4.6 Article

Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

期刊

ADVANCES IN ATMOSPHERIC SCIENCES
卷 33, 期 5, 页码 559-570

出版社

SCIENCE PRESS
DOI: 10.1007/s00376-015-5147-y

关键词

climate simulation; dynamical downscaling; GCM; RCM

资金

  1. National Natural Science Foundation of China [41130103, 41421004, 41405087]

向作者/读者索取更多资源

Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM. However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM (VFRGCM) and from RCM dynamical downscaling. The results showed that: (1) The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation. (2) The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method. (3) The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall, the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future, and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据