全球升温1.5℃和2.0℃情景下澜沧江流域极端降水的变化特征

doi:10.12006/j.issn.1673-1719.2019.126

摘要/Abstract

摘要：

澜沧江是我国为数不多的跨境河流,流域内多发暴雨、洪水灾害,因此定量、科学地评估澜沧江流域未来全球升温情景下极端降水的变化特征,能够为澜沧江-湄公河沿线国家共同管理流域水资源和抵御自然灾害提供一定的科学指导。文中基于部门间影响模式比较计划（ISI-MIP）下5个全球气候模式降水数据,通过偏差校正增强其在澜沧江流域极端降水的模拟能力,使用降水强度、日最大降水量和强降水量等9个指标评价未来全球升温1.5℃和2.0℃下澜沧江流域极端降水的变化情况,并对结果的不确定性和可信度进行研究,得出以下主要结论：随着全球温度的升高,澜沧江流域年降水和极端降水均呈现增大趋势,其中极强降水量（R99p）升幅最大,升温1.5℃和2.0℃下升幅分别为37%和75%;相对于基准期,全球升温2.0℃下各极端降水指数增幅明显大于升温1.5℃,前者升幅甚至超出后者一倍;未来全球升温情景下,澜沧江流域湿季会变得更湿润,而干季则会更干燥;澜沧江流域降水集中程度会增大,使得流域内洪涝灾害发生的风险增大;ISI-MIP气候模式对澜沧江流域未来极端降水模拟存在较大不确定性,升温2.0℃较升温1.5℃情景下不确定性更大,但相对于基准期,前者极端降水增大的可信度更高。

关键词: 全球升温1.5℃, 全球升温2.0℃, 极端降水, ISI-MIP, 澜沧江流域, 不确定性

Abstract:

Lancang River is one of the few cross-border rivers in China, and there are many torrential rains and flood disasters in the basin. Therefore, the scientific assessment of the changes of extreme precipitation in the Lancang River basin under the future global warming scenario can provide some scientific guidance for the countries along the Lancang-Mekong River to jointly manage the basin water resources and resist natural disasters. Based on the rainfall data of five global climate models under the Inter-Sectoral Impact Model Comparison Program (ISI-MIP), the simulation ability of extreme precipitation was enhanced in the Lancang River basin through bias correction. A total of 9 indicators, such as precipitation intensity, daily maximum precipitation, and heavy precipitation, were used to evaluate the changes of extreme precipitation in the Lancang River basin under the global warming of 1.5℃ and 2.0℃ in the future, and the uncertainty and reliability of the results were also studied. The main conclusions are as follows. With the increase of global temperature, the annual precipitation and extreme precipitation in Lancang River basin show an increasing trend, in which extremely strong precipitation increase the most by 37% and 75%, respectively under the global warming of 1.5℃ and 2.0℃. Compared with the historical benchmark period, the increase of extreme precipitation index at 2.0℃ is obviously larger than that at 1.5℃, and the amplification of the former is almost twice that of the later. In the future global warming scenarios, the wet season in the Lancang River basin will become more humid, while the dry season drier. The concentration of precipitation in the Lancang River basin will increase, which increases the risk of flood disasters in the wet season. The future extreme precipitation simulation from the ISI-MIP climate models has great uncertainty over the Lancang River basin, which is greater under 2.0℃ global warming than that under 1.5℃, but the reliability of increasing extreme precipitation relative to the historical benchmark period for the former is higher.

Key words: Global warming of 1.5℃, Global warming of 2.0℃, Extreme precipitation, ISI-MIP, Lancang River basin, Uncertainty

丁凯熙, 张利平, 佘敦先, 张琴, 向竣文. 全球升温1.5℃和2.0℃情景下澜沧江流域极端降水的变化特征[J]. 气候变化研究进展, 2020, 16(4): 466-479.

DING Kai-Xi, ZHANG Li-Ping, SHE Dun-Xian, ZHANG Qin, XIANG Jun-Wen. Variation of extreme precipitation in Lancang River basin under global warming of 1.5℃ and 2.0℃[J]. Climate Change Research, 2020, 16(4): 466-479.

图/表 11

图1 澜沧江流域地形图

Fig. 1 Topographic map of Lancang River basin

表1 校正后MME对澜沧江流域极端降水的模拟能力评估

Table 1 Evaluation of simulation ability of extreme precipitation in Lancang River basin by multi-model ensemble (MME) after correction

图2 1961—2005年澜沧江流域极端降水空间分布注：图中序号1代表实测数据,2代表历史MME。

Fig. 2 Spatial distribution of extreme precipitation index in Lancang River basin from 1961 to 2005

图3 澜沧江流域RCP2.6和RCP4.5情景下各极端降水指标时间变化

Fig. 3 Time-varying process of various extreme precipitation indexes under different scenarios in Lancang River basin

图4 澜沧江流域不同情景下各极端指标箱形图

Fig. 4 Box map of various extreme precipitation indexes under different scenarios in Lancang River basin

图5 澜沧江流域不同情景下各极端降水指标空间变化注：序号1代表升温1.5℃减去基准期,2代表升温2.0℃减升温1.5℃;年降水量与PRCPTOT、Rx5d与Rx1d、R99p与R95p空间变化相近,故图略。

Fig. 5 Spatial variation of extreme precipitation indexes under different scenarios in Lancang River basin

图6 核密度估计下的澜沧江流域极端降水概率密度分布注：年降水量与PRCPTOT、Rx5d与Rx1d、R99p与R95p概率分布相近,故图略。

Fig. 6 Probability density distribution of extreme precipitation in Lancang River basin based on kernel density estimation

图7 澜沧江流域各时期降水基尼系数变化特征

Fig. 7 Characteristics of Gini coefficient of precipitation in Lancang River basin in different periods

图8 澜沧江流域不同升温情景下降水基尼系数空间变化注：(a)基准期,(b)升温1.5℃减基准期,(c)升温2.0℃减基准期,(d)升温2.0℃减升温1.5℃。

Fig. 8 Spatial variation of Gini coefficient of precipitation under different warming scenarios in Lancang River basin

图9 不同升温情景下澜沧江流域极端降水差异系数

Fig. 9 Extreme precipitation difference coefficients in Lancang River basin under different warming scenarios

图10 不同升温情景下澜沧江流域极端降水信噪比变化

Fig. 10 Variation of signal-to-noise ratio of extreme precipitation in Lancang River basin under different warming scenarios

参考文献 36

[1]	IPCC. Climate change 2013: the physical science basis [M]. Cambridge: Cambridge University Press, 2013
[2]	Tiro N, Mark N, Modathir Z. Temperature and precipitation extremes under current, 1.5℃ and 2.0℃ global warming above pre-industrial levels over Botswana, and implications for climate change vulnerability[J]. Environmental Research Letters, 2018,13(6):065016 doi: 10.1088/1748-9326/aac2f8 URL
[3]	Trenberth K E, Dai A, Rasmussen R M, et al. The changing character of precipitation[J]. Bulletin of the American Meteorological Society, 2010,84(9):1205-1217 doi: 10.1175/BAMS-84-9-1205 URL
[4]	刘超, 胡海波, 张媛, 等. CAM3.0模式中东亚气溶胶浓度变化的直接效应及全球海面温度年代际变化对东亚夏季降水的影响研究[J]. 热带气象学报, 2014,30(6):1048-1060.
	Liu C, Hu H B, Zhang Y, et al. The direct effects of aerosols and decadal variation of global sea surface temperature on east Asia summer precipitation in CAM3.0[J]. Journal of Tropical Meteorology, 2014,30(6):1048-1060 (in Chinese)
[5]	Bao J, Sherwood S C, Alexander L V, et al. Future increases in extreme precipitation exceed observed scaling rates[J]. Nature Climate Change, 2017,7(2):128-132 doi: 10.1038/nclimate3201 URL
[6]	IPCC. Climate change 2014: synbook report[M]. Geneva: IPCC, 2014: 1-151
[7]	UNFCCC. Decision 1/CP. 21. The Paris Agreement[M]. Paris: UNFCCC, 2015
[8]	Pfahl S, O'Gorman P A, Fischer E M. Understanding the regional pattern of projected future changes in extreme precipitation[J]. Nature Climate Change, 2017,7(6):423-427 doi: 10.1038/nclimate3287 URL
[9]	Wuebbles D, Meehl G, Hayhoe K, et al. CMIP5 climate model analyses: climate extremes in the United States[J]. Bulletin of the American Meteorological Society, 2014,95(4):571-583 doi: 10.1175/BAMS-D-12-00172.1 URL
[10]	Zollo A L, Rillo V, Bucchignani E, et al. Extreme temperature and precipitation events over Italy: assessment of high-resolution simulations with COSMO-CLM and future scenarios[J]. International Journal of Climatology, 2016,36(2):987-1004 doi: 10.1002/joc.4401 URL
[11]	Schwarzak S, Hänsel S, Matschullat J. Projected changes in extreme precipitation characteristics for Central Eastern Germany (21st century, model-based analysis)[J]. International Journal of Climatology, 2015,35(10):2724-2734 doi: 10.1002/joc.4166 URL
[12]	Pinto I, Lennard C, Tadross M, et al. Evaluation and projections of extreme precipitation over southern Africa from two CORDEX models[J]. Climatic Change, 2016,135(3-4):655-668 doi: 10.1007/s10584-015-1573-1 URL
[13]	Nangombe S, Zhou T, Zhang W, et al. Record-breaking climate extremes in Africa under stabilized 1.5℃ and 2℃ global warming scenarios[J]. Nature Climate Change, 2018 (8):375-380
[14]	杭月荷. CMIP5多模式对中国极端降水的模拟评估及未来情景预估[D]. 南京: 南京信息工程大学, 2013.
	Hang Y H. Projection and evaluation of the precipitation extreme indices over China by CMIP5 models[D]. Nanjing: Nanjing University of Information Science & Technology, 2013 (in Chinese)
[15]	陈晓晨, 徐影, 姚遥. 不同升温阈值下中国地区极端气候事件变化预估[J]. 大气科学, 2015 (6):1123-1135. doi: 10.3878/j.issn.1006-9895.1502.14224 URL
	Chen X C, Xu Y, Yao Y. Changes in climate extremes over China in a 2℃, 3℃, and 4℃ warmer world[J]. Chinese Journal of Atmospheric Sciences, 2015 (6):1123-1135 (in Chinese)
[16]	贺振, 贺俊平. 1960年至2012年黄河流域极端降水时空变化[J]. 资源科学, 2014,36(3):490-501.
	He Z, He J P. Temporal and spatial variation of extreme precipitation in the Yellow River basin from 1960 to 2012[J]. Resources Science, 2014,36(3):490-501 (in Chinese)
[17]	刘俸霞, 王艳君, 赵晶, 等. 全球升温1.5℃与2.0℃情景下长江中下游地区极端降水的变化特征[J]. 长江流域资源与环境, 2017,26(5):133-143.
	Liu F X, Wang Y J, Zhao J, et al. Variations of the extreme precipitation under the global warming of 1.5℃ and 2.0℃ in the Mid Lower reaches of the Yangtze River basin[J]. Resources and Environment in the Yangtze Basin, 2017,26(5):133-143 (in Chinese)
[18]	史婉丽, 禹雪中, 冯时, 等. 澜沧江流域近51年来极端降水事件的变化趋势和突变特征[J]. 水电能源科学, 2012 (11):1-5.
	Shi W L, Yu X Z, Feng S, et al. Sudden change and variation trend of extreme precipitation in Lancangjiang River basin during 1960-2010[J]. Water Resources and Power, 2012 (11):1-5 (in Chinese)
[19]	唐海行. 澜沧江-湄公河流域资源环境与可持续发展[J]. 地理学报, 1999,66(S1):101-109. doi: 10.11821/xb1999S1013 URL
	Tang H X. Resources environment in the Lancang-Mekong River basin and it's sustainable development[J]. Acta Geographica Sinica, 1999,66(S1):101-109 (in Chinese)
[20]	刘波, 肖子牛. 澜沧江流域1951—2008年气候变化和2010—2099年不同情景下模式预估结果分析[J]. 气候变化研究进展, 2010,6(3):170-174.
	Liu B, Xiao Z N. Observed (1951-2008) and projected (2010-2099) climate change in the Lancang River basin[J]. Advances in Climate Change Research, 2010,6(3):170-174 (in Chinese)
[21]	Warszawski L, Frieler K, Huber V, et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): project framework[J]. Proceedings of the National Academy of Sciences, 2014,111(9):3228-3232 doi: 10.1073/pnas.1312330110 URL
[22]	赵煜飞, 朱江. 近50年中国降水格点日值数据集精度及评估[J]. 高原气象, 2015,34(1):50-58. doi: 10.7522/j.issn.1000-0534.2013.00141 URL
	Zhao Y F, Zhu J. Assessing quality of grid daily precipitation datasets in China in recent 50 years[J]. Plateau Meteorology, 2015,34(1):50-58 (in Chinese)
[23]	Li H X, Chen H P, Wang H J., et al. Future precipitation changes over China under 1.5℃. Future precipitation changes over China under 1.5℃ and 2.0℃ global warming targets by using CORDEX regional climate models[J]. Science of the Total Environment, 2018, 640-641:543-554 doi: 10.1016/j.scitotenv.2018.05.324 URL pmid: 29864667
[24]	Su B D, Huang J L, Fischer T, et al. Drought losses in China might double between the 1.5℃ and 2.0℃ warming[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018,115(42):10600-10605 doi: 10.1073/pnas.1802129115 URL pmid: 30275323
[25]	Ma S M, Zhou T J. Observed trends in the timing of wet and dry season in China and the associated changes in frequency and duration of daily precipitation[J]. International Journal of Climatology, 2015,35(15):4631-4641 doi: 10.1002/joc.4312 URL
[26]	Zhang X B, Alexander L, Jones P, et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data[J]. Wiley Interdisciplinary Reviews Climate Change, 2011,2(6):851-870 doi: 10.1002/wcc.147 URL
[27]	Monjo R, Martin-Vide J. Daily precipitation concentration around the world according to several indices[J]. International Journal of Climatology, 2016,36(11):3828-3838 doi: 10.1002/joc.4596 URL
[28]	Rajah K, O'Leary T, Turner A, et al. Changes to the temporal distribution of daily precipitation[J]. Geophysical Research Letters, 2015,41(24):8887-8894 doi: 10.1002/2014GL062156 URL
[29]	李建芳, 粟晓玲, 王素芬. 基于基尼系数的内陆河流域用水公平性评价: 以石羊河流域为例[J]. 西北农林科技大学学报: 自然科学版, 2010,38(8):217-222.
	Li J F, Su X L, Wang S F. Evaluating the fairness of water use based on the Gini coefficient in inland river basins: a case study on Shiyang River basin[J]. Journal of Northwest A & F University: Natural Science Edition, 2010,38(8):217-222 (in Chinese)
[30]	吴蔚, 穆海振, 梁卓然, 等. CMIP5全球气候模式对上海极端气温和降水的情景预估[J]. 气候与环境研究, 2016,21(3):269-281.
	Wu W, Mu H Z, Liang Z R, et al. Projected changes in extreme temperature and precipitation events in Shanghai based on CMIP5 simulations[J]. Climatic and Environmental Research, 2016,21(3):269-281 (in Chinese)
[31]	江晓菲, 李伟, 游庆龙. 中国未来极端气温变化的概率预估及其不确定性[J]. 气候变化研究进展, 2018,14(3):12-20.
	Jiang X F, Li W, You Q L. Probability projection and uncertainties of the temperature extreme indices change over China[J]. Climate Change Research, 2018,14(3):12-20 (in Chinese)
[32]	Tian D, Guo Y, Dong W. Future changes and uncertainties in temperature and precipitation over China based on CMIP5 models[J]. Advances in Atmospheric Sciences, 2015,32(4):487-496 doi: 10.1007/s00376-014-4102-7 URL
[33]	陈晓晨, 徐影, 许崇海, 等. CMIP5全球气候模式对中国地区降水模拟能力的评估[J]. 气候变化研究进展, 2014,10(3):217-225. doi: 10.3969/j.issn.1673-1719.2014.03.011 URL
	Chen X C, Xu Y, Xu C H, et al. Assessment of precipitation simulations in China by CMIP5 multi-models[J]. Climate Change Research, 2014,10(3):217-225 (in Chinese)
[34]	胡芩, 姜大膀, 范广洲 . CMIP5全球气候模式对青藏高原地区气候模拟能力评估[J]. 大气科学, 2014,38(5):924-938. doi: 10.3878/j.issn.1006-9895.2013.13197 URL
	Hu Q, Jiang D B, Fan G Z, et al. Evaluation of CMIP5 models over the Qinghai-Tibetan Plateau[J]. Chinese Journal of Atmospheric Sciences, 2014,38(5):924-938 (in Chinese)
[35]	Ren D, Leslie L M, Lynch M J. Trends in Storm-Triggered Landslides over Southern California[J]. Journal of Applied Meteorology and Climatology, 2014,53(2):217-233 doi: 10.1175/JAMC-D-12-0253.1 URL
[36]	Teegavarapu R S V, Goly A, Obeysekera J. Influences of Atlantic multidecadal oscillation phases on spatial and temporal variability of regional precipitation extremes[J]. Journal of Hydrology, 2013,495(15):74-93 doi: 10.1016/j.jhydrol.2013.05.003 URL