CWRF模式对长江流域极端降水气候事件的模拟能力评估

doi:10.12006/j.issn.1673-1719.2021.132

气候变化研究进展 ›› 2022, Vol. 18 ›› Issue (1): 44-57.doi: 10.12006/j.issn.1673-1719.2021.132

CWRF模式对长江流域极端降水气候事件的模拟能力评估

孙晨¹, 汪方²^,³(), 周月华¹, 李兰¹

1 武汉区域气候中心,武汉 430074
2 国家气候中心中国气象局气候研究开放实验室,北京 100081
3 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,南京 210044

收稿日期:2021-07-14 修回日期:2021-10-29 出版日期:2022-01-30 发布日期:2021-12-23
通讯作者: 汪方
作者简介:孙晨,男,工程师
基金资助:
国家重点研发计划项目(2018YFC1508002);湖北省气象局科技课题(2020Q05);国家重点研发计划项目(2018YFE0196000)

An assessment on extreme precipitation events in Yangtze River basin as simulated by CWRF regional climate model

SUN Chen¹, WANG Fang²^,³(), ZHOU Yue-Hua¹, LI Lan¹

1 Wuhan Regional Climate Center, Wuhan 430074, China
2 National Climate Center/Climate Research Open Laboratory of China Meteorological Administration, Beijing 100081, China
3 Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2021-07-14 Revised:2021-10-29 Online:2022-01-30 Published:2021-12-23
Contact: WANG Fang

摘要/Abstract

摘要：

基于1980—2016年长江流域站点观测降水,评估了CWRF区域气候模式对长江流域面雨量和极端降水气候事件的模拟能力。结果表明：CWRF模式能较好地再现1980—2016年长江流域及不同分区降水空间分布及月/季面雨量年际变率,且在冬、春季表现较好,夏、秋季次之。CWRF模式对长江流域面雨量存在系统性高估,对面雨量的模拟能力在长江中下游明显优于长江上游和金沙江,这可能和长江流域上游及金沙江地区地形复杂、站点稀少导致的面雨量实况代表性不足,以及CWRF模式自身模拟能力欠缺均有关。CWRF模式对长江流域极端降水事件也具备一定的模拟能力,能较好反映出长江中下游的变湿趋势,对长江上游极端强降水减弱而长江中下游地区极端强降水增强的趋势均有所体现,但对于日尺度极端降水和复杂地形下的降水模拟效果不佳。

关键词: CWRF, 长江流域, 面雨量, 极端降水

Abstract:

Based on the station observation data from 1980 to 2016, the CWRF model’s ability to simulate area rainfall and extreme precipitation events in the Yangtze River basin was evaluated. The results showed that the CWRF model well reproduced the spatial distribution of precipitation in the Yangtze River basin and the monthly/seasonal variation characteristics of area rainfall in different regions from 1980 to 2016, and the CWRF model performance was better in winter and spring than in summer and autumn. CWRF model had a systematic overestimation on area rainfall in the Yangtze River basin, and the simulation capability for area rainfall in the middle and lower reaches of the Yangtze River was higher than that in the upper reaches and the Jinsha River. This may be due to the inadequate representation of observation area rainfall caused by the complex topography and the scarcity of stations in the upper reaches of the Yangtze River basin and the Jinsha River area, as well as the lack in simulation capabilities of the CWRF model. CWRF model also presented certain capabilities to simulate extreme precipitation events, which could well reflect the wetting trend of the middle and lower reaches of the Yangtze River. Additionally, the weakening trend of extreme precipitation in the upper reaches of the Yangtze River and the increasing trend in the middle and lower reaches of the Yangtze River could be reflected. However, the numerical simulation capability for extreme daily precipitation and precipitation in complex terrain were insufficient.

Key words: CWRF, Yangtze River basin, Area rainfall, Extreme precipitation

孙晨, 汪方, 周月华, 李兰. CWRF模式对长江流域极端降水气候事件的模拟能力评估[J]. 气候变化研究进展, 2022, 18(1): 44-57.

SUN Chen, WANG Fang, ZHOU Yue-Hua, LI Lan. An assessment on extreme precipitation events in Yangtze River basin as simulated by CWRF regional climate model[J]. Climate Change Research, 2022, 18(1): 44-57.

图/表 13

图1 长江流域分区及气象站点位置

Fig. 1 Division of Yangtze River basin and distribution of meteorological stations

图2 1980—2016年长江流域年平均降水量空间分布

Fig. 2 Spatial distribution of annual mean precipitation in the period of 1980-2016

图3 1980—2016年长江流域年平均降水量模式与观测差异的空间分布注：阴影部分为通过0.05的显著性检验,下同。

Fig. 3 Spatial distribution of bias of annual mean precipitation between model and observation in 1980-2016. (The shaded regions indicate the bias passing the significance test at the 95% confidence level)

图4 1980—2016年长江流域季节平均降水量空间分布

Fig. 4 Geographic distributions of seasonal average precipitation in 1980-2016

图5 1980—2016年长江流域季节平均降水量模式与观测差异的空间分布注：阴影部分为通过0.05的显著性检验。

Fig. 5 Spatial distribution of bias of annual total precipitation between model and observation in 1980-2016. (The shaded regions indicate the bias passing the significance test at the 95% confidence level)

图6 1980—2016年CWRF模式(a)、ERI (b)与站点极端降水偏差百分比

Fig. 6 Geographic distributions of deviation percentage between CWRF (a), ERI (b) and OBS extreme precipitation

图7 1980—2016年不同流域分区年和季节面雨量时间序列泰勒图

Fig. 7 Taylor diagram of annual and seasonal area rainfall time series in different basins during 1980-2016

图8 1980—2016年不同流域分区面雨量偏差百分比时间序列

Fig. 8 Deviation percentage of area rainfall in different basins during 1980-2016

图9 1980—2016年不同流域分区逐月面雨量距平时间序列

Fig. 9 Monthly area rainfall anomaly in different basins during 1980-2016

表1 极端降水气候指数

Table 1 Extreme precipitation climate index

图10 1980—2016年CWRF模式与站点极端降水气候指数偏差百分比注：阴影部分通过0.1的显著性检验。

Fig. 10 Geographic distributions of deviation percentage between CWRF and OBS extreme precipitation climate index. (The shaded regions indicate the bias passing the significance test at the 90% confidence level)

图11 同图10,但为相关系数

Fig. 11 Same as Fig. 10 except for the correlation coefficient

图12 各极端降水指数线性趋势注：阴影部分通过0.1的显著性检验。

Fig. 12 Linear trend of extreme precipitation indexes. (The shaded regions indicate the bias passing the significance test at the 90% confidence level)

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CWRF模式对长江流域极端降水气候事件的模拟能力评估

An assessment on extreme precipitation events in Yangtze River basin as simulated by CWRF regional climate model

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