Assessments of temperature extremes in China by using RegCM4 and WRF models

Authors: Xianghui Kong*, Institute of Atmospheric Physics,Chinese Academy of Sciences, Aihui Wang, Institute of Atmospheric Physics,Chinese Academy of Sciences, Xunqiang Bi, Institute of Atmospheric Physics,Chinese Academy of Sciences, Dan Wang, Institute of Atmospheric Physics,Chinese Academy of Sciences
Topics: China
Keywords: dynamical downscaling, extreme temperature indices, observation, RegCM, WRF
Session Type: Lightning Paper
Day: 4/6/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Marshall North, Marriott, Mezzanine Level
Presentation File: No File Uploaded

This study assesses the performance of temperature extremes over China in two regional climate models (RCMs), RegCM4 and WRF, driven by theERA-20C. Based on the Expert Team on Climate Change Detection and Indices (ETCCDI), twelve extreme temperature indices (TXx, TXn, TNx, TNn, TX90p, TN90p, TX10p, TN10p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs as compared with those from the daily station-based observation for the period of 1981-2010. Overall, two RCMs have satisfactory capability in representing the spatial and temporal distribution of those extreme indices over majority regions in China. The RegCM generally performs better than that WRF in reproducing the mean temperature extremes over China, especially over the Tibetan Plateau. Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10p, and TN10p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90p, and TN90p over China. Compared with observation, the RegCM tends to underestimate the trends of temperature extremes, while WRF tends to overestimate the trends of temperature extremes over TP. For instance, the linear trends of TXx for CN05, RegCM, and WRF are 0.53°C/Dec, 0.44°C/Dec, and 0.75°C/Dec over TP, respectively. However, WRF has better performance than RegCM in the reproduction of the interannual variability of extreme temperature indices. Thus, it is crucial to improve our understanding the physical realism of a regional climate model in term of different time-scales, so that we can address the sources of biases in RCMs’ simulations in the future work

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