RegCM4对中国东部区域气候模拟的辐射收支分析
投稿时间: 2015-11-04  最后修改时间: 2016-01-14  点此下载全文
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作者单位E-mail
韩振宇 国家气候中心 hanzy@cma.gov.cn 
王宇星 国家海洋局海洋减灾中心  
聂羽 国家气候中心  
中文摘要:大气顶净辐射是地球气候系统的主要驱动因子之一,气候系统内的辐射收支直接影响着大气和海洋运动、大气水循环等。本文利用卫星和再分析数据,评估了区域气候模式RegCM4对中国东部地区辐射收支的基本模拟能力,关注地表净短波(SNS)、地表净长波(SNL)、大气顶净短波(TNS)、大气顶净长波(TNL)四个辐射分量。结果表明:短波辐射的误差值在夏季较大,而长波辐射的误差值在冬季较大。但各辐射分量模拟误差的空间分布在冬、夏季都有较好的一致性。对于地表辐射通量,SNS表现为正偏差(向下净短波偏多),在各分量中误差最大,区域平均误差值近50W/m2;SNL表现为负偏差(向上净长波偏多)。对于大气顶辐射通量,TNS和TNL分别表现为“北负南正”的误差分布和整体正偏差。利用空间相关、散点线性回归和简化物理模型估算的方法,对四个辐射分量的模拟误差进行归因分析。结果显示,在云量、地表反照率、地表温度三个直接影响因子中,云量模拟误差的贡献最大,中国东部地区云量模拟显著偏少。
中文关键词:辐射收支  模拟评估  区域气候模式
 
The Radiation Budget in Regional Climate Simulation of RegCM4 for Eastern China
Abstract:The net radiation at the top of the atmosphere (TOA) is one of the most important driving forces of the Earth's climate system, while radiation budget of the climate system directly influences the atmospheric and oceanic motion, as well as atmospheric water cycle. In this paper, the performance of regional climate model RegCM4 in simulating the radiation budget over eastern China is evaluated. The simulation is compared with the satellite dataset and reanalysis, concerning the surface net shortwave (SNS), the surface net longwave (SNL), the TOA net shortwave (TNS), and the TOA net longwave (TNL). Results show that biases in short-wave fluxes are larger in summer, and biases in long-wave fluxes are larger in winter. However, spatial distributions of the biases in summer and winter are similar. For surface fluxes, biases of SNS are positive (overestimate the net downward shortwave flux), with regional average of nearly 50W/m2, which is the maximum among these radiation flux biases. Biases of SNL are negative (overestimate the net upward longwave flux). For TOA fluxes, biases of TNS are negative in north while positive in south, and biases of TNL are overall positive. Attributing analysis on the biases in radiation fluxes is conducted by three methods - spatial correlation, linear regression, and a simplified physical model. The biases in the cloud fraction, surface albedo, and surface temperature are considered. We find that biases in the radiation fluxes can be explained, in a large proportion, by significant negative biases in cloud fraction.
keywords:radiation budget  model evaluation  regional climate model
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