the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models
Wang Yangjun
Liu Kefeng
Zhang Ren
Qian Longxia
Zhang Yu
Abstract. 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.
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Wang Yangjun et al.


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RC1: 'Review of tc-2020-86', Anonymous Referee #1, 20 Jul 2020
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AC1: 'Response to reviewer1', Liu Kefeng, 05 Dec 2020
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AC1: 'Response to reviewer1', Liu Kefeng, 05 Dec 2020
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RC2: 'Ensemble weighting for sea ice thickness projections', Anonymous Referee #2, 22 Sep 2020
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AC2: 'Response to reviewer2', Liu Kefeng, 05 Dec 2020
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AC2: 'Response to reviewer2', Liu Kefeng, 05 Dec 2020


-
RC1: 'Review of tc-2020-86', Anonymous Referee #1, 20 Jul 2020
-
AC1: 'Response to reviewer1', Liu Kefeng, 05 Dec 2020
-
AC1: 'Response to reviewer1', Liu Kefeng, 05 Dec 2020
-
RC2: 'Ensemble weighting for sea ice thickness projections', Anonymous Referee #2, 22 Sep 2020
-
AC2: 'Response to reviewer2', Liu Kefeng, 05 Dec 2020
-
AC2: 'Response to reviewer2', Liu Kefeng, 05 Dec 2020
Wang Yangjun et al.
Wang Yangjun et al.
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