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Journal of Arid Land  2021, Vol. 13 Issue (6): 568-580    DOI: 10.1007/s40333-021-0101-6
Research article     
Projections of temperature extremes based on preferred CMIP5 models: a case study in the Kaidu-Kongqi River basin in Northwest China
CHEN Li1, XU Changchun1,2,*(), LI Xiaofei1
1MOE Key Laboratory of Oasis Ecology, College of Resource and Environment Sciences, Xinjiang University, Urumqi 830000, China
2School of Civil Engineering and Environmental Science, University of Oklahoma, Norman OK 73072, USA
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The extreme temperature has more outstanding impact on ecology and water resources in arid regions than the average temperature. Using the downscaled daily temperature data from 21 Coupled Model Inter-comparison Project (CMIP) models of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and the observation data, this paper analyzed the changes in temporal and spatiotemporal variation of temperature extremes, i.e., the maximum temperature (Tmax) and minimum temperature (Tmin), in the Kaidu-Kongqi River basin in Northwest China over the period 2020-2050 based on the evaluation of preferred Multi-Model Ensemble (MME). Results showed that the Partial Least Square ensemble mean participated by Preferred Models (PM-PLS) was better representing the temporal change and spatial distribution of temperature extremes during 1961-2005 and was chosen to project the future change. In 2020-2050, the increasing rate of Tmax (Tmin) under RCP (Representative Concentration Pathway) 8.5 will be 2.0 (1.6) times that under RCP4.5, and that of Tmin will be larger than that of Tmax under each corresponding RCP. Tmin will keep contributing more to global warming than Tmax. The spatial distribution characteristics of Tmax and Tmin under the two RCPs will overall the same; but compared to the baseline period (1986-2005), the increments of Tmax and Tmin in plain area will be larger than those in mountainous area. With the emission concentration increased, however, the response of Tmax in mountainous area will be more sensitive than that in plain area, and that of Tmin will be equivalently sensitive in mountainous area and plain area. The impacts induced by Tmin will be universal and far-reaching. Results of spatiotemporal variation of temperature extremes indicate that large increases in the magnitude of warming in the basin may occur in the future. The projections can provide the scientific basis for water and land plan management and disaster prevention and mitigation in the inland river basin.

Key wordstemperature extremes      multi-model ensemble      RCP      projection      Kaidu-Kongqi River basin     
Received: 22 September 2020      Published: 10 June 2021
Corresponding Authors: XU Changchun     E-mail:
About author: XU Changchun (E-mail:
Cite this article:

CHEN Li, XU Changchun, LI Xiaofei. Projections of temperature extremes based on preferred CMIP5 models: a case study in the Kaidu-Kongqi River basin in Northwest China. Journal of Arid Land, 2021, 13(6): 568-580.

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Fig. 1 Sketch map of the Kaidu-Kongqi River basin
Model name/Country
ACCESS1-0/Australia CSIRO-MK3-6-0/Australia MIROC-ESM/Japan
CNRM-CM5/France IPSL-CM5A-MR/France NorESM1-M/Norway
Table 1 List of 21 GCMs (Global Climate Models) used in NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections)
No. Model name Maximum temperature Minimum temperature
Linear trend Correlation coefficient Preferred model Linear trend Correlation coefficient Preferred model
1 ACCESS1-0 0.30** 0.42** 0.31** 0.58**
2 BCC-CSM1-1 0.17** 0.23 \ 0.20** 0.38**
3 BNU-ESM 0.29** 0.23 \ 0.36** 0.51**
4 CanESM2 0.22** 0.32* \ 0.29** 0.63**
5 CCSM4 0.28** 0.29 \ 0.32** 0.51**
6 CESM1-BGC 0.28** 0.23 \ 0.26** 0.49**
7 CNRM-CM5 0.11 0.39** \ 0.18** 0.51**
8 CSIRO-MK3-6-0 0.13* 0.38** 0.21** 0.58**
9 GFDL-CM3 0.19** 0.22 \ 0.20** 0.45**
10 GFDL-ESM2G 0.23** 0.27 \ 0.23** 0.48**
11 GFDL-ESM2M 0.19** 0.64** 0.20** 0.57**
12 INMCM4 0.06 0.07 \ 0.13* 0.17 \
13 IPSL-CM5A-LR 0.28** 0.30* \ 0.31** 0.59**
14 IPSL-CM5A-MR 0.28** 0.26 \ 0.29** 0.50**
15 MIROC5 0.25** 0.34* \ 0.25** 0.55**
16 MIROC-ESM 0.13** 0.16 \ 0.14** 0.51**
17 MIROC-ESM-CHEM 0.20** 0.03 \ 0.24** 0.36* \
18 MPI-ESM-LR 0.17** 0.01 \ 0.17** 0.23 \
19 MPI-ESM-MR 0.25** 0.30 \ 0.26** 0.49**
20 MRI-CGCM3 0.10 -0.04 \ 0.12 0.06 \
21 NorESM1-M 0.27** 0.41** 0.25** 0.57**
22 EE 0.21** 0.47** 0.23** 0.80**
23 PLS 0.21** 0.51** 0.25** 0.82**
24 PM-EE 0.22** 0.62** 0.25** 0.82**
25 PM-PLS 0.23** 0.61** 0.26** 0.83**
CN05.1 0.16* 0.41**
Table 2 Simulated linear trends and the correlation coefficients between the simulated and observed temperature series for the period of 1961-2005
Fig. 2 Taylor diagram for temporal variability of Tmax (maximum temperature) (a) and Tmin (minimum temperature) (b) in the Kaidu-Kongqi River basin between the observations and simulations for the period of 1961-2005. Each number representing a model ID was listed in Table 2.
Fig. 3 Simulated Tmax and Tmin in the Kaidu-Kongqi River basin for the period 1961-2005 from CN05.1 observations denoted by black line and from CMIP5 models denoted by colored lines. CN05.1, observation; EE equal-weight ensemble mean; PLS, Partial Least Square; PM-EE, equal-weight ensemble mean participated by preferred models; PM-PLS, Partial Least Square ensemble mean participated by Preferred Models.
Fig. 4 Simulated Tmax and Tmin in the Kaidu-Kongqi River basin during 1961-2005 for spatial change. T, temperature.
Fig. 5 Spatial correlation coefficient (CC) distribution of Tmax and Tmin in the Kaidu-Kongqi River basin during 1961-2005 between PM-EE and observation (CN05.1) (a, b) and between PM-PLS and CN05.1 (c, d)
Fig. 6 Projected Tmax and Tmin under RCP (Representative Concentration Pathway) 4.5 and RCP8.5, respectively, in the Kaidu-Kongqi River basin during 2020-2050. The dashed lines denote the corresponding linear trends and the symbols on the line denote the STD (standard deviation).
Fig. 7 Projected Tmax under RCP4.5 and RCP8.5 in 2020-2050 (a, b) and the changes relative to the baseline period 1986-2005 (c, d)
Fig. 8 Projected Tmin under RCP4.5 and RCP8.5 in 2020-2050 (a, b) and the changes relative to the baseline period 1986-2005 (c, d)
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