Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models
This paper aims to find a possible ensemble method to combine the global climate models, providing an accuracy forecast of sea ice thickness. Conventional multimodel superensemble, the advanced method that is widely used in atmosphere, ocean and other fields, cannot be well performed in sea ice thic...
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ftcopernicus:oai:publications.copernicus.org:tcd84712 2023-05-15T18:16:23+02:00 Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models Yangjun, Wang Kefeng, Liu Ren, Zhang Longxia, Qian Yu, Zhang 2020-06-16 application/pdf https://doi.org/10.5194/tc-2020-86 https://tc.copernicus.org/preprints/tc-2020-86/ eng eng doi:10.5194/tc-2020-86 https://tc.copernicus.org/preprints/tc-2020-86/ eISSN: 1994-0424 Text 2020 ftcopernicus https://doi.org/10.5194/tc-2020-86 2020-07-20T16:22:06Z This paper aims to find a possible ensemble method to combine the global climate models, providing an accuracy forecast of sea ice thickness. Conventional multimodel superensemble, the advanced method that is widely used in atmosphere, ocean and other fields, cannot be well performed in sea ice thickness simulation. Hence, an adaptive forecasting through exponential re-weighting (AFTER) algorithm is adopted to improve the conventional multimodel superensemble. Results show our proposed methods perform better than any other mainstream ensemble methods by using a multi-criteria evaluation. The proposed method is used to predict the future sea ice thickness in the period of 2020–2049, where the possible biases are discussed. Text Sea ice Copernicus Publications: E-Journals |
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Open Polar |
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Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
This paper aims to find a possible ensemble method to combine the global climate models, providing an accuracy forecast of sea ice thickness. Conventional multimodel superensemble, the advanced method that is widely used in atmosphere, ocean and other fields, cannot be well performed in sea ice thickness simulation. Hence, an adaptive forecasting through exponential re-weighting (AFTER) algorithm is adopted to improve the conventional multimodel superensemble. Results show our proposed methods perform better than any other mainstream ensemble methods by using a multi-criteria evaluation. The proposed method is used to predict the future sea ice thickness in the period of 2020–2049, where the possible biases are discussed. |
format |
Text |
author |
Yangjun, Wang Kefeng, Liu Ren, Zhang Longxia, Qian Yu, Zhang |
spellingShingle |
Yangjun, Wang Kefeng, Liu Ren, Zhang Longxia, Qian Yu, Zhang Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models |
author_facet |
Yangjun, Wang Kefeng, Liu Ren, Zhang Longxia, Qian Yu, Zhang |
author_sort |
Yangjun, Wang |
title |
Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models |
title_short |
Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models |
title_full |
Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models |
title_fullStr |
Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models |
title_full_unstemmed |
Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models |
title_sort |
improved multimodel superensemble forecast for sea ice thickness using global climate models |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-2020-86 https://tc.copernicus.org/preprints/tc-2020-86/ |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2020-86 https://tc.copernicus.org/preprints/tc-2020-86/ |
op_doi |
https://doi.org/10.5194/tc-2020-86 |
_version_ |
1766189961469493248 |