Climate Dynamics

, Volume 47, Issue 9–10, pp 3235–3251 | Cite as

Biases and improvements in three dynamical downscaling climate simulations over China

Article

Abstract

A dynamical downscaling is performed to improve the regional climate simulation in China. It consists of using a variable resolution model LMDZ4 nested into three global climate models (GCMs): BCC-csm1-1-m, FGOALS-g2 and IPSL-CM5A-MR, respectively. The regional climate from different simulations is assessed in terms of surface air temperature and rainfalls through a comparison to observations (both station data and gridded data). The comparison includes climatic trends during the last 40 years, statistical distribution of sub-regional climate, and the seasonal cycle. For surface air temperature, a significant part of the improvement provided by LMDZ4 is related to the effect of surface elevation which is more realistic in high-resolution simulations; the rest is related to changes in regional or local atmospheric general circulation. All GCMs and the downscaling model LMDZ4 are, more or less, able to describe the spatial distribution of surface air temperature and precipitation in China. LMDZ4 does show its superiority, compared to GCMs, in depicting a good regional terrain including the Tibetan Plateau, the Sichuan Basin and the Qilian Mountains.

Notes

Acknowledgments

This work is supported jointly by the National Natural Science Foundation of China (Grant No. 41230528), National Program on Key Basic Research Project of China (Grant No. 2012CB955204), China R&D Special Fund for Public Welfare Industry (meteorology) (GYHY201306024) and a Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions. Helpful comments from the anonymous reviewers are greatly appreciated.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy RainChina Meteorological AdministrationWuhanChina
  3. 3.Laboratoire de Météorologie DynamiqueCNRS/UPMCParisFrance

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