摘要
世界气候研究计划(WCRP)正在组织实施第6次国际耦合模式比较计划(CMIP6)。本文选取了参加CMIP6的9个中国大陆地球气候系统模式的北极海冰输出结果与同时段海冰遥感观测数据进行比较,评估了各个模式1980年至2014年北极海冰密集度和其长期趋势的空间分布。研究表明,所有的模式都可以较好地模拟出3月北极海盆海冰的分布情况,误差主要分布在海冰边缘地区,其中鄂霍茨克海的中部以及巴伦支海地区误差最大,最高值可达90%。与3月相比,模式对9月海冰空间分布的模拟效果不佳,在北极海盆地区以及海冰边缘地区均存在15%以上的误差。在海冰密集度长期趋势空间分布方面,3月,9个模式总体高估了海冰下降区的海域面积,在鄂霍茨克海、巴伦支海以及格陵兰海北部海域为模式误差大值区(大于50%)。模式在模拟9月海冰下降趋势的区域及量级上较3月都有更大的偏差。另外,9个模式对海冰密集度多年平均季节变化的模拟能力与其对长期趋势的模拟能力有一定关联,对海冰密集度季节变化模拟准确的模式,其海冰长期趋势的模拟也较接近观测。海冰分量模式中参数化方案的改进可以明显提高模式的模拟能力。
The Coupled Model Intercomparison Project phase six(CMIP6)organized by the World Climate Research Project(WCRP)is currently on-going.We compared the outputs from 9 earth climate system models that are developed in China with the observations and evaluated the simulation of the spatial distribution of Arctic sea ice concentration and its long-term trend from 1980 to 2014.It shows that all models perform well in simulating the distribution of sea ice in the Arctic basin in March.The errors exist mainly in marginal areas around the sea ice cover,with maximum errors being in the central Sea of Okhotsk and the Barents Sea,reaching 90%.However,the simulated sea ice concentration in September has above 15%errors in both the marginal areas around sea ice cover and the central part of the Arctic basin.In terms of the spatial distribution of the long-term trend of sea ice concentration,the 9 models overestimate the declining of the area of sea ice concentration in March,with large errors(over 50%)existing in the Sea of Okhotsk,the Barents Sea,and northern area to the Greenland.Compared with the situation in March,the models tend to have larger errors in the area and magnitude of the sea ice declining in September.In addition,the skill of the models on simulating the seasonality of sea ice concentration is related to their performance on simulating the long-term trend of sea ice concentration.The models that can well reproduce the seasonality of sea ice concentration usually performs better on simulating the long-term trend.New parameterization schemes in sea ice model show great potential in improving the performance of climate system models.
作者
李佳琦
王晓春
赵立清
LI Jiaqi;WANG Xiaochun;ZHAO Liqing(School of Marine Sciences,Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《海洋科学进展》
CAS
CSCD
北大核心
2024年第2期254-268,共15页
Advances in Marine Science
基金
国家重点研发计划项目(2018YFA0605904)。