极地研究

极地研究 ›› 2022, Vol. 34 ›› Issue (4): 460-470.DOI: 10.13679/j.jdyj.20210071

• 研究论文 • 上一篇    下一篇

第六次国际耦合模式比较计划中我国地球气候系统模式海冰范围的模拟评价

赵立清  王晓春  李佳琦   

  1. 南京信息工程大学海洋科学学院, 江苏 南京 210044
  • 出版日期:2022-12-31 发布日期:2023-01-12
  • 通讯作者: 王晓春
  • 作者简介:赵立清, 女, 1982年生。副教授, 主要从事海冰模式分析和海洋湍流混合等研究。E-mail: zhaoliqing@nuist.edu.cn
  • 基金资助:
    国家重点研发计划 (2018YFA0605904)资助

Evaluation of Arctic sea ice extent according to Chinese CMIP6 models

Zhao Liqing, Wang Xiaochun, Li Jiaqi   

  1. School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210014, China
  • Online:2022-12-31 Published:2023-01-12

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摘要： 世界气候研究计划(WCRP)正在组织实施第六次国际耦合模式比较计划(CMIP6), 该计划中, 我国提供了9个地球气候系统模式的结果。本文利用这9个地球气候系统模式的北极海冰输出以及同时段海冰的观测数据, 评价了这些模式1980—2014年北极海冰范围的季节变化、长期趋势及年内变率, 并与CMIP6多模式平均进行了比较。结果表明, 与观测数据对比, 多数模式(8/9)都能反映出北极海冰范围季节变化的时间特征, 其中1个模式海冰范围最大值的出现时间延迟了1个月。多数模式(8/9)高估季节变化的最大值。在长期趋势方面, 5个模式高估了3月北极海冰范围减小的趋势, 4个模式低估了9月北极海冰范围减小的趋势。与CMIP6多模式平均结果相比, 其中1个模式的季节变化和长期趋势在多模式平均值的标准差范围内。观测表明, 1980—2014年, 9月海冰范围的减少趋势为3月减少趋势的2倍, 这导致了海冰范围年内变率呈现上升趋势, 有两个模式较好地再现了这一特征。此外, 参加CMIP5及CMIP6具有传承关系的我国4个模式在北极海冰范围季节变化及长期趋势方面有了明显的改善。

关键词: 地球气候系统模式, CMIP6, 北极海冰, 海冰范围

Abstract: The Coupled Model Intercomparison Project Phase Six (CMIP6) organized by the World Climate Research Project (WCRP) is in progress. Nine earth climate system models from China contribute to CMIP6. The seasonal cycle, long-term linear trend, and intra-annual variability of Arctic sea ice extent (SIE) from the nine models are evaluated by comparing them with observations from 1980 to 2014. The results show that eight models are capable of reproducing the seasonal cycles of Arctic SIE well, except one of nine models in which the maximum value of seasonal cycle is delayed by one month. Most of the models (8/9) overestimate the maximum sea ice extent values of seasonal cycle. In terms of long-term trends, five models overestimate the declining trends of Arctic sea ice in March, and four models underestimate the declining trends of Arctic sea ice in September. Compared with the results of the CMIP6 multi-model ensemble mean, it is found that there is one model for which the seasonal cycle and long-term linear trend of SIE are both within the range of the multi-model ensemble mean’s standard deviation. The difference in long-term September and March SIE trends leads to a significant increasing trend of SIE intra-annual variability as measured by the standard deviation of SIE within a calendar year. Two models can reproduce this feature reasonably well. Finally, it is worth pointing out that four models from the same institution that contributed to both CMIP5 and CMIP6 show improvements in terms of SIE seasonal cycle and its long-term linear trend of annual averaged SIE.

Key words: earth climate system model, CMIP6, Arctic sea ice, sea ice extent