不同分辨率CWRF模式对中国区域气温模拟的比较研究

doi:10.12006/j.issn.1673-1719.2023.067

气候变化研究进展 ›› 2024, Vol. 20 ›› Issue (2): 129-145.doi: 10.12006/j.issn.1673-1719.2023.067

不同分辨率CWRF模式对中国区域气温模拟的比较研究

董李丽¹, 张焓², 李清泉¹^,³(), 汪方¹, 赵崇博¹, 谢冰¹

1 中国气象局气候预测研究重点开放实验室，国家气候中心，北京 100081
2 中山大学大气科学学院，珠海 519082
3 南京信息工程大学气象灾害预报预警与评估协同创新中心，南京 210044

收稿日期:2023-04-04 修回日期:2023-08-15 出版日期:2024-03-30 发布日期:2024-01-15
通讯作者: 李清泉，女，研究员，liqq@cma.gov.cn
作者简介:董李丽，女，高级工程师
基金资助:
国家自然科学基金联合基金项目(U2242207);国家重点研发计划项目(2022YFE0136000);国家自然科学基金青年基金项目(42105037);气象能力提升联合研究专项(22NLTSQ007);第二次青藏高原综合科学考察研究(2019QZKK0208)

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

DONG Li-Li¹, ZHANG Han², LI Qing-Quan¹^,³(), WANG Fang¹, ZHAO Chong-Bo¹, XIE Bing¹

1 China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Centre, Beijing 100081, China
2 School of Atmospheric Science, Sun Yat-sen University, Zhuhai 519082, China
3 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2023-04-04 Revised:2023-08-15 Online:2024-03-30 Published:2024-01-15

摘要/Abstract

摘要：

针对现有区域气候模式分辨率较为粗糙的现状，瞄准精细化气候预测和服务的需求，基于30 km分辨率CWRF区域气候模式研发了水平分辨率和下垫面信息进一步精细化至15 km的新版本模式，利用欧洲中期预报中心的ERA-Interim大气再分析资料和美国的OISSTv2海表面温度资料驱动两种版本的CWRF模式，与中国均一化气温数据集CN05.1的观测数据相比，系统分析了CWRF模式对1982—2016年中国区域2 m气温的模拟效果及其对水平分辨率和下垫面信息的敏感性。结果表明：单纯修改模式的下垫面信息，不提高模式分辨率，对模拟结果的影响不大；将分辨率提高至15 km的CWRF模式对地形复杂区域的气温模拟效果更好，对气温的空间分布、年际变化以及极端气温的模拟都有较好的表现。从气候平均气温分布看，与30 km CWRF模式相比，在春、秋、冬季西南地区和青藏高原，以及夏季西南、华南、华中等冷偏差较为显著的地区，15 km模式模拟结果将冷偏差减小到1℃以内；从气温年际变化来看，15 km模式对夏季华中、华北和东北南部地区及冬季气温的模拟结果优于30 km模式，相关系数最高提升0.4；在极端事件的模拟方面，与30 km模式相比，15 km模式对于在华南到福建、江西、湖南、湖北东部的夏季日数极大值区域模拟具有较大的改善，均方根误差减小1 d；对于新疆东部、东北的极端高温和新疆、青藏高原、华南的极端低温模拟也有明显改善，均方根误差减小1℃。因此，高分辨率区域气候模式有利于提高中国气温的精细化模拟能力。

关键词: 中国, 区域模式, 水平分辨率, 气温, 极端事件

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

董李丽, 张焓, 李清泉, 汪方, 赵崇博, 谢冰. 不同分辨率CWRF模式对中国区域气温模拟的比较研究[J]. 气候变化研究进展, 2024, 20(2): 129-145.

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.

图/表 11

图1 CWRF-R30 (a)和CWRF-R15 (b)模式地形高度注：11个子区域包括东北（NE）、华北（NC）、华中（CC）、华南（SC）、内蒙古（IM）、西南（SW）、青藏高原东部（ET）、青藏高原西部（WT)、青藏高原南部（ST）、新疆南部（SX）、新疆北部（NX）。

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))

图2 CWRF-R30 (a)、CWRF-R30 S (b)和CWRF-R15 (c)模式的植被覆盖度分布

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

图3 CWRF-R30、CWRF-R30 S、CWRF-R15模式的春、夏、秋、冬季节平均叶面积指数（LAI）分布

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

表1 极端气温指数

Table 1 Extreme temperature climate index

图4 春、夏、秋、冬季观测2 m气温以及CWRF-R30、CWRF-R30 S和CWRF-R15模拟与观测的差值分布

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

图5 CWRF-R30和CWRF-R15模拟的夏(a)、冬 (b)季各个子区域和全区2 m气温与观测的偏差密度函数

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

图6 CWRF-R30和CWRF-R15模拟的各季节各子区域和全区2 m气温泰勒图

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

图7 CWRF-R30和CWRF-R15模拟的春、夏、秋、冬季节2 m气温与观测的时间相关系数的空间分布，以及CWRF-R15减去CWRF-R30的相关系数分布注：相关系数>0.32通过95%的信度检验。

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

图8 夏、冬季节观测以及CWRF-R30和CWRF-R15模拟的各个子区域和全区2 m气温随时间变化曲线以及9点平滑曲线

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

图9 1982—2016年观测以及CWRF-R30和CWRF-R15模拟夏季日数（SU）及极端气温（TXx和TNn）

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

表2 CWRF-R30和CWRF-R15模拟与观测的2 m气温极端事件的空间相关系数和均方根误差（RMSE)

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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不同分辨率CWRF模式对中国区域气温模拟的比较研究

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

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