21世纪开都-孔雀河流域未来气候变化情景预估
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摘要
利用Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections(DCHP)提供的31个CMIP5降尺度数据和CRU逐月气温、降水格点数据集,通过评估PLS(偏最小二乘回归)、RR(岭回归)和EE(等权平均) 3种多模式集合平均预估模型对历史气候变化的模拟能力,确定最优集合方法,进而预估开都-孔雀河流域21世纪气候变化情景。结果表明:(1)所建立的PLS模型对流域的气温和降水具有较好的模拟能力,尤其对气温的模拟,r值均达到了0. 64以上,明显优于降水(0. 19～0. 36),但存在空间异质性;(2) 21世纪开都-孔雀河流域各子区气温呈显著增加趋势,且RCP8. 5情景下的增温速率[0. 58～0. 67℃·(10a)~(-1)]是RCP4. 5情景下[0. 25～0. 31℃·(10a)~(-1)]的2倍以上,21世纪中叶是2种情景产生明显差异的开始。整个流域增温速率由西北山区向东南荒漠区逐渐增大;(3)未来降水在不同排放情景下变化速率的分布状况略有不同,但均呈显著增加趋势,且RCP8. 5情景下的增加速率[1. 22%～1. 54%·(10a)~(-1)]总体上高于RCP4. 5[0. 80%～1. 32%·(10a)~(-1)]。
Climate change assessments on both global and regional scales rely strongly on the global climate models( GCMs) which are dominantly provided by the Coupled Model Inter Comparison Project Phase 5( CMIP5).Based on the grid datasets of monthly air temperature and precipitation from CRU( Climate Research Unit) and 31 CMIP5 GCMS data from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections( DCHP),in this paper the performance of three Multi-Model Ensemble Mean methods( PLS,RR and EE) in simulating the historical climate change processes was evaluated,and the optimal ensemble method was determined and estimated for predicting the future climate change in the Kaidu-Konqi River Basin in the 21 st century. The results indicated that:(1) The performance of the established Partial Least Squares( PLS) model was the best in simulating air temperature and precipitation in the study area. The r values of simulated temperature were all higher than 0. 64,they were obviously better than those of simulated precipitation( 0. 19-0. 36). However,there was a spatial heterogeneity in both temperature and precipitation simulations;(2) In the 21 st century,the air temperature in the 4 sub-basins of KaiduKongqi River Basin would be in a significant increase trend. The increase rates of air temperature [0. 58-0. 67 ℃ ·( 10 a)~(-1)]under the RCP8. 5 scenario would be doubled compared with those under RCP4. 5 scenario[0. 25-0. 31 ℃·( 10 a)~(-1)]. The significant difference between the two scenarios would begin from the mid-21 st century. From the perspective of entire watershed,the warming rate increased gradually from the mountainous area in the northwest to the desert in the southeast;(3) The distribution of change rates of precipitation was slightly different under different discharge scenarios,but both of them would be in a significant increase trend. The increase rate under RCP8. 5 scenario[1. 22%~(-1). 54% ·( 10 a)~(-1)]would be holistically higher than that under RCP4. 5 scenario[0. 80%~(-1). 32% ·( 10 a)~(-1)].
Climate change assessments on both global and regional scales rely strongly on the global climate models( GCMs) which are dominantly provided by the Coupled Model Inter Comparison Project Phase 5( CMIP5).Based on the grid datasets of monthly air temperature and precipitation from CRU( Climate Research Unit) and 31 CMIP5 GCMS data from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections( DCHP),in this paper the performance of three Multi-Model Ensemble Mean methods( PLS,RR and EE) in simulating the historical climate change processes was evaluated,and the optimal ensemble method was determined and estimated for predicting the future climate change in the Kaidu-Konqi River Basin in the 21 st century. The results indicated that:(1) The performance of the established Partial Least Squares( PLS) model was the best in simulating air temperature and precipitation in the study area. The r values of simulated temperature were all higher than 0. 64,they were obviously better than those of simulated precipitation( 0. 19-0. 36). However,there was a spatial heterogeneity in both temperature and precipitation simulations;(2) In the 21 st century,the air temperature in the 4 sub-basins of KaiduKongqi River Basin would be in a significant increase trend. The increase rates of air temperature [0. 58-0. 67 ℃ ·( 10 a)~(-1)]under the RCP8. 5 scenario would be doubled compared with those under RCP4. 5 scenario[0. 25-0. 31 ℃·( 10 a)~(-1)]. The significant difference between the two scenarios would begin from the mid-21 st century. From the perspective of entire watershed,the warming rate increased gradually from the mountainous area in the northwest to the desert in the southeast;(3) The distribution of change rates of precipitation was slightly different under different discharge scenarios,but both of them would be in a significant increase trend. The increase rate under RCP8. 5 scenario[1. 22%~(-1). 54% ·( 10 a)~(-1)]would be holistically higher than that under RCP4. 5 scenario[0. 80%~(-1). 32% ·( 10 a)~(-1)].
引文
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[2]IPCC.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[M].Cambridge:Cambridge University Press,2013.
[3]IPCC,2012:Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation.A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change[M].Cambridge:Cambridge University Press,2012.
[4]符淙斌,马柱国.全球变化与区域干旱化[J].大气科学,2008,32(4):752-760.[Fu Congbin,Ma Zhuguo.Global change and regional aridification[J].Chinese Journal of Atmospheric Sciences,2008,32(4):752-760.]
[5]成爱芳,冯起,张健恺,等.未来气候情景下气候变化响应过程研究综述[J].地理科学,2015,35(1):84-90.[Cheng Aifang,Feng Qi,Zhang Jiankai,et al.A review of climate change scenario for impacts process study[J].Scientia Geographica Sinica,2015,35(1):84-90.]
[6]吴迪,严登华.SRES情景下多模式集合对淮河流域未来气候变化的预估[J].湖泊科学,2013,25(4):565-575.[Wu Di,Yan Denghua.Projections of future climate change over Huaihe River basin by multimodel ensembles under SRES scenarios[J].Journal of Lake Science,2013,25(4):565-575.]
[7]Guo Yan,Dong Wenjie,Ren Fumin,et al.Surface air temperature simulations over China with CMIP5 and CMIP3[J].Advances in Climate Change Research,2013,4(3):145-152.
[8]Taylor K E,Stouffer R J,Gerald A M.An overview of CMIP5 and the experiment design[J].Bulletin of the American Meteorological Society,2012,93(4):485-498.
[9]Qu X,Huang G,Zhou W.Consistent responses of East Asian summer mean rainfall to global warming in CMIP5 simulations[J].Theoretical&Applied Climatology,2014,117(1-2):123-131.
[10]Moon S,Ha K J.Temperature and precipitation in the context of the annual cycle over Asia:Model evaluation and future change[J].Asia-Pacific Journal of Atmospheric Sciences,2017,53(2):229-242.
[11]Chen H P.Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models[J].Science Bulletin,2013,58(12):1 462-1 472.
[12]陶纯苇,姜超,孙建新.CMIP5多模式集合对东北三省未来气候变化的预估研究[J].地球物理学报,2016,59(10):3 580-3 591.[Tao Chunwei,Jiang Chao,Sun Jianxin.Projection of future changes in climate in Northeast China using a CMIP5 multi-model ensemble[J].Chinese Journal of Geophysics,2016,59(10):3 580-3 591.]
[13]苏琪骅.基于CMIP5模式在中国地区温度与降水的模拟评估及集合预报方法研究[D].合肥:中国科学技术大学,2017.[Su Qihua.Evaluation and Ensemble Forecast of the Surface Air Temperature and Precipitation in China Based on CMIP5 MultiModels[D].Hefei:University of Science and Technology of China,2017.
[14]智协飞,赵欢,朱寿鹏,等.基于CMIP5多模式回报资料的地面气温超级集合研究[J].大气科学学报,2016,39(1):64-71.[Zhi Xiefei,Zhao Huan,Zhu Shoupeng,et al.Superensemble hindcast of surface air temperature using CMIP5 Multi-Model data[J].Transactions of Atmospheric Sciences,2016,39(1):64-71.]
[15]Zhu C,Park C K,Lee W S,et al.Statistical downscaling for MultiModel ensemble prediction of summer monsoon rainfall in the AsiaPacific region using geopotential height field[J].Advances in Atmospheric Sciences,2008,25(5):867-884.
[16]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.[Zhou Li,Lan Mingcai,Cai Ronghui,et al.Projection and uncertainties of extreme precipitation over the Yangtze River valley in the early 21st century[J].Acta Meteorologica Sinica,2018,76(1):47-61.]
[17]Semenov M A,Stratonovitch P.Use of multi-model ensembles from global climate models for assessment of climate change impacts[J].Climate Research,2010,41(1):1-14.
[18]陈鹏翔,江志红,彭冬梅.基于BP-CCA统计降尺度的中亚春季降水的多模式集合模拟与预估[J].气象学报,2017,75(2):236-247.[Chen Pengxiang,Jiang Zhihong,Peng Dongmei.Multimodel statistical downscaling of spring precipitation simulation and projection in Central Asia based on canonical correlation analysis[J].Acta Meteorologica,2017,75(2):236-247.]
[19]吴晶,罗毅,李佳,等.CMIP5模式对中国西北干旱区模拟能力评价[J].干旱区地理,2014,37(3):499-508.[Wu Jing,Luo Yi,Li Jia,et al.Evaluation of CMIP5 model’s simulation ability in the Northwest arid areas of China[J].Arid Land Geography,2014,37(3):499-508.]
[20]祁晓凡,李文鹏,李海涛,等.基于CMIP5模式的干旱内陆河流域未来气候变化预估[J].干旱区地理,2017,40(5):987-996.[Qi Xiaofan,Li Wenpeng,Li Haitao,et al.Future climate change prediction of arid inland river basin based on CMIP5 model[J].Arid Land Geography,2017,40(5):987-996.]
[21]黄秋霞,赵勇,何清.基于CRU资料的中亚地区气候特征[J].干旱区研究,2013,30(3):396-403.[Huang Qiuxia,Zhao Yong,He Qing.Climatic characteristics in Central Asia based on CRU data[J].Arid Zone Research,2013,30(3):396-403.]
[22]周成虎,程维明.中国1∶400万数字地貌数据集.http://westdc.westgis.ac.cn,2014.[Zhou Chenghu,Cheng Weiming.1∶4 000 000 Digital Geomorphology Database in China.http://westdc.westgis.ac.cn,2014.]
[23]Geladi P,Kowalski B R.Partial least-squares regression:A tutorial[J].Analytica Chimica Acta,1985,185(86):1-17.
[24]Hoerl A,Kennard R.Ridge regression:Biased estimation for nonorthogonal problems[J].Technometrics,2000,42(1):80-86.
[25]谭娇,丁建丽,张钧永,等.1961-2014年新疆北部地区气温时空变化特征[J].干旱区研究,2018,35(5):1 181-1 191.[Tan Jiao,Ding Jianli,Zhang Junyong,et al.Spatiotemporal variation of temperature in North Xinjiang during the period of 1961-2014[J].Arid Zone Research,2018,35(5):1 181-1 191.]
[26]张艳武,张莉,徐影.CMIP5模式对中国地区气温模拟能力评估与预估[J].气候变化研究进展,2016,12(1):10-19.[Zhang Yanwu,Zhang Li,Xu Ying.Simulations and projections of the surface air temperature in China by CMIP5 models[J].Advances in Climate Change,2016,12(1):10-19.]
[27]商沙沙,廉丽姝,马婷,等.近54 a中国西北地区气温和降水的时空变化特征[J].干旱区研究,2018,35(1):68-76.[Shang Shasha,Lian Lishu,Ma Ting,et al.The temporal and spatial characteristics of temperature and precipitation in Northwestern China in recent 54 years[J].Arid Zone Research,2018,35(1):68-76.]
[28]Aloysius N R,Sheffield J,Saiers J E,et al.Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models[J].Journal of Geophysical Research Atmospheres,2016,121(1):130-152.
[29]赵天保,陈亮,马柱国.CMIP5多模式对全球典型干旱半干旱区气候变化的模拟与预估[J].科学通报,2014,59(12):1 148-1 163.[Zhao Tianbao,Chen Liang,Ma Zhuguo.Simulation of historical and projected climate change in arid and semiarid areas by CMIP5 models[J].Chinese Science Bulletin,2014,59(12):1 148-1 163.]
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