基于站点的非线性回归降尺度模型及其在CMIP5降水产品降尺度分析中的应用
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摘要
降水是最重要的水循环过程,在全球气候变化影响下,降水过程出现显著时空变异,极端气象事件频发,对我国社会经济发展造成了严重影响.除降水观测数据以外,CMIP5数据已成为未来降水预估研究的重要数据源,而目前CMIP5数据空间分辨率低,如何对CMIP5数据进行降尺度研究,已成为区域降水研究的关键.本文以中国北部农牧交错带为研究区,提出了基于站点的非线性统计降尺度模型(station-based non-linear statistical downscaling model,SNSDM),并得出了以下结论:1)SNSDM降尺度降水序列相对于BCSD(bias corrected spatial downscaling)降尺度降水序列,在同等分辨率的情况下,SNSDM提高了对降水低值的模拟精度,可更为准确地模拟中国北部农牧交错带降水时空特性;2)相比于BCSD降尺度方法,SNSDM模拟结果与实测降水相关性提高最高达1.66%,且明显减少了对实测降水过高估计的误差,最大误差仅为0.2～0.3mm·d-1(每月6～9mm);3)CMIP5降水产品在较为湿润地区对于降水强度及趋势模拟精度要普遍高于对较为干旱地区的模拟精度.本研究提出的SNSDM方法对CMIP5降水数据过高估计实测降水的改进,进一步提高了利用CMIP5数据集对未来气候变化预估的精度及研究结果的可信度.
Precipitation is a critical component in the hydrological cycle.Precipitation processes are subject to remarkable spatiotemporal alterations in the backdrop of global climate changes and high frequency extreme weathers.Warming climate-induced amplification of weather extremes has apparent impacts on socioeconomic development.It should be noted here that,other than in situ precipitation observations,CMIP5 precipitation products can act as a major data source for prediction of precipitation changes.Lower spatial resolution of CMIP5 precipitation dataset however,enables only limited theoretical and practical application of CMIP5 precipitation datasets in the evaluation of regional precipitation variations.In the current study,a new stationbased Non-linear Statistical Downscaling Model,SNSDM,was proposed for the agricultural and livestock farming transitional zone in northern China.Downscaling performance of this model was evaluated.It was found that downscaled precipitation data by SNSDM,when compared to Bias Correlated Spatial Downscaling(BCSD),greatly improved the spatial resolution of lower precipitation value and could better describe the spatiotemporal pattern of precipitation regimes across the whole agricultural and livestock farming transitional zone in northern China.In comparison with spatial resolution of BCSD downscaled precipitation datasets,the improvement of spatial resolution was 1.66% with SNSDM.This also resulted in greatly reduced estimation error in downscaled precipitation data when compared to in situ precipitation observations.CMIP5 datasets could better quantify precipitation processes in terms of precipitation intensity and precipitation trends in humid regions when compared to arid regions.In comparison to BCSD,SNSDM could greatly improve prediction accuracy and validity for CMIP5 precipitation datasets.
Precipitation is a critical component in the hydrological cycle.Precipitation processes are subject to remarkable spatiotemporal alterations in the backdrop of global climate changes and high frequency extreme weathers.Warming climate-induced amplification of weather extremes has apparent impacts on socioeconomic development.It should be noted here that,other than in situ precipitation observations,CMIP5 precipitation products can act as a major data source for prediction of precipitation changes.Lower spatial resolution of CMIP5 precipitation dataset however,enables only limited theoretical and practical application of CMIP5 precipitation datasets in the evaluation of regional precipitation variations.In the current study,a new stationbased Non-linear Statistical Downscaling Model,SNSDM,was proposed for the agricultural and livestock farming transitional zone in northern China.Downscaling performance of this model was evaluated.It was found that downscaled precipitation data by SNSDM,when compared to Bias Correlated Spatial Downscaling(BCSD),greatly improved the spatial resolution of lower precipitation value and could better describe the spatiotemporal pattern of precipitation regimes across the whole agricultural and livestock farming transitional zone in northern China.In comparison with spatial resolution of BCSD downscaled precipitation datasets,the improvement of spatial resolution was 1.66% with SNSDM.This also resulted in greatly reduced estimation error in downscaled precipitation data when compared to in situ precipitation observations.CMIP5 datasets could better quantify precipitation processes in terms of precipitation intensity and precipitation trends in humid regions when compared to arid regions.In comparison to BCSD,SNSDM could greatly improve prediction accuracy and validity for CMIP5 precipitation datasets.
引文
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[30]LIANG X Z,WU Y,CHAMBERS R G,et al.Determining climate effects on US total agricultural productivity[J].Proceedings of the National Academy of Sciences,2017,114(12):E2285
[31]MOORE F C,BALDOS U,HERTEL T,et al.New science of climate change impacts on agriculture implies higher social cost of carbon[J].Nature Communications,2017,8(1):1607
[32]VARGAS R,BALDOCCHI D D,BAHN M,et al.On the multi-temporal correlation between photosynthesis and soil CO2 efflux:reconciling lags and observations[J].New Phytologist,2011,191(4):1006
[33]AHLSTROM A,RAUPACH M R,SCHURGERS G,et al.The dominant role of semi-arid ecosystems in the trend and variability of the land CO2sink[J].Science,2015,348(6237):895
[34]TANG J,NIU X,WANG S,et al.Statistical downscaling and dynamical downscaling of regional climate in China:present climate evaluations and future climate projections[J].Journal of Geophysical Research:Atmospheres,2016,121(5):2110
[35]JIANG Y,KIM J B,STILL C J,et al.Intercomparison of multiple statistically downscaled climate datasets for the Pacific Northwest,USA[J].Scientific Data,2018,5:180016
[36]GANGULI P,KUMAR D,GANGULY A R.USPower Production at Risk from Water Stress in a Changing Climate[J].Scientific Reports,2017,7(1):11983
[37]YOON J H,RUBY LEUNG L,CORREIA J.Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States[J].Journal of Geophysical Research:Atmospheres,2012,117(D21):109
[38]SHI W,LIU Y,SHI X.Contributions of climate change to the boundary shifts in the farming-pastoral ecotone in northern China since 1970[J].Agricultural Systems,2018,161:16
[39]LIU J H,GAO J X,GENG B,et al.Changes of land use and landscape pattern in the boundary change areas in farming-pastoral ecotone of northern China[J].Chinese Society of Agricultural Engineering,2008,2008(11):222
[40]ZHANG Q,QI T Y,LI J F,et al.Spatiotemporal variations of pan evaporation in China during 1960-2005:changing patterns and causes[J].Int J Climatol,2015,35(6):903
[41]ZHANG Q,QI T,SINGH V P,et al.Regional frequency analysis of droughts in China:a multivariate perspective[J].Water Resources Management,2015,29(6):1767
[42]TAYLOR K E,STOUFFER R J,MEEHL G A.An overview of CMIP5and the experiment design[J].Bull Amer Meteor Soc,2012,93(4):485
[43]MANZ B,BUYTAERT W,ZULKAFLI Z,et al.High-resolution satellite-gauge merged precipitation climatologies of the Tropical Andes[J].Journal of Geophysical Research:Atmospheres,2016,121(3):1190
[44]WOOD A W,LEUNG L R,SRIDHAR V,et al.Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs[J].Climatic change,2004,62(1/2/3):189
[45]WOOD A W,MAURER E P,KUMAR A,et al.Long-range experimental hydrologic forecasting for the eastern United States[J].Journal of Geophysical Research:Atmospheres,2002,107(D20):ACL-6
[46]SCHNORBUS M,WERNER A,BENNETT K.Impacts of climate change in three hydrologic regimes in British Columbia,Canada[J].Hydrological Processes,2014,28(3):1170
[47]SHUKLA S,LETTENMAIER D P.Multi-RCMensemble downscaling of NCEP CFS winter season forecasts:implications for seasonal hydrologic forecast skill[J].Journal of Geophysical Research:Atmospheres,2013,118(19):1077
[48]SEO S B,SINHA T,MAHINTHAKUMAR G,et al.Identification of dominant source of errors in developing streamflow and groundwater projections under nearterm climate change[J].Journal of Geophysical Research:Atmospheres,2016,121(13):7652
[49]WERNER A T,CANNON A J.Hydrologic extremesan intercomparison of multiple gridded statistical downscaling methods[J].Hydrology and Earth System Sciences,2016,20(4):1483
[50]KOVACS A,ABONYI J.Vizualization of fuzzy clustering results by modified sammon mapping[C]∥Proceedings of the 3rd international symposium of Hungarian researchers on computational intelligence,2002
[51]JANSSEN E,SRIVER R L,WUEBBLES D J,et al.Seasonal and regional variations in extreme precipitation event frequency using CMIP5[J].Geophysical Research Letters,2016,43(10):5385
[52]ROSA D,COLLINS W D.A case study of subdaily simulated and observed continental convective precipitation:CMIP5 and multiscale global climate models comparison[J].Geophys Res Lett,2013,40(22):5999
[53]RING C,MANNING B,POLLINGER F,et al.Uncertainties in the simulation of precipitation in selected regions of humid and dry climate[J].Int JClimatol,2016,36(10):3521
[2]SWAIN S,HAYHOE K.CMIP5projected changes in spring and summer drought and wet conditions over North America[J].Climate Dyn,2015,44(9/10):2737
[3]BABAR Z A,ZHI X F,FEI G.Precipitation assessment of Indian summer monsoon based on CMIP5 climate simulations[J].Arabian Journal of Geosciences,2015,8(7):4379
[4]MEEHL G A,BOER G J,COVEY C,et al.The coupled model intercomparison project(CMIP)[J].Bull Amer Meteor Soc,2000,81(2):313
[5]KNUTTI R,SEDLACEK J.Robustness and uncertainties in the new CMIP5climate model projections[J].Nature Climate Change,2013(4):369
[6]CHEN L,FRAUENFELD O W.Surface air temperature changes over the twentieth and twenty-first centuries in China simulated by 20 CMIP5 models[J].J Climate,2014,27(11):3920
[7]CHEN W,JIANG Z,LI L.Probabilistic projections of climate change over China under the SRES A1Bscenario using 28AOGCMs[J].J Climate,2011,24(17):4741
[8]DING Y,REN G,ZHAO Z,et al.Detection,causes and projection of climate change over China:an overview of recent progress[J].Adv Atmos Sci,2007,24(6):954
[9]JIANG D B,WANG H J,LANG X M.Multimodel ensemble prediction for climate change trend of China under SRES A2 scenario[J].Chinese Journal of Geophysics,2004,47(5):878
[10]ZHOU B,WEN Q H,XU Y,et al.Projected changes in temperature and precipitation extremes in China by the CMIP5multimodel ensembles[J].J Climate,2014,27(17):6591
[11]FOWLER H J,BLENKINSOP S,TEBALDI C.Linking climate change modelling to impacts studies:recent advances in downscaling techniques for hydrological modelling[J].Int J Climatol,2007,27(12):1547
[12]WILBY R L,WIGLEY T M L.Downscaling general circulation model output:a review of methods and limitations[J].Progress in Physical Geography,1997,21(4):530
[13]BENESTAD R E,HANSSEN-BAUER I,CHEN D.Empirical-statistical Downscaling[M].World Scientific Publishing Company,2008
[14]WILBY R L,WIGLEY T M L,CONWAY D,et al.Statistical downscaling of general circulation model output:a comparison of methods[J].Water Resour Res,1998,34(11):2995
[15]FAN L,CHEN D,FU C,et al.Statistical downscaling of summer temperature extremes in northern China[J].Adv Atmos Sci,2013,30(4):1085
[16]SALATHE E P,MOTE P W,WILEY M W.Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States Pacific Northwest[J].Int J Climatol,2007,27(12):1611
[17]WILBY R L,DAWSON C W,BARROW E M.SDSM:a decision support tool for the assessment of regional climate change impacts[J].Environmental Modelling and Software,2002,17(2):145
[18]WILB Y R L,DAWSON C W.The statistical downscaling model:insights from one decade of application[J].Int J Climatol,2013,33(7):1707
[19]HU BY,TANG J P,WANG S Y.Evaluation and projection of extreme events over China under IPCC A1Bscenario by MM5V3 model[J].Chinese Journal of Geophysics,2013,56(7):2195
[20]HUANG J,ZHANG J,ZHANG Z,et al.Simulation of extreme precipitation indices in the Yangtze River basin by using statistical downscaling method(SDSM)[J].Theor Appl Climatol,2012,108(3/4):325
[21]LIU W,FU G,LIU C,et al.A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain[J].Theor Appl Climatol,2013,111(3/4):585
[22]LIU Z,XU Z,CHARLES S P,et al.Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China[J].Int J Climatol,2011,31(13):2006
[23]PIAO S,FANG J,ZHOU L,et al.Variations in satellite-derived phenology in Chinas temperate vegetation[J].Global Change Biology,2006,12(4):672
[25]FU Y H,ZHAO H,PIAO S,et al.Declining global warming effects on the phenology of spring leaf unfolding[J].Nature,2015,526(7571):104
[26]MALO A R,NICHOLSON S E.A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index[J].Journal of Arid Environments,1990,19(1):1
[26]WANG X,PIAO S,CIAIS P,et al.Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006[J].Proceedings of the National Academy of Sciences,2011,108(4):1240
[27]REICHMANN L G,SALA O E,PETERS D P.Precipitation legacies in desert grassland primary production occur through previous-year tiller density[J].Ecology,2013,94(2):435
[28]ZENG F W,COLLATZ G J,PINZON J E,et al.Evaluating and quantifying the climate-driven interannual variability in Global Inventory Modeling and Mapping Studies(GIMMS)Normalized Difference Vegetation Index(NDVI3g)at global scales[J].Remote Sensing,2013,5(8):3918
[29]SALA O E,GHERARDI L A,REICHMANN L,et al.Legacies of precipitation fluctuations on primary production:theory and data synthesis[J].Philosophical Transactions of the Royal Society B:Biological Sciences,2012,367(1606):3135
[30]LIANG X Z,WU Y,CHAMBERS R G,et al.Determining climate effects on US total agricultural productivity[J].Proceedings of the National Academy of Sciences,2017,114(12):E2285
[31]MOORE F C,BALDOS U,HERTEL T,et al.New science of climate change impacts on agriculture implies higher social cost of carbon[J].Nature Communications,2017,8(1):1607
[32]VARGAS R,BALDOCCHI D D,BAHN M,et al.On the multi-temporal correlation between photosynthesis and soil CO2 efflux:reconciling lags and observations[J].New Phytologist,2011,191(4):1006
[33]AHLSTROM A,RAUPACH M R,SCHURGERS G,et al.The dominant role of semi-arid ecosystems in the trend and variability of the land CO2sink[J].Science,2015,348(6237):895
[34]TANG J,NIU X,WANG S,et al.Statistical downscaling and dynamical downscaling of regional climate in China:present climate evaluations and future climate projections[J].Journal of Geophysical Research:Atmospheres,2016,121(5):2110
[35]JIANG Y,KIM J B,STILL C J,et al.Intercomparison of multiple statistically downscaled climate datasets for the Pacific Northwest,USA[J].Scientific Data,2018,5:180016
[36]GANGULI P,KUMAR D,GANGULY A R.USPower Production at Risk from Water Stress in a Changing Climate[J].Scientific Reports,2017,7(1):11983
[37]YOON J H,RUBY LEUNG L,CORREIA J.Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States[J].Journal of Geophysical Research:Atmospheres,2012,117(D21):109
[38]SHI W,LIU Y,SHI X.Contributions of climate change to the boundary shifts in the farming-pastoral ecotone in northern China since 1970[J].Agricultural Systems,2018,161:16
[39]LIU J H,GAO J X,GENG B,et al.Changes of land use and landscape pattern in the boundary change areas in farming-pastoral ecotone of northern China[J].Chinese Society of Agricultural Engineering,2008,2008(11):222
[40]ZHANG Q,QI T Y,LI J F,et al.Spatiotemporal variations of pan evaporation in China during 1960-2005:changing patterns and causes[J].Int J Climatol,2015,35(6):903
[41]ZHANG Q,QI T,SINGH V P,et al.Regional frequency analysis of droughts in China:a multivariate perspective[J].Water Resources Management,2015,29(6):1767
[42]TAYLOR K E,STOUFFER R J,MEEHL G A.An overview of CMIP5and the experiment design[J].Bull Amer Meteor Soc,2012,93(4):485
[43]MANZ B,BUYTAERT W,ZULKAFLI Z,et al.High-resolution satellite-gauge merged precipitation climatologies of the Tropical Andes[J].Journal of Geophysical Research:Atmospheres,2016,121(3):1190
[44]WOOD A W,LEUNG L R,SRIDHAR V,et al.Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs[J].Climatic change,2004,62(1/2/3):189
[45]WOOD A W,MAURER E P,KUMAR A,et al.Long-range experimental hydrologic forecasting for the eastern United States[J].Journal of Geophysical Research:Atmospheres,2002,107(D20):ACL-6
[46]SCHNORBUS M,WERNER A,BENNETT K.Impacts of climate change in three hydrologic regimes in British Columbia,Canada[J].Hydrological Processes,2014,28(3):1170
[47]SHUKLA S,LETTENMAIER D P.Multi-RCMensemble downscaling of NCEP CFS winter season forecasts:implications for seasonal hydrologic forecast skill[J].Journal of Geophysical Research:Atmospheres,2013,118(19):1077
[48]SEO S B,SINHA T,MAHINTHAKUMAR G,et al.Identification of dominant source of errors in developing streamflow and groundwater projections under nearterm climate change[J].Journal of Geophysical Research:Atmospheres,2016,121(13):7652
[49]WERNER A T,CANNON A J.Hydrologic extremesan intercomparison of multiple gridded statistical downscaling methods[J].Hydrology and Earth System Sciences,2016,20(4):1483
[50]KOVACS A,ABONYI J.Vizualization of fuzzy clustering results by modified sammon mapping[C]∥Proceedings of the 3rd international symposium of Hungarian researchers on computational intelligence,2002
[51]JANSSEN E,SRIVER R L,WUEBBLES D J,et al.Seasonal and regional variations in extreme precipitation event frequency using CMIP5[J].Geophysical Research Letters,2016,43(10):5385
[52]ROSA D,COLLINS W D.A case study of subdaily simulated and observed continental convective precipitation:CMIP5 and multiscale global climate models comparison[J].Geophys Res Lett,2013,40(22):5999
[53]RING C,MANNING B,POLLINGER F,et al.Uncertainties in the simulation of precipitation in selected regions of humid and dry climate[J].Int JClimatol,2016,36(10):3521
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