Evaluation of the surface air temperature over the Tibetan Plateau among different reanalysis datasets

Abstract

The surface air temperature (SAT) over the Tibetan Plateau (TP) not only affects the physical processes such as local evaporation, snow melting, and glacier ablation, but also has a great impact on the downstream regions and even the global climate change. The development of reanalysis data has gradually overcome the problem of sparse stations over the TP, but there are still some deficiencies. Therefore, the distance between indices of simulation and observation (DISO) method is used to calculate the distance between five reanalysis datasets (ERA5, JRA-55, ERA-Interim, MERRA2, NCEP2) and the CMFD to evaluate the abilities of different reanalysis datasets to capture the SAT over the TP in different seasons. The results indicate that ERA-Interim has a higher ability to reproduce the SAT over the TP in spring and summer, while it is ERA5 in autumn and winter. It should be noted that although the optimal reanalysis has a better performance in capturing the SAT of the TP, there are still a certain degree of deviations in their spatial fields. We further show the spatial deviation fields of SAT over the TP corresponding to the optimal reanalysis data in different seasons, and analyze the possible reasons. The result implies that the SAT deviation field is mainly related to the snow in winter and spring, while the summer SAT deviation field is mainly related to the water vapor, and the autumn is related to both the snow and the water vapor fields. Overall, the quality of reanalysis data needs to be further improved in the future.