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Acta Oceanologica Sinica

, Volume 38, Issue 9, pp 48–58 | Cite as

Assessment of Arctic sea ice simulations in CMIP5 models using a synthetical skill scoring method

  • Liping Wu
  • Xiao-Yi YangEmail author
  • Jianyu Hu
Article

Abstract

The Arctic sea ice cover has declined at an unprecedented pace since the late 20th century. As a result, the feedback of sea ice anomalies for atmospheric circulation has been increasingly evidenced. While climatic models almost consistently reproduced a decreasing trend of sea ice cover, the reported results show a large distribution. To evaluate the performance of models for simulating Arctic sea ice cover and its potential role in climate change, this study constructed a reasonable metric by synthesizing both linear trends and anomalies of sea ice. This study particularly focused on the Barents Sea and the Kara Sea, where sea ice anomalies have the highest potential to affect the atmosphere. The investigated models can be grouped into three categories according to their normalized skill scores. The strong contrast among the multi-model ensemble means of different groups demonstrates the robustness and rationality of this method. Potential factors that account for the different performances of climate models are further explored. The results show that model performance depends more on the ozone datasets that are prescribed by the model rather than on the chemical representation of ozone.

Key words

Arctic sea ice climate model Barents and Kara Seas multi-model ensemble mean 

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Notes

Acknowledgements

We acknowledge the climate modelling groups, the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling (WGCM), and the Met Office Hadley Centre’s sea ice for the open datasets used in this study.

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Copyright information

© Chinese Society for Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Marine Environmental Science, College of Ocean and Earth SciencesXiamen UniversityXiamenChina
  2. 2.Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)ZhuhaiChina

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