Comparative study on regional temperature simulation in China by different resolution CWRF models

doi:10.12006/j.issn.1673-1719.2023.067

Abstract

Abstract:

Based on the 30 km CWRF regional climate model, a new model was developed with a horizontal resolution further refined to 15 km. ERA-Interim atmospheric reanalysis data from the European Center for Medium Range Forecasts and OISSTv2 sea surface temperature data from the United States were used to drive two CWRF models. Compared with the observed data of China’s homogenized air temperature dataset CN05.1, the simulation effect of CWRF models on 2 m-air temperature over China during 1982-2016 and its sensitivity to horizontal resolution were systematically analyzed. The results show that simply modifying the underlying surface information of the model has little effect on the simulation results without improving the model resolution, the 15 km resolution CWRF model has a better simulation effect on the temperature in the complex terrain area, and has a better performance on the spatial distribution, inter-annual variation and extreme temperature simulation. From the perspective of climate mean temperature distribution, the 15 km model reduces the cold deviation to less than 1℃ in spring, autumn and winter over Southwest China and Qinghai-Tibet Plateau, and Southwest, South China and Central China in summer, where the cold deviation is more significant simulated by the 30 km CWRF model. From the perspective of inter-annual temperature variation, the simulation results of 15 km model over Central China, North China and southern Northeast China in summer and winter are better than those of 30 km model, and the correlation coefficient increases by 0.4 at the highest. In terms of the simulation of extreme events, compared with the 30 km model, the 15 km model has a great improvement in the simulation of the maximum number of days with the annual daily maximum temperature >25℃ from South China to Fujian, Jiangxi, Hunan and eastern Hubei, the root mean square error decreases by 1 d, and the extreme high and low temperature in eastern Xinjiang, Northeast China, the Qinghai-Tibet Plateau and South China are also significantly improved, the root mean square error is reduced by 1℃. Therefore, higher resolution regional climate models are helpful to improve the precision simulation ability of temperature in China.

Key words: China, Regional model, Horizontal resolution, Temperature, Extreme events

DONG Li-Li, ZHANG Han, LI Qing-Quan, WANG Fang, ZHAO Chong-Bo, XIE Bing. Comparative study on regional temperature simulation in China by different resolution CWRF models[J]. Climate Change Research, 2024, 20(2): 129-145.

Figures/Tables 11

Fig. 1 Terrain height (HGT) of CWRF-R30 (a) and CWRF-R15 (b) models. (Eleven subregions include Northeast China (NE), North China (NC), Central China (CC), South China (SC), Inner Mongolia (IM), Southwest China (SW), Eastern Plateau (ET), Western Plateau (WT), Southern Plateau (ST), Southern Xinjiang (SX) and Northern Xinjiang (NX))

Fig. 2 Distribution of XFVEG (vegetation fraction) for CWRF-R30 (a), CWRF-R30 S (b) and CWRF-R15 (c) models

Fig. 3 Distribution of mean leaf area index (LAI) in spring, summer, autumn and winter of CWRF-R30, CWRF-R30 S and CWRF-R15 models

Table 1 Extreme temperature climate index

Fig. 4 Observation temperature at 2 m in spring, summer, autumn and winter, and differences between simulations of CWRF-R30, CWRF-R30 S, CWRF-R15 and observations, respectively

Fig. 5 Deviation density function between 2 m temperature simulated by CWRF-R30 and CWRF-R15 and observation in each sub-region and the whole region in summer (a) and winter (b) season

Fig. 6 Taylor charts of 2 m temperature simulated by CWRF-R30 and CWRF-R15 in spring (a), summer (b), autumn (c), and winter (d) in each sub-region and the whole region

Fig. 7 The spatial distribution of correlation coefficients between 2 m temperature simulated by CWRF-R30 and CWRF-R15 and observation in spring, summer, autumn and winter, and the correlation coefficient difference distribution of CWRF-R15 minus CWRF-R30

Fig. 8 Inter-annual variation and 9-point smooth curve of observed and CWRF-R30 and CWRF-R15 simulated 2 m temperature in sub-regions and whole region by observations and simulations in summer and winter

Fig. 9 SU, TXx and TNn of 2 m temperature simulated by CWRF-R30, CWRF-R15 and observation during 1982-2016

Table 2 Spatial correlation coefficient and Root Mean Square Error (RMSE) for 2 m temperature extremes between observation and simulation by CWRF-R30 and CWRF-R15

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