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Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent MinimumThe rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. A recent modeling study that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July. Our results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state.
Document ID
20160001390
Document Type
Reprint (Version printed in journal)
Authors
Liu, Jiping (State Univ. of New York Albany, NY, United States)
Song, Mirong (Academia Sinica Beijing, China)
Horton, Radley M. (Columbia Univ. New York, NY, United States)
Hu, Yongyun (Peking Univ. Beijing, China)
Date Acquired
February 2, 2016
Publication Date
May 19, 2015
Publication Information
Publication: Environmental Research Letters
Volume: 10
Issue: 5
ISSN: 1748-9326
Subject Category
Meteorology and Climatology
Oceanography
Report/Patent Number
GSFC-E-DAA-TN23862
Funding Number(s)
CONTRACT_GRANT: NNSF-China-41176169
CONTRACT_GRANT: NNX14AB99A
CONTRACT_GRANT: NOAA-NA14OAR4310216
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
sea ice
simulation
Arctic Ocean
ponds
satellite observation

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