Assessing CMIP5 general circulation model simulations of precipitation and temperature over China

• Published in: International journal of climatology 2015-07, Vol.35 (9), p.2431-2440
• Main Authors: Gu, Huanghe, Yu, Zhongbo, Wang, Jigan, Wang, Guiling, Yang, Tao, Ju, Qin, Yang, Chuanguo, Xu, Feng, Fan, Chuanhao
• Format: Article
• Language: English
• Subjects: China; Climate change; CMIP5; General circulation models; Global warming; model assessment; Precipitation; Temperature
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.4152

ABSTRACT Given the availability of a new generation of general circulation model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive, we attempt to evaluate the model output by using three variants of the transformed Mielke measure to assess (1) the performance of the models in simulating historical surface temperature and precipitation, (2) the climate change response of the models to future greenhouse gases (GHGs) scenarios, and (3) the consistency of the projected change of each model with that of the multi‐model ensemble (MME) mean. Most models exhibited varying degrees of skills, depending on the region and season, whereas a few models were identified as performing well globally, including the CMCC models, IPSL‐CM5A‐MR, and BCC‐CSM1.1M. Models with the highest and lowest climate sensitivities, as well as those that project future climate changes most resembling the MME mean, were identified. The future precipitation and temperature changes projected by the MPI models and NCAR‐CESM1 models were found to best resemble the overall MME. Finer resolution was found to improve model performance in simulating historical climate in most regions and seasons, particularly for temperature; however, it does not have a significant effect on the response of model climates to future GHGs scenarios. We found that no model can simultaneously exhibits good performance in simulating historical climate and in projecting a future climate that is close to the MME mean. Determining the ‘best’ overall model is difficult because ‘best’ is dependent on the specific applications for which a model will be used. Evaluating climate models is an important step to build confidence in their application for impact assessment. Our study provides a basis for concerned groups choosing climate models for their subsequent studies.