Subseasonal forecast of Arctic sea ice concentration via statistical approaches
Subseasonal forecast of Arctic sea ice has received less attention than the seasonal counterpart, as prediction skill of dynamical models generally exhibits a significant drop in the extended range (> 2 weeks). The predictability of pan-Arctic sea ice concentration is evaluated by statistical mod...
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ftcolumbiauniv:oai:academiccommons.columbia.edu:10.7916/cf3g-tt37 2023-05-15T14:43:55+02:00 Subseasonal forecast of Arctic sea ice concentration via statistical approaches Wang, Lei Yuan, Xiaojun Li, Cuihua 2019 https://doi.org/10.7916/cf3g-tt37 English eng https://doi.org/10.7916/cf3g-tt37 Sea ice Statistical weather forecasting Climatology Articles 2019 ftcolumbiauniv https://doi.org/10.7916/cf3g-tt37 2022-01-29T23:21:24Z Subseasonal forecast of Arctic sea ice has received less attention than the seasonal counterpart, as prediction skill of dynamical models generally exhibits a significant drop in the extended range (> 2 weeks). The predictability of pan-Arctic sea ice concentration is evaluated by statistical models using weekly time series for the first time. Two statistical models, the vector auto-regressive model and the vector Markov model, are evaluated for predicting the 1979–2014 weekly Arctic sea ice concentration (SIC) anomalies at the subseasonal time scale, using combined information from the sea ice, atmosphere and ocean. The vector auto-regressive model is slightly inferior to the vector Markov model for the subseasonal forecast of Arctic SIC, as the latter captures more effectively the subseasonal transition of the underlying dynamics. The cross-validated forecast skill of the vector Markov model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times > 3 weeks. Surface air and ocean temperatures can be included to further improve the forecast skill for lead times > 4 weeks. The long-term trends in SIC due to global warming and its polar amplification contribute significantly to the subseasonal sea ice predictability in summer and fall. The vector Markov model shows much higher skill than the NCEP CFSv2 model for lead times of 3–6 weeks, as evaluated for the period of 1999–2010. Article in Journal/Newspaper Arctic Global warming Sea ice Columbia University: Academic Commons Arctic |
institution |
Open Polar |
collection |
Columbia University: Academic Commons |
op_collection_id |
ftcolumbiauniv |
language |
English |
topic |
Sea ice Statistical weather forecasting Climatology |
spellingShingle |
Sea ice Statistical weather forecasting Climatology Wang, Lei Yuan, Xiaojun Li, Cuihua Subseasonal forecast of Arctic sea ice concentration via statistical approaches |
topic_facet |
Sea ice Statistical weather forecasting Climatology |
description |
Subseasonal forecast of Arctic sea ice has received less attention than the seasonal counterpart, as prediction skill of dynamical models generally exhibits a significant drop in the extended range (> 2 weeks). The predictability of pan-Arctic sea ice concentration is evaluated by statistical models using weekly time series for the first time. Two statistical models, the vector auto-regressive model and the vector Markov model, are evaluated for predicting the 1979–2014 weekly Arctic sea ice concentration (SIC) anomalies at the subseasonal time scale, using combined information from the sea ice, atmosphere and ocean. The vector auto-regressive model is slightly inferior to the vector Markov model for the subseasonal forecast of Arctic SIC, as the latter captures more effectively the subseasonal transition of the underlying dynamics. The cross-validated forecast skill of the vector Markov model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times > 3 weeks. Surface air and ocean temperatures can be included to further improve the forecast skill for lead times > 4 weeks. The long-term trends in SIC due to global warming and its polar amplification contribute significantly to the subseasonal sea ice predictability in summer and fall. The vector Markov model shows much higher skill than the NCEP CFSv2 model for lead times of 3–6 weeks, as evaluated for the period of 1999–2010. |
format |
Article in Journal/Newspaper |
author |
Wang, Lei Yuan, Xiaojun Li, Cuihua |
author_facet |
Wang, Lei Yuan, Xiaojun Li, Cuihua |
author_sort |
Wang, Lei |
title |
Subseasonal forecast of Arctic sea ice concentration via statistical approaches |
title_short |
Subseasonal forecast of Arctic sea ice concentration via statistical approaches |
title_full |
Subseasonal forecast of Arctic sea ice concentration via statistical approaches |
title_fullStr |
Subseasonal forecast of Arctic sea ice concentration via statistical approaches |
title_full_unstemmed |
Subseasonal forecast of Arctic sea ice concentration via statistical approaches |
title_sort |
subseasonal forecast of arctic sea ice concentration via statistical approaches |
publishDate |
2019 |
url |
https://doi.org/10.7916/cf3g-tt37 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Global warming Sea ice |
genre_facet |
Arctic Global warming Sea ice |
op_relation |
https://doi.org/10.7916/cf3g-tt37 |
op_doi |
https://doi.org/10.7916/cf3g-tt37 |
_version_ |
1766315488834486272 |