ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Original Paper

The uncertainty of projection of precipitation change in the middle and lower reaches of the Yangtze River under the RCP8.5 scenario

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.07.018
  • Received Date: 25 February 2020
  • Accepted Date: 29 April 2020
  • Rev Recd Date: 29 April 2020
  • Publish Date: 31 July 2020
  • Summer precipitation changes in the middle and lower reaches of the Yangtze River under global were estimated, using the historical simulation of the fifth coupled mode comparison plan (CMIP5) and the experimental data under the high concentration scenario of the typical concentration path (RCP8.5). The results show that the multi-mode average predicted global warming has a small increase in precipitation variation in the middle and lower reaches of the Yangtze River. On the one hand, global warming leads to an increase in water vapor content, which is conducive to the increase of precipitation in the Yangtze River Basin. On the other hand, global warming has caused the Summer monsoon circulation to weaken, and the weakened summer monsoon is not conducive to the increase of precipitation in the Yangtze River basin. Under the combined effect of the two, the multi-model average estimated mid-downstream watershed in the Yangtze River has no obvious changes in summer precipitation. However, there is a large inter-mode uncertainty in this estimate. The analysis shows that the uncertainty of the prediction of precipitation change is mainly due to the uncertainty of precipitation prediction results caused by large-scale Summer monsoon circulation changes, and the increase of water vapor caused by climate warming has less influence on the uncertainty of precipitation variation. Further research on this source of uncertainty shows that precipitation in the middle and lower reaches of the Yangtze River and changes in the East Asian summer monsoon circulation are strongly correlated with the warming of the North Atlantic and the the Western North Pacific SST. This indicates that in the context of global warming, the warming of the North Atlantic and the Western North Pacific SST has a great impact on the prediction of precipitation changes in the middle and lower reaches of the Yangtze River. If the uncertainty of the prediction of the North Atlantic and the Western North Pacific SST is reduced, the precipitation prediction results in the middle and lower reaches of the Yangtze River will be more reliable.
    Summer precipitation changes in the middle and lower reaches of the Yangtze River under global were estimated, using the historical simulation of the fifth coupled mode comparison plan (CMIP5) and the experimental data under the high concentration scenario of the typical concentration path (RCP8.5). The results show that the multi-mode average predicted global warming has a small increase in precipitation variation in the middle and lower reaches of the Yangtze River. On the one hand, global warming leads to an increase in water vapor content, which is conducive to the increase of precipitation in the Yangtze River Basin. On the other hand, global warming has caused the Summer monsoon circulation to weaken, and the weakened summer monsoon is not conducive to the increase of precipitation in the Yangtze River basin. Under the combined effect of the two, the multi-model average estimated mid-downstream watershed in the Yangtze River has no obvious changes in summer precipitation. However, there is a large inter-mode uncertainty in this estimate. The analysis shows that the uncertainty of the prediction of precipitation change is mainly due to the uncertainty of precipitation prediction results caused by large-scale Summer monsoon circulation changes, and the increase of water vapor caused by climate warming has less influence on the uncertainty of precipitation variation. Further research on this source of uncertainty shows that precipitation in the middle and lower reaches of the Yangtze River and changes in the East Asian summer monsoon circulation are strongly correlated with the warming of the North Atlantic and the the Western North Pacific SST. This indicates that in the context of global warming, the warming of the North Atlantic and the Western North Pacific SST has a great impact on the prediction of precipitation changes in the middle and lower reaches of the Yangtze River. If the uncertainty of the prediction of the North Atlantic and the Western North Pacific SST is reduced, the precipitation prediction results in the middle and lower reaches of the Yangtze River will be more reliable.
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  • [1]
    周莉, 兰明才, 蔡荣辉,等. 21世纪前期长江中下游流域极端降水预估及不确定性分析[J]. 气象学报, 2018, 76(1): 47-61.
    [2]
    陈活泼, 孙建奇, 陈晓丽. 我国夏季降水及相关大气环流场未来变化的预估及不确定性分析[J]. 气候与环境研究, 2012, 17 (2): 171-183.
    [3]
    韩乐琼, 韩哲, 李双林. 不同代表性浓度路径(RCPs)下21世纪长江中下游强降水预估[J]. 大气科学学报, 2014, 37(5): 529-540.
    [4]
    姚世博, 姜大膀, 范广洲. 中国降水季节性的预估[J]. 大气科学, 2018, 42(6): 1378-1392.
    [5]
    初祁, 徐宗学, 刘文丰, 等. 24个CMIP5模式对长江流域模拟能力评估[J]. 长江流域资源与环境,2015, 24(1): 81-89.
    [6]
    孙颖, 丁一汇.未来百年东亚夏季降水和季风预测的研究[J].中国科学(D辑:地球科学), 2009, 39(11): 1487-1504.
    [7]
    谢安, 毛江玉, 宋焱云,等. 长江中下游地区水汽输送的气候特征[J]. 应用气象学报, 2002, 13(1): 67-77.
    [8]
    WANG C Z, ZHANG L P, LEE S K. A global perspective on CMIP5 climate model biases[J]. Nature Climate Change, 2014, 4(3): 201-205.
    [9]
    LI G, DU Y, XU H.An intermodel approach to identify the source of excessive equatorial Pacific cold tongue in CMIP5 models and uncertainty in observational datasets[J].Journal of Climate, 2015, 28: 7630-7640.
    [10]
    LI G, XIE S P, DU Y.Climate model errors over the South Indian Ocean thermocline dome and their effect on the basin mode of interannual variability[J]. Journal of Climate, 2015, 28: 3093-3098.
    [11]
    陈晓龙, 周天军. 使用订正的“空间型标度”法预估1.5 ℃温升阈值下地表气温变化[J].地球科学进展, 2017, 32(4): 435-445.
    [12]
    CHOU C, NEELIN J D, CHEN C A, et al. Evaluating the "rich-get-richer" mechanism in tropical precipitation change under global warming[J]. Journal of Climate, 2009, 22(8): 1982-2005.
    [13]
    DUAN Q, PHILLIPS T J. Bayesian estimation of local signal and noise in multimodel simulations of climate change[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D18): D18123; doi: 10.1029/2009JD013654.
    [14]
    SONG F, ZHOU T. Interannual variability of East Asian summer monsoon simulated by CMIP3 and CMIP5 AGCMs: Skill dependence on Indian Ocean-western Pacific anticyclone teleconnection[J]. Journal of Climate, 2014, 27(4): 1679-1697.
    [15]
    ZHANG R H. Relation of water vapor transport from Indian monsoon with that over East Asia and summer rainfall in China[J]. Advances in Atmospheric Sciences,2001, 18: 1005-1007.
    [16]
    SIMMONDS I, BI D, HOPE P. Atmospheric water vapor flux and its association with rainfall over China in summer[J]. Journal of Climate, 1999, 12(5): 1353-1367.
    [17]
    DING Y H, CHAN J C L.The East Asian summer monsoon: An overview[J]. Meteorology and Atmospheric Physics, 2005, 89: 117-142.
    [18]
    ZHOU T J, YU R C. Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China[J]. Journal of Geophysical Research: Atmospheres, 2005, 110(D8): D08104; doi: 10.1029/2004JD005413.
    [19]
    王蕾. 基于大气环流指数与区域降水关系的模式评估方法及其应用[D]. 南京:南京大学, 2015.
    [20]
    丁一汇, 司东, 柳艳菊, 等. 论东亚夏季风的特征、驱动力与年代际变化[J]. 大气科学, 2018, 42(3): 533-558.
    [21]
    KITOH A, KUSUNOKI S. East Asian summer monsoon simulation by a 20-km mesh AGCM[J]. Climate Dynamics, 2008, 31(4): 389-401.
    [22]
    姜江, 姜大膀, 林一骅. RCP4.5情景下中国季风区及降水变化预估[J]. 大气科学, 2015, 39 (5): 901-910.
    [23]
    周天军, 吴波, 郭准, 等. 东亚夏季风变化机理的模拟和未来变化的预估: 成绩和问题、机遇和挑战[J]. 大气科学,2018, 42(4): 902-934.
    [24]
    WENTZ F J, RICCIARDULLI L, HILBURN K, et al. How much more rain will global warming bring?[J]. Science, 2007, 317(5835): 233-235.
    [25]
    LIN R, ZHOU T, QIAN Y. Evaluation of global monsoon precipitation changes based on five reanalysis datasets[J]. Journal of Climate, 2014, 27(3): 1271-1289.
    [26]
    周佰铨. 基于大尺度环流型的我国江淮流域夏季降水变化归因及情景预估研究[D].北京:中国气象科学研究院, 2018.
    [27]
    FENG J, LI T, ZHU W. Propagating and nonpropagating MJO events over Maritime Continent[J]. Journal of Climate, 2015, 28(21): 8430-8449.
    [28]
    LEE J Y, WANG B. Future change of global monsoon in the CMIP5[J]. Climate Dynamics, 2014, 42: 101-119.
    [29]
    张蓓, 戴新刚. 2006~2013 年 CMIP5 模式中国降水预估误差分析[J]. 大气科学,2016, 40 (5): 981-994.
    [30]
    蒋兴文, 李跃清. 长江流域地区水汽输送及其对旱涝影响研究综述[J]. 气象科学, 2009, 29(1): 138-142.
    [31]
    陈红. CMIP5气候模式对中国东部夏季降水年代际变化的模拟性能评估[J]. 气候与环境研究, 2014, 19(6): 773-786.
    [32]
    LIU B, HUANG G, HU K M. The multidecadal variations of the interannual relationship between the East Asian summer monsoon and ENSO in a coupled model[J]. Climate Dynamics, 2017, 51: 1671-1686.
    [33]
    KUHLBRODT T, GRIESEL A, MONTOYA M, et al. On the driving processes of the Atlantic meridional overturning circulation[J]. Reviews of Geophysics, 2007, 45(2): RG2001, doi: 10.1029/2004RG000166.
    [34]
    李双林, 王彦明, 郜永祺. 北大西洋年代际振荡(AMO)气候影响的研究评述[J]. 大气科学学报, 2009, 32(3): 458-465.)
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    [1]
    周莉, 兰明才, 蔡荣辉,等. 21世纪前期长江中下游流域极端降水预估及不确定性分析[J]. 气象学报, 2018, 76(1): 47-61.
    [2]
    陈活泼, 孙建奇, 陈晓丽. 我国夏季降水及相关大气环流场未来变化的预估及不确定性分析[J]. 气候与环境研究, 2012, 17 (2): 171-183.
    [3]
    韩乐琼, 韩哲, 李双林. 不同代表性浓度路径(RCPs)下21世纪长江中下游强降水预估[J]. 大气科学学报, 2014, 37(5): 529-540.
    [4]
    姚世博, 姜大膀, 范广洲. 中国降水季节性的预估[J]. 大气科学, 2018, 42(6): 1378-1392.
    [5]
    初祁, 徐宗学, 刘文丰, 等. 24个CMIP5模式对长江流域模拟能力评估[J]. 长江流域资源与环境,2015, 24(1): 81-89.
    [6]
    孙颖, 丁一汇.未来百年东亚夏季降水和季风预测的研究[J].中国科学(D辑:地球科学), 2009, 39(11): 1487-1504.
    [7]
    谢安, 毛江玉, 宋焱云,等. 长江中下游地区水汽输送的气候特征[J]. 应用气象学报, 2002, 13(1): 67-77.
    [8]
    WANG C Z, ZHANG L P, LEE S K. A global perspective on CMIP5 climate model biases[J]. Nature Climate Change, 2014, 4(3): 201-205.
    [9]
    LI G, DU Y, XU H.An intermodel approach to identify the source of excessive equatorial Pacific cold tongue in CMIP5 models and uncertainty in observational datasets[J].Journal of Climate, 2015, 28: 7630-7640.
    [10]
    LI G, XIE S P, DU Y.Climate model errors over the South Indian Ocean thermocline dome and their effect on the basin mode of interannual variability[J]. Journal of Climate, 2015, 28: 3093-3098.
    [11]
    陈晓龙, 周天军. 使用订正的“空间型标度”法预估1.5 ℃温升阈值下地表气温变化[J].地球科学进展, 2017, 32(4): 435-445.
    [12]
    CHOU C, NEELIN J D, CHEN C A, et al. Evaluating the "rich-get-richer" mechanism in tropical precipitation change under global warming[J]. Journal of Climate, 2009, 22(8): 1982-2005.
    [13]
    DUAN Q, PHILLIPS T J. Bayesian estimation of local signal and noise in multimodel simulations of climate change[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D18): D18123; doi: 10.1029/2009JD013654.
    [14]
    SONG F, ZHOU T. Interannual variability of East Asian summer monsoon simulated by CMIP3 and CMIP5 AGCMs: Skill dependence on Indian Ocean-western Pacific anticyclone teleconnection[J]. Journal of Climate, 2014, 27(4): 1679-1697.
    [15]
    ZHANG R H. Relation of water vapor transport from Indian monsoon with that over East Asia and summer rainfall in China[J]. Advances in Atmospheric Sciences,2001, 18: 1005-1007.
    [16]
    SIMMONDS I, BI D, HOPE P. Atmospheric water vapor flux and its association with rainfall over China in summer[J]. Journal of Climate, 1999, 12(5): 1353-1367.
    [17]
    DING Y H, CHAN J C L.The East Asian summer monsoon: An overview[J]. Meteorology and Atmospheric Physics, 2005, 89: 117-142.
    [18]
    ZHOU T J, YU R C. Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China[J]. Journal of Geophysical Research: Atmospheres, 2005, 110(D8): D08104; doi: 10.1029/2004JD005413.
    [19]
    王蕾. 基于大气环流指数与区域降水关系的模式评估方法及其应用[D]. 南京:南京大学, 2015.
    [20]
    丁一汇, 司东, 柳艳菊, 等. 论东亚夏季风的特征、驱动力与年代际变化[J]. 大气科学, 2018, 42(3): 533-558.
    [21]
    KITOH A, KUSUNOKI S. East Asian summer monsoon simulation by a 20-km mesh AGCM[J]. Climate Dynamics, 2008, 31(4): 389-401.
    [22]
    姜江, 姜大膀, 林一骅. RCP4.5情景下中国季风区及降水变化预估[J]. 大气科学, 2015, 39 (5): 901-910.
    [23]
    周天军, 吴波, 郭准, 等. 东亚夏季风变化机理的模拟和未来变化的预估: 成绩和问题、机遇和挑战[J]. 大气科学,2018, 42(4): 902-934.
    [24]
    WENTZ F J, RICCIARDULLI L, HILBURN K, et al. How much more rain will global warming bring?[J]. Science, 2007, 317(5835): 233-235.
    [25]
    LIN R, ZHOU T, QIAN Y. Evaluation of global monsoon precipitation changes based on five reanalysis datasets[J]. Journal of Climate, 2014, 27(3): 1271-1289.
    [26]
    周佰铨. 基于大尺度环流型的我国江淮流域夏季降水变化归因及情景预估研究[D].北京:中国气象科学研究院, 2018.
    [27]
    FENG J, LI T, ZHU W. Propagating and nonpropagating MJO events over Maritime Continent[J]. Journal of Climate, 2015, 28(21): 8430-8449.
    [28]
    LEE J Y, WANG B. Future change of global monsoon in the CMIP5[J]. Climate Dynamics, 2014, 42: 101-119.
    [29]
    张蓓, 戴新刚. 2006~2013 年 CMIP5 模式中国降水预估误差分析[J]. 大气科学,2016, 40 (5): 981-994.
    [30]
    蒋兴文, 李跃清. 长江流域地区水汽输送及其对旱涝影响研究综述[J]. 气象科学, 2009, 29(1): 138-142.
    [31]
    陈红. CMIP5气候模式对中国东部夏季降水年代际变化的模拟性能评估[J]. 气候与环境研究, 2014, 19(6): 773-786.
    [32]
    LIU B, HUANG G, HU K M. The multidecadal variations of the interannual relationship between the East Asian summer monsoon and ENSO in a coupled model[J]. Climate Dynamics, 2017, 51: 1671-1686.
    [33]
    KUHLBRODT T, GRIESEL A, MONTOYA M, et al. On the driving processes of the Atlantic meridional overturning circulation[J]. Reviews of Geophysics, 2007, 45(2): RG2001, doi: 10.1029/2004RG000166.
    [34]
    李双林, 王彦明, 郜永祺. 北大西洋年代际振荡(AMO)气候影响的研究评述[J]. 大气科学学报, 2009, 32(3): 458-465.)

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