Evaluation of a high-resolution historical simulation over China: climatology and extremes View Full Text


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Article Info

DATE

2015-10

AUTHORS

Entao Yu, Jianqi Sun, Huopo Chen, Weiling Xiang

ABSTRACT

China faces an increasing challenge in water resources in the coming decades; thus high-confidence climate projection is of particular importance for the country’s future. In this paper, we evaluate the performance of a long high-resolution continuous simulation over China based on multiple observations and the corresponding historical simulation. The simulation is completed using the Weather Research and Forecasting (WRF) model driven by the Model for Interdisciplinary Research on Climate version 5 (MIROC5) in the context of the Coupled Model Intercomparison Project Phase 5. The results show that both MIROC5 and WRF can capture the distribution and variability of temperature over China, whereas WRF shows improvements, particularly for simulation of regional features. Compared with MIROC5, WRF can reproduce the spatial distribution, annual cycle, probability distribution, and seasonal evolution of the precipitation over mainland China and the sub-regions with better performance. The trend is of fundamental importance in the future projection estimations, and WRF shows better skill in simulating the annual mean precipitation trend. However, there is overestimation of precipitation in Southeast China while negative one in the middle latitude of China in WRF simulation, which can be traced back to model bias in atmospheric circulation and water vapor transportation in these regions. Several extreme climate indices are selected to further assess the model’s performance in simulating climate extremes, WRF can well reproduce the main features with better model skill compared with MIROC5. The better performance of WRF indicates the necessity of the dynamical downscaling technique and the robustness of regional climate simulation in future regional climate projection over China. More... »

PAGES

2013-2031

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    http://scigraph.springernature.com/pub.10.1007/s00382-014-2452-6

    DOI

    http://dx.doi.org/10.1007/s00382-014-2452-6

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    https://app.dimensions.ai/details/publication/pub.1036632508


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