Assessment of CMIP6 Model Performance for Air Temperature in the Arid Region of Northwest China and Subregions

• Published in: Atmosphere Vol. 13; no. 3; p. 454
• Main Authors: Liu, Fang, Xu, Changchun, Long, Yunxia, Yin, Gang, Wang, Hongyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Air temperature; Annual temperatures; Arid regions; Arid zones; Climate; Climate change; Climate models; CMIP6; Coefficients; Correlation coefficient; Correlation coefficients; Global climate; Global climate models; Global warming; Historic temperatures; Intercomparison; Maximum temperatures; Mean temperatures; Minimum temperatures; Modelling; Performance evaluation; Precipitation; preferred-model ensemble; Simulation; Standard deviation; temperature; the arid region of northwest China; Time series; Topography; Trends; Junggar Basin; Altai Mountains; Tien Shan Mountains; China; Tarim Basin
• DOI: 10.3390/atmos13030454
• ISSN: 2073-4433

The arid region of northwest China (ARNC) is one of the most sensitive areas to global warming. However, the performance of new Global Climate Models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating climate in this region, especially in the subregions, is not clear yet. Based on the temperature dataset from historical runs of CMIP6, this paper analyzed and evaluated the simulation ability of 29 GCMs in reproducing the annual mean temperature (tas), annual mean maximum temperature (tasmax) and annual mean minimum temperature (tasmin) in the ARNC and subregions from 1961 to 2014. The results show that (1) the correlation coefficients (CCs) between simulation and observation time series for the mean of two model ensembles (MME for equal-weight multi-model ensemble and PME for preferred-model ensemble) are generally better than those of 29 individual GCMs, with CCs ranging from 0.38 to 0.87 (p < 0.01). (2) All the models can simulate the significant warming trend of the three temperature elements in the study area well. However, the warming magnitude simulated by most of the models (41%) is smaller than the observations except for tasmax, which is also shown in the MME. (3) The spatial pattern of the three temperature elements can be better reflected by most models. Model simulation ability for the ARNC is better compared to that of the four subregions, with a spatial CC greater than 0.7 (p < 0.01). Among the subregions, the simulation performance of the north of Xinjiang for spatial pattern is superior to that of the other regions. (4) The preferred models for each subregion are various and should be treated differently when used. Overall, the PME outperforms both the MME and the individual models; it can not only simulate the linear trend accurately but also reduce the deviation effectively.