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CMIP6 Projections of the “Warming-Wetting” Trend in Northwest China and Related Extreme Events Based on Observational Constraints

  • Detection, Attribution, and Projection of Regional Extreme Weather and Climate Events in China
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Abstract

This study presents the improved future projections of the climate “warming—wetting” trend and climate extremes with different return periods in Northwest China at different global warming levels. The projections are based on the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations constrained by the high-resolution observation dataset using the equidistant cumulative distribution functions (EDCDF) method. The results indicate that the climate will experience continuous warming and wetting as reflected by average temperature and total precipitation over Northwest China, especially under the scenario of the shared socioeconomic pathway 5—representative concentration pathway 8.5 (SSP5–8.5). Most parts of Northwest China will continue to warm in the future more than global average. Spatially, areas with prominent “warming—wetting” trends will be mainly distributed in western Northwest China. It is worth noting that extreme heat and precipitation events will also increase with the climate warming and wetting over Northwest China. Moreover, frequencies of rarer extreme events will increase more apparently than weaker extreme events and frequency increase of extreme heat events responds to global warming faster than that of extreme precipitation events. Limiting global warming within 2°C relative to 1850–1900 would slowdown the increase in extreme heat events and considerably suppress the increase in frequencies of extreme precipitation events, especially the rare (i.e., 50-yr) extreme events.

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References

  • Ben Alaya, M. A., F. Zwiers, and X. Zhang, 2020: An evaluation of block-maximum-based estimation of very long return period precipitation extremes with a large ensemble climate simulation. J. Climate, 33, 6957–6970, doi: https://doi.org/10.1175/JCLI-D-19-0011.1.

    Article  Google Scholar 

  • Chen, H. P., J. Q. Sun, W. Q. Lin, et al., 2020: Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull., 65, 1415–1418, doi: https://doi.org/10.1016/j.scib.2020.05.015.

    Article  Google Scholar 

  • Du, H. Y., C. Zhou, H. Q. Tang, et al., 2021: Simulation and estimation of future precipitation changes in arid regions: a case study of Xinjiang, Northwest China. Climatic Change, 167, 43, doi: https://doi.org/10.1007/s10584-021-03192-z.

    Article  Google Scholar 

  • IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.

  • IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, C. B. Field, V. Barros, T. F. Stocker, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 582 pp.

  • 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, T. F. Stocker, D. Qin, G.-K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.

  • IPCC, 2018: Global Warming of 1.5°C. 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, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, et al., Eds., in press. Available online at https://www.ipcc.ch/sr15/. Accessed on 10 March 2022.

  • IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, V. Masson-Delmotte, P. Zhai, A. Pirani, et al., Eds., Cambridge University Press, in press. Available online at https://www.ip-cc.ch/report/sixth-assessment-report-working-group-i/. Accessed on 10 March 2022.

  • Kendall, M. G., 1975: Rank Correlation Methods. 4th ed., Charles Griffin, London, 202 pp.

    Google Scholar 

  • Kharin, V. V., F. W. Zwiers, X. B. Zhang, et al., 2007: Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Climate, 20, 1419–1444, doi: https://doi.org/10.1175/JCLI4066.1.

    Article  Google Scholar 

  • Kharin, V. V., F. W. Zwiers, X. Zhang, et al., 2013: Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119, 345–357, doi: https://doi.org/10.1077/s10584-013-0705-8.

    Article  Google Scholar 

  • Kharin, V. V., G. M. Flato, X. Zhang, et al., 2018: Risks from climate extremes change differently from 1.5°C to 2.0°C depending on rarity. Earth’s Future, 6, 704–715, doi: https://doi.org/10.1002/2018EF000813.

    Article  Google Scholar 

  • La, M. K., Y. Zhou, H. C. Zhu, et al., 2019: On the precipitation changes over Xinjiang in summers from 2006 to 2035 through the dynamical downscaling of CMIP5 model results. J. Meteor. Sci., 39, 413–120. (in Chinese)

    Google Scholar 

  • Li, C., F. Zwiers, X. B. Zhang, et al., 2021: Changes in annual extremes of daily temperature and precipitation in CMIP6 models. J. Climate, 34, 3441–3460, doi: https://doi.org/10.1175/JCLI-D-19-1013.1.

    Article  Google Scholar 

  • Li, H. B., J. Sheffield, and E. F. Wood, 2010: Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J. Geophys. Res. Atmos., 115, D10101, doi: https://doi.org/10.1029/2009JD012882.

    Article  Google Scholar 

  • Li, S.-Y., L.-J. Miao, Z.-H. Jiang, et al., 2020: Projected drought conditions in Northwest China with CMIP6 models under combined SSPs and RCPs for 2015–2099. Adv. Climate Change Res., 11, 210–217, doi: https://doi.org/10.1016/j.accre.2020.09.003.

    Article  Google Scholar 

  • Lu, S., Z. Y. Hu, H. P. Yu, et al., 2021: Changes of extreme precipitation and its associated mechanisms in Northwest China. Adv. Atmos. Sci., 38, 1665–1681, doi: https://doi.org/10.1007/s00376-021-0409-3.

    Article  Google Scholar 

  • O’Neill, B. C., C. Tebaldi, D. P. van Vuuren, et al., 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 3461–3482, doi: https://doi.org/10.5194/gmd-9-3461-2016.

    Article  Google Scholar 

  • Pan, X. D., L. Zhang, and C. L. Huang, 2020: Future climate projection in Northwest China with RegCM4.6. Earth Space Sci., 7, e2019EA000819, doi: https://doi.org/10.1029/2019EA000819.

    Article  Google Scholar 

  • Qin, J. C., B. D. Su, H. Tao, et al., 2021: Projection of temperature and precipitation under SSPs-RCPs scenarios over North-west China. Front. Earth Sci., 15, 23–37, doi: https://doi.org/10.1007/s11707-020-0847-8.

    Article  Google Scholar 

  • Riahi, K., D. P. van Vuuren, E. Kriegler, et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environ. Change, 42, 153–168, doi: https://doi.org/10.1016/j.gloenvcha.2016.05.009.

    Article  Google Scholar 

  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Statist. Assoc., 63, 1379–1389, doi: https://doi.org/10.1080/01621459.1968.10480934.

    Article  Google Scholar 

  • Shi, Y. F., Y. P. Shen, D. L. Li, et al., 2003: Discussion on the present climate change from warm-dry to warm-wet in North-west China. Quat. Sci., 23, 152–164, doi: https://doi.org/10.3321/j.issn:1001-7410.2003.02.005. (in Chinese)

    Google Scholar 

  • Shi, Y. F., Y. P. Shen, E. S. Kang, et al., 2007: Recent and future climate change in Northwest China. Climatic Change, 80, 379–393, doi: https://doi.org/10.1007/s10584-006-9121-7.

    Article  Google Scholar 

  • Wang, Q., P.-M. Zhai, and D.-H. Qin, 2020: New perspectives on ‘warming—wetting’ trend in Xinjiang, China. Adv. Climate Change Res., 11, 252–260, doi: https://doi.org/10.1016/j.accre.2020.09.004.

    Article  Google Scholar 

  • Wang, Z. Q., X. J. Gao, Y. Tong, et al., 2021: Future climate change projection over Xinjiang based on an ensemble of regional climate model simulations. Chinese J. Atmos. Sci., 45, 407–423. (in Chinese)

    Google Scholar 

  • Watanabe, S., K. Takahashi, Y. Hijioka, et al., 2016: Report of the IPCC workshop on regional climate projections and their use in impacts and risk analysis studies. J. Japan Soc. Hydrol. Water Resour., 29, 79–84, doi: https://doi.org/10.3178/jjshwr.29.79.

    Article  Google Scholar 

  • Wu, J., and X. J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese J. Geophys., 56, 1102–1111. (in Chinese)

    Google Scholar 

  • Wu, P., Y. H. Ding, Y. J. Liu, et al., 2019: The characteristics of moisture recycling and its impact on regional precipitation against the background of climate warming over Northwest China. Int. J. Climatol., 39, 5241–5255, doi: https://doi.org/10.1002/joc.6136.

    Article  Google Scholar 

  • Wu, Z. T., H. J. Zhang, C. M. Krause, et al., 2010: Climate change and human activities: a case study in Xinjiang, China. Climatic Change, 99, 457–472, doi: https://doi.org/10.1007/s10584-009-9760-6.

    Article  Google Scholar 

  • Xu, Y., Y. H. Ding, Z. C. Zhao, et al., 2003: A scenario of seasonal climate change of the 21st century in Northwest China. Climatic Environ. Res., 8, 19–25, doi: https://doi.org/10.3969/j.issn.1006-9585.2003.01.003. (in Chinese)

    Google Scholar 

  • Xu, Y., X. J. Gao, F. Giorgi, et al., 2018: Projected changes in temperature and precipitation extremes over China as measured by 50-yr return values and periods based on a CMIP5 ensemble. Adv. Atmos. Sci., 35, 376–388, doi: https://doi.org/10.1007/s00376-017-6269-1.

    Article  Google Scholar 

  • Yang, X., E. F. Wood, J. Sheffield, et al., 2018: Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models. J. Hydrometeor., 19, 609–623, doi: https://doi.org/10.1175/JHM-D-17-0180.1.

    Article  Google Scholar 

  • Yu, E. T., J. Q. Sun, G. H. Lv, et al., 2015: High-resolution projection of future climate change in the northwestern arid regions of China. Arid Land Geography, 38, 429–437. (in Chinese)

    Google Scholar 

  • Zhai, P. M., F. M. Ren, and Q. Zhang, 1999: Detection of trends in China’s precipitation extremes. Acta Meteor. Sinica, 57, 208–216. (in Chinese)

    Google Scholar 

  • Zhang, Q., J. H. Yang, W. Wang, et al., 2021: Climatic warming and humidification in the arid region of Northwest China: Multi-scale characteristics and impacts on ecological vegetation. J. Meteor. Res., 35, 113–127, doi: https://doi.org/10.1007/s13351-021-0105-3.

    Article  Google Scholar 

  • Zhao, W. Y., Y. N. Chen, J. L. Li, et al., 2010: Periodicity of plant yield and its response to precipitation in the steppe desert of the Tianshan Mountains region. J. Arid Environ., 74, 445–449, doi: https://doi.org/10.1016/j.jaridenv.2009.09.022.

    Article  Google Scholar 

  • Zhou, T. J., and X. L. Chen, 2015: Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback. J. Meteor. Res., 29, 884–895, doi: https://doi.org/10.1007/s13351-015-5036-4.

    Article  Google Scholar 

  • Zhu, B. L., L. Q. Xue, G. H. Wei, et al., 2019: CMIP5 projected changes in temperature and precipitation in arid and humid basins. Theor. Appl. Climatol., 136, 1133–1144, doi: https://doi.org/10.1007/s00704-018-2542-1.

    Article  Google Scholar 

  • Zhu, H. H., Z. H. Jiang, J. Li, et al., 2020: Does CMIP6 inspire more confidence in simulating climate extremes over China? Adv. Atmos. Sci., 37, 1119–1132, doi: https://doi.org/10.1007/s00376-020-9289-1.

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge the World Climate Research Program’s Working Group on Coupled Modeling and thank the climate modeling groups for producing and sharing their model outputs.

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Correspondence to Panmao Zhai.

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Supported by the National Key Research and Development Program of China (2018YFC1507700).

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Wang, Q., Zhai, P. CMIP6 Projections of the “Warming-Wetting” Trend in Northwest China and Related Extreme Events Based on Observational Constraints. J Meteorol Res 36, 239–250 (2022). https://doi.org/10.1007/s13351-022-1157-8

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  • DOI: https://doi.org/10.1007/s13351-022-1157-8

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