Stochastic Environmental Research and Risk Assessment

, Volume 29, Issue 8, pp 2061–2071

Simulated and projected climate extremes in the Tarim River Basin using the regional climate model CCLM

Original Paper

Abstract

The reduction of uncertainty in simulations and projections of regional climate models is a critical issue for regional climate impact studies, especially in the context of climate extremes. In this study, the regional climate model COSMO-CLM (CCLM) is evaluated in terms of daily precipitation and temperature characteristics, in order to obtain reliable projections of climate extremes for the Tarim River Basin (TRB) in Northwest China. The results show that CCLM can acceptably reproduce the annual cycle of maximum and minimum temperature, as well as the spatial distribution of precipitation pattern. Nonetheless, some systematic biases have been encountered. The equidistant cumulative distribution function matching method has been applied, which led to an efficient reduction of the systematic biases of observed and simulated climate variables. The bias correction has further been applied to climate projections for the period of 2016–2035 under the Representative Concentration Pathway 4.5. The projected indices of climate extremes as calculated from the bias-corrected CCLM projections show that most of the TRB is likely to experience a decrease in daily temperature range, and an increase in minimum temperature as well as consecutive wet days. The total precipitation on very wet days is projected to slightly increase at most stations, while the annual total precipitation will mostly increase in the southwestern parts of the TRB. The findings on the spatial–temporal patterns of these climate extremes will enable decision makers, especially in the water and agricultural sectors, to adapt and be better prepared for future climate impacts in the region.

Keywords

Climate extremes Bias correction Regional climate model CCLM Tarim River Basin 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Environmental Science and EngineeringFudan UniversityShanghaiChina
  2. 2.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjingChina
  3. 3.Department of GeosciencesEberhard Karls UniversityTübingenGermany

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