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Extreme climate changes over three major river basins in China as seen in CMIP5 and CMIP6

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Abstract

Climate extremes can severely impact socio-economic development. Climate trends of three temperature and three precipitation climate indices were evaluated in observational data, 23 models from the 5th Coupled Model Intercomparison Projects (CMIP5), and 20 models from CMIP6. The climate indices were calculated over the whole of China, and individually over the basins of its three major rivers. The indices are the spatio-temporal evolution characteristics of annual mean temperature (Tas), minimum of daily minimum temperature (TNn), maximum of daily maximum temperature (TXx), number of tropical nights (TR), daily precipitation (Pre), very heavy precipitation days (R20mm), maximum consecutive 5-day precipitation (Rx5day) and consecutive dry days (CDD). From 1961 to 2018, most of China has warmed; Tas, TNn, TXx and TR over China has increased by 1.7 °C, 2.8 °C, 1.1 °C and 9 days, respectively. Changes of Tas, TNn and TXx over the Yellow River Basin, Yangtze River Basin and Pearl River Basin were generally similar in sign. The most significant increase of TR was seen over the Pearl River Basin. Historical Tas was well reproduced by both CMIP5 and CMIP6 over the study regions, but obvious uncertainties exist in the simulation of Pre. In general, CMIP6 models were improved from CMIP5 models. Climate projections were calculated for the 2021–2100 period. Future warming over China would be stronger with higher SSP scenarios; TNn over China would warm seven times more under the SSP5-8.5 scenario (5.6 °C) compared to the SSP1-2.6 scenario. Future wetting over China would be stronger with higher the SSP scenarios; Under the SSP5-8.5 scenario, Pre, R20mm, Rx5day would increase by 28%, 150%, and 38%, respectively. Projected changes of CDD different by region—decreasing over most of China and the Yellow River Basin, but increasing over the Yangtze and Pearl River Basins. The higher of the emission scenario, the less significant the reduction of CDD over the two basins. This suggests that the temporal distribution of precipitation over China will become more uneven in the future, especially under the higher SSP scenarios.

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References

  • Bai H, Xiao DP et al (2020) Multi-model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain. Int J Climatol. https://doi.org/10.1002/joc.6674

    Article  Google Scholar 

  • Birkinshaw S (2017) Yangtze climate change data and Shetran simulation input files and results. Newcastle Univ. https://doi.org/10.17634/120693-2

    Article  Google Scholar 

  • Brunner L, Pendergrass AG, Lehner F, Merrifield AL, Lorenz R, Knutti R (2020) Reduced global warming from CMIP6 projections when weighting models by performance and independence. Earth Syst Dyn 11:995–1012

    Article  Google Scholar 

  • Chen HP, Sun JQ (2015) Changes in drought characteristics over China using the standardized precipitation evapotranspiration index. J Clim 28:5430–5447

    Article  Google Scholar 

  • Chen Y, Zhai PM (2017) Revisiting summertime hot extremes in China during 1961–2015: overlooked compound extremes and significant changes. Geophys Res Lett 44:5096–5103

    Article  Google Scholar 

  • Chen HP, Sun JQ, Fan K (2012) Decadal features of heavy rainfall events in eastern China. Acta Meteor Sin 26:289–303

    Article  Google Scholar 

  • Chen HP, Sun JQ, Lin WQ, Xu HW (2020) Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci Bull 65(17):1415–1418

    Article  Google Scholar 

  • Cheng J, Wu JJ, Xu ZW et al (2014) Associations between extreme precipitation and childhood hand, foot and mouth disease in urban and rural areas in Hefei, China. Sci Total Env 1(497–498):484–490

    Article  Google Scholar 

  • Compo GP, Sardeshmukh PD et al (2013) Independent confirmation of global land warming without the use of station temperatures. Geophys Res Lett 40:3170–3174

    Article  Google Scholar 

  • Diffenbaugh NS et al (2017) Quantifying the influence of global warming on unprecedented extreme climate events. Proc Natl Acad Sci 114:4881–4886

    Article  Google Scholar 

  • Eyring V, Bony S, Meehl GA et al (2016) Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958

    Article  Google Scholar 

  • Eyring V et al (2019) Taking climate model evaluation to the next level. Nat Clim Change 9:102–110

    Article  Google Scholar 

  • Fan X, Miao C, Duan Q, Shen C, Wu Y (2020) The performance of CMIP6 versus CMIP5 in simulating temperature extremes over the global land surface. J Geophys Res Atmos 125:e2020JD033031

    Article  Google Scholar 

  • Gou J, Miao C, Duan Q, Tang Q, Di Z, Liao W, Wu J, Zhou R (2020) Sensitivity analysis-based automatic parameter calibration of the variable infiltration capacity (VIC) model for streamflow simulations over China. Water Resour Res 56:e2019WR025968

    Article  Google Scholar 

  • Grose MR, Narsey S, Delage FP et al (2020) Insights from CMIP6 for Australia’s future climate. Earth Future 8:e2019EF001469

    Article  Google Scholar 

  • Guan YH, Zhang XC, Zheng FL, Wang B (2015) Trends and variability of daily temperature extremes during 1960–2012 in the Yangtze River Basin, China. Glob Planet Change 124:79–94

    Article  Google Scholar 

  • Gusain A, Ghosh S, Karmakar S (2019) Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmos Res 232:104680

    Article  Google Scholar 

  • Hartmann DL, Klein TAMG, Rusticucci M et al (2013) Observations: atmosphere and surface. In: Stocker TF, Qin D, Plattner GK (eds) Climate change 2013 the physical science basis: working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699

    Article  Google Scholar 

  • IPCC (2013) Climate Change 2013: the physical science basis. In: Contribution of working group I to the Fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, New York

  • IPCC (2018) Summary for policymakers. In: Global warming of 1.5℃. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways (World Meteorological Organization, Geneva, Switzerland, 32 pp)

  • Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758

    Article  Google Scholar 

  • Lang XM, Sui Y (2013) Changes in mean and extreme climates over China with a 2 °C global warming. Chin Sci Bull 58:1453–1461

    Article  Google Scholar 

  • Liu R, Chen LS, Cicerone RJ, Chein-Jung S, Jun LI, Wang J, Zhang Y (2015) Trends of extreme precipitation in eastern China and their possible causes. Adv Atmos Sci 32:1027–1037

    Article  Google Scholar 

  • Liu A, Soneja SI, Jiang CS et al (2017) Frequency of extreme weather events and increased risk of motor vehicle collision in Maryland. Sci Total Environ 580:550–555

    Article  Google Scholar 

  • Liu CM, Tian W, Liu XM et al (2019) Analysis and understanding on runoff variation of the yellow river in recent 100 years. Yellow River 41:11–15

    Google Scholar 

  • Luo N, Guo Y, Gao ZB, Chen KX, Chou JM (2020) Assessment of CMIP6 and CMIP5 model performance for extreme temperature in China. Atmos Ocean Sci Lett 13(6):589–597

    Article  Google Scholar 

  • Lv MX, Ma ZG, Lv MZ (2018) Effects of climate/land surface changes on streamflow with consideration of precipitation intensity and catchment characteristics in the Yellow River Basin. J Gerontol Ser A Biol Med Sci 123:1942–1958

    Google Scholar 

  • Lv MX, Ma ZG, Li MX et al (2019) Quantitative analysis of terrestrial water storage changes under the Grain for Green program in the Yellow River basin. J Gerontol Ser A Biol Med Sci 124:1336–1351

    Google Scholar 

  • Ma ZG (2005) Historical regular patterns of the discharge in the Yellow River and the cause of their formation. Chin J Geophys (Chin) 48:1270–1275

    Google Scholar 

  • Marotzke J et al (2017) Climate research must sharpen its view. Nat Clim Change 7:89–91

    Article  Google Scholar 

  • Mudryk L, Santolaria-Otín M, Krinner G, Ménégoz M, Derk-sen C, Brutel-Vuilmet C, Brady M, Essery R (2020) Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble. Cryosphere 14:2495–2514

    Article  Google Scholar 

  • O’Neill BC, Tebaldi C, van Vuuren DP et al (2016) The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci Model Dev 9:3461–3482

    Article  Google Scholar 

  • Piao S et al (2010) The impacts of climate change on water resources and agriculture in China. Nature 467:43–51

    Article  Google Scholar 

  • Ridder NN, Pitman AJ, Ukkola AM (2021) Do CMIP6 climate models simulate global or regional compound events skillfully? Geophys Res Lett 48:e2020GL091152

    Article  Google Scholar 

  • Seddon AWR, Macias-Fauria M, Long PR, Benz D, Willis KJ (2016) Sensitivity of global terrestrial ecosystems to climate variability. Nature 531:229–232

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013a) Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J Geophys Res 118:1716–1733

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013b) Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J Geophys Res 118:2473–2493

    Article  Google Scholar 

  • Su B, Gemmer M, Jiang T (2008) Spatial and temporal variation of extreme precipitation over the Yangtze River Basin. Quat Int 186:22–31

    Article  Google Scholar 

  • Sun JQ, Ao J (2013) Changes in precipitation and extreme precipitation in a warming environment in China. Sci Bull 58:1395–1401

    Article  Google Scholar 

  • Sun QH, Miao CY, Duan QY, Kong DX et al (2014) Would the “real” observed dataset stand up? A critical examination of eight observed gridded climate datasets for China. Environ Res Lett 9:015001

    Article  Google Scholar 

  • Sun Q, Miao C, Duan Q (2015) Projected changes in temperature and precipitation in ten river basins over China in 21st century. Int J Climatol 35:1125–1141

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498

    Article  Google Scholar 

  • Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Clim Change 79:185–211

    Article  Google Scholar 

  • Tokarska KB, Stolpe MB, Sippel S, Fischer EM, Smith CJ, Lehner F, Knutti R (2020) Past warming trend constrains future warming in CMIP6 models. Sci Adv 6:eaaz9549

    Article  Google Scholar 

  • Trenberth KE, Fasullo JT, Shepherd TG (2015) Attribution of climate extreme events. Nat Clim Chang 5:725–730

    Article  Google Scholar 

  • Wang HJ, Sun JQ, Chen HP et al (2012) Extreme climate in China: facts, simulation and projection. Meteorol Z 21:279–304

    Article  Google Scholar 

  • Wang X, Yang T, Li X, Shi P, Zhou X (2016) Spatio-temporal changes of precipitation and temperature over the Pearl River basin based on CMIP5 multi-model ensemble. J Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-016-1286-7

    Article  Google Scholar 

  • Wu ZY, Lu GH, Liu ZY, Wang JX, Xiao H (2013) Trends of extreme flood events in the Pearl river basin during 1951–2010. Adv Clim Change Res 4:110–116

    Article  Google Scholar 

  • Wu J, Gao XJ, Giorgi F, Chen DL (2017) Changes of effective temperature and cold/hot days in late decades over China based on a high resolution gridded observation dataset. Int J Climatol 37:788–800

    Article  Google Scholar 

  • Wu J, Han Z, Xu Y, Zhou B, Gao X (2020) Changes in extreme climate events in China under 1.5 °C-4°C global warming targets: projections using an ensemble of regional climate model simulations. J Geophys Res Atmos 125:e2019JD031057

    Google Scholar 

  • Xia J, Peng SM, Wang C et al (2014) Impact of climate change on water resources and adaptive management in the Yellow River basin. Yellow River 36:1–15

    Google Scholar 

  • Xin X, Wu T, Zhang J, Yao J, Fang Y (2020) Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. Int J Climatol 40:6423–6440

    Article  Google Scholar 

  • Xu Y, Gao XJ, Shen Y et al (2009) A daily temperature dataset over China and its application in validating a RCM simulation. Adv Atmos Sci 26:763–772

    Article  Google Scholar 

  • Xu K, Xu B, Ju J, Wu C, Dai H, Hu BX (2019) Projection and uncertainty of precipitation extremes in the CMIP5 multimodel ensembles over nine major basins in China. Atmos Res 226:122–137

    Article  Google Scholar 

  • Yang HL, Xu YL, Zhang L et al (2010a) Projected change in heat waves over China using the PRECIS climate model. Clim Res 42:79–88

    Article  Google Scholar 

  • Yang T, Shao Q, Hao Z, Chen X, Zhang Z, Xu C, Sun L (2010b) Regional frequency analysis and spatio-temporal pattern characterization of rainfall extremes in the Pearl River basin. J Hydrol 380:386–405

    Article  Google Scholar 

  • Yang XL, Zhou BT, Xu Y, Han Z-Y (2021) CMIP6 evaluation and projection of temperature andprecipitation over China. Adv Atmos Sci. https://doi.org/10.1007/s00376-021-0351-4

    Article  Google Scholar 

  • Ye JS, Pei JY, Fang C (2018) Under which climate and soil conditions the plant productivity–precipitation relationship is linear or nonlinear? Sci Total Environ 616–617:1174–1180

    Article  Google Scholar 

  • Yin H, Li C (2001) Human impact on floods and flood disasters in the Yangtze River. Geomorphology 41:105–109

    Article  Google Scholar 

  • Zelinka MD, Myers TA, McCoy DT, Po-Chedley S, Caldwell PM, Ceppi P, Klein SA, Taylor KE (2020) Causes of higher climate sensitivity in CMIP6 models. Geophys Res Lett 47:1–12

    Article  Google Scholar 

  • Zeng QC, Zhou GQ, Pu YF et al (2008) Research on the earth system dynamic model and some related numerical simulations. J Atmos Sci (Chin) 32:653–690

    Google Scholar 

  • Zhai P, Pan X (2003) Trends in temperature extremes during 1951–1999 in China. Geophys Res Lett 30:1913

    Article  Google Scholar 

  • Zhai P, Zhang X, Wan H, Pan XH (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–1108

    Article  Google Scholar 

  • Zhang Q, Xu CY, Becker S, Zhang ZX, Chen YD, Coulibaly M (2009) Trends and abrupt changes of precipitation maxima in the Pearl River basin, China. Atmos Sci Lett 10:132–144

    Article  Google Scholar 

  • Zhang LX, Chen XL, Xin XG (2019) Short commentary on CMIP6 scenario model intercomparison project (ScenarioMIP). Clim Change Res 15:519–525

    Google Scholar 

  • Zhou BT, Xu Y, Wu J et al (2015) Changes in temperature and precipitation extreme indices over China: analysis of a high-resolution grid dataset. Int J Climatol 36:1051–1066

    Article  Google Scholar 

  • Zhou TJ, Zou LW, Chen XL (2019) Commentary on the coupled model intercomparison project phase 6 (CMIP6). Clim Change Res 15:445–456

    Google Scholar 

  • Zhu HH, Jiang ZH, Li J, Li W, Sun CX, Li L (2020) Does CMIP6 inspire more confidence in simulating climate extremes over China? Adv Atmos Sci 37(10):1119–1132

    Article  Google Scholar 

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Acknowledgements

This work was funded by National Key R&D Program of China (2016YFA0602703), the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0103), the China Postdoctoral Science Foundation (Grant no. 2020M672942), the Fundamental Research Funds for the Central Universities from Sun Yat-Sen University (Grant no. 19lgpy31). We express sincere gratitude to the reviewers for their constructive comments and suggestions. Their advices will benefit the improvement of the paper and our future researches.

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Authors and Affiliations

  1. School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, 519082, China

    Xian Zhu, Shao-Yi Lee, Zhenming Ji, Lei Lin, Zhiyuan Zheng & Wenjie Dong

  2. Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, 519082, China

    Xian Zhu, Shao-Yi Lee, Zhenming Ji, Lei Lin, Zhiyuan Zheng & Wenjie Dong

  3. Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, 519082, China

    Xian Zhu, Shao-Yi Lee, Zhenming Ji, Lei Lin, Zhiyuan Zheng, Danya Xu & Wenjie Dong

  4. Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, 610225, China

    Xiaohang Wen

  5. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

    Zhigang Wei

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  1. Xian Zhu
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Zhu, X., Lee, SY., Wen, X. et al. Extreme climate changes over three major river basins in China as seen in CMIP5 and CMIP6. Clim Dyn 57, 1187–1205 (2021). https://doi.org/10.1007/s00382-021-05767-z

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