| 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 |
| 利用综合评分方法评估CMIP5模式对北极海冰的模拟能力 |
| Assessment of Arctic sea ice simulations in CMIP5 models using a synthetical skill scoring method |
| 投稿时间：2019-05-06 |
| DOI：10.1007/s13131-019-1474-0 |
| 中文关键词: 北极海冰 气候模式 巴伦支与卡拉海 多模式集合平均 |
| 英文关键词:Arctic sea ice climate model Barents and Kara Seas multi-model ensemble mean |
| 基金项目:The National Natural Science Foundation of China under contract Nos 41576178 and 41630963; the National Basic Research Program (973 program) of China under contract No. 2015CB954004. |
| 作者 | 单位 | E-mail | | 吴利萍 | 厦门大学近海海洋环境科学国家重点实验室, 海洋与地球学院, 厦门, 361102 | | | 杨小怡 | 厦门大学近海海洋环境科学国家重点实验室, 海洋与地球学院, 厦门, 361102 | xyyang@xmu.edu.cn | | 胡建宇 | 厦门大学近海海洋环境科学国家重点实验室, 海洋与地球学院, 厦门, 361102
南方海洋科学与技术广东省实验室, 珠海 | |
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| 中文摘要: |
| 北极海冰冰盖自20世纪以来经历了前所未有的缩减，这使得北极海冰异常对大气环流的反馈作用日益显现。尽管目前的气候模式模拟北极海冰均为减少的趋势，但各模式间仍然存在较大的分散性。为了评估模式对于北极海冰变化及其气候效应的模拟能力，我们将海冰线性趋势和年际异常两者结合起来构造了一种合理的衡量指标。我们还强调巴伦支与卡拉海的重要性，因为前人研究证明此区域海冰异常是近年来影响大尺度大气环流变异的关键因子。根据我们设定的标准，CMIP5模式对海冰的模拟可被归为三种类型。这三组多模式集合平均之间存在巨大的差异，验证了这种分组方法的合理性。此外，我们还进一步探讨了造成模式海冰模拟能力差别的潜在物理因子。结果表明模式所采用的臭氧资料集对海冰模拟能力有显著的影响。 |
| 英文摘要: |
| 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. |
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