Climate change scenarios generated by using GCM outputs and statistical downscaling in an arid region

Document Type: Research Paper


1 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China

2 Key Laboratory of Water and Sediment Sciences, Ministry of Education; College of Water Sciences, Beijing Normal University, Beijing 100875, China


Two statistical downscaling models, the non-homogeneous hidden Markov model (NHMM) and the Statistical Down–
Scaling Model (SDSM) were used to generate future scenarios of both mean and extremes in the Tarim River basin,
which were based on nine combined scenarios including three general circulation models (GCMs) (CSIRO30, ECHAM5,
and GFDL21) predictor sets and three special report on emission scenarios (SRES) (SRES A1B, SRES A2, and SRES
B1). Local climate change scenarios generated from statistical downscaling models was also compared with that
projected by raw GCMs outputs. The results showed that the magnitude of changes for annual precipitation projected by
raw GCMs outputs was greater than that generated by using statistical downscaling model. The difference between
changes of annual maximum air temperature projected by statistical downscaling model and raw GCMs outputs was not
as significant as that for annual precipitation. In total, the magnitude of these increasing trends projected by both
statistical downscaling models and raw GCMs outputs was the greatest under SRES A2 scenario and the smallest under
B1 scenario, with A1B scenario in–between. Generally, the magnitude of these increasing trends in the period of 2081 to
2100 was greater than that in the period of 2046 to 2065. The magnitude of standard deviation changes for daily
precipitation projected by raw GCMs outputs was greater than that generated by statistical downscaling model under
most of combined scenarios in both periods.


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