Arctic Cloud Properties and Radiative Forcing From Observations and Their Role in Sea Ice Decline Predicted by the NCAR CCSM3 Model During the 21st Century - Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications - page 57

of cloud changes plays an important role. Overestimation of
cloud LWP during summer decreases changes in both the
shortwave and the longwave cloud forcing. During winter,
CCSM3 cloud LWP is comparable to the observed values
when cloud LWP changes are largest and the sensitivity of
the cloud longwave forcing to LWP is high.
The NCAR CCSM3 model predicts an increase in cloud
fraction, cloud liquid water content, and near-surface atmo-
sphere warming in the Arctic during the 21st century. These
atmospheric changes provide additional energy to the sur-
face; thus increase in cloudiness facilitates rather than re-
duces the surface warming accompanying sea ice decline.
The ultimate sea ice changes depend on a combination of
factors. The present study showed that clouds directly re-
spond to initial Arctic warming and are among the key fac-
tors accelerating the Arctic sea ice decline. Given the larger
magnitude of the longwave flux increase induced by clouds
compared to the absorbed shortwave flux, as predicted by
CCSM3, the cloud surface warming may be a more domi-
nant driver of sea ice loss than the sea ice albedo feedback.

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Acknowledgments. We are grateful to Matthew Shupe for his
continuous help with the SHEBA data, Martin Vancoppenolle and
Nikolay koldunov for their positive and critical input, and Gerhard
krinner and Annette Rinke for helpful discussions. Our great ap-
preciation goes to everyone involved in the SHEBA fieldwork and
subsequent data processing. We acknowledge the NCAR CCSM3
group for providing the model output for analysis, the Program for
Climate Model Diagnosis and Intercomparison (PCMDI) for col-
lecting and archiving the model data, the JSC/CLIVAR Working
Group on Coupled Modeling (WGCM) and their Coupled Model
Intercomparison Project (CMIP) and Climate Simulation Panel for
organizing the model data analysis activity, and the IPCC WG1
TSU for technical support. The IPCC Data Archive at Lawrence
Livermore National Laboratory is supported by the Office of Sci-
ence, U.S. Department of Energy. We thank the National Snow
and Ice Data Center, the Hadley Centre for Climate Prediction
and Research, and the Earth Radiation Budget Experiment team
for providing satellite data. We appreciate the efforts of the two
anonymous reviewers and the Editor for both the positive feedback
and constructive criticism that helped to improve the manuscript.
I.G. was supported by NASA Fellowship ESSF0400000163 and
Agence Nationale de la Recherche (France) grant OTP 232333.
B.T. was supported by the Natural Sciences and Engineering Re-
search Council of Canada Discovery Grant Program and by the
National Science Foundation under grant OPP-0230325 from the
Office of Polar Programs and grant ARC-05-20496 from the Arctic
Science Program.

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