Impact of Interannual Soil Moisture Anomaly on Simulation of Extreme Climate Events in China. Part I: Model Evaluation of CAM3.1
土壤湿度年际变化对中国区域极端气候事件模拟的影响研究 I. 基于CAM3.1的模式评估

Extreme climate events over China in recent 40 years are simulated by using NCAR Community Atmosphere Model (CAM3.1). Based on the observed daily data of maximum/minimum temperature and precipitation at 452 stations from 1961 to 2000 in China, the performance of CAM3.1 is evaluated from three aspects, i.e., climatology, interannual variations, and long-term trends. Results show that: 1)CAM3.1 can generally reproduce the basic features of the large-scale spatial patterns of the annual-mean extreme climate indices. The model performs better in simulating the spatial patterns of extreme precipitation than extreme temperature simulation. Systematic bias is found in the simulation of extreme climate events, and the bias in extreme precipitation simulation is evidently larger than that in the extreme temperature simulation on the whole. 2) CAM3.1 can ideally reproduce the interannual variations of the temperature extreme indices, but has poor performance in simulating the interannual variations of the precipitation extreme indices. Large bias is found in the amplitude of the interannual variations between the simulated and observed extreme precipitation events. 3) Increasing trends of both Tn95p (warm nights) and Tx95p (warm days) over most areas of China are well reproduced by the model, but the observed trends are underestimated to some extent. In contrast, the capability of the model in simulating the long-term trends of HWDI (heat wave duration) is poor. Overall, the capability of the model in simulating the long-term trends of precipitation extremes is poorer than those of temperature extremes. CAM3.1 can also capture the long-term trends of extreme precipitation events such as P95p (frequency of extreme heavy precipitation) and R10 (number of days with precipitation greater than 10 mm) in some regions of China, but could not reproduce the long-term trends of CWD (consecutive wet days) very well. Results can provide some references for using CAM3.1 in extreme climate simulation.