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

Fang Liu,Changchun Xu,Gang Yin,Yunxia Long,Hongyu Wang

doi:10.3390/atmos13030454

Abstract

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.

Highlights

Global Climate Models (GCMs; see Table 1 for nomenclature, hereinafter) are important tools for historical climate simulation and future climate projection [1]
The results demonstrate that the mean of all models (MME) and preferred-model ensemble (PME) exhibit better simulation performance for climate mean fields than individual models, with the PME outperforming the MME
Based on HadEX3 observations and four reanalysis datasets, Kim et al evaluated the capability of CMIP6 to simulate global climate extreme indices using root-mean-square errors and found that the performance of the multimodel ensemble mean outperformed that of the individual models [43]

Summary

Introduction

Global Climate Models (GCMs; see Table 1 for nomenclature, hereinafter) are important tools for historical climate simulation and future climate projection [1]. The. Coupled Model Intercomparison Project (CMIP) promoted by the World Climate Research. Programme (WRCP) has contributed significantly to the various assessment reports produced by the Intergovernmental Panel on Climate Change (IPCC) [1–3]. From CMIP3 to CMIP5, many scholars have investigated model performance [4–6]. CMIP5 performed better than CMIP3 in simulating large-scale precipitation and temperature [7–9]. The CMIP is in its sixth phase (CMIP6). Compared with CMIP3/5, the physical process of CMIP6 considers is more complex, and the models have higher spatial resolution [10]. The most significant feature of CMIP6 is that it considers the Representative Concentration Pathway (RCP) of CMIP5 and the Shared Socioeconomic

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Methods

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Discussion

Conclusion