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Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging

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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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References

  • Aguirre-Salado C A, Treviño-Garza E J, Aguirre-Calderón O A, et al. 2014. Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico. Journal of Arid Land, 6(1): 80–96.

    Article  Google Scholar 

  • Aizen V B, Aizen E M, Melack J M, et al. 1997. Climatic and hydrologic changes in the Tien Shan, central Asia. Journal of Climate, 10(6): 1393–1404.

    Article  Google Scholar 

  • Berghuijs W R, Woods R A, Hrachowitz M. 2014. A precipitation shift from snow towards rain leads to a decrease in streamflow. Nature Climate Change, 4(7): 583–586.

    Article  Google Scholar 

  • Bocchiola D. 2014. Long term (1921–2011) hydrological regime of Alpine catchments in Northern Italy. Advances in Water Resources, 70: 51–64.

    Article  Google Scholar 

  • Chen Y N. 2014. Water Resources Research in Northwest China. Dordrecht, Netherlands: Springer, 1–440.

    Book  Google Scholar 

  • Chen Y N, Li W H, Deng H J, et al. 2016. Changes in central Asia’s water tower: past, present and future. Scientific Reports, 6: 35458, doi: 10.1038/srep35458.

    Article  Google Scholar 

  • Davis R E, Lowit M B, Knappenberger P C, et al. 1999. A climatology of snowfall-temperature relationships in Canada. Journal of Geophysical Research: Atmospheres, 104(D10): 11985–11994.

    Article  Google Scholar 

  • Dedieu J P, Lessard-Fontaine A, Ravazzani G, et al. 2014. Shifting mountain snow patterns in a changing climate from remote sensing retrieval. Science of the Total Environment, 493: 1267–1279.

    Article  Google Scholar 

  • Dietz A, Conrad C, Kuenzer C, et al. 2014. Identifying changing snow cover characteristics in central Asia between 1986 and 2014 from remote sensing data. Remote Sensing, 6(12): 12752–12775.

    Article  Google Scholar 

  • Duan Q Y, Ajami N K, Gao X G, et al. 2007. Multi-model ensemble hydrologic prediction using Bayesian model averaging. Advances in Water Resources, 30(5): 1371–1386.

    Article  Google Scholar 

  • Feng S, Hu Q. 2007. Changes in winter snowfall/precipitation ratio in the contiguous United States. Journal of Geophysical Research: Atmospheres, 112(D15): D15109.

    Article  Google Scholar 

  • Fraley C, Raftery A E, Gneiting T. 2010. Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging. Monthly Weather Review, 138(1): 190–202.

    Article  Google Scholar 

  • Guo L P, Li L H. 2015. Variation of the proportion of precipitation occurring as snow in the Tian Shan Mountains, China. International Journal of Climatology, 35(7): 1379–1393.

    Article  Google Scholar 

  • Hagg W, Hoelzle M, Wagner S, et al. 2013. Glacier and runoff changes in the Rukhk catchment, upper Amu-Darya basin until 2050. Global and Planetary Change, 110: 62–73.

    Article  Google Scholar 

  • Hersbach H. 2000. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15(5): 559–570.

    Article  Google Scholar 

  • Hezel P J, Zhang X, Bitz C M, et al. 2012. Projected decline in spring snow depth on Arctic sea ice caused by progressively later autumn open ocean freeze-up this century. Geophysical Research Letters, 39(17): L17505.

    Article  Google Scholar 

  • Hoeting J A, Madigan D, Raftery A E, et al. 1999. Bayesian model averaging: a tutorial. Statistical Science, 14(4): 382–401.

    Article  Google Scholar 

  • IPCC. 2013. Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom, New York, NY, USA: Cambridge University Press.

    Google Scholar 

  • Kapnick S B, Delworth T L, Ashfaq M, et al. 2014. Snowfall less sensitive to warming in Karakoram than in Himalayas due to a unique seasonal cycle. Nature Geoscience, 7(11): 834–840.

    Article  Google Scholar 

  • Kawase H, Nagashima T, Sudo K, et al. 2011. Future changes in tropospheric ozone under Representative Concentration Pathways (RCPs). Geophysical Research Letters, 38(5): L05801.

    Article  Google Scholar 

  • Kure S, Jang S, Ohara N, et al. 2013. Hydrologic impact of regional climate change for the snow-fed and glacier-fed river basins in the Republic of Tajikistan: statistical downscaling of global climate model projections. Hydrological Processes, 27(26): 4071–4090.

    Article  Google Scholar 

  • Li B F, Chen Y N, Chen Z S, et al. 2013. Variations of temperature and precipitation of snowmelt period and its effect on runoff in the mountainous areas of Northwest China. Journal of Geographical Sciences, 23(1): 17–30.

    Article  Google Scholar 

  • Li P Y, Qian H, Howard K W F, et al. 2015. Building a new and sustainable “Silk Road economic belt”. Environmental Earth Sciences, 74(10): 7267–7270.

    Article  Google Scholar 

  • Liu J G, Xie Z H. 2014. BMA probabilistic quantitative precipitation forecasting over the Huaihe basin using TIGGE multimodel ensemble forecasts. Monthly Weather Review, 142(4): 1542–1555.

    Article  Google Scholar 

  • Mir R A, Jain S K, Saraf A K, et al. 2015. Decline in snowfall in response to temperature in Satluj basin, western Himalaya. Journal of Earth System Science, 124(2): 365–382.

    Article  Google Scholar 

  • Monaghan A J, Bromwich D H, Schneider D P. 2008. Twentieth century Antarctic air temperature and snowfall simulations by IPCC climate models. Geophysical Research Letters, 35(7): L07502.

    Article  Google Scholar 

  • Najafi M R, Moradkhani H. 2015. Multi-model ensemble analysis of runoff extremes for climate change impact assessments. Journal of Hydrology, 525(0): 352–361.

    Article  Google Scholar 

  • Najafi M R, Moradkhani H, Jung I W. 2011. Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrological Processes, 25(18): 2814–2826.

    Article  Google Scholar 

  • O’Gorman P A. 2014. Contrasting responses of mean and extreme snowfall to climate change. Nature, 512(7515): 416–418.

    Article  Google Scholar 

  • Ososkova T, Gorelkin N, Chub V. 2000. Water resources of central Asia and adaptation measures for climate change. Environmental Monitoring and Assessment, 61(1): 161–166.

    Article  Google Scholar 

  • Piazza M, Boé J, Terray L, et al. 2014. Projected 21st century snowfall changes over the French Alps and related uncertainties. Climatic Change, 122(4): 583–594.

    Article  Google Scholar 

  • Raftery A E, Gneiting T, Balabdaoui F, et al. 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133(5): 1155–1174.

    Article  Google Scholar 

  • Schmeits M J, Kok K J. 2010. A comparison between raw ensemble output, (Modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Monthly Weather Review, 138(11): 4199–4211.

    Article  Google Scholar 

  • Serquet G, Marty C, Dulex J P, et al. 2011. Seasonal trends and temperature dependence of the snowfall/precipitation-day ratio in Switzerland. Geophysical Research Letters, 38(7): L07703.

    Article  Google Scholar 

  • Shi Y F, Shen Y P, Kang E S, et al. 2007. Recent and future climate change in northwest China. Climatic Change, 80(3): 379–393.

    Article  Google Scholar 

  • Sloughter J M, Raftery A E, Gneiting T, et al. 2007. Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Monthly Weather Review, 135(9): 3209–3220.

    Article  Google Scholar 

  • Sloughter J M, Gneiting T, Raftery A E, 2010. Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. Journal of the American Statistical Association, 105(489): 25–35.

    Article  Google Scholar 

  • Tebaldi C, Mearns L O, Nychka D, et al. 2004. Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations. Geophysical Research Letters, 31(24): L24213, doi:10.1029/2004GL021276.

    Article  Google Scholar 

  • van Vuuren D P, Edmonds J, Kainuma M, et al. 2011. The representative concentration pathways: an overview. Climatic Change, 109(1–2): 5–31.

    Article  Google Scholar 

  • Winkelmann R, Levermann A, Martin M A, et al. 2012. Increased future ice discharge from Antarctica owing to higher snowfall. Nature, 492(7428): 239–242.

    Article  Google Scholar 

  • Yang T, Hao X B, Shao Q X, et al. 2012. Multi-model ensemble projections in temperature and precipitation extremes of the Tibetan Plateau in the 21st century. Global and Planetary Change, 80–81: 1–13.

    Article  Google Scholar 

  • Yasutomi N, Hamada A, Yatagai A. 2011. Development of a long-term daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation. Global Environmental Research, 15: 165–172.

    Google Scholar 

  • Yatagai A, Kamiguchi K, Arakawa O, et al. 2012. APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society, 93(9): 1401–1415.

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported by the Thousand Youth Talents Plan (Xinjiang Project), the National Natural Science Foundation of China (41630859) and the West Light Foundation of Chinese Academy of Sciences (2016QNXZB12). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for their efforts in producing their model outputs. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Authors and Affiliations

  1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Jing Yang, Gonghuan Fang & Yaning Chen

  2. National Institute of Water and Atmospheric Research, Christchurch, 8011, New Zealand

    Jing Yang

  3. Department of Geography, Ghent University, Ghent, 9000, Belgium

    Gonghuan Fang & Philippe De-Maeyer

  4. Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, 830011, China

    Gonghuan Fang & Philippe De-Maeyer

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  1. Jing Yang
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  4. Philippe De-Maeyer
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Correspondence to Jing Yang.

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Yang, J., Fang, G., Chen, Y. et al. Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging. J. Arid Land 9, 622–634 (2017). https://doi.org/10.1007/s40333-017-0100-9

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