Abstract
This article evaluates the performance of 20 Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating temperature and precipitation over China through comparisons with gridded observation data for the period of 1995–2014, with a focus on spatial patterns and interannual variability. The evaluations show that the CMIP6 models perform well in reproducing the climatological spatial distribution of temperature and precipitation, with better performance for temperature than for precipitation. Their interannual variability can also be reasonably captured by most models, however, poor performance is noted regarding the interannual variability of winter precipitation. Based on the comprehensive performance for the above two factors, the “highest-ranked” models are selected as an ensemble (BMME). The BMME outperforms the ensemble of all models (AMME) in simulating annual and winter temperature and precipitation, particularly for those subregions with complex terrain but it shows little improvement for summer temperature and precipitation. The AMME and BMME projections indicate annual increases for both temperature and precipitation across China by the end of the 21st century, with larger increases under the scenario of the Shared Socioeconomic Pathway 5/Representative Concentration Pathway 8.5 (SSP585) than under scenario of the Shared Socioeconomic Pathway 2/Representative Concentration Pathway 4.5 (SSP245). The greatest increases of annual temperature are projected for higher latitudes and higher elevations and the largest percentage-based increases in annual precipitation are projected to occur in northern and western China, especially under SSP585. However, the BMME, which generally performs better in these regions, projects lower changes in annual temperature and larger variations in annual precipitation when compared to the AMME projections.
摘 要
通过与1995–2014年中国温度和降水格点观测数据的对比, 评估了第六次耦合模式比较计划 (CMIP6) 中的20个全球气候模式对中国温度和降水空间型态与年际变率的模拟能力. 评估结果表明:CMIP6模式能够较好地再现观测中温度和降水的气候态分布, 其中对温度的模拟优于降水. 其年际变率也能被大多数模式合理模拟出, 不过对冬季降水年际变率的模拟较差. 基于模式对温度和降水空间型态和年际变率模拟能力的综合表现, 选择了“排名最高”的模式集合 (BMME), 发现BMME对年平均和冬季的温度与降水的模拟优于所有模式集合 (AMME), 尤其在具有复杂地形的区域; 而对于夏季温度和降水的模拟与AMME相比没有明显改善. AMME和BMME的预估结果均表明, 到21世纪末, 中国区域温度和降水都将增加, 其中SSP585情景下的增幅大于SSP245. 年平均温度最大增幅出现在高纬度和高海拔地区, 年降水量最大百分比增幅出现在中国西北部地区. 不过, 对于上述BMME模拟明显改善的地区, BMME预估的年平均温度变化幅度要比AMME预估的小, 而预估的年降水量变化幅度则比AMME预估的要大.
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References
Bador, M., and Coauthors, 2020: Impact of higher spatial atmospheric resolution on precipitation extremes over land in Global Climate Models. J. Geophys. Res., 125, e2019JD032184, https://doi.org/10.1029/2019JD032184.
Bao, J. W., and J. M. Feng, 2016: Intercomparison of CMIP5 simulations of summer precipitation, evaporation, and water vapor transport over Yellow and Yangtze River basins. Theor. Appl. Climatol., 123, 437–452, https://doi.org/10.1007/s00704-014-1349-y.
Chen, H. P., and J. Q. Sun, 2009: How the “best” models project the future precipitation change in China. Adv. Atmos. Sci., 26, 773–782, https://doi.org/10.1007/s00376-009-8211-7.
Chen, H. P., and J. Q. Sun, 2013: Projected change in East Asian summer monsoon precipitation under RCP scenario. Meteorol. Atmos. Phys., 121, 55–77, https://doi.org/10.1007/s00703-013-0257-5.
Chen, H. P., J. Q. Sun, W. Q. Lin, and H. W. Xu, 2020: Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Science Bulletin, 61, 1415–1418, https://doi.org/10.1016/j.scib.2020.05.015.
Chen, L., and O. W. Frauenfeld, 2014: A comprehensive evaluation of precipitation simulations over China based on CMIP5 multimodel ensemble projections. J. Geophys. Res., 119, 5767–5786, https://doi.org/10.1002/2013JD021190.
Chen, W. L., Z. H. Jiang, and L. Li, 2011: Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. J. Climate, 24, 4741–4756, https://doi.org/10.1175/2011JCLI4102.1.
Chen, X. C., Y. Xu, C. H. Xu, and Y. Yao, 2014: Assessment of precipitation simulations in China by CMIP5 multi-models. Progressus Inquisitiones de Mutatione Climatis, 10, 217–225, https://doi.org/10.3969/j.issn.1673-1719.2014.03.011. (in Chinese with English abstract)
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.
Gao, X. J., Y. Xu, Z. C. Zhao, J. S. Pal, and F. Giorgi, 2006: On the role of resolution and topography in the simulation of East Asia precipitation. Theor. Appl. Climatol., 86, 173–185, https://doi.org/10.1007/s00704-005-0214-4.
Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007JD008972.
Guo, Y., W. J. Dong, F. M. Ren, Z. C. Zhao, and J. B. Huang, 2013: Assessment of CMIP5 simulations for China annual average surface temperature and its comparison with CMIP3 simulations. Progressus Inquisitiones de Mutatione Climatis, 9, 181–186, https://doi.org/10.3969/j.issn.1673-1719.2013.03.004. (in Chinese with English abstract)
Ha, K. J., S. Moon, A. Timmermann, and D. Kim, 2020: Future changes of summer monsoon characteristics and evaporative demand over Asia in CMIP6 simulations. Geophys. Res. Lett., 47, e2020GL087492, https://doi.org/10.1029/2020GL087492.
Hu, Q., D. B. Jiang, and G. Z. Fan, 2015: Climate change projection on the Tibetan Plateau: Results of CMIP5 models. Chinese Journal of Atmospheric Sciences, 39(2), 260–270, https://doi.org/10.3878/j.issn.1006-9895.1406.13325. (in Chinese with English abstract)
Huang, D. Q., J. Zhu, Y. C. Zhang, and A. N. Huang, 2013: Uncertainties on the simulated summer precipitation over Eastern China from the CMIP5 models. J. Geophys. Res., 118, 9035–9047, https://doi.org/10.1002/jgrd.50695.
Jiang, D. B., H. J. Wang, and X. M. Lang, 2005: Evaluation of East Asian climatology as simulated by seven coupled models. Adv. Atmos. Sci., 22, 479–495, https://doi.org/10.1007/BF02918482.
Jiang, D. B., Z. P. Tian, and X. M. Lang, 2016: Reliability of climate models for China through the IPCC Third to Fifth Assessment Reports. International Journal of Climatology, 36, 1114–1133, https://doi.org/10.1002/joc.4406.
Jiang, D. B., D. Hu, Z. P. Tian, and X. M. Lang, 2020: Differences between CMIP6 and CMIP5 models in simulating climate over China and the East Asian monsoon. Adv. Atmos. Sci., 37, 1102–1118, https://doi.org/10.1007/s00376-020-2034-y.
Kumar, D., E. Kodra, and A. R. Ganguly, 2014: Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation. Climate Dyn., 43, 2491–2518, https://doi.org/10.1007/s00382-014-2070-3.
National Report Committee, 2007: China’s National Assessment Report on Climate Change. Science Press, Beijing, 148 pp. (in Chinese)
Nie, S. P., S. W. Fu, W. H. Cao, and X. L. Jia, 2020: Comparison of monthly air and land surface temperature extremes simulated using CMIP5 and CMIP6 versions of the Beijing Climate Center climate model. Theor. Appl. Climatol., 140, 487–502, https://doi.org/10.1007/s00704-020-03090-x.
O’Neill, B. C., and Coauthors, 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016.
Rao, X. Q., X. Lu, and W. J. Dong, 2019: Evaluation and projection of extreme precipitation over Northern China in CMIP5 models. Atmosphere, 10, 691, https://doi.org/10.3390/atmos10110691.
Riahi, K., and Coauthors, 2017: The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009.
Scherrer, S. C., 2011: Present-day interannual variability of surface climate in CMIP3 models and its relation to future warming. International Journal of Climatology, 31, 1518–1529, https://doi.org/10.1002/joc.2170.
Simpkins, G., 2017: Progress in climate modelling. Nature Climate Change, 4, 684–685, https://doi.org/10.1038/nclimate3398.
Stouffer, R. J., V. Eyring, G. A. Meehl, S. Bony, C. Senior, B. Stevens, and K. E. Taylor, 2017: CMIP5 scientific gaps and recommendations for CMIP6. Bull. Amer. Meteor. Soc., 98, 95–105, https://doi.org/10.1175/BAMS-D-15-00013.1.
Su, F. G., X. L. Duan, D. L. Chen, Z. C. Hao, and L. Cuo, 2013: Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau. J. Climate, 26, 3187–3208, https://doi.org/10.1175/JCLI-D-12-00321.1.
Sui, Y., D. B. Jiang, and Z. P. Tian, 2013: Latest update of the climatology and changes in the seasonal distribution of precipitation over China. Theor. Appl. Climatol., 113, 599–610, https://doi.org/10.1007/s00704-012-0810-z.
Sun, Q. H., C. Y. Miao, and Q. Y. Duan, 2015: Comparative analysis of CMIP3 and CMIP5 global climate models for simulating the daily mean, maximum, and minimum temperatures and daily precipitation over China. J. Geophys. Res., 120, 4806–4824, https://doi.org/10.1002/2014JD022994.
Tan, J. L., Z. H. Jiang, and T. T. Ma, 2016: Projections of future surface air temperature change and uncertainty over China based on the Bayesian model averaging. Acta Meteorologica Sinica, 44, 583–597, https://doi.org/10.11676/qxxb2016.044. (in Chinese with English abstract)
Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719.
Tian, D., Y. Guo, Y., and W. J. Dong, 2015: Future changes and uncertainties in temperature and precipitation over China based on CMIP5 models. Adv. Atmos. Sci., 32, 487–496, https://doi.org/10.1007/s00376-014-4102-7.
Tokarska, K. B., M. B. Stolpe, S. Sippel, E. M. Fischer, C. J. Smith, F. Lehner, and R. Knutti, 2020: Past warming trend constrains future warming in CMIP6 models. Science Advances, 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549.
Wang, Y. J., B. T. Zhou, D. H. Qin, J. Wu, R. Gao, and L. C. Song, 2017: Changes in mean and extreme temperature and precipitation over the arid region of northwestern China: Observation and projection. Adv. Atmos. Sci., 34, 289–305, https://doi.org/10.1007/s00376-016-6160-5.
Wu, J., and X. J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics, 56, 1102–1111, https://doi.org/10.6038/cjg20130406.(inChinesewithEng-lishabstract). (in Chinese with English abstract)
Wu, J., B. T. Zhou, and Y. Xu, 2015: Response of precipitation and its extremes over China to warming: CMIP5 simulation and projection. Chinese Journal of Geophysics, 58, 461–473, https://doi.org/10.1002/cjg2.20187.
Xin, X. G., T. W. Wu, J. Zhang, J. C. Yao, and Y. J. Fang, 2020: Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. International Journal of Climatology, 40, 6423–6440, https://doi.org/10.1002/joc.6590.
Xu, C. H., and Y. Xu, 2012a: The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble. Atmos. Ocean. Sci. Lett., 5, 527–533, https://doi.org/10.1080/16742834.2012.11447042.
Xu, Y., and C. H. Xu, 2012b: Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models. Atmos. Ocean. Sci. Lett., 5, 489–494, https://doi.org/10.1080/16742834.2012.11447041.
Yao, J. C., T. J. Zhou, Z. Guo, X. L. Chen, L. W. Zou, and Y. Sun, 2017: Improved performance of high-resolution atmospheric models in simulating the East Asian summer monsoon rain belt. J. Climate, 30, 8825–8840, https://doi.org/10.1175/JCLI-D-16-0372.1.
Zelinka, M. D., T. A. Myers, D. T. McCoy, S. Po-Chedley, P. M. Caldwell, P. Ceppi, S. A. Klein, and K. E. Taylor, 2020: Causes of higher climate sensitivity in CMIP6 Models. Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782.
Zhang, X. Z., X. X. Li, X. C. Xu, and L. J. Zhang, 2017: Ensemble projection of climate change scenarios of China in the 21st century based on the preferred climate models. Acta Geographica Sinica, 22, 1555–1568, https://doi.org/10.11821/dlxb201709002. (in Chinese with English abstract)
Zhou, B. T., H. Q. Z. Wen, Y. Xu, L. C. Song, and X. B. Zhang, 2014: Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Climate, 27, 6591–6611, https://doi.org/10.1175/JCLI-D-13-00761.1.
Zhou, B. T., Y. Xu, and Y. Shi, 2018a: Present and future connection of Asian-Pacific Oscillation to large-scale atmospheric circulations and East Asian rainfall: Results of CMIP5. Climate Dyn., 50, 17–29, https://doi.org/10.1007/s00382-017-3579-z.
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., 9, 884–895, https://doi.org/10.1007/s13351-015-5036-4.
Zhou, T. J., and Coauthors, 2018b: A review of East Asian summer monsoon simulation and projection: Achievements and problems, opportunities and challenges. Chinese Journal of Atmospheric Sciences, 22, 902–934, https://doi.org/10.3878/j.issn.1006-9895.1802.17306. (in Chinese with English abstract)
Zhou, T. J., L. W. Zou, and X. L. Chen, 2019: Commentary on the Coupled Model Intercomparison Project Phase 6 (CMIP6). Climate Change Research, 11, 445–456, https://doi.org/10.12006/j.issn.1673-1719.2019.193. (in Chinese with English abstract)
Zhu, H. H., Z. H. Jiang, J. Li, W. Li, C. X. Sun, and L. Li, 2020: Does CMIP6 inspire more confidence in simulating climate extremes over China? Adv. Atmos. Sci., 37, 1119–1132, https://doi.org/10.1007/s00376-020-9289-1.
Acknowledgements
We 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. This research was jointly supported by the National Key Research and Development Program of China (2018YFA0606301) and the National Natural Science Foundation of China (42025502, 41991285, 42088101).
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Article Highlights
• Most CMIP6 models perform reasonably well in reproducing the spatial patterns and interannual variability of annual temperature and precipitation.
• BMME outperforms AMME for simulating annual and winter temperature and precipitation, particularly in subregions with complex terrain.
• BMME projects lower (higher) increases in annual temperature (precipitation) compared to the AMME projection over subregions with large changes.
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Yang, X., Zhou, B., Xu, Y. et al. CMIP6 Evaluation and Projection of Temperature and Precipitation over China. Adv. Atmos. Sci. 38, 817–830 (2021). https://doi.org/10.1007/s00376-021-0351-4
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DOI: https://doi.org/10.1007/s00376-021-0351-4