主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

气象与环境学报 ›› 2023, Vol. 39 ›› Issue (6): 69-79.doi: 10.3969/j.issn.1673-503X.2023.06.009

• 论文 • 上一篇    下一篇

全球升温1.5 ℃和2.0 ℃辽河流域极端降水变化预估

敖雪1(),翟晴飞2,赵春雨1,*(),崔妍1,耿树江3,于怡秋1,周晓宇1,李经纬1   

  1. 1. 沈阳区域气候中心,辽宁沈阳 110166
    2. 辽宁省气象信息中心,辽宁沈阳 110166
    3. 辽宁省人工影响天气办公室,辽宁沈阳 110166
  • 收稿日期:2022-08-02 出版日期:2023-12-28 发布日期:2024-01-27
  • 通讯作者: 赵春雨 E-mail:aoxuefyh@163.com;springrainscn@163.com
  • 作者简介:敖雪, 女, 1986年出生, 高级工程师, 主要从事气候、气候变化与气候预测方面的研究, E-mail: aoxuefyh@163.com
  • 基金资助:
    辽宁省气象局科研课题(202108);沈阳区域气候中心课题(202002);中国气象局创新发展专项(CXFZ2021J047);中国气象局气候变化专项(CCSF201841)

Projected changes of extreme precipitation in Liaohe River Basin at global warming levels of 1.5 ℃ and 2.0 ℃

Xue AO1(),Qingfei ZHAI2,Chunyu ZHAO1,*(),Yan CUI1,Shujiang GENG3,Yiqiu YU1,Xiaoyu ZHOU1,Jingwei LI1   

  1. 1. Shenyang Regional Climate Center of Liaoning, Shenyang 110166, China
    2. Liaoning Provincial Meteorological Information Center, Shenyang 110166, China
    3. Liaoning Weather Modification Office, Shenyang 110166, China
  • Received:2022-08-02 Online:2023-12-28 Published:2024-01-27
  • Contact: Chunyu ZHAO E-mail:aoxuefyh@163.com;springrainscn@163.com

摘要:

基于国家气候中心中等分辨率模式BCC-CSM2-MR开展的第六次耦合模式比较计划(CMIP6)预估数据,采用双线性插值、趋势分析、偏差分析等方法,分析全球升温1.5 ℃和2.0 ℃辽河流域极端降水变化。结果表明:全球升温1.5 ℃辽河流域年平均降水量距平百分率增幅随排放情景的升高而增大,SSP5-8.5排放情景下增幅达5.82%。全球升温2.0 ℃辽河流域年和四季降水均为增加趋势,夏季降水增幅明显;SSP2-4.5和SSP5-8.5情景下降水量均为自西南向东北递减,辽宁西部地区降水增幅较为显著,超过15%。不同排放情景下辽河流域极端降水指数均为增加趋势,日降水强度、强降水日数、强降水比例增长显著;随排放情景升高,极端降水指数增长速率增大,SSP5-8.5情景下的增长速率为SSP2-4.5情景下的两倍以上。SSP5-8.5情景下,21世纪末降水强度、强降水日数、强降水比例、强降水阈值、最长连续湿日数、最大十日降水量将达11.66 mm/d、15.15 d、59.08%、32.94 mm、9.69 d、201.29 mm,较基准期增加5.58 mm/d、5.15 d、37.08%、10.15 mm、5.55 d、102.86 mm。相比全球升温1.5 ℃,全球升温2.0 ℃情况下,6种极端降水指数的增幅更显著,且SSP3-7.0和SSP5-8.5情景下辽河流域极端降水呈一致增加趋势。

关键词: BCC-CSM2-MR, 气候变暖, 极端降水指数

Abstract:

Based on the estimated data of the Sixth Coupled Model Intercomparison Project (CMIP6) conducted by the median resolution model BCC-CSM2-MR from the National Climate Center, methods including bilinear interpolation, trend analysis, anomaly analysis, etc. are used to analyze the changes of extreme precipitation over the Liaohe River Basin under the global warming of 1.5 ℃ and 2.0 ℃. The results show that the anomalous percentage increase of the annual average precipitation over the Liaohe River Basin at the global warming of 1.5 ℃ rises with the increase of emission scenarios, reaching 5.82% under the SSP5-8.5 emission scenario. Under the global warming of 2.0 ℃, the annual and seasonal precipitations over the Liaohe River Basin all show increasing trends, especially for summer precipitation; under the SSP2-4.5 and SSP5-8.5 scenarios, the precipitation decreases from southwest to northeast, with a remarkable increase of over 15% in western Liaoning. Under different emission scenarios, the extreme precipitation indices over the Liaohe River Basin all show increasing trends, with significant growths in daily precipitation intensity, the number of heavy precipitation days, and the proportion of heavy precipitation. With the increase of emission scenarios, the growth rate of extreme precipitation indices rises and is two times more than under the SSP5-8.5 scenario compared to that under the SSP2-4.5 scenario. Under the SSP5-8.5 scenario, the precipitation intensity, the number of heavy precipitation days, the proportion of heavy precipitation, the threshold of heavy precipitation, the maximum number of consecutive wet days, and the maximum 10-day precipitation amount will reach 11.66 mm/d, 15.15 d, 59.08%, 32.94 mm, 9.69 d, 201.29 mm by the end of 21st century, increased by 5.58 mm/d, 5.15 d, 37.08%, 10.15 mm, 5.55 d, 102.86 mm compared to those in the baseline period. Under the global warming of 2.0 ℃, the increases of six extreme precipitation indices are more significant than those under 1.5 ℃ warming, and the extreme precipitation over the Liaohe River Basin shows a consistent increasing trend under the SSP3-7.0 and SSP5-8.5 scenarios.

Key words: BCC-CSM-MR, Global warming, Extreme precipitation index

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