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Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models
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ScienceDirect Advances in Climate Change Research xx (2017) 1e9 www.keaipublishing.com/en/journals/accr/
Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models WU Jie a,b, XU Ying c,*, GAO Xue-Jie a,b a
Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China b University of Chinese Academy of Sciences, Beijing 100049, China c National Climate Center, China Meteorological Administration, Beijing 100081, China Received 16 December 2016; revised 19 January 2017; accepted 13 March 2017
Abstract Based on the outputs from 21 CMIP5 (Coupled Model Intercomparison Project phase 5) models, future changes in the mean temperature, precipitation and four climate extreme indices (annual maximum of daily maximum temperature (TXx), minimum of daily minimum temperature (TNn), annual total precipitation when the daily amount exceeds the 95th percentile of wet-day precipitation (R95p), and maximum consecutive 5-day precipitation (RX5day)) over Hindu Kush Himalayan (HKH) region are investigated under the greenhouse gas concentration pathways of RCP4.5 and RCP8.5. Two periods of the 21st century, 2036e2065 and 2066e2095, are selected, with the reference period is considered as 1976e2005. Results show general increase of the mean temperature, TXx and TNn under both scenarios, with the largest increases found during 2066e2095 under RCP8.5. Future precipitation is projected to increase over most part of HKH, except for the northwestern part. Intensification of the precipitation extremes is projected over the region. The uncertainties of mean temperature, TXx and TNn over the HKH1 subregions are the largest compared to the other three subregions and the overall HKH. Besides RX5day during 2036e2065 over HKH1, the uncertainties of R95p and RX5day tend to be larger following the increase of greenhouse gas concentrations. The multimodel ensemble medians of temperature and four extreme indices under RCP8.5 are projected to be larger than those under RCP4.5 in each of the subregions. Keywords: Hindu Kush Himalayan region; CMIP5; Mean climate; Extreme climate events; Climate change projection
1. Introduction Hindu Kush Himalayan (HKH) region (Fig. 1) is the source of ten prominent Asian rivers, thus the region is called as the Water Tower of Asia. With high elevations and holding large mass of ice, HKH region has been recognized as one of the
* Corresponding author. E-mail address: [email protected] (XU Y.). Peer review under responsibility of National Climate Center (China Meteorological Administration).
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most fragile areas susceptible to climate change (Xu et al., 2009). Additionally, significant warming over the Tibetan Plateau has been observed (Liu and Chen, 2000; Wang et al., 2008), even during the global hiatus period of the late decade (You et al., 2016). The role of snow-albedo feedback may contribute to the larger warming over the region (Ghatak et al., 2014). Global climate models (GCMs) are the primary tools in studying the historical climate change and projecting future climate. Most of the present day CMIP5 (Coupled Model Intercomparison Project phase 5) models show good performances in reproducing the present climatology, climate variability and climate extremes over China (Xu and Xu, 2012; Yao et al., 2013; Zhang et al., 2013; Jiang et al., 2016). Based on these evaluations, some researchers projected
http://dx.doi.org/10.1016/j.accre.2017.03.001 1674-9278/Copyright © 2017, National Climate Center (China Meteorological Administration). Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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temperature (TXx), minimum of daily minimum temperature (TNn), annual total precipitation when the daily amount exceeds the 95th percentile of wet-day precipitation (R95p), and maximum consecutive 5-day precipitation (RX5day). In the total of 21 CMIP5 models are used, with the model details listed in Table 1. The model outputs are interpolated onto a common 1 1 grid to facilitate the inter-comparison. Multimodel ensemble (MME) in this paper refers to the unweighted average of all individual models. The mean and extreme climates are projected under the RCP4.5 and RCP8.5
Fig. 1. Hindu Kush Himalayan (HKH) region and the three subregions (rectangular in red color) of interest: the northwest Himalaya and Karakoram (HKH1), central Himalaya (HKH2), and southeast Himalaya and Tibetan Plateau (HKH3). Shaded are the topography above 2500 m.
Table 1 List of the 21 CMIP5 models used in the study. Model ACCESS1-0
climate extreme events over China, using CMIP5 models (Xu et al., 2015; Guo et al., 2016a, 2016b). Analysis of their future changes based on the CMIP5 outputs over the HKH region is of importance for the society and will provide help for policymakers in developing proper adaptation strategies with sound scientific basis. Several studies have been conducted to investigate the future climate changes over the region. Rangwala et al. (2013) found that high correlations between the raised temperature and elevation over the Tibetan Plateau in the winters of the 21st century under 4.5 Representative Concentration Pathways scenario (RCP4.5). Panday et al. (2015) reported the analysis on the simulated and projected temperature, precipitation and their extremes over the eastern Himalaya and western Himalaya-Karakoram regions based on the CMIP3 and CMIP5 models. They showed that these regions were projected to be exposed to a warmer and wetter climate. Consistent to the study by Yang et al. (2012) based on five CMIP3 models, Li et al. (2015) showed also both increase in temperature and precipitation extremes over the Tibetan Plateau in future in the CMIP5 simulations. Ali et al. (2015) analyzed the climatic and hydrological changes over Upper Indus Basin using the Conformal-Cubic Atmospheric Model and a regional climate model (RegCM4). They found the increases of temperature and precipitation, especially over the northern parts, and river flows were projected to increase. However, most of the previous studies are focused on the different portions of the HKH region only, while very limited studies have been carried out covering the whole region, which we will address in this paper. 2. Data and methods CMIP5 models output consist a large variety of climate data and variables, providing greatly benefit the climate change studies. In this study, daily scale data of mean surface (2 m) air temperature, minimum and maximum temperature, and precipitation were selected and employed to calculate the mean climatology and extremes. Following Frich et al. (2002), four indices were used to describe the extreme events of temperature and precipitation: annual maximum of daily maximum
Institution/Country
Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM)/Australia BCC-CSM1-1 Beijing Climate Center, China Meteorological Administration/China BNU-ESM Global Change and Earth System Science (GCESS)/China CanESM2 Canadian Centre for Climate Modeling and Analysis/Canada CCSM4 National Center for Atmospheric Research (NCAR)/United States CNRM-CM5 Centre National de Recherches Meteorologiques and Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique/France CSIRO-Mk3-6-0 Queensland Climate Change Centre of Excellence and Commonwealth Scientific and Industrial Research Organization/Australia FGOALS-s2 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS, Tsinghua University GFDL-ESM2G National Oceanic and Atmospheric Administration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL)/ United States GFDL-ESM2M National Oceanic and Atmospheric Administration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL)/ United States HadGEM2-ES Met Office Hadley Centre (MOHC)/ United Kingdom INMCM4 Institute of Numerical Mathematics (INM)/Russia IPSL-CM5A-LR Institute Pierre-Simon Laplace/France IPSL-CM5A-MR Institute Pierre-Simon Laplace/France MIROC-ESM Model for Interdisciplinary Research on Climate (MIROC)/Japan MIROC-ESM-CHEM Model for Interdisciplinary Research on Climate (MIROC)/Japan MIROC5 Model for Interdisciplinary Research on Climate (MIROC)/Japan MPI-ESM-LR Max Planck Institute for Meteorology/ Germany MPI-ESM-MR Max Planck Institute for Meteorology/ Germany MRI-CGCM3 Meteorological Research Institute/Japan NorESM1-M Norwegian Climate Centre (NCC)/ Norway
Resolution (lon lat) 192 145 128 64 128 64 128 64 288 192 256 128
192 96
128 108 144 90
144 90
192 145 180 120 96 96 144 143 128 64 128 64 256 128 192 96 192 96 320 160 144 96
Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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WU J. et al. / Advances in Climate Change Research xx (2017) 1e9 Table 2 Numbers of grid points over HKH1, HKH2 and HKH3 for each model. Model
HKH1
HKH2
HKH3
ACCESS1-0 BCC-CSM1-1 BNU-ESM CanESM2 CCSM4 CNRM-CM5 CSIRO-Mk3-6-0 FGOALS-s2 GFDL-ESM2G GFDL-ESM2M HadGEM2-ES INMCM4 IPSL-CM5A-LR IPSL-CM5A-MR MIROC-ESM MIROC-ESM-CHEM MIROC5 MPI-ESM-LR MPI-ESM-MR MRI-CGCM3 NorESM1-M
30 9 9 9 49 30 20 15 12 12 30 20 12 15 9 9 30 20 20 42 12
36 6 6 6 70 48 27 18 21 21 36 27 12 28 6 6 48 27 27 75 21
42 12 12 12 80 48 28 20 20 20 42 30 16 30 12 12 48 28 28 77 20
scenarios in the periods of 2036e2065 and 2066e2095, with the period of 1976e2005 as the reference. HKH is a region largely uninhabited with its high elevations, and very few observation stations located (Wu and Gao, 2013). This condition leads to the difficulties in finding a proper observation dataset to validate the performances of the models. In the present study, the daily data from the National
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Center for Environmental Precipitation/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 (Kalnay et al., 1996; Kistler et al., 2001) are thus employed as the “observation”. The mean climatology and extreme indices were calculated interpolated onto the 1 1 grid. The rootmean-square error (RMSE), as well as the relative model error (RMSE0 ) following Gleckler et al. (2008) and Zhou et al. (2014) are calculated for each model. RMSE0 is defined as RMSE0 ¼
RMSE EMSEmedian RMSEmedian
ð1Þ
where RMSEmedian represents the median of RMSEs for all CMIP5 models. The negative value of RMSE0 of one individual model indicates that its performance is better than the majority (50%) of all models (Chen et al., 2014). The HKH region is further divided into three subregions as northwest Himalaya and Karakoram (HKH1), central Himalaya (HKH2), and southeast Himalaya and Tibetan Plateau (HKH3), as shown in Fig. 1. The topography higher than 2500 m in the region is also shown. The numbers of grid points over the three subregions for each model are listed in Table 2. 3. Results 3.1. Assessment of CMIP5 models' performance The RMSE0 s of the individual models and MME during the reference period (1976e2005) over the HKH region are
Fig. 2. The relative root-mean-square errors over HKH region (RMSE0 ) for the mean and extreme climate indices TXx (maximum of daily maximum temperature), TNn (minimum of daily minimum temperature), R95p (annual total precipitation when the daily precipitation exceeds the 95th percentile of the wet-day precipitation), and RX5day (maximum consecutive 5-day precipitation) in 1976e2005. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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presented in Fig. 2. Performances of the models in simulating different climate variables and indices vary widely. Some models perform relatively well for most indices, including CSIRO-MK3-6-0, GFDL-ESM2G, GFDL-ESM2M, INMCM4, IPSL-CM5A-LR, MPI-ESM-LR and MRI-CGCM3. In the meantime, some other models show relatively poor performances for most indices, including FGOALS-s2, MIROC5 and MPI-ESM-MR. As for MME, the RMSE0 s for all indices are negative, indicating its better skill as expected.
3.2. Spatial distributions of projected mean and extreme climate indices 3.2.1. Temperature and precipitation The spatial distributions of surface air temperature changes for 2036e2065 and 2066e2095 in relative to 1976e2005 are provided in Fig. 3. Significant warming is projected over the HKH region in the future. The warming is greater under RCP8.5 compared to RCP4.5 in each of
Fig. 3. The projected multimodel ensemble mean changes in surface (2 m) air temperature in 2036e2065 and 2066e2095 under RCP4.5 (a, c) and RCP8.5 (b, d), relative to 1976e2005 (Only the changes statistically significant at the 95% level are shown).
Fig. 4. Same as Fig. 3, but for precipitation. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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the periods, and greater in 2066e2095 compared to 2036e2065 under both scenarios. The largest values of temperature increase are found in the Tibetan Plateau in both of the periods and both of the scenarios. A warming in excess of 5 C is projected in 2066e2095 under RCP8.5. The projected percentage changes of precipitation are presented in Fig. 4. Increase over regions east of 70 E, and decrease over the northwestern part of HKH are found in the
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different periods and under different scenarios. The projected changes are in general less than 12% in 2036e2065 under RCP4.5. The change pattern and amount in 2066e2095 under RCP4.5 show consistencies with those in 2036e2065 under RCP8.5, with the increase greater than 16% found over the northeastern part of HKH. The largest change is found in 2066e2095 under RCP8.5, with the maxima (~24%) located in northern part of the Tibetan Plateau and along the western coast of India.
Fig. 5. Same as Fig. 3, but for TXx.
Fig. 6. Same as Fig. 3, but for TNn. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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3.2.2. Extreme climate indices Spatial distribution of the projected changes in TXx and TNn over HKH is shown in Figs. 5 and 6, respectively. Overwhelming increases of both TXx and TNn are found in the figures, indicating more extreme warm events and less extreme cold events in the future. Greater increase in TNn compared to TXx is evident within the same period. The maxima of TXx increase is around 2.8 C under RCP4.5 and 3.4 C under RCP8.5 in 2036e2065, 4.0 C under RCP4.5 and
5.2 C under RCP8.5 in 2066e2095, respectively. The northwestern part of HKH region is projected to experience larger increases of TXx compared to the other areas, while the most pronounced warming in TNn is projected along the southern border of the Tibetan Plateau. Greater than 5.2 C increase of TNn dominates the whole Tibetan Plateau in 2066e2095 under RCP8.5. Fig. 7 presents projected changes in R95p over the HKH region. As shown in Fig. 7, significant increases of R95ps are
Fig. 7. Same as Fig. 3, but for R95p.
Fig. 8. Same as Fig. 3, but for RX5day. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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WU J. et al. / Advances in Climate Change Research xx (2017) 1e9 Table 3 Six HKH-averaged mean temperature, precipitation and four extreme indices projected by the MME in 2036e2065 and 2066e2095 under RCP4.5 and RCP8.5, relative to 1976e2005. Indices
2036e2065
2066e2095
RCP4.5
RCP8.5
RCP4.5
RCP8.5
Temperature ( C) Precipitation (%) TXx ( C) TNn ( C) R95p (%) RX5day (%)
1.97 4.80 2.10 2.15 23.46 8.11
2.62 6.44 2.71 2.94 31.82 11.20
2.56 7.03 2.71 2.87 32.38 11.18
4.64 10.56 4.80 5.26 57.84 20.20
found in both of the periods and scenarios, indicating intensification of the extreme events in the future. For 2036e2065 under RCP4.5, the largest increase of R95p (>40%) is located in the north-central part of the region. Similar pattern and amount of the changes are found in 2066e2095 under RCP4.5 and in 2036e2065 under RCP8.5, with the maxima of the increase (~60%) located in the central and north of the Tibetan Plateau. Greater than 70% increase dominates most parts of the region in 2066e2095 under RCP8.5. –
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The general increase of RX5day as shown in Fig. 8, indicates also the future intensification of precipitation extremes. Similar to R95p, greater increases of RX5day are found in central part of the Tibetan Plateau and the north of it. The largest increase is in the range of 12%e15% in 2036e2065 under RCP4.5, and exceeding 24% in 2066e2095 under RCP8.5. 3.3. Uncertainty analysis of the projected climate indices The box-and-whisker plots are employed to illustrate the inter-model agreement or disagreement on the projected changes thus their uncertainties over HKH (HKH-averaged values are listed in Table 3) and its three subregions, as summarized in Fig. 9. In general, the MME medians under RCP8.5 are larger compared to RCP4.5. This is more significant for temperature than precipitation indices. The model spreads are larger for the precipitation indices compared to temperature. Greater increases of the mean temperature and TXx are found in HKH1, while TNn under RCP8.5 shows the largest
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Fig. 9. Projected changes in temperature (a, b), precipitation (c, d), TXx (e, f), TNn (g, h), R95p (i, j), and RX5day (k, l) over HKH and its three subregions in 2036e2065 and 2066e2095 under the RCP4.5 (blue color) and RCP8.5 (red color). The ranges between the 25th and 75th quantiles are indicated by boxes, the MME medians are indicated by the horizontal lines within boxes, and the extreme ranges of models are indicated by whiskers. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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increase in HKH1 (Fig. 9a, b, eeh). The model spreads of the temperature indices for RCP4.5 and RCP8.5 are mostly in the same order, although larger of the later is found, e.g. for TXx in 2066e2095 in HKH1. Differences of the median of the indices under RCP4.5 and RCP8.5 are around 0.5 C in 2036e2065, and 2 C in 2066e2095, respectively. For the mean precipitation, the median change is all positive, but the full inter-model spread is large, with negative tails found in many cases. The model spreads are in general wider under RCP8.5 compared to RCP4.5, with the largest found in HKH1 in 2066e2095 (<20% and >40% for the lower and upper tail). For the extreme indices of R95p and RX5day, all the median changes are positive in the different regions, periods and scenarios, as is the interquartile range. Larger increase in HKH2 and HKH while the least increase in HKH1 of the indices are found. The upper tails of both R95p and RX5day are large over HKH1, indicating the presence of individual models with very high sensitivity of the precipitation extremes. 4. Conclusions and discussion In this study, validation of 21 CMIP5 models and their ensemble in simulating the temperature and precipitation mean and extremes is firstly carried out over the HKH region. Analysis shows better performances of the MME mean. Then the changes of mean temperature, precipitation and four extreme indices in 2036e2065 and 2066e2095 in relative to 1976e2005 under RCP4.5 and RCP8.5 are conducted, and their uncertainties (model spreads) are analyzed. Increases of the mean temperature, TXx and TNn are projected, with the largest increase occurring in 2066e2095 under RCP8.5. While the greatest warming is found over the Tibetan Plateau, the largest increases of TXx and TNn are located in the northwestern part of HKH region, and along the southern edge of Tibetan Plateau, respectively. The increase of TNn is in general larger than TXx. Greater than 5 C increases of the indices are found in the end of the century under RCP8.5. The multimodel medians of temperature, TXx and TNn under RCP8.5 are larger than those under RCP4.5 over HKH region and its three subregions in each period. The warming over the Tibetan Plateau was also projected by Rangwala et al. (2013). General increases of precipitation and its extremes are projected, indicating a wetter climate, but with more intensified extreme events future of the region. The increases of R95ps and RX5days are greater under RCP8.5 compared to RCP4.5. The largest increase is found in the central and north of the Tibetan Plateau. The increases of extreme indices are more significant than that of the precipitation mean. These changes are consistence with previous projections (Ali et al., 2015; Panday et al., 2015). Larger model spreads and uncertainties are found for precipitation than temperature, and the spreads are larger in 2066e2095 under RCP8.5. Finally, it is noted that the resolutions of GCMs in present are still low, which calls for the future application of high resolution regional models over the region (Shi et al., 2011; Gao et al., 2013).
Acknowledgments We acknowledge the World Climate Research Program's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank climate modeling groups for producing and making available their model outputs as well. This research was jointly supported by the National Key Research and Development Program of China (2016YFA0600704), R&D Special Fund for Public Welfare Industry (meteorology) (GYHY201306019) and the Climate Change Foundation of China Meteorological Administration (CCSF201508). References Ali, S., Li, D., Congbin, F., et al., 2015. Twenty first century climatic and hydrological changes over Upper Indus Basin of Himalayan region of Pakistan. Environ. Res. Lett. 10 (1), 014007. Chen, X.-C., Xu, Y., Xu, C.-H., et al., 2014. Assessment of precipitation simulations in China by CMIP5 multi-models. Adv. Clim. Change Res. 10 (3), 217e225 (in Chinese). Frich, P., Alexander, L.V., Della-Marta, P., et al., 2002. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19 (3), 193e212. Gao, X.-J., Wang, M.-L., Giorgi, F., 2013. Climate change over China in the 21st century as simulated by BCC_CSM1.1-RegCM4.0. Atmos. Ocean. Sci. Lett. 6 (5), 381e386. Ghatak, D., Sinsky, E., Miller, J., 2014. Role of snow-albedo feedback in higher elevation warming over the Himalayas, Tibetan Plateau and Central Asia. Environ. Res. Lett. 9 (11), 1586e1590. Gleckler, P.J., Taylor, K.E., Doutriaux, C., 2008. Performance metrics for climate models. J. Geophys. Res. 113 (D6). Guo, X.-J., Huang, J.-B., Luo, Y., et al., 2016a. Projection of heat waves over China for eight different global warming targets using 12 CMIP5 models. Theor. Appl. Climatol. 1e16. Guo, X.-J., Huang, J.-B., Luo, Y., et al., 2016b. Projection of precipitation extremes for eight global warming targets by 17 CMIP5 models. Nat. Hazards 84 (3), 2299e2319. Jiang, D.-B., Tian, Z.-P., Lang, X.-M., 2016. Reliability of climate models for China through the IPCC third to fifth assessment reports. Int. J. Climatol. 36 (3), 1114e1133. Kalnay, E., Kanamitsu, M., Kistler, R., et al., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77 (3), 437e471. Kistler, R., Collins, W., Saha, S., et al., 2001. The NCEPeNCAR 50eyear reanalysis: monthly means CDeROM and documentation. Bull. Am. Meteorol. Soc. 82 (2), 247e267. Li, S.-S., Lu¨, S.-H., Gao, Y.-H., et al., 2015. The change of climate and terrestrial carbon cycle over Tibetan Plateau in CMIP5 models. Int. J. Climatol. 35 (14), 4359e4369. Liu, X.-D., Chen, B.-D., 2000. Climatic warming in the Tibetan Plateau during recent decades. Int. J. Climatol. 20 (14), 1729e1742. Panday, P.K., Thibeault, J., Frey, K.E., 2015. Changing temperature and precipitation extremes in the Hindu Kush-Himalayan region: an analysis of CMIP3 and CMIP5 simulations and projections. Int. J. Climatol. 35 (10), 3058e3077. Rangwala, I., Sinsky, E., Miller, J.R., 2013. Amplified warming projections for high altitude regions of the northern hemisphere mid-latitudes from CMIP5 models. Environ. Res. Lett. 8 (2), 279e288. Shi, Y., Gao, X.-J., Zhang, D.-F., et al., 2011. Climate change over the Yarlung ZangboeBrahmaputra River Basin in the 21st century as simulated by a high resolution regional climate model. Quat. Int. 244 (2), 159e168. Wang, B., Bao, Q., Hoskins, B., et al., 2008. Tibetan Plateau warming and precipitation changes in East Asia. Geophys. Res. Lett. 35 (14). Wu, J., Gao, X.-J., 2013. A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys. Chin. Ed. 56 (4), 1102e1111 (in Chinese).
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Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models WU Jie a,b, XU Ying c,*, GAO Xue-Jie a,b a
Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China b University of Chinese Academy of Sciences, Beijing 100049, China c National Climate Center, China Meteorological Administration, Beijing 100081, China Received 16 December 2016; revised 19 January 2017; accepted 13 March 2017
Abstract Based on the outputs from 21 CMIP5 (Coupled Model Intercomparison Project phase 5) models, future changes in the mean temperature, precipitation and four climate extreme indices (annual maximum of daily maximum temperature (TXx), minimum of daily minimum temperature (TNn), annual total precipitation when the daily amount exceeds the 95th percentile of wet-day precipitation (R95p), and maximum consecutive 5-day precipitation (RX5day)) over Hindu Kush Himalayan (HKH) region are investigated under the greenhouse gas concentration pathways of RCP4.5 and RCP8.5. Two periods of the 21st century, 2036e2065 and 2066e2095, are selected, with the reference period is considered as 1976e2005. Results show general increase of the mean temperature, TXx and TNn under both scenarios, with the largest increases found during 2066e2095 under RCP8.5. Future precipitation is projected to increase over most part of HKH, except for the northwestern part. Intensification of the precipitation extremes is projected over the region. The uncertainties of mean temperature, TXx and TNn over the HKH1 subregions are the largest compared to the other three subregions and the overall HKH. Besides RX5day during 2036e2065 over HKH1, the uncertainties of R95p and RX5day tend to be larger following the increase of greenhouse gas concentrations. The multimodel ensemble medians of temperature and four extreme indices under RCP8.5 are projected to be larger than those under RCP4.5 in each of the subregions. Keywords: Hindu Kush Himalayan region; CMIP5; Mean climate; Extreme climate events; Climate change projection
1. Introduction Hindu Kush Himalayan (HKH) region (Fig. 1) is the source of ten prominent Asian rivers, thus the region is called as the Water Tower of Asia. With high elevations and holding large mass of ice, HKH region has been recognized as one of the
* Corresponding author. E-mail address: [email protected] (XU Y.). Peer review under responsibility of National Climate Center (China Meteorological Administration).
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most fragile areas susceptible to climate change (Xu et al., 2009). Additionally, significant warming over the Tibetan Plateau has been observed (Liu and Chen, 2000; Wang et al., 2008), even during the global hiatus period of the late decade (You et al., 2016). The role of snow-albedo feedback may contribute to the larger warming over the region (Ghatak et al., 2014). Global climate models (GCMs) are the primary tools in studying the historical climate change and projecting future climate. Most of the present day CMIP5 (Coupled Model Intercomparison Project phase 5) models show good performances in reproducing the present climatology, climate variability and climate extremes over China (Xu and Xu, 2012; Yao et al., 2013; Zhang et al., 2013; Jiang et al., 2016). Based on these evaluations, some researchers projected
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Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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temperature (TXx), minimum of daily minimum temperature (TNn), annual total precipitation when the daily amount exceeds the 95th percentile of wet-day precipitation (R95p), and maximum consecutive 5-day precipitation (RX5day). In the total of 21 CMIP5 models are used, with the model details listed in Table 1. The model outputs are interpolated onto a common 1 1 grid to facilitate the inter-comparison. Multimodel ensemble (MME) in this paper refers to the unweighted average of all individual models. The mean and extreme climates are projected under the RCP4.5 and RCP8.5
Fig. 1. Hindu Kush Himalayan (HKH) region and the three subregions (rectangular in red color) of interest: the northwest Himalaya and Karakoram (HKH1), central Himalaya (HKH2), and southeast Himalaya and Tibetan Plateau (HKH3). Shaded are the topography above 2500 m.
Table 1 List of the 21 CMIP5 models used in the study. Model ACCESS1-0
climate extreme events over China, using CMIP5 models (Xu et al., 2015; Guo et al., 2016a, 2016b). Analysis of their future changes based on the CMIP5 outputs over the HKH region is of importance for the society and will provide help for policymakers in developing proper adaptation strategies with sound scientific basis. Several studies have been conducted to investigate the future climate changes over the region. Rangwala et al. (2013) found that high correlations between the raised temperature and elevation over the Tibetan Plateau in the winters of the 21st century under 4.5 Representative Concentration Pathways scenario (RCP4.5). Panday et al. (2015) reported the analysis on the simulated and projected temperature, precipitation and their extremes over the eastern Himalaya and western Himalaya-Karakoram regions based on the CMIP3 and CMIP5 models. They showed that these regions were projected to be exposed to a warmer and wetter climate. Consistent to the study by Yang et al. (2012) based on five CMIP3 models, Li et al. (2015) showed also both increase in temperature and precipitation extremes over the Tibetan Plateau in future in the CMIP5 simulations. Ali et al. (2015) analyzed the climatic and hydrological changes over Upper Indus Basin using the Conformal-Cubic Atmospheric Model and a regional climate model (RegCM4). They found the increases of temperature and precipitation, especially over the northern parts, and river flows were projected to increase. However, most of the previous studies are focused on the different portions of the HKH region only, while very limited studies have been carried out covering the whole region, which we will address in this paper. 2. Data and methods CMIP5 models output consist a large variety of climate data and variables, providing greatly benefit the climate change studies. In this study, daily scale data of mean surface (2 m) air temperature, minimum and maximum temperature, and precipitation were selected and employed to calculate the mean climatology and extremes. Following Frich et al. (2002), four indices were used to describe the extreme events of temperature and precipitation: annual maximum of daily maximum
Institution/Country
Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM)/Australia BCC-CSM1-1 Beijing Climate Center, China Meteorological Administration/China BNU-ESM Global Change and Earth System Science (GCESS)/China CanESM2 Canadian Centre for Climate Modeling and Analysis/Canada CCSM4 National Center for Atmospheric Research (NCAR)/United States CNRM-CM5 Centre National de Recherches Meteorologiques and Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique/France CSIRO-Mk3-6-0 Queensland Climate Change Centre of Excellence and Commonwealth Scientific and Industrial Research Organization/Australia FGOALS-s2 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS, Tsinghua University GFDL-ESM2G National Oceanic and Atmospheric Administration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL)/ United States GFDL-ESM2M National Oceanic and Atmospheric Administration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL)/ United States HadGEM2-ES Met Office Hadley Centre (MOHC)/ United Kingdom INMCM4 Institute of Numerical Mathematics (INM)/Russia IPSL-CM5A-LR Institute Pierre-Simon Laplace/France IPSL-CM5A-MR Institute Pierre-Simon Laplace/France MIROC-ESM Model for Interdisciplinary Research on Climate (MIROC)/Japan MIROC-ESM-CHEM Model for Interdisciplinary Research on Climate (MIROC)/Japan MIROC5 Model for Interdisciplinary Research on Climate (MIROC)/Japan MPI-ESM-LR Max Planck Institute for Meteorology/ Germany MPI-ESM-MR Max Planck Institute for Meteorology/ Germany MRI-CGCM3 Meteorological Research Institute/Japan NorESM1-M Norwegian Climate Centre (NCC)/ Norway
Resolution (lon lat) 192 145 128 64 128 64 128 64 288 192 256 128
192 96
128 108 144 90
144 90
192 145 180 120 96 96 144 143 128 64 128 64 256 128 192 96 192 96 320 160 144 96
Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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WU J. et al. / Advances in Climate Change Research xx (2017) 1e9 Table 2 Numbers of grid points over HKH1, HKH2 and HKH3 for each model. Model
HKH1
HKH2
HKH3
ACCESS1-0 BCC-CSM1-1 BNU-ESM CanESM2 CCSM4 CNRM-CM5 CSIRO-Mk3-6-0 FGOALS-s2 GFDL-ESM2G GFDL-ESM2M HadGEM2-ES INMCM4 IPSL-CM5A-LR IPSL-CM5A-MR MIROC-ESM MIROC-ESM-CHEM MIROC5 MPI-ESM-LR MPI-ESM-MR MRI-CGCM3 NorESM1-M
30 9 9 9 49 30 20 15 12 12 30 20 12 15 9 9 30 20 20 42 12
36 6 6 6 70 48 27 18 21 21 36 27 12 28 6 6 48 27 27 75 21
42 12 12 12 80 48 28 20 20 20 42 30 16 30 12 12 48 28 28 77 20
scenarios in the periods of 2036e2065 and 2066e2095, with the period of 1976e2005 as the reference. HKH is a region largely uninhabited with its high elevations, and very few observation stations located (Wu and Gao, 2013). This condition leads to the difficulties in finding a proper observation dataset to validate the performances of the models. In the present study, the daily data from the National
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Center for Environmental Precipitation/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 (Kalnay et al., 1996; Kistler et al., 2001) are thus employed as the “observation”. The mean climatology and extreme indices were calculated interpolated onto the 1 1 grid. The rootmean-square error (RMSE), as well as the relative model error (RMSE0 ) following Gleckler et al. (2008) and Zhou et al. (2014) are calculated for each model. RMSE0 is defined as RMSE0 ¼
RMSE EMSEmedian RMSEmedian
ð1Þ
where RMSEmedian represents the median of RMSEs for all CMIP5 models. The negative value of RMSE0 of one individual model indicates that its performance is better than the majority (50%) of all models (Chen et al., 2014). The HKH region is further divided into three subregions as northwest Himalaya and Karakoram (HKH1), central Himalaya (HKH2), and southeast Himalaya and Tibetan Plateau (HKH3), as shown in Fig. 1. The topography higher than 2500 m in the region is also shown. The numbers of grid points over the three subregions for each model are listed in Table 2. 3. Results 3.1. Assessment of CMIP5 models' performance The RMSE0 s of the individual models and MME during the reference period (1976e2005) over the HKH region are
Fig. 2. The relative root-mean-square errors over HKH region (RMSE0 ) for the mean and extreme climate indices TXx (maximum of daily maximum temperature), TNn (minimum of daily minimum temperature), R95p (annual total precipitation when the daily precipitation exceeds the 95th percentile of the wet-day precipitation), and RX5day (maximum consecutive 5-day precipitation) in 1976e2005. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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presented in Fig. 2. Performances of the models in simulating different climate variables and indices vary widely. Some models perform relatively well for most indices, including CSIRO-MK3-6-0, GFDL-ESM2G, GFDL-ESM2M, INMCM4, IPSL-CM5A-LR, MPI-ESM-LR and MRI-CGCM3. In the meantime, some other models show relatively poor performances for most indices, including FGOALS-s2, MIROC5 and MPI-ESM-MR. As for MME, the RMSE0 s for all indices are negative, indicating its better skill as expected.
3.2. Spatial distributions of projected mean and extreme climate indices 3.2.1. Temperature and precipitation The spatial distributions of surface air temperature changes for 2036e2065 and 2066e2095 in relative to 1976e2005 are provided in Fig. 3. Significant warming is projected over the HKH region in the future. The warming is greater under RCP8.5 compared to RCP4.5 in each of
Fig. 3. The projected multimodel ensemble mean changes in surface (2 m) air temperature in 2036e2065 and 2066e2095 under RCP4.5 (a, c) and RCP8.5 (b, d), relative to 1976e2005 (Only the changes statistically significant at the 95% level are shown).
Fig. 4. Same as Fig. 3, but for precipitation. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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the periods, and greater in 2066e2095 compared to 2036e2065 under both scenarios. The largest values of temperature increase are found in the Tibetan Plateau in both of the periods and both of the scenarios. A warming in excess of 5 C is projected in 2066e2095 under RCP8.5. The projected percentage changes of precipitation are presented in Fig. 4. Increase over regions east of 70 E, and decrease over the northwestern part of HKH are found in the
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different periods and under different scenarios. The projected changes are in general less than 12% in 2036e2065 under RCP4.5. The change pattern and amount in 2066e2095 under RCP4.5 show consistencies with those in 2036e2065 under RCP8.5, with the increase greater than 16% found over the northeastern part of HKH. The largest change is found in 2066e2095 under RCP8.5, with the maxima (~24%) located in northern part of the Tibetan Plateau and along the western coast of India.
Fig. 5. Same as Fig. 3, but for TXx.
Fig. 6. Same as Fig. 3, but for TNn. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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3.2.2. Extreme climate indices Spatial distribution of the projected changes in TXx and TNn over HKH is shown in Figs. 5 and 6, respectively. Overwhelming increases of both TXx and TNn are found in the figures, indicating more extreme warm events and less extreme cold events in the future. Greater increase in TNn compared to TXx is evident within the same period. The maxima of TXx increase is around 2.8 C under RCP4.5 and 3.4 C under RCP8.5 in 2036e2065, 4.0 C under RCP4.5 and
5.2 C under RCP8.5 in 2066e2095, respectively. The northwestern part of HKH region is projected to experience larger increases of TXx compared to the other areas, while the most pronounced warming in TNn is projected along the southern border of the Tibetan Plateau. Greater than 5.2 C increase of TNn dominates the whole Tibetan Plateau in 2066e2095 under RCP8.5. Fig. 7 presents projected changes in R95p over the HKH region. As shown in Fig. 7, significant increases of R95ps are
Fig. 7. Same as Fig. 3, but for R95p.
Fig. 8. Same as Fig. 3, but for RX5day. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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WU J. et al. / Advances in Climate Change Research xx (2017) 1e9 Table 3 Six HKH-averaged mean temperature, precipitation and four extreme indices projected by the MME in 2036e2065 and 2066e2095 under RCP4.5 and RCP8.5, relative to 1976e2005. Indices
2036e2065
2066e2095
RCP4.5
RCP8.5
RCP4.5
RCP8.5
Temperature ( C) Precipitation (%) TXx ( C) TNn ( C) R95p (%) RX5day (%)
1.97 4.80 2.10 2.15 23.46 8.11
2.62 6.44 2.71 2.94 31.82 11.20
2.56 7.03 2.71 2.87 32.38 11.18
4.64 10.56 4.80 5.26 57.84 20.20
found in both of the periods and scenarios, indicating intensification of the extreme events in the future. For 2036e2065 under RCP4.5, the largest increase of R95p (>40%) is located in the north-central part of the region. Similar pattern and amount of the changes are found in 2066e2095 under RCP4.5 and in 2036e2065 under RCP8.5, with the maxima of the increase (~60%) located in the central and north of the Tibetan Plateau. Greater than 70% increase dominates most parts of the region in 2066e2095 under RCP8.5. –
–
–
The general increase of RX5day as shown in Fig. 8, indicates also the future intensification of precipitation extremes. Similar to R95p, greater increases of RX5day are found in central part of the Tibetan Plateau and the north of it. The largest increase is in the range of 12%e15% in 2036e2065 under RCP4.5, and exceeding 24% in 2066e2095 under RCP8.5. 3.3. Uncertainty analysis of the projected climate indices The box-and-whisker plots are employed to illustrate the inter-model agreement or disagreement on the projected changes thus their uncertainties over HKH (HKH-averaged values are listed in Table 3) and its three subregions, as summarized in Fig. 9. In general, the MME medians under RCP8.5 are larger compared to RCP4.5. This is more significant for temperature than precipitation indices. The model spreads are larger for the precipitation indices compared to temperature. Greater increases of the mean temperature and TXx are found in HKH1, while TNn under RCP8.5 shows the largest
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–
–
–
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Fig. 9. Projected changes in temperature (a, b), precipitation (c, d), TXx (e, f), TNn (g, h), R95p (i, j), and RX5day (k, l) over HKH and its three subregions in 2036e2065 and 2066e2095 under the RCP4.5 (blue color) and RCP8.5 (red color). The ranges between the 25th and 75th quantiles are indicated by boxes, the MME medians are indicated by the horizontal lines within boxes, and the extreme ranges of models are indicated by whiskers. Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001
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increase in HKH1 (Fig. 9a, b, eeh). The model spreads of the temperature indices for RCP4.5 and RCP8.5 are mostly in the same order, although larger of the later is found, e.g. for TXx in 2066e2095 in HKH1. Differences of the median of the indices under RCP4.5 and RCP8.5 are around 0.5 C in 2036e2065, and 2 C in 2066e2095, respectively. For the mean precipitation, the median change is all positive, but the full inter-model spread is large, with negative tails found in many cases. The model spreads are in general wider under RCP8.5 compared to RCP4.5, with the largest found in HKH1 in 2066e2095 (<20% and >40% for the lower and upper tail). For the extreme indices of R95p and RX5day, all the median changes are positive in the different regions, periods and scenarios, as is the interquartile range. Larger increase in HKH2 and HKH while the least increase in HKH1 of the indices are found. The upper tails of both R95p and RX5day are large over HKH1, indicating the presence of individual models with very high sensitivity of the precipitation extremes. 4. Conclusions and discussion In this study, validation of 21 CMIP5 models and their ensemble in simulating the temperature and precipitation mean and extremes is firstly carried out over the HKH region. Analysis shows better performances of the MME mean. Then the changes of mean temperature, precipitation and four extreme indices in 2036e2065 and 2066e2095 in relative to 1976e2005 under RCP4.5 and RCP8.5 are conducted, and their uncertainties (model spreads) are analyzed. Increases of the mean temperature, TXx and TNn are projected, with the largest increase occurring in 2066e2095 under RCP8.5. While the greatest warming is found over the Tibetan Plateau, the largest increases of TXx and TNn are located in the northwestern part of HKH region, and along the southern edge of Tibetan Plateau, respectively. The increase of TNn is in general larger than TXx. Greater than 5 C increases of the indices are found in the end of the century under RCP8.5. The multimodel medians of temperature, TXx and TNn under RCP8.5 are larger than those under RCP4.5 over HKH region and its three subregions in each period. The warming over the Tibetan Plateau was also projected by Rangwala et al. (2013). General increases of precipitation and its extremes are projected, indicating a wetter climate, but with more intensified extreme events future of the region. The increases of R95ps and RX5days are greater under RCP8.5 compared to RCP4.5. The largest increase is found in the central and north of the Tibetan Plateau. The increases of extreme indices are more significant than that of the precipitation mean. These changes are consistence with previous projections (Ali et al., 2015; Panday et al., 2015). Larger model spreads and uncertainties are found for precipitation than temperature, and the spreads are larger in 2066e2095 under RCP8.5. Finally, it is noted that the resolutions of GCMs in present are still low, which calls for the future application of high resolution regional models over the region (Shi et al., 2011; Gao et al., 2013).
Acknowledgments We acknowledge the World Climate Research Program's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank climate modeling groups for producing and making available their model outputs as well. This research was jointly supported by the National Key Research and Development Program of China (2016YFA0600704), R&D Special Fund for Public Welfare Industry (meteorology) (GYHY201306019) and the Climate Change Foundation of China Meteorological Administration (CCSF201508). References Ali, S., Li, D., Congbin, F., et al., 2015. Twenty first century climatic and hydrological changes over Upper Indus Basin of Himalayan region of Pakistan. Environ. Res. Lett. 10 (1), 014007. Chen, X.-C., Xu, Y., Xu, C.-H., et al., 2014. Assessment of precipitation simulations in China by CMIP5 multi-models. Adv. Clim. Change Res. 10 (3), 217e225 (in Chinese). Frich, P., Alexander, L.V., Della-Marta, P., et al., 2002. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19 (3), 193e212. Gao, X.-J., Wang, M.-L., Giorgi, F., 2013. Climate change over China in the 21st century as simulated by BCC_CSM1.1-RegCM4.0. Atmos. Ocean. Sci. Lett. 6 (5), 381e386. Ghatak, D., Sinsky, E., Miller, J., 2014. Role of snow-albedo feedback in higher elevation warming over the Himalayas, Tibetan Plateau and Central Asia. Environ. Res. Lett. 9 (11), 1586e1590. Gleckler, P.J., Taylor, K.E., Doutriaux, C., 2008. Performance metrics for climate models. J. Geophys. Res. 113 (D6). Guo, X.-J., Huang, J.-B., Luo, Y., et al., 2016a. Projection of heat waves over China for eight different global warming targets using 12 CMIP5 models. Theor. Appl. Climatol. 1e16. Guo, X.-J., Huang, J.-B., Luo, Y., et al., 2016b. Projection of precipitation extremes for eight global warming targets by 17 CMIP5 models. Nat. Hazards 84 (3), 2299e2319. Jiang, D.-B., Tian, Z.-P., Lang, X.-M., 2016. Reliability of climate models for China through the IPCC third to fifth assessment reports. Int. J. Climatol. 36 (3), 1114e1133. Kalnay, E., Kanamitsu, M., Kistler, R., et al., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77 (3), 437e471. Kistler, R., Collins, W., Saha, S., et al., 2001. The NCEPeNCAR 50eyear reanalysis: monthly means CDeROM and documentation. Bull. Am. Meteorol. Soc. 82 (2), 247e267. Li, S.-S., Lu¨, S.-H., Gao, Y.-H., et al., 2015. The change of climate and terrestrial carbon cycle over Tibetan Plateau in CMIP5 models. Int. J. Climatol. 35 (14), 4359e4369. Liu, X.-D., Chen, B.-D., 2000. Climatic warming in the Tibetan Plateau during recent decades. Int. J. Climatol. 20 (14), 1729e1742. Panday, P.K., Thibeault, J., Frey, K.E., 2015. Changing temperature and precipitation extremes in the Hindu Kush-Himalayan region: an analysis of CMIP3 and CMIP5 simulations and projections. Int. J. Climatol. 35 (10), 3058e3077. Rangwala, I., Sinsky, E., Miller, J.R., 2013. Amplified warming projections for high altitude regions of the northern hemisphere mid-latitudes from CMIP5 models. Environ. Res. Lett. 8 (2), 279e288. Shi, Y., Gao, X.-J., Zhang, D.-F., et al., 2011. Climate change over the Yarlung ZangboeBrahmaputra River Basin in the 21st century as simulated by a high resolution regional climate model. Quat. Int. 244 (2), 159e168. Wang, B., Bao, Q., Hoskins, B., et al., 2008. Tibetan Plateau warming and precipitation changes in East Asia. Geophys. Res. Lett. 35 (14). Wu, J., Gao, X.-J., 2013. A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys. Chin. Ed. 56 (4), 1102e1111 (in Chinese).
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Please cite this article in press as: WU, J., et al., Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models, Advances in Climate Change Research (2017), http://dx.doi.org/10.1016/j.accre.2017.03.001