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

Wu Liping; Yang Xiao-Yi; Hu Jianyu

doi:10.1007/s13131-019-1474-0

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Wu Liping, Yang Xiao-Yi, Hu Jianyu. Assessment of Arctic sea ice simulations in CMIP5 models using a synthetical skill scoring method[J]. Acta Oceanologica Sinica, 2019, 38(9): 48-58. doi: 10.1007/s13131-019-1474-0

Citation:

Wu Liping, Yang Xiao-Yi, Hu Jianyu. Assessment of Arctic sea ice simulations in CMIP5 models using a synthetical skill scoring method[J]. Acta Oceanologica Sinica, 2019, 38(9): 48-58. doi: 10.1007/s13131-019-1474-0

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

doi: 10.1007/s13131-019-1474-0

1.
State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
2.
State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China;Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China

Received Date: 2019-05-06

Abstract

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

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