四川盆地是中国重要的农业和经济中心, 湿季降水尤其极端降水情况显得尤为重要。基于“国际耦合模式比较计划第五阶段”（CMIP5）的多个模式结果评估未来湿季降水和极端降水的可能变化。首先, 利用CRU数据检验了模式对1971 -2000年5 -9月四川盆地降水的模拟能力。结果显示, 31个模式中有17个模式的模拟能力较好, 通过99%的空间相关性信度检验, 重现了“东多西少”的空间形态。有21个模式与观测值的标准差之比小于2.5, 所有模式的平均偏差率都小于50%。在此基础上, 选取表现最好的3个模式, 在订正后做模式集合平均（MME）, 展示在RCP2.6、 RCP4.5、 RCP8.5的情形下, 21世纪初期（2010 -2039年）、 中期（2040 -2069年）、 末期（2070 -2099年）四川盆地湿季平均降水和湿季极端降水阈值的空间分布特征。结果显示, 在RCP2.6的情形下, 相对于1971 -2000年的气候平均态, 四川盆地湿季降水自东向西呈现“减-增-减”的形势, 并且随着时间变化并无明显变化。在RCP4.5与RCP8.5的情形下, 特征与RCP2.6不同, 自东向西呈现“增-减-增”的形势。盆地东部湿季平均降水普遍增多, 部分区域的变率达到了20%。对于极端降水阈值空间分布, 特征与平均降水类似, 三种情形下高值区均为在盆地中部偏东的一条南北走向的狭长带状区域。该区域内包括了成都、 雅安、 眉山、 乐山等四川主要城市, 并且随时间变化, 该区域还有扩大的趋势, 而RCP8.5模拟的降水要显著多于另两种情形。此外研究还发现, 在全球变暖的背景下, 四川盆地某一区域湿季平均降水减少的情况下, 其极端降水阈值一定降低, 反之不一定成立。只有当平均降水增幅超过10%时, 该地区极端降水阈值才会增加。
Sichuan Basin is an important agricultural and economic center in China.Precipitation in wet season, especially extreme precipitation, is particularly important.This paper evaluates possible future changes in wet season precipitation and extreme precipitation based on multiple model results of the Coupled Model Inter-comparison Project Phase 5(CMIP5).First, the CRU data was used to test the models' ability to simulate the May-September precipitation in Sichuan Basin from 1971 to 2000.The results show that 17 of the 31 models had better simulation capabilities, and pass the 99% of the spatial correlation reliability test.The results successfully reproduce the spatial pattern of “east more and west less”.The ratio of the standard deviation of 21 patterns to observations is less than 2.5.The average precipitation bias of all models is less than 50%.On this basis, we select three best-performing models, and after the correction we will perform the MME to display the spatial distribution characteristics of average precipitation and wet season extreme precipitation thresholds in the Sichuan Basin in early 21st century (2010 -2039)、 middle period (2040 -2069) and late period (2070 -2099), in the case of RCP2.6, RCP4.5, and RCP8.5.The results show that in the case of RCP2.6, compared to the climatology of 1971 -2000, precipitation in wet season of Sichuan Basin shows a "decreasing-increasing-decreasing" trend from east to west, and there is no obvious change over time.In the case of RCP4.5 and RCP8.5, the characteristics are different from those of RCP2.6, showing a "increasing-decreasing-increasing" situation from east to west.The average precipitation in wet season in the east of basin generally increases, and the variability in some regions even reached 20%.For the spatial distribution of extreme precipitation thresholds, the characteristics are similar to the average precipitation.In the three cases, the large-value areas are in a north-south trending strip-like zone in the east of the basin.This area includes major cities in Sichuan, such as Chengdu, Ya'an, Meishan, Leshan, and it has an increasing trend over time.The precipitation simulated by RCP8.5 is significantly more than the other two cases.In addition, the study finds that under the background of global warming, if the average precipitation in wet season in a certain area of the Sichuan Basin decreases, and the extreme precipitation threshold must be reduced, but the opposite is not necessarily true.Only when the average precipitation increases by more than 10% will the threshold of extreme precipitation in that area increase.
[1]Alexander L V, Zhang X, Peterson T C, al et, 2006.Global observed changes in daily climate extremes of temperature and precipitation[J].Journal of Geophysical Research Atmospheres, 111(D5): 1042-1063.
[2]Beniston M, Stephenson D B, Christensen O B, al et, 2007.Future extreme events in European climate: An exploration of regional climate model projections[J].Climatic Change, 81(1): 71-95.
[3]Brown P, Bradley R, Keimig F, 2010.Changes in extreme climate indices for the Northeastern United States,1870-2005[J].Journal of Climate, 23(24): 6555 - 6572.
[4]Gutowski W J, Arritt R W, Kawazoe S, al et, 2010.Regional Extreme monthly precipitation simulated by NARCCAP RCMs[J].Journal of Hydrometeorology, 11(6): 1373-1379.
[5]Hartmann D L, Tank A M G K, Rusticucci M, al et, 2013.Observations: atmosphere and surface[M]//Climate change 2013 the physical science basis: Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change.London: Cambridge University Press, 159-254.
[6]Hugo C, Naumann G, Barbosa P, 2018.Global projections of drought hazard in a warming climate: A prime for disaster risk management[J].Climate Dynamics, 50:1-19.
[7]Kunkel K E, Changnon S A, Angel J R, 1994.Climatic aspects of the 1993 upper Mississippi River basin flood[J].Bulletin of the American Meteorological Society, 75(5): 811-822.
[8]Liu J, Zhai P, 2014.Changes in climate regionalization indices in China during 1961 -2010[J].Advances in Atmospheric Sciences, 31(2): 374-384.
[9]Miroslava U, To?i? Ivana, 2011.A statistical analysis of the daily precipitation over Serbia: Trends and indices[J].Theoretical & Applied Climatology, 106(1/2): 69-78.
[10]Nath R, Cui X, Nath D, al et, 2017.CMIP5 multimodel projections of extreme weather events in the humid subtropical Gangetic Plain region of India[J].Earth's Future, 2017, 5(2): 224-239.
[11]Nath R, Nath D, Li Q, al et, 2017.Impact of drought on agriculture in the Indo-Gangetic Plain, India[J].Advances in Atmospheric Sciences, 34(3): 335-346.
[12]Rodrigo B, Leila C, 2011.The South Atlantic dipole and variations in the characteristics of the South American Monsoon in the WCRP-CMIP3 multi-model simulations[J].Climate Dynamics, 36(11):2091-2102.
[13]Scibek J, Allen D M, 2006.Modeled impacts of predicted climate change on recharge and groundwater levels[J].Water Resources Research, 42(11).
[14]Stocker, T F, Qin D, Plattner G K, al et, 2013.PCC, 2013: Climate Change 2013: The Physical Science Basis[M].London: Cambridge University Press.
[15]Taylor K E, 2001.Summarizing multiple aspects of model performance in a single diagram[J].Journal of Geophysical Research Atmospheres, 106(D7): 7183-7192.
[16]Wan S, Hu Y, You Z, al et, 2013, Extreme monthly precipitation pattern in China and its dependence on Southern Oscillation[J].International Journal of Climatology, 33(4): 806-814.
[17]Wang L, Chen W, 2014.CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China[J].International Journal of Climatology, 34(6): 2059-2078.
[18]常燕, 吕世华, 罗斯琼, 等, 2016.CMIP5耦合模式对青藏高原冻土变化的模拟和预估［J］.高原气象, 35（5）: 1157-1168.DOI: 10.7522/j.issn.1000-0534.2015.00090.
[19]陈姣, 张耀存, 2016.气候变化背景下陆地极端降水和温度变化区域差异[J].高原气象, 35(4): 955-968.DOI: 10.7522/j.issn. 1000-0534.2015.00075.
[20]江志红, 陈威霖, 宋洁, 等, 2009.7个IPCCAR4模式对中国地区极端降永指数模拟能力的评估及其未来情景预估[J].大气科学, 33(1): 109-120.
[21]孔锋, 史培军, 方建, 等, 2017.全球变化背景下极端降水时空格局变化及其影响因素研究进展和展望[J].灾害学, 32(2): 165-174.
[22]李永华, 徐海明, 白莹莹, 等, 2010.我国西南地区东部夏季降水的时空特征[J].高原气象, 29(2): 523-530.
[23]李振朝, 韦志刚, 吕世华, 等, 2013.CMIP5部分模式气温和降水模拟结果在北半球及青藏高原的检验[J].高原气象, 32(4): 921-928.DOI: 10.7522/j.issn.1000-0534.2012.00088.
[24]邵远坤, 沈桐立, 游泳, 等, 2005.四川盆地近40年来的降水特征分析[J].西南大学学报(自然科学版), 27(6): 749-752.
[25]卿清涛, 陈文秀, 詹兆渝, 2013.四川省暴雨洪涝灾害损失时空演变特征分析[J].高原山地气象研究, 33(1): 47-51.
[26]吴绍洪, 尹云鹤, 2012.极端事件对人类系统的影响[J].气候变化研究进展, 8(2): 99-102.
[27]闻新宇, 王绍武, 朱锦红, 等, 2006.英国CRU高分辨率格点资料揭示的20世纪中国气候变化[J].大气科学, 30(5): 894-904.
[28]伍清, 蒋兴文, 谢洁, 2017.CMIP5模式对西南地区气温的模拟能力评估[J].高原气象, 36( 2): 358-370.DOI: 10.7522/j.issn. 1000-0534.2016.00046.
[29]袁文德, 郑江坤, 董奎, 2014.1962-2012年西南地区极端降水事件的时空变化特征[J].资源科学, 36(4): 766-772.
[30]杨红娟, 韦方强, 马振峰, 等, 2017.四川省泥石流灾害的时空分布规律和降水特征[J]．灾害学, 32(4): 102-107.
[31]杨金虎, 江志红, 王鹏祥, 等, 2008.中国年极端降水事件的时空分布特征气候与环境研究[J], 13(1): 75-83.
[32]郁淑华, 2003.四川盆地泥石流、 滑坡的时空分布特征及其气象成因分析[J].高原气象, 22(10): 83-89.
[33]翟盘茂, 潘晓华, 2003.中国北方近50年温度和降水极端事件变化[J].地理学报, 58(suppl1): 1-10.
[34]翟盘茂, 王萃萃, 李威, 2007.极端降水事件变化的观测研究[J].气候变化研究进展, 3(3): 144-148.
[35]张蓓, 戴新刚, 2017.基于CMIP5的2006－2015年中国气温预估偏差分析及订正[J].高原气象, 36( 6): 1619-1629.DOI: 10. 7522/j.issn.1000-0534.2016.00136.
[36]周长艳, 岑思弦, 李跃清, 等, 2011.四川省近50年降水的变化特征及影响[J].地理学报, 66(5): 619-630.
[37]智协飞, 王晶, 林春泽, 等, 2015.CMIP5多模式资料中气温的BMA预测方法研究[J].气象科学, 35(4): 405-412.
