Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum

The 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...

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Main Authors: Hu, Yongyun, Horton, Radley M., Liu, Jiping, Song, Mirong
Format: Other/Unknown Material
Language:unknown
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/2060/20160001390
id ftnasantrs:oai:casi.ntrs.nasa.gov:20160001390
record_format openpolar
spelling ftnasantrs:oai:casi.ntrs.nasa.gov:20160001390 2023-05-15T14:51:35+02:00 Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum Hu, Yongyun Horton, Radley M. Liu, Jiping Song, Mirong Unclassified, Unlimited, Publicly available May 19, 2015 application/pdf http://hdl.handle.net/2060/20160001390 unknown Document ID: 20160001390 http://hdl.handle.net/2060/20160001390 Copyright, Distribution under U.S. Government purpose rights CASI Meteorology and Climatology Oceanography GSFC-E-DAA-TN23862 Environmental Research Letters (ISSN 1748-9326); 10; 5; 054017 2015 ftnasantrs 2019-07-20T23:57:38Z The 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. Other/Unknown Material Arctic Sea ice NASA Technical Reports Server (NTRS) Arctic
institution Open Polar
collection NASA Technical Reports Server (NTRS)
op_collection_id ftnasantrs
language unknown
topic Meteorology and Climatology
Oceanography
spellingShingle Meteorology and Climatology
Oceanography
Hu, Yongyun
Horton, Radley M.
Liu, Jiping
Song, Mirong
Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum
topic_facet Meteorology and Climatology
Oceanography
description The 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.
format Other/Unknown Material
author Hu, Yongyun
Horton, Radley M.
Liu, Jiping
Song, Mirong
author_facet Hu, Yongyun
Horton, Radley M.
Liu, Jiping
Song, Mirong
author_sort Hu, Yongyun
title Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum
title_short Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum
title_full Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum
title_fullStr Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum
title_full_unstemmed Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum
title_sort revisiting the potential of melt pond fraction as a predictor for the seasonal arctic sea ice extent minimum
publishDate 2015
url http://hdl.handle.net/2060/20160001390
op_coverage Unclassified, Unlimited, Publicly available
geographic Arctic
geographic_facet Arctic
genre Arctic
Sea ice
genre_facet Arctic
Sea ice
op_source CASI
op_relation Document ID: 20160001390
http://hdl.handle.net/2060/20160001390
op_rights Copyright, Distribution under U.S. Government purpose rights
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