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| Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging |
Jing YANG1,2,*( ), Gonghuan FANG1,3,4, Yaning CHEN1, DE-MAEYER Philippe3,4 |
1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2 National Institute of Water and Atmospheric Research, Christchurch 8011, New Zealand
3 Department of Geography, Ghent University, Ghent 9000, Belgium
4 Sino-Belgian Joint Laboratory for Geo-Information, Urumqi 830011, China |
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Abstract Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21st 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 21st 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.
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Received: 03 September 2016
Published: 10 August 2017
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| Fund: National Science Foundation, Department of Energy, National Center for Atmospheric Research |
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Corresponding Authors:
Jing YANG
E-mail: yangjing@ms.xjb.ac.cn
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