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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2018-26
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2018-26
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Mar 2018

21 Mar 2018

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This preprint was under review for the journal TC but the revision was not accepted.

Assessment of Arctic sea ice simulations in CMIP5 models

Liping Wu, Xiao-Yi Yang, and Jianyu Hu Liping Wu et al.
  • State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen , 361102, China

Abstract. The Arctic sea ice cover has experienced an unprecedented decline since the late 20th century. As a result, the feedback of sea ice anomalies to atmospheric circulation has been increasingly evidenced. While the climate models almost consistently reproduce the downward trend of sea ice cover, great dispersion between them still exists. To evaluate the model performance in simulating Arctic sea ice and its potential role in climate change, we constructed a reasonable metric by synthesizing the linear trends and anomalies of the sea ice. We particularly focus on the Barents and Kara seas, where the sea ice anomalies have the greatest potential to feedback the atmosphere. Models can be grouped into three categories according to this criterion. The strong contrast among the multi-model ensemble means in different groups demonstrates the robustness and rationality of this method. The potential factors accounting for the different performance of climate models are further explored. The result shows that the model performance depends more on the ozone datasets prescribed by model rather than on the chemistry representation of ozone.

Liping Wu et al.

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Liping Wu et al.

Liping Wu et al.

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Short summary
In this study, we constructed a objectively and comprehensively assessment framework to quantify the models’ ability of sea ice simulation, by which we can sort out some better models to constrain the biases of models and set a better basis for the study of future Arctic climate change prediction. Moreover, we further scrutinized on the model parameters and suggested the possible way to improve models’ performance on Arctic sea ice simulation.
In this study, we constructed a objectively and comprehensively assessment framework to quantify...
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