ACPDAtmospheric Chemistry and Physics DiscussionsACPDAtmos. Chem. Phys. Discuss.1680-7375Copernicus GmbHGöttingen, Germany10.5194/acpd-15-17527-2015Effect of retreating sea ice on Arctic cloud cover in
simulated recent global warmingAbeM.abe.mnb@gmail.comhttps://orcid.org/0000-0002-4822-3321NozawaT.OguraT.TakataK.Japan Agency for Marine-Earth Science and Technology, Yokohama,
JapanOkayama University, Okayama, JapanNational Institute for Environmental Studies, Tsukuba, JapanNational Institute of Polar Research, Tachikawa, JapanM. Abe (abe.mnb@gmail.com)30June20151512175271755205June201510June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/preprints/15/17527/2015/acpd-15-17527-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/preprints/15/17527/2015/acpd-15-17527-2015.pdf
This study investigates the effect of sea ice reduction on Arctic
cloud cover in historical simulations with the coupled
atmosphere–ocean general circulation model MIROC5. During simulated
global warming since the 1970s, the Arctic sea ice extent has
reduced substantially, particularly in September. This simulated
reduction is consistent with satellite observation results.
However, the Arctic cloud cover increases significantly during
October at grids with significant reductions in sea ice because of
the enhanced heat and moisture flux from the underlying ocean. Cloud
fraction increases in the lower troposphere. However, the cloud
fraction in the surface thin layers just above the ocean decreases
despite the increased moisture because the surface air temperature
rises strikingly in the thin layers and the relative humidity
decreases. As the cloud cover increases, the cloud radiative effect
in surface downward longwave radiation (DLR) increases by
approximately 40–60 % compared to a change in clear-sky surface
DLR. These results suggest that an increase in the Arctic cloud
cover as a result of a reduction in sea ice could further melt the
sea ice and enhance the feedback processes of the Arctic
amplification in future projections.
Introduction
Satellite observations have shown that the Arctic sea ice extent has
reduced gradually since the 1980s (Comiso et al., 2008). Recent
significant reductions in the Arctic sea ice occurred in 2007 and
2012. A further reduction in Arctic sea ice is likely to result from
global warming. In turn, the reduction in sea ice can accelerate
surface warming in the Arctic region and at a global scale through
various feedback processes. A major feedback process in climate change
is the ice-albedo feedback, in which the reduced sea ice extent
decreases the global albedo and then increases shortwave radiation
entering into the climate system (e.g., Curry et al., 1995; Dickinson
et al., 1987; Manabe and Stouffer, 1980; Perovich et al., 2007). This
feedback is likely to occur in high-latitude regions, where snow cover
and sea ice are seasonally extended. Thus, the ice-albedo feedback is
more dominant in the autumn, when the incoming shortwave radiation is
not at its lowest values in the high-latitude region (Yoshimori
et al., 2014).
However, the reduction in sea ice involves other feedback processes in
the Arctic region (Serreze and Barry, 2011). Previous studies suggest
that extended periods open ocean resulting from reductions in sea ice
increased the Arctic cloud cover and enhanced the Arctic amplification
(AA) (e.g., Holland and Bitz, 2003; Screen and Simmonds, 2010; Serreze
and Barry, 2011; Vavrus et al., 2009). With regard to recent changes
in the Arctic cloud cover, Schweiger (2004) reported that both
satellite data from TIROS Operational Vertical Sounder (TOVS) Polar
Pathfinder retrievals and Advanced Very High Resolution Radiometer
(AVHRR) data revealed significant decreases in cloud fractions over
the Arctic sea during winter (December–January–February, DJF) and
striking increases in spring (March–April–May, MAM) cloudiness. Wang
and Key (2003) also showed an increase in the spring cloud fraction.
However, the negative trend in spring cloudiness reported by Comiso
(2003) is not consistent with these previous findings. These results
suggest that uncertainty remains with respect to these observations.
Therefore, continuous monitoring and investigation of the changes in
Arctic cloudiness are required for a robust evaluation.
To monitor the change in cloudiness resulting from reduced sea ice,
several recent studies have used satellite data. Liu et al. (2012)
evaluated the use of satellite data to show that a 1 % decrease in
sea ice concentration leads to a 0.36–0.47 % increase in cloud
cover. These authors also suggested that the total variance in the
cloud cover from July to November can be explained by sea ice-cloud
feedback. Using satellite data, Wu and Lee (2012) showed that the
autumn low cloud cover over the Beaufort Sea and East Siberian Sea
increased during 2000–2010, notably in October. The authors suggested
that the enhanced downward longwave radiation (DLR) resulting from the
increased cloud cover may be responsible for the enhanced autumnal
warming in surface air temperature (SAT). In addition, the enhanced
DLR can prolong the sea ice melt seasons and lead to a positive
feedback involving Arctic sea ice loss.
Furthermore, a strong link between cloud cover variability and sea ice
variability near the sea ice margins was found in autumn using the
40 year ECMWF Re-Analysis (ERA40) data and TOVS polar Pathfinder
satellite datasets (Schweiger et al., 2008). However, this previous
study concluded that the radiative effect of this change is relatively
small because the direct radiative effects of the cloud cover changes
are compensated for by changes in the temperature and humidity
profiles associated with varying ice conditions. A regional climate
model simulation has also shown that the radiative effect of the
changes in cloud cover is likely to be smaller than that of changes in
air temperature and humidity (Rinke et al., 2013). Because of the
deficiency of observed radiation data at the surface, correctly
evaluating the radiative effect of the cloud cover change is
difficult. Therefore, the radiative effect of cloud clover changes in
the AA is controversial.
Recent ship observations found that the cloud base height in September
tends to increase over the Arctic ocean without sea ice cover because
of heating from the ocean. This heating is enhanced because of the
increased temperature gradient between the atmosphere and the ocean,
weakening the stable conditions in the atmospheric boundary layer
(Sato et al., 2012). This previous study indicated that convective
clouds increase in the Arctic Ocean. However, inconsistent results
have been reported concerning the vertical profile of cloud cover
change. Whereas Kay and Gettelman (2009) showed that increased
turbulent transport of heat and moisture promotes low-cloud
formations, Schweiger et al. (2008) showed that low-level clouds may
decrease and simultaneously middle-level clouds may increase because
the decreased static stability and a deepening atmospheric boundary
layer contribute to a rise in the cloud level. Simulations run by
Porter et al. (2012) with the Weather Research Forecasting (WRF) model
support an increase in middle-level clouds in September and increases
in low-level clouds from October to November. The vertical profile of
the cloud cover change resulting from sea ice loss is under debate and
may alter the evaluation of the radiative effect of cloud cover
change.
In addition to the results from the observed data, several studies
employ climate model simulations. Climate models that have simulated
sea ice reduction show that the Arctic cloud cover increases in the
autumn (Vavrus et al., 2011, 2009). An increased area of open ocean
enhances the heat and moisture transport from the ocean to the
atmosphere, resulting in an increased cloudiness. These studies
analyzed the change in cloudiness resulting from sea ice losses in
simulations with increasing greenhouse gas concentrations. The effects
of reduced sea ice in these analyses are stronger than those occurring
in the late 20th century. Therefore, these results are not always
appropriate for the change in Arctic cloudiness occurring in the late
20th century and the present situation, in which the sea ice reduces
only in limited regions. These investigations may be insufficient to
understand the events observed recently and unable to effectively
explain recent processes in simulated climate models.
As noted above, studies have investigated the Arctic cloud cover
change during recent global warming. However, a debate remains
surrounding the change in Arctic cloudiness and the lack of
understanding of the effect of the reduced sea ice on Arctic cloud
cover because of insufficient observed data. In addition, the
radiative effect of the cloud cover change at the surface is difficult
to measure accurately because of the dark seasons and sea ice
cover. In this study, we investigate the temporal trends of Arctic
cloud cover change during the recent global warming using a climate
model simulation and focus on the effects of reduced sea ice. The
vertical structure of the cloud cover change is analyzed using the
composite analysis technique. Furthermore, changes in the cloud
radiative effect in the surface DLR are evaluated to provide
information on the role of Arctic clouds in the mechanism of the
AA. The change in Arctic cloud resulting from the reduced sea ice in
climate model simulations should be informative for understanding the
mechanism underlying future changes in Arctic clouds and the AA.
The next section explains the coupled atmosphere–ocean general
circulation model, MIROC5, used in this study and its 20th century
simulation. The third section reports the results for the Arctic cloud
cover change resulting from the retreating sea ice. We then discuss
the relationship between changes in Arctic cloud cover and sea ice
change, and the paper concludes with a summary.
Model and experiments
We analyze historical simulations using a coupled atmosphere–ocean
general circulation model, MIROC5 (Watanabe et al., 2010), a model
that was used in the Coupled Model Intercomparison Project Phase 5
(CMIP5). The atmospheric portion of MIROC5 is based on the global
spectral dynamical core and includes a standard physical package. The
atmospheric resolution is T85L40, with the peak at 3 hPa. The
ocean general circulation model in MIROC5 is the CCSR (Center for
Climate System Research, University of Tokyo) Ocean Component Model
(COCO) version 4.5 (Hasumi, 2007). The zonal resolution of the ocean
is fixed at 1.4∘, whereas the meridional resolution is
0.5∘ at latitudes equatorward of 8∘ and 1.4∘
at higher latitudes (poleward of 65∘), with a smooth
transition in between (256×224 grid points for zonal and
meridional resolutions). The model has 49 vertical levels, and the
spacing varies with a depth of 2.5 m at the surface,
20 m at a depth of 100, 100 m at a depth of
1000 m, and 250 m below the 2000 m depth. The
sea ice in each horizontal grid is divided into five categories in
addition to open water. The sea ice concentration, ice thickness, and
energy of ice melting are predicted for the five categories in a grid
cell (Komuro et al., 2012). The lower bounds of the ice thickness for
these categories are 0.3, 0.6, 1.0, 2.5, and 5.0 m.
The historical simulations from 1850 to 2005 were performed using
anthropogenic forcings recommended by the CMIP5 project. The
historical change in the solar constant is considered according to
Lean et al. (2005). The historical changes in the optical thickness of
volcanic stratospheric aerosols are given by Sato et al. (1993), and
subsequent updates are available
(http://data.giss.nasa.gov/modelforce/strataer/index.html). Beginning in
1998, the optical thickness of the volcanic stratospheric aerosols
exponentially reduced with a one-year relaxation time.
The historical simulation by MIROC5 has five ensemble members with
different initial conditions. In this study, monthly mean data are
used, and sea ice concentration data were interpolated to be in the
identical horizontal grids as the atmosphere.
ResultsTemporal trend of the Arctic sea ice and clouds
Figure 1a shows the time series of SAT anomalies (Δ SAT) from
the 1951–1980 average, which are averaged for the global and the
high-latitude regions (60–90∘ N) during 1900–2005. Before
1970, the global mean Δ SAT varied between -0.5 and
0.5 ∘C at an inter-annual variation scale. However, a small
increasing trend occurred during 1900–1960 in the global mean
Δ SAT; the interannual variations of the global mean Δ SAT
are dominant. Since the 1970s, however, the global mean Δ SAT
has increased with interannual variations. The warming rate of the
global mean Δ SAT is approximately 0.2 Kdecade-1.
However, the Δ SAT (60–90∘ N) has also varied between
-1.0 and +1.0 ∘C until 1970. The Δ SAT
(60–90∘ N) started to increase in the 1970s, reaching
1 ∘C in the 2000s. The warming rate from 1976 to 2005 was
approximately 0.6 Kdecade-1, which is twice as high or
higher than the warming rate for the global mean Δ SAT. This
result clearly reveals the AA and demonstrates that the MIROC5 is able
to simulate the AA in historical simulations. The positive trend for
the Δ SAT (60–90∘ N) for 1970–2005 in MIROC5 is
similar to that of the observation-based Δ SAT
(60–90∘ N) data from the Merged Land and Ocean Temperature
Analysis (MLOST) (Smith et al., 2008), HadCRUT4 (Morice et al., 2012)
and GISS Surface Temperature Analysis (GISTEMP) (Hansen et al., 2010).
Figure 1b shows the time series of the September Arctic sea ice
area. As the SAT in the northern high latitude increases, the Arctic
sea ice area significantly decreases. This decrease from the 1970s is
common in all ensemble members. The ensemble average of the September
sea ice area is approximately 5.25×10-6km2 in
2005. This simulated negative trend in the Arctic sea ice area is
consistent with that from the Hadley Center Sea Ice and Sea Surface
Temperature data set (HadISST) (Rayner et al., 2003) (Fig. 1b),
although the simulated area is slightly larger than that from the
HadISST.
Figure 2a shows the seasonal cycle of the Arctic sea ice area averaged
for 1976–1985 (blue line) and 1991–2005 (red line), and Fig. 2b
displays the differences in the seasonal cycle. The maximum sea ice
area occurs in March, and the sea ice then decreases to reach
a minimum in August. This seasonal cycle of sea ice area in the MIROC5
is slightly different from the observed seasonal cycle (Komuro et al.,
2012). According to the observations, the seasonal minimum sea ice
area occurs in September, and generally, the Arctic sea ice cover
starts to recover in October. Although such discrepancies are found,
the basic features of the seasonal cycle of the Arctic sea ice such as
the summer reduction and autumn recovery in sea ice are simulated in
the MIROC5. During recent global warming, the simulated Arctic sea ice
decreased in all months from 1976 to 2005, displaying a maximum
reduction in September. The maximum reduction in the Arctic sea ice
area in September is consistent with the observations of the Arctic
sea ice (Comiso et al., 2008).
For the cloud cover over the Arctic Ocean, Fig. 2c and d are identical
to Fig. 2a and b except for the total and low-level cloud cover,
respectively. The Arctic Ocean is covered by low-level clouds during
the summer. From summer to autumn, the cloud cover over the Arctic
Ocean decreases, reaching a minimum during April. The seasonal cycle
of the total cloud cover is similar to that of the low-level
clouds. Therefore, the seasonal cycle of the total cloud cover is able
to be explained by that of the low-level clouds. When compared with
the seasonal cycle of cloud cover observed by TOVS satellite and
surface-based cloud climatology reported by Schweiger et al. (1999)
and Hahn et al. (1995), the seasonal cycle of the total cloud cover
averaged over the Arctic Ocean is realistically simulated using this
method. As shown in Fig. 2d, the Arctic cloud cover for
autumn-winter-spring increases during (1976–85)–(1996–2005) but not
substantially. The increase in cloud cover is the largest in
October. The increase in the total cloud cover is also explained by
the increase in low-level clouds. This result agrees with previous
studies using satellite data and climate model simulations (Liu
et al., 2012; Vavrus et al., 2011; Wu and Lee, 2012). Compared to the
low-level cloud cover, the middle- and high-level cloud covers are
small, and their changes during (1976–85)–(1996–2005) are
approximately zero (not shown).
Figure 3 shows the geographical distributions of the linear trends in
the total cloud cover and sea ice concentrations from 1976 to 2005 in
September, October, and November. These linear trends were obtained
with the least squares method. As shown in Fig. 3a and b, the negative
trends in sea ice concentrations are found widely in the Laptev Sea,
the East Siberian Sea and the Beaufort Sea in September.
Additionally, in the Atlantic sector, the negative trends are seen in
the Kara Sea and the Barents Sea. For the cloud cover, a significant
trend is scarcely observed, limited to only the coast of the East
Siberian Sea and northern Bering Strait.
Negative trends in sea ice concentration remain in October (Fig. 3b),
although the area of significant negative trends becomes narrower than
that in September. However, positive trends in cloud cover exist
broadly over the Arctic Ocean. In the region of the East Siberian and
Beaufort Sea, where the sea ice significantly decreases, larger
positive trends in the cloud cover are found. Therefore, the increased
cloud cover is confirmed to result from the reduction in sea ice. Note
that the cloud cover increases significantly over the Arctic Ocean
north of the Beaufort Sea, although significant negative trends in sea
ice concentrations are not found.
In November, Fig. 3c shows that the significant negative trends in sea
ice concentration are limited to the Barents Sea, the Bering Strait
and the coasts of Greenland. Over these regions, a significant
increase in cloud cover is found. This result also supports the model
that the cloud cover increases because of reduced sea ice. In winter
months, the cloud cover increases over grids with reduced sea ice,
similar to that in November (not shown). However, because the sea ice
reductions in November and winter months are smaller than that in
October, the cloud cover change averaged over the Arctic Ocean in
these months is less significant than that in October. The following
subsections focus on the increased cloud cover in October.
Cloud cover changes resulting from reduced sea ice
As shown in Fig. 3, the retreating Arctic sea ice in September and
October is significant, but the positive trends in cloud cover in
September are less than those in October. As the open ocean extends
because of the reduced sea ice, vertical heat and moisture fluxes from
the ocean to the atmosphere are enhanced. Figure 4 shows linear trends
in the latent heat (LE) and sensible heat (SH) fluxes in September and
October. Positive trends are exhibited in the LE and SH at grids,
where sea ice reduced remarkably. The increase in both fluxes is
larger in October than in September because of the large temperature
difference between the atmosphere and the sea surface in October.
Because the air temperature normally decreases from September to
October along with the seasonal cycle, the difference between the air
temperature and sea surface temperature is greater in October compared
with that in September, causing the two fluxes to increase. The
increase in the LE and SH fluxes can play a role in the increased
cloud cover in October. These results are also consistent with
previous studies (Blüthgen et al., 2012; Schweiger et al., 2008;
Vavrus et al., 2011).
Figure 5 shows comparisons of the vertical profiles of the cloud
fraction, relative humidity, air temperature and specific humidity in
October between grids with and without significant reductions of sea
ice. In this figure, the case “Δ ai-” is defined by grids
with a linear trend of sea ice concentration less than -0.1 decade-1.
The case “Δ ai+” is defined by grids with linear trends of
sea ice concentration of more than 0 decade-1. In the case
Δ ai-, an increase in the cloud fraction is found in the lower
troposphere centered at the σ=0.9 level (approximately
830 m) (Fig. 5a and b). At the σ=0.9 level, the cloud
fraction increases by approximately 15 %. For the increase in the
cloud fraction, the cloud liquid water grows through a large-scale
condensation process, but the cloud ice shows little change. However,
the cloud fraction decreases at levels below σ=0.95. The
cloud base height rises because of the reduced sea ice in the case
Δ ai- (not shown). Figure 5c and d shows that the relative
humidity increases at levels between σ=0.9 and σ=0.8
(approximately 1839 m) for the case Δ ai-. This result
is consistent with the increase in the cloud fraction. Decreases in
the relative humidity are also found in levels below σ=0.9,
consistent with the decrease in the cloud fraction at levels below
σ=0.95 (approximately 460 m).
Figure 5e and f shows that the air temperature increases with the
maximum increase at the surface. The significant increases in air
temperature are found in layers between the surface and σ=0.85 (approximately 1200 m) (Fig. 5f). Above σ=0.85,
the increases in the air temperature are smaller than in the
underlying layers. Figure 5g and h shows that the specific humidity
increases significantly in the lower troposphere. At the lowest level,
the specific humidity increases by approximately 27 %. Compared
with the change in the saturated specific humidity (qsat,
dot-dot-dash lines in Fig. 5g and h), the increase in the specific
humidity is near to that in the qsat at levels with increases in the
cloud fraction. Therefore, the relative humidity increases and
enhances the cloudiness in these levels (Fig. 5b and d). However,
increases in the specific humidity are smaller than those in the
qsat at thin layers near the surface. Therefore, in the surface thin
layers, the relative humidity decreases and reduces the cloudiness.
Figure 6 shows the lapse rate of the air temperature and specific
humidity. In the case Δ ai-, the lapse rate of the air
temperature is large in the thin layers close to surface
(Fig. 6a). Thus, the strong vertical diffusion effect of heat is
confined in the surface thin layers. However, larger lapse rates are
noted for the specific humidity between the surface and σ=0.8
(Fig. 6b). The specific humidity is not limited to the surface thin
layer, in contrast to the air temperature. Therefore, the vertical
diffusion effect of moisture is enhanced in all levels of the lower
troposphere. Hence, much more water vapor is transported from the
ocean to higher atmospheric levels relative to the heat
transferred. These effects cause different vertical profile changes in
the air temperature and specific humidity.
The large lapse rate in the air temperature is limited to the surface
thin layer because of the radiative cooling in all atmospheric
levels. The air temperature is therefore unlikely to increase
significantly in the lower troposphere. The air temperature rises
significantly only in the surface thin layer. However, because no
stationary sink is noted for moisture similar to the radiative cooling
in the atmosphere over the Arctic Ocean, the water vapor from the
ocean increases in the lower troposphere.
In the case Δ ai+, the humidity and air temperature increase
slightly in the lower troposphere because of global warming. Thus, the
effect of global warming on the atmosphere, particularly in the
boundary layer, appears in a region of the Arctic Ocean without
a reduction in sea ice; however, the effect is small.
Cloud radiative effect
Cloud cover change affects the energy balance through the cloud
radiative effect (CRE). During the autumn–winter–spring seasons in the
Arctic region and because of the reduced or absent incoming shortwave
radiation, the DLR by clouds may play an important role in the surface
energy balance in the Arctic region. In addition, increasing the DLR
because of increasing both the water vapor content and air temperature
is an important factor contributing to the AA during global warming
(Rinke et al., 2013). Here, we introduce an index defined by the
ratio ((ΔCRE/ΔCS)SDLR)
between the change in the CRE of the surface DLR
(Δ CRESDLR) and the change in the clear-sky
surface DLR (Δ CSSDLR). Figure 7 shows the annual
time series of the index, (ΔCRE/ΔCS)SDLR. The index is averaged only for
Δ ai- grids. The Δ CRESDLR is positive
in grids in which the sea ice is reduced because the cloud cover
increases as a result of reduced sea ice. Additionally, the
Δ CSSDLR is positive over the entire Arctic Ocean
because of the increased air temperature and moisture. The indexes in
Fig. 7 are approximately 0.4–0.6 during the fall, winter and early
spring, varying little among the seasons. An increase in the cloud
cover as a result of reduced sea ice enhances the surface DLR. The
all-sky surface DLR increases by approximately 40–60 % compared
to the clear-sky surface DLR change. Thus, the change in the CRE
because of the reduced sea ice cannot be disregarded as a factor
affecting the AA. This finding disagrees with Rinke
et al. (2013). However, the index shown in Fig. 7 is different from
the averaged value over the Arctic Ocean. The averaged value is
smaller in the winter and early spring because the area with
a significant sea ice reduction is small during these
seasons. However, the index is close to zero in the summer because of
the minimal changes in Arctic cloud cover because of the reduced sea
ice. This behavior implies that the CRE change in the surface DLR in
the summer is less important than the albedo change resulting from the
reduced sea ice.
Discussion
As shown in Fig. 3b, increases in the cloud cover are found in the
Arctic Ocean near the North Pole, where the sea ice does not decrease
significantly. Figure 8a shows the vertical profile of the cloud
fraction averaged for the region (210–240∘ E,
75–83∘ N) indicated with a gray line in Fig. 8b. Compared
with the cloud fraction for 1976–1985, the cloud fraction for
1996–2005 increases by approximately 20 % in the lower
troposphere. The height at which the increase in the cloud fraction
occurs is lower than that in the Δ ai- case shown in
Fig. 5a. Figure 8b shows the linear trend of the sea level pressure
(SLP), moisture flux at 925 hPa, and their convergence in
October. A negative SLP trend is noted in the East Siberian Ocean in
which the sea ice is reduced during the period. This change in SLP
enhances the atmospheric moisture transported from the lower latitude
to the North Pole. Then, the moisture flux converges in the region
including the area covered by the gray lines. Accordingly, the cloud
fraction in the region near the North Pole increases in the lower
troposphere despite the absence of a significant reduction in sea ice.
During global warming, both the air temperature and humidity increase,
complicating the changes in Arctic cloud cover. Therefore, considering
future Arctic cloud cover changes, increases in both air temperature
and humidity are crucial components in addition to the sea ice
loss. With regard to the vertical profile of the cloud cover change,
the level at which the air temperature and humidity increases under
global warming conditions is important. Thus, the fine vertical
resolution and boundary process in the model may be a primary factor
for improving the projections of the Arctic cloud cover change related
to global warming and sea ice loss in the future.
Previous studies have argued for a role of changes in Arctic cloud in
the AA. Significantly increased DLR because of cloud cover occurs in
grids with significant reductions in sea ice, whereas select studies
have noted a lower effect of the increase in cloud cover on the
surface DLR. These discrepancies in the effects should be related to
the uncertainties of dealing with clouds and cloud radiative forcing
among models. The vertical profile of cloud cover change is also
strongly connected with the changes in cloud radiative forcing. The
uncertainties in the air temperature and humidity increases may be
among the causes. Therefore, further investigations into the Arctic
cloud cover change and on the feedback process related to clouds are
needed.
With regard to the feedback between the sea ice and clouds, the
effects of cloud cover on sea ice are also considerable. This study
focused on the changes in Arctic cloud cover as a result of the
reduced sea ice. However, we were unable to observe a significant
effect of the increased cloud cover on the sea ice reduction in our
statistical analysis of inter-seasonal variations using monthly mean
data, despite the increased surface DLR resulting from the increased
cloud cover.
In the future, if the sea ice retreats further not only in the summer
but also in the autumn and spring, then the Arctic cloud cover could
increase more and the effects of the cloud cover could become stronger
than the current period. Thus, a further understanding and correct
projection of the relationship between the sea ice and cloud cover is
important in the analysis of future global and Arctic climate change.
Summary
This study investigated the Arctic cloud cover change resulting from
the reduced sea ice in global warming simulated by MIROC5 to
understand the effect of changes in the extent of Arctic sea ice on
cloud cover.
A significant negative trend for the Arctic sea ice is found in the
summer and autumn during 1976–2005, although small negative trends in
the winter and spring are found in limited regions. The temporal trend
of the Arctic cloud cover is positive in the autumn-spring, with
a maximum in October. This study focused on the increases in the cloud
fraction in October resulting from the reduced sea ice.
In grids with reduced sea ice concentrations (trends below
-0.1 decade-1), the cloud fraction in October increases
at levels between σ=0.9 and σ=0.7. However, the
cloud fraction in grids without a reduction in the sea ice is unlikely
to change. Because of the reduced sea ice, a more extensive open ocean
area increases the latent and sensible heat fluxes from the ocean to
atmosphere. Along with the seasonal progression, atmospheric
temperature cooling increases the temperature gradient between the air
and sea surface in October. Therefore, the fluxes from the ocean to
the atmosphere are enhanced in October rather than in September. This
effect results in a greater increase in the cloud fraction in October
than in September.
Significant warming is found at the surface thin layer because the
longwave radiative cooling suppresses warming in the overlying
layers. However, because no stationary sink is available for the
moisture such as the radiative cooling process in the atmosphere,
moistening occurs throughout the lower troposphere. Thus, the cloud
fraction increases throughout the lower troposphere except near the
surface thin layers, in which the cloud fraction drops. Further, the
cloud cover increases in regions close to the North Pole, at which no
significant reductions in sea ice occur because the lower-tropospheric
circulation is varied in response to the reduced sea ice.
The change in the CRE as a result of the reduced sea ice in the
surface DLR is approximately 40–60 % compared to a change in
clear-sky surface DLR in grids with significant sea ice reduction from
autumn to spring. Therefore, the change in CRE resulting from the
reduced sea ice must be considered as a factor influencing the Arctic
amplification.
This study analyzed data from only one climate model,
MIROC5. Therefore, the sea ice–cloud cover relationship in multiple
models and its contribution to the uncertainty of future climate
change projections among models are suitable future research topics.
Acknowledgements
This study was supported by the GRENE Arctic Climate Change Research
Project conducted by the Ministry of Education, Culture, Sports,
Science and Technology of the Japanese Government. We thank
Y. Komuro and T. Suzuki for providing the land fraction data of
MIROC5 to enable the calculations of the Arctic sea ice area. The
Earth Simulator at JAMSTEC was employed to perform the AOGCM
simulations.
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(a) Time series of the surface air temperature (SAT)
anomaly from the 1951–1980 mean. Solid black, green, orange, and
blue lines are the SAT anomalies averaged for the 60–90∘ N
in MIROC5's ensemble mean, MLOST, GISTEMP, and HadCRUT4,
respectively. The broken black line is the global and ensemble mean
SAT anomaly in MIROC5. The gray shaded area indicates the maximum
and minimum SAT anomalies between the ensemble members of
MIROC5. (b) Time series of the September sea ice
extent. The black lines are the ensemble mean. The gray shaded area
indicates the maximum and minimum extent between the ensemble
members. The purple line is the September sea ice extent calculated
from HadISST. The units of the SAT anomaly and sea ice extent
anomaly are K and 106km2, respectively.
Seasonal cycle of (a) Arctic mean sea ice area
averaged for 1976–1985 and 1996–2005 and (b) the
difference between the means. (c) and (d) are
identical to (a) and (b) except for the total and
low cloud covers. The unit of the sea ice area is
106km2.
Geographical map of the linear trend in the total cloud cover
(shade) and sea ice concentration (contour) in (a)
September, (b) October, and (c) November during
1976–2005. The units are decade-1.
Geographical map of the linear trend in (a, b) the
latent heat and (c, d) the sensible heat flux in
(a, c) September and (b, d) October during
1976–2005. The units are Wm-2decade-1. A linear
trend for the sea ice concentration (contour) is overlaid, and the
units are decade-1.
(a, c, e, g) Vertical profiles of the average for
1976–1985 (blue) and 1996–2005 (red) in the (a) cloud
fraction, (c) relative humidity, (e) air
temperature, and (g) specific humidity. The solid (broken)
line represents the case Δ ai- (Δ ai+). See the
text for the definition of the Δ ai- and Δ ai+.
(b, d, f, h) Vertical profiles of the differences between
the averages for 1976–1985 and for 1991–2005 in the (b)
cloud fraction, (d) relative humidity, (f) air
temperature, and (h) specific humidity. The solid (broken)
line represents the case Δ ai- (Δ ai+). The
dot-dot-dash lines in g and h indicate the saturated specific
humidity. The units of the air temperature and specific humidity are
K and gkg-1, respectively.
Vertical profiles of the lapse rate in the (a) air
temperature and (b) specific humidity for 1976–1985 (blue)
and 1996–2005 (red). The solid (broken) line represents the case
Δ ai- (Δ ai+). The units of the lapse rate of
the air temperature and the specific humidity are Km-1
and gkg-1m-1, respectively.
Annual time series of the index, (ΔCRE/ΔCS)SDLR. See the text for the definition of the
index. The indexes are for the case Δ ai-.
(a) Vertical profile of the cloud fraction for
1976–1985 (blue) and 1996–2005 (red) averaged in the region
(210–240∘ E, 75–83∘ N) delineated by solid gray
lines in (b). (b) Linear trend of the sea level
pressure (contour), moisture flux at 925 hPa (vector), and
its convergence (shade). The unit of the moisture flux trend is
(kgkg-1) (ms-1) decade-1.