期刊文献+

Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging 被引量:2

Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging
下载PDF
导出
摘要 Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21^(st) century for the Tianshan and northern Kunlun Mountains(TKM) based on the general circulation model(GCM) simulation ensemble from the coupled model intercomparison project phase 5(CMIP5) under the representative concentration pathway(RCP) lower emission scenario RCP4.5 and higher emission scenario RCP8.5 using the Bayesian model averaging(BMA) technique. Results show that(1) BMA significantly outperformed the simple ensemble analysis and BMA mean matches all the three observed climate variables;(2) at the end of the 21^(st) century(2070–2099) under RCP8.5, compared to the control period(1976–2005), annual mean temperature and mean annual precipitation will rise considerably by 4.8°C and 5.2%, respectively, while mean annual snowfall will dramatically decrease by 26.5%;(3) precipitation will increase in the northern Tianshan region while decrease in the Amu Darya Basin. Snowfall will significantly decrease in the western TKM. Mean annual snowfall fraction will also decrease from 0.56 of 1976–2005 to 0.42 of 2070–2099 under RCP8.5; and(4) snowfall shows a high sensitivity to temperature in autumn and spring while a low sensitivity in winter, with the highest sensitivity values occurring at the edge areas of TKM. The projections mean that flood risk will increase and solid water storage will decrease. Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21^(st) century for the Tianshan and northern Kunlun Mountains(TKM) based on the general circulation model(GCM) simulation ensemble from the coupled model intercomparison project phase 5(CMIP5) under the representative concentration pathway(RCP) lower emission scenario RCP4.5 and higher emission scenario RCP8.5 using the Bayesian model averaging(BMA) technique. Results show that(1) BMA significantly outperformed the simple ensemble analysis and BMA mean matches all the three observed climate variables;(2) at the end of the 21^(st) century(2070–2099) under RCP8.5, compared to the control period(1976–2005), annual mean temperature and mean annual precipitation will rise considerably by 4.8°C and 5.2%, respectively, while mean annual snowfall will dramatically decrease by 26.5%;(3) precipitation will increase in the northern Tianshan region while decrease in the Amu Darya Basin. Snowfall will significantly decrease in the western TKM. Mean annual snowfall fraction will also decrease from 0.56 of 1976–2005 to 0.42 of 2070–2099 under RCP8.5; and(4) snowfall shows a high sensitivity to temperature in autumn and spring while a low sensitivity in winter, with the highest sensitivity values occurring at the edge areas of TKM. The projections mean that flood risk will increase and solid water storage will decrease.
出处 《Journal of Arid Land》 SCIE CSCD 2017年第4期622-634,共13页 干旱区科学(英文版)
基金 supported by the Thousand Youth Talents Plan(Xinjiang Project) the National Natural Science Foundation of China(41630859) the West Light Foundation of Chinese Academy of Sciences(2016QNXZB12)
关键词 climate change GCM ensemble Bayesian model averaging Tianshan and northern Kunlun Mountains climate change GCM ensemble Bayesian model averaging Tianshan and northern Kunlun Mountains
  • 相关文献

参考文献2

二级参考文献17

  • 1CHEN Yaning & XU Zongxue Xinjiang Institute of Ecology & Geography, Chinese Academy of Sciences, Urumqi 830011, China,College of Environmental Sciences, Beijing Normal University, Beijing 100875, China.Plausible impact of global climate change on water resources in the Tarim River Basin[J].Science China Earth Sciences,2005,48(1):65-73. 被引量:56
  • 2CHEN Hao,CHEN LiJun,Thomas P. ALBRIGHT.Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches:A case of ragweed(Ambrosia artemisiifolia L.)distribution in China[J].Chinese Science Bulletin,2007,52(9):1223-1230. 被引量:13
  • 3SAS Institute Inc.SAS/STAT? 9.1 User’s Guide,2004.
  • 4Susan Barati,Behzad Rayegani,Mehdi Saati,Alireza Sharifi,Masoud Nasri.Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas[J].The Egyptian Journal of Remote Sensing and Space Sciences.2011(1)
  • 5Johannes Breidenbach,Erik N?sset,Terje Gobakken.Improving k-nearest neighbor predictions in forest inventories by combining high and low density airborne laser scanning data[J].Remote Sensing of Environment.2011
  • 6Xin Tian,Zhongbo Su,Erxue Chen,Zengyuan Li,Christiaan van der Tol,Jianping Guo,Qisheng He.Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area[J].International Journal of Applied Earth Observations and Geoinformation.2011(1)
  • 7B. Lasserre,G. Chirici,U. Chiavetta,V. Garfì,R. Tognetti,R. Drigo,P. DiMartino,M. Marchetti.Assessment of potential bioenergy from coppice forests trough the integration of remote sensing and field surveys[J].Biomass and Bioenergy.2010(1)
  • 8Latifur Rahman Sarker,Janet E. Nichol.Improved forest biomass estimates using ALOS AVNIR-2 texture indices[J].Remote Sensing of Environment.2010(4)
  • 9Rock B,Vogelmann J,Williams D,Vogelmann A,Hoshizaki.Remote detection of forest damage[].Bioscience.1986
  • 10Jan de Leeuw,Yola Georgiadou,Norman Kerle,Alfred de Gier,Yoshio Inoue,Jelle Ferwerda,Maarten Smies,Davaa Narantuya.The Function of Remote Sensing in Support of Environmental Policy[].Remote Sensing of Environment.2010

共引文献16

同被引文献14

引证文献2

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部