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The Cryosphere An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/tc-2020-86
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-86
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Jun 2020

Submitted as: research article | 16 Jun 2020

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This preprint is currently under review for the journal TC.

Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models

Wang Yangjun1, Liu Kefeng1, Zhang Ren1, Qian Longxia2, and Zhang Yu3 Wang Yangjun et al.
  • 1Institute of Meteorology and Oceanology, National University of Defense Technology, Nanjing 211101, China
  • 2School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
  • 3PLA 95746 troops, Sichuan 611530, China

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.

Wang Yangjun et al.

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Wang Yangjun et al.

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Latest update: 04 Jul 2020
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Short summary
This paper aims to find an ensemble method that combines the global climate models, providing an accurate forecast of sea ice thickness (SIT). An improved multimodel superensemble is proposed in SIT prediction, showing better performance than other mainstream methods. Large biases between the proposed model and observations in SIT simulation are found along the coastline in the west Arctic, and in August, being in accordance with the largest SIT anomaly in time and space.
This paper aims to find an ensemble method that combines the global climate models, providing an...
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