基于SDSM的珠江中上游气候模拟及未来情景预估

许 燕; 王世杰; 白晓永; 李雄耀; 史晓明; 田义超; 吴路华

doi:10.11932/karst20180209

基于SDSM的珠江中上游气候模拟及未来情景预估

doi: 10.11932/karst20180209

1.
中国科学院地球化学研究所环境地球化学国家重点实验室/中国科学院大学/普定喀斯特生态系统观测研究站
2.
中国科学院地球化学研究所环境地球化学国家重点实验室/普定喀斯特生态系统观测研究站
3.
中国科学院地球化学研究所环境地球化学国家重点实验室

基金项目: 国家重点研发计划（2016YFC0502300、2016YFC0502102）；国家973计划（2013CB956700）;国家科技支撑计划（2014BAB03B02）；国家自然科学基金（U1612441、41571130074 & 1571130042）；贵州省农业攻关计划（2014-3039；中国科学院院地合作项目2014-3）；贵阳市科技攻关计划（2012-205）

计量
- 文章访问数: 2298
- HTML浏览量: 531
- PDF下载量: 883
- 被引次数: 0
出版历程
- 发布日期: 2018-04-25

Simulation of future scenarios of climate change in the middle and upper reaches of the Peal River using the Statistical Down Scaling Model (SDSM)

1.
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, CAS/University of Chinese Academy of Sciences/Puding Karst Ecosystem Observation Research Station, CAS
2.
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, CAS/Puding Karst Ecosystem Observation Research Station, CAS
3.
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, CAS

摘要

摘要: 预估喀斯特生态脆弱区的未来气候变化对于区域资源的合理开发利用及生态环境保护具有重要参考价值，而目前应用降尺度方法模拟喀斯特地区的未来气候情景仍存在较大的探讨空间。本文依据珠江流域红柳江区13个气象站1961-2001年的实测日气温、日降水量资料和全球大气NCEP再分析资料，采用SDSM模型预测流域在HadCM3模式SRES A2和B2两种排放情景下未来年份气温和降水的变化趋势。结果表明：（1）SDSM模型可以较为准确地模拟研究区的气温和降水变化，确定性系数分别可达99%和65%左右；（2）A2、B2两种情景下，21世纪气温和降水均表现出明显的上升趋势，且随时间推移增幅逐渐增大。截至21世纪末，A2、B2两种情景下的年平均气温变化分别为+3.39 ℃和+2.49 ℃，日均降水将分别增加117.30 %和80.90 %；（3）未来的气温上升以秋季和春季变化最为明显，降水则表现为夏季降水增幅最大。分析成果可为喀斯特区的气候变化影响评价与应对决策提供数据基础和理论依据。
- 气候变化 /
- 珠江中上游 /
- 统计降尺度 /
- SDSM /
- 情景分析
Abstract: Due to the vulnerability and sensitivity of the karst eco-system, the prediction of future climate change in the karst area has great significance to reasonably exploit the regional resources and effectively protect the ecological environment. However, the research on the climate downscaling in the karst area is nearly blank. In this paper, the daily mean temperature and daily precipitation from 13 meteorological stations in the Hongliujiang zone of the Pearl River Basin during 1961-2001 were used as the predictors. Moreover, the NCEP reanalysis data was used as the forecast factor. The SDSM model was applied to predict the trends of temperature and precipitation in future years under SRES A2 and B2 emission scenarios of the HadCM3 model. The results show that,(1) SDSM mode can be applied in the accurate simulation of the temperature and precipitation in the study area, and the deterministic coefficient of observation and simulation value is about 99% and 65% respectively; (2) Compared with the reference period, the future temperature and precipitation in the A2 and B2 scenarios show a clear upward trend until the end of the 21th century, and the increment is increasing with time prolonged. Meanwhile,by the end of 21th century,the relative variation of the future mean annual temperature in the A2 and B2 scenarios are +3.39 ℃ and +2.49 ℃ respectively, and the 117.30 % and 80.90 % growth respectively in daily precipitation under the A2 and B2 scenarios; (3) The rise of temperature is the most obvious in autumn and spring, and the precipitation increase is the largest in summer. Therefore, this analysis not only provides the foundation data and the theoretical basis for setting strategies, but also has great significance for impact study of climate changes in karst areas.
- climate change /
- middle and upper reaches of the Pearl River /
- statistical downscaling /
- SDSM /
- scenario analysis

HTML

参考文献(26)

[1]	Varis O，Kajander T，Lemmela R. Climate and water: From climate models to water resources management and vice versa［J］. Climatic Change, 2004, 66(3):321-344.
[2]	Wilby R L，Whitehead P G，Wade A J，et al. Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK［J］. Journal of Hydrology, 2006, 330(1-2):204-220.
[3]	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):530-548.
[4]	Mearns L O，Bogardi I，Giorgi F，et al. Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling［J］. Journal of Geophysical Research-Atmospheres, 1999, 104(D6):66036621.
[5]	范丽军，符淙斌，陈德亮. 统计降尺度法对未来区域气候变化情景预估的研究进展［J］. 地球科学进展, 2005(3):320329.
[6]	Hay L E，Clark M P. Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States［J］. Journal of Hydrology, 2003, 282(1-4):56-75.
[7]	Boe J，Terray L，Habets F，et al. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies［J］. International Journal of Climatology, 2007, 27(12):1643-1655.
[8]	Chen S T，Yu P S，Tang Y H. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis［J］. Journal of Hydrology, 2010, 385(1-4):13-22.
[9]	Chu J T，Xia J，Xu C Y，et al. Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China［J］. Theoretical and Applied Climatology, 2010, 99(1-2):149-161.
[10]	Teutschbein C，Wetterhall F，Seibert J. Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale［J］. Climate Dynamics, 2011, 37(9-10):2087-2105.
[11]	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 & Software, 2002, 17(2):147-159.
[12]	Khan M S，Coulibaly P，Dibike Y. Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors［J］. Hydrological Processes, 2006, 20(14):3085-3104.
[13]	Huang J,Zhang J C，Zhang Z X. Simulation of extreme precipitation indices in the Yangze River basin by using statistical down scaling method(SDSM)［J］. Theoretical and Applied Climatology, 2012, 108:325-343.
[14]	徐影，丁一汇，赵宗慈. 近30年人类活动对东亚地区气候变化影响的检测与评估［J］. 应用气象学报, 2002(5):513-525.
[15]	施小英，徐祥德，徐影. 中国600个站气温和IPCC模式产品气温的比较［J］. 气象, 2005(7):49-53.
[16]	苏志侠，吕世华，罗四维. 美国NCEP/NCAR 40年全球再分析资料及其解码和图形显示软件简介［J］. 高原气象, 1999 (2):74-83.
[17]	Wilby R L，Hassan H，Hanaki K. Statistical downscaling of hydrometeorological variables using general circulation model output［J］. Journal of Hydrology, 1998,205(1-2):1-19.
[18]	陈威霖，江志红，黄强. 基于统计降尺度模型的江淮流域极端气候的模拟与预估［J］. 大气科学学报, 2012(5):578-590.
[19]	Hessami M，Gachon P，Ouarda T B M J，et al. Automated regression-based statistical downscaling tool［J］. Environmental Modelling & Software, 2008, 23(6):813-834.
[20]	Sascha S. Downscaling Local Extreme Temperature Changes in South- eastern Australia From the CS IRO M ARK2 GCM［J］. International Journal of Climatology, 1998, 18:1419-1438.
[21]	Wetterhall F，Halldin S，Xu C Y. Seasonality properties of four statistical-downscaling methods in central Sweden［J］. Theoretical and Applied Climatology, 2007, 87(1-4):123-137.
[22]	郝振纯，时芳欣，王加虎. 统计降尺度法在黄河源区未来降水变化分析中的应用［J］. 水电能源科学, 2011 (3):2-4.
[23]	王晓燕，杨涛，郝振纯. 基于统计降尺度的黄河源区气象极值预测［J］. 水电能源科学, 2011(4):1-5.
[24]	姜大膀，王会军，郎咸梅. 全球变暖背景下东亚气候变化的最新情景预测［J］. 地球物理学报, 2004(4):590-596.
[25]	Houghton J T，Ding Y,Griggs D J,et al. Climate change 2001: The scientific basis［R］. Cambridge:Cambridge University Press,2001:944.
[26]	许吟隆，薛峰，林一骅. 不同温室气体排放情景下中国21世纪地面气温和降水变化的模拟分析［J］. 气候与环境研究, 2003(2):209-217.