease in sea ice extent, suggesting a
stronger coupling between these two variables that may become stronger in
the future. However, the direction of causality between the two variables is
unclear and also requires further study.
Conclusion
The paper addresses two important climate science topics, namely the
role of clouds and the fate of polar sea ice. The work is grounded in a long
time series of robust satellite observations that allowed us to document an
important damping effect in the polar cloud–sea-ice system using a unique
inter-annual approach. Our results agree with several previous works that
approached the problem from a different perspective (Hartmann and Ceppi,
2014; Sledd and L'Ecuyer, 2019). In addition, we show how 32 state-of-the-art
climate models represent aspects of the surface radiation budget over the
polar seas.
Our data-driven analysis shows that polar sea ice and clouds interplay in a
way that substantially reduces the impact of the sea ice loss on the surface
radiation budget. We found that when sea ice cover is reduced between 2 consecutive years, the cloud radiative effect becomes more negative, damping
the total change in the net surface energy budget. The magnitude of this
effect is important. Satellite data indicate that the more negative cloud
radiative effect reduces the potential increase of net radiation at the
surface by approximately half. One-third of this cloud radiative effect
change is induced by the direct change in cloud cover and thickness, while
two-thirds of this change results from the surface albedo change.
In addition, we demonstrated that the models that show larger trends in
polar sea ice extent also show larger trends in surface incoming solar radiation.
In order to understand current and future climate trajectories, model
developments should aim at reducing uncertainties in the representation of
polar cloud processes in order to improve the simulation of present-day
cloud properties over the polar seas. Present-day Arctic and Antarctic cloud
properties strongly influence the model-simulated cloud damping effect on
the radiative impacts of sea ice loss.
Future cloud changes and sea ice evolution represent major uncertainties in
climate projections due to the multiple relevant pathways through which
cloudiness and sea ice feed back on Earth's climate system
(Solomon et al., 2007). Our evidence
derived from Earth observations provides additional insight into the coupled
radiative impacts of polar clouds and the changing sea ice cover (Fig. 8)
that may provide a useful constraint on model projections and ultimately
improve our understanding of present and future polar climate. On a
practical level, our results demonstrate a simple correlation analysis
between the net surface radiation budget and individual radiation budget
terms that can be used to quickly evaluate climate models for realistic
surface radiation budget variability in polar regions. Ultimately, our
findings on the interplay between clouds and sea ice may support an
improvement in the model representation of the cloud–ice interactions,
mechanisms that may substantially affect the speed of the polar sea ice
retreat, which in turn has a broad impact on the climate system, on the
Arctic environment and on potential economic activities in the Arctic region
(Buixadé Farré et al., 2014).
Code and data availability
The programmes used to generate all the results are
made with Python. Analysis scripts are available upon request to Ramdane Alkama.
Clouds and the Earth's
Radiant Energy System (CERES) satellite data version 4.0 are available at
https://ceres.larc.nasa.gov/order_data.php (last access: May 2018, NASA, 2018). Sea ice concentration data are available from the
National Snow and Ice Data Center (NSIDC; http://nsidc.org/data/G02202, last access: May 2018). Data of the modelling groups that contributed to the CMIP5 data archive are available at
PCMDI (https://pcmdi.llnl.gov/mips/cmip5/, last access: 18 August 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-14-2673-2020-supplement.
Author contributions
RA directed the study with contributions from
all authors. RA performed the analysis. RA, PCT, AC and GD drafted the
paper. All authors commented on the text.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge the use of Clouds and the Earth's
Radiant Energy System (CERES) satellite data, sea ice concentration data from the
National Snow and Ice Data Center (NSIDC), and data of the modelling groups that contributed to the CMIP5 data archive.
Review statement
This paper was edited by Dirk Notz and reviewed by two anonymous referees.
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