Advanced Search
Article Contents

CMIP6 Evaluation and Projection of Temperature and Precipitation over China

Fund Project:

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)


doi: 10.1007/s00376-021-0351-4

  • 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。
  • 加载中
  • Figure 1.  Domains and topography (shading, units: m) of eight sub-regions in China. NEC: Northeast China; NC: North China; EC: East China; CC: Central China; NWC: Northwest China; SWC1: Tibetan Plateau; SWC2: Southwest China; SC: South China.

    Figure 2.  Spatial distributions of (a−c) observed temperature (units: °C), (d−f) AMME simulated temperature (units: °C), and (g−i) AMME simulation biases from the observation (simulation minus observation, units: °C) for the period 1995–2014. The panels from the left to right side are for annual (ANN), winter (DJF), and summer (JJA), respectively. The black lines in (g)−(i) show the boundary of subregions. Note that the scales of color bars are different.

    Figure 3.  Spatial distributions of (a−c) observed precipitation (units: mm), (d−f) AMME simulated precipitation (units: mm), and (g−i) AMME simulation biases from the observation ((simulation minus observation)/observation, units: %) for the period 1995–2014. The panels from the left to right side are for annual (ANN), winter (DJF), and summer (JJA), respectively. The black lines in (g)−(i) show the boundary of subregions. Note that the scales of color bars are different.

    Figure 4.  Taylor diagrams of (a) annual (ANN), (b) winter (DJF), and (c) summer (JJA) temperature (red dots; units: °C) and precipitation (blue dots; units: mm) over China for the period 1995–2014. The black dot in each panel represents AMME.

    Figure 5.  Interannual variability skill score (IVS) of the CMIP6 models for annual (ANN), winter (DJF), and summer (JJA) (a) temperature and (b) precipitation over China. Note that the IVS for winter precipitation is divided by 10.

    Figure 6.  Portrait diagram of the rankings of model performance for annual (ANN), winter (DJF), and summer (JJA) (a) temperature (units: °C) and (b) precipitation (units: mm). The colors in the label bar indicate the rankings. A smaller ranking number indicates a better model performance. Columns from the left to the right side in each group show the rankings of the SCC, ratio of variances, and RMSE, mean rankings of the three factors in the Taylor diagram, and IVS rankings, respectively.

    Figure 7.  Spatial distributions of (a, c) BMME and (b, d) WMME simulation biases for annual (a, b) temperature (simulation minus observation, units: °C) and (c, d) precipitation [(simulation minus observation)/observation, units: %]. The black lines show the boundary of subregions.

    Figure 8.  Biases of the BMME, AMME, and WMME simulations for annual (ANN), winter (DJF), and summer (JJA) temperature (left panel, units: °C) and precipitation (right panel, units: %) in eight subregions of China. Boxes indicate the range of biases from the ensemble models and the black lines show the ensemble mean values. Note that the vertical scales are different.

    Figure 9.  Time series of annual (a) temperature (units: °C) and (b) precipitation (units: %) anomalies (relative to 1995–2014) over China for the observation (green), historical simulation (black), SSP245 (red), and SSP585 (blue). Solid, dashed, and dotted lines indicate the BMME, AMME, and WMME simulations, respectively. The shadings show the AMME ensemble spread. The time series are smoothed with a 20-yr running mean filter.

    Figure 10.  Projected changes in annual temperature (units: °C) under (a–c) SSP245 and (d–f) SSP585 over the period 2081–2100 relative to the reference period 1995–2014 from (a, d) BMME, (b, e) AMME, and (c, f) WMME. Black solid dots indicate those grids with statistically significant changes at the 95% level. The values in the lower-left corners represent the changes averaged over China.

    Figure 11.  Projected changes in (a) annual (ANN), (b) winter (DJF), and (c) summer (JJA) temperature (units: °C) under SSP245 and SSP585 over the period 2081–2100 relative to the reference period 1995–2014. Boxes indicate the interquartile model spread (i.e., 25th and 75th quantiles), horizontal lines indicate the AMME values, and whiskers show the AMME ensemble ranges. Black rectangles and blue solid dots represent the BMME and WMME values, respectively.

    Figure 12.  Same as in Fig. 10, but for annual precipitation (units: %).

    Figure 13.  Same as in Fig. 11, but for precipitation (units: %). Note that the vertical scales are different.

    Table 1.  Basic information for the CMIP6 models used in this study.

    IDModel nameInstitution and countryAtmospheric resolution (lon×lat: number of grids, L: vertical levels)
    1ACCESS-CM2Commonwealth Scientific and Industrial Research Organization, Australian Research Council Centre of Excellence for Climate System Science, Australia192×144, L85
    2ACCESS-ESM1-5Commonwealth Scientific and Industrial Research Organization, Australia192×145, L38
    3BCC-CSM2-MRBeijing Climate Center, China320×160, L46
    4CanESM5Canadian Centre for Climate Modelling and Analysis, Canada128 × 64, L49
    5CESM2National Center for Climate Research, USA288 × 192, L32
    6CESM2-WACCMNational Center for Climate Research, USA288 × 192, L70
    7EC-Earth3EC-Earth Consortium, Europe512 × 256, L91
    8EC-Earth3-VegEC-Earth Consortium, Europe512 × 256, L91
    9FGOALS-g3Chinese Academy of Sciences, China180 × 80, L26
    10GFDL-CM4National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA288 × 180, L33
    11GFDL-ESM4National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA288 × 180, L49
    12INM-CM4-8Institute for Numerical Mathematics, Russia180 × 120, L21
    13INM-CM5-0Institute for Numerical Mathematics, Russia180 × 120, L73
    14IPSL-CM6A-LRInstitute Pierre Simon Laplace, France144 × 143, L79
    15MIROC6Atmosphere and Ocean Research Institute, The University of Tokyo, Japan256 × 128, L81
    16MPI-ESM1-2-HRMax Planck Institute for Meteorology, Germany384 × 192, L95
    17MPI-ESM1-2-LRMax Planck Institute for Meteorology, Alfred Wegener Institute, Germany192× 96, L47
    18MRI-ESM2-0Meteorological Research Institute, Japan320 ×160, L80
    19NorESM2-LMNorESM Climate modeling Consortium, Norway144 × 96, L32
    20NorESM2-MMNorESM Climate modeling Consortium, Norway288 × 192, L32
    DownLoad: CSV

    Table 2.  Highest and lowest ranking models selected for the ensembles for annual (ANN), winter (DJF), and summer (JJA) temperature and precipitation.

    ANNDJFJJA
    Highest ranked modelsLowest ranked models Highest ranked modelsLowest ranked models Highest ranked modelsLowest ranked models
    TasCESM2-WACCMCanESM5ACCESS-CM2CanESM5CESM2CanESM5
    GFDL-ESM4IPSL-CM6A-LRCESM2-WACCMIPSL-CM6A-LRCESM2-WACCMFGOALS-g3
    MPI-ESM1-2-HRMIROC6NorESM2-MMMIROC6NorESM2-MMINM-CM5-0
    PreEC-Earth3ACCESS-ESM1-5EC-Earth3MPI-ESM1-2-LRACCESS-CM2ACCESS-ESM1-5
    EC-Earth3-VegCESM2EC-Earth3-VegINM-CM4-8BCC-CSM2-MRCESM2
    MRI-ESM2-0CESM2-WACCMGFDL-CM4INM-CM5-0INM-CM4-8FGOALS-g3
    FGOALS-g3MPI-ESM1-2-LR
    DownLoad: CSV
  • 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, 65, 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, 7, 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, 74, 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.(inChinesewithEnglishabstract). (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, 72, 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., 29, 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, 42, 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, 15, 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.
  • [1] JIE Weihua, WU Tongwen, WANG Jun, LI Weijing, LIU Xiangwen, 2014: Improvement of 6-15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 293-304.  doi: 10.1007/s00376-013-3037-8
    [2] REN Guoyu, DING Yihui, ZHAO Zongci, ZHENG Jingyun, WU Tongwen, TANG Guoli, XU Ying, 2012: Recent Progress in Studies of Climate Change in China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 958-977.  doi: 10.1007/s00376-012-1200-2
    [3] WANG Shaowu, ZHU Jinhong, CAI Jingning, 2004: Interdecadal Variability of Temperature and Precipitation in China since 1880, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 307-313.  doi: 10.1007/BF02915560
    [4] SONG Lianchun, A. J. CANNON, P. H. WHITFIELD, 2007: Changes in Seasonal Patterns of Temperature and Precipitation in China During 1971--2000, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 459-473.  doi: 10.1007/s00376-007-0459-1
    [5] GE Quansheng, WANG Shaowu, WEN Xinyu, Caiming SHEN, HAO Zhixin, 2007: Temperature and Precipitation Changes in China During the HoloceneTemperature and Precipitation Changes in China During the Holocene, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 1024-1036.  doi: 10.1007/s00376-007-1024-7
    [6] Feng ZHANG, Xin-Zhong LIANG, ZENG Qingcun, Yu GU, and Shenjian SU, 2013: Cloud-Aerosol-Radiation (CAR) ensemble monitoring system: Overall accuracy and efficiency, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 955-973.  doi: 10.1007/s00376-012-2171-z
    [7] ZHANG Xinping, JIN Huijun, SUN Weizhen, 2006: Stable Isotopic Variations in Precipitation in Southwest China, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 649-658.  doi: 10.1007/s00376-006-0649-2
    [8] ZHANG Xinping, LIU Jingmiao, TIAN Lide, HE Yuanqing, YAO Tandong, 2004: Variations of 18O in Precipitation along Vapor Transport Paths, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 562-572.  doi: 10.1007/BF02915724
    [9] Reshmita NATH, Debashis NATH, Qian LI, Wen CHEN, Xuefeng CUI, 2017: Impact of Drought on Agriculture in the Indo-Gangetic Plain, India, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 335-346.  doi: 10.1007/s00376-016-6102-2
    [10] Imoleayo Gbode, Toju Babalola, Gulilat Diro, Joseph Daniel Intsiful, 2022: Assessment of ERA5 and ERA-Interim in reproducing mean and extreme climates over West Africa, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-022-2161-8
    [11] TIAN Di, GUO Yan*, DONG Wenjie, 2015: Future Changes and Uncertainties in Temperature and Precipitation over China Based on CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 487-496.  doi: 10.1007/s00376-014-4102-7
    [12] HU Yichang, HE Yong, DONG Wenjie, 2009: Changes in Temperature Extremes Based on a 6-Hourly Dataset in China from 1961--2005, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1215-1225.  doi: 10.1007/s00376-009-8140-5
    [13] Yao YAO, Dehai LUO, 2018: An Asymmetric Spatiotemporal Connection between the Euro-Atlantic Blocking within the NAO Life Cycle and European Climates, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 796-812.  doi: 10.1007/s00376-017-7128-9
    [14] LIU Ge, WU Renguang, ZHANG Yuanzhi, and NAN Sulan, 2014: The Summer Snow Cover Anomaly over the Tibetan Plateau and Its Association with Simultaneous Precipitation over the Mei-yu-Baiu region, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 755-764.  doi: 10.1007/s00376-013-3183-z
    [15] CHEN Huopo, SUN Jianqi, 2009: How the “Best” Models Project the Future Precipitation Change in China, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 773-782.  doi: 10.1007/s00376-009-8211-7
    [16] Yin ZHAO, Tianjun ZHOU, Wenxia ZHANG, Jian LI, 2022: Change in Precipitation over the Tibetan Plateau Projected by Weighted CMIP6 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1133-1150.  doi: 10.1007/s00376-022-1401-2
    [17] Li Wei, Yu Rucong, Zhang Xuehong, 2001: Impacts of Sea Surface Temperature in the Tropical Pacific on Interannual Variability of Madden-Julian Oscillation in Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 429-444.  doi: 10.1007/BF02919322
    [18] LIU Ge, WU Renguang, SUN Shuqing, WANG Huimei, 2015: Synergistic Contribution of Precipitation Anomalies over Northwestern India and the South China Sea to High Temperature over the Yangtze River Valley, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1255-1265.  doi: 10.1007/s00376-015-4280-y
    [19] Xiaying ZHU, Mingzhu YANG, Ge LIU, Yanju LIU, Weijing LI, Sulan NAN, Linhai SUN, 2022: A Precursory Signal of June–July Precipitation over the Yangtze River Basin: December–January Tropospheric Temperature over the Tibetan Plateau, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-022-2079-1
    [20] Jie ZHANG, Tongwen WU, Fang ZHANG, Kalli FURTADO, Xiaoge XIN, Xueli SHI, Jianglong LI, Min CHU, Li ZHANG, Qianxia LIU, Jinghui Yan, Min WEI, Qiang MA, 2021: BCC-ESM1 Model Datasets for the CMIP6 Aerosol Chemistry Model Intercomparison Project (AerChemMIP), ADVANCES IN ATMOSPHERIC SCIENCES, 38, 317-328.  doi: 10.1007/s00376-020-0151-2

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 16 October 2020
Manuscript revised: 23 December 2020
Manuscript accepted: 18 January 2021
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

CMIP6 Evaluation and Projection of Temperature and Precipitation over China

    Corresponding author: Botao ZHOU, zhoubt@nuist.edu.cn
  • 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 3. National Climate Center, China Meteorological Administration, Beijing 100081, China

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。

    • The Coupled Model Intercomparison Project (CMIP) has been coordinating simulations conducted by international modeling groups since the 1990s, with the aim of improving the performance of climate models and enhancing the scientific understanding of the climate system. This project has become a major tool for climate science, and remarkable progress has been achieved for global climate models (GCMs) (Eyring et al., 2016; Stouffer et al., 2017). At present, the CMIP phase 6 (CMIP6) is being carried out (Simpkins, 2017). Compared with previous phases, the number of experiments that have been designed in the CMIP6 is the largest. The physical processes of the CMIP6 models are more complicated, and their resolutions are enhanced (Eyring et al., 2016; Zhou et al., 2019). These CMIP6 simulations will support climate change research in the upcoming several years (Zhou et al., 2019).

      Temperature and precipitation are essential indicators for climate change. In the past few years, many research efforts have been devoted to model evaluations and projections of temperature and precipitation over China within the CMIP phase 5 (CMIP5) framework. The evaluations indicated that the CMIP5 models, in general, show reasonable performance in capturing the geographical distributions of surface temperature and precipitation (e.g., Xu and Xu, 2012b; Guo et al., 2013; Huang et al., 2013; Kumar et al., 2014; Sun et al., 2015; Jiang et al., 2016). Compared with CMIP phase 3 (CMIP3), the CMIP5 performances were improved for temperature while there was little performance change for precipitation (Kumar et al., 2014; Sun et al., 2015; Jiang et al., 2016). Some common biases, such as topography-related cold biases, underestimations of southeast-northwest precipitation gradients, and overestimations of the magnitudes of the interannual variability of temperature and precipitation, are also present in the CMIP5 simulations (Su et al., 2013; Chen et al., 2014; Bao and Feng, 2016; Jiang et al., 2016). For the CMIP5 projections, an overall warming of temperature and a general increase in precipitation are projected over China by the end of the 21st century under the Representative Concentration Pathways (RCPs), further noting the expectation of larger changes to occur for higher RCPs (e.g., Xu and Xu, 2012a; Su et al., 2013; Chen and Frauenfeld, 2014; Hu et al., 2015; Wu et al., 2015; Tan et al., 2016; Zhang et al., 2017).

      In general, these findings improve our knowledge of the simulation abilities of CMIP5 models and future climate changes under the RCP scenarios. Questions naturally arise regarding the performance of CMIP6 models for the climate in China and how China’s climate would change in the context of the new CMIP6 scenarios (i.e., Shared Socioeconomic Pathways, SSPs), which represents the motivation of this study. Some recent studies have begun to evaluate and project the East Asian monsoon climate using CMIP6 data (Chen et al., 2020; Ha et al., 2020; Jiang et al., 2020; Nie et al., 2020; Zhu et al., 2020; Xin et al., 2020) and have indicated a general improvement of CMIP6 models compared with CMIP5 models regarding simulations of both mean temperature and precipitation as well as extreme temperature and precipitation events. For example, a smaller spread is observed among CMIP6 models as well as a weaker cold bias of temperature and a weaker underestimation of the southeast–northwest precipitation gradient (Chen et al., 2020; Jiang et al., 2020). However, more detailed regional analysis is still needed. In addition, some previous CMIP studies have used the multimodel ensemble for projecting the climate of China (e.g., Xu and Xu, 2012a; Chen and Frauenfeld, 2014; Zhou et al., 2014; Tian et al., 2015; Wu et al., 2015; Wang et al., 2017) and some have used the optimal model ensemble (e.g., Chen and Sun, 2009, 2013; Chen et al., 2011; Hu et al., 2015; Zhou et al., 2018a; Rao et al., 2019). Determining the nature of the differences between the optimal model ensemble and the multimodel ensemble for CMIP6 projections of climate over China is also an area of concern in this study. Addressing this topic will aid in the understanding of uncertainties in the projections.

    2.   Data and methods
    • Simulation data from 20 CMIP6 models (Table 1) are used in this study. For each model, the near-surface air temperature and precipitation results from the historical simulation and the SSP245 and SSP585 experiments are employed. The SSP245 and SSP585 reflect a set of alternative futures of social development and greenhouse gas emission. The SSP245 represents the combined scenario of a moderate socio-economic development path (i.e., SSP2) with the medium-low radiation forcing which peaks at 4.5 W m−2 by 2100. The SSP585 represents the combined scenario of a high energy-intensive, socio-economic developmental path (i.e., SSP5) with strong radiative forcing which peaks at 8.5 W m−2 by 2100 (O'Neill et al., 2016; Riahi et al., 2017).

      IDModel nameInstitution and countryAtmospheric resolution (lon×lat: number of grids, L: vertical levels)
      1ACCESS-CM2Commonwealth Scientific and Industrial Research Organization, Australian Research Council Centre of Excellence for Climate System Science, Australia192×144, L85
      2ACCESS-ESM1-5Commonwealth Scientific and Industrial Research Organization, Australia192×145, L38
      3BCC-CSM2-MRBeijing Climate Center, China320×160, L46
      4CanESM5Canadian Centre for Climate Modelling and Analysis, Canada128 × 64, L49
      5CESM2National Center for Climate Research, USA288 × 192, L32
      6CESM2-WACCMNational Center for Climate Research, USA288 × 192, L70
      7EC-Earth3EC-Earth Consortium, Europe512 × 256, L91
      8EC-Earth3-VegEC-Earth Consortium, Europe512 × 256, L91
      9FGOALS-g3Chinese Academy of Sciences, China180 × 80, L26
      10GFDL-CM4National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA288 × 180, L33
      11GFDL-ESM4National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA288 × 180, L49
      12INM-CM4-8Institute for Numerical Mathematics, Russia180 × 120, L21
      13INM-CM5-0Institute for Numerical Mathematics, Russia180 × 120, L73
      14IPSL-CM6A-LRInstitute Pierre Simon Laplace, France144 × 143, L79
      15MIROC6Atmosphere and Ocean Research Institute, The University of Tokyo, Japan256 × 128, L81
      16MPI-ESM1-2-HRMax Planck Institute for Meteorology, Germany384 × 192, L95
      17MPI-ESM1-2-LRMax Planck Institute for Meteorology, Alfred Wegener Institute, Germany192× 96, L47
      18MRI-ESM2-0Meteorological Research Institute, Japan320 ×160, L80
      19NorESM2-LMNorESM Climate modeling Consortium, Norway144 × 96, L32
      20NorESM2-MMNorESM Climate modeling Consortium, Norway288 × 192, L32

      Table 1.  Basic information for the CMIP6 models used in this study.

      The observed temperature and precipitation data of CN05.1 with a resolution of 0.25°×0.25° (Wu and Gao, 2013) are used to validate the performance of the CMIP6 models. For convenience, all data are converted to the same 1°×1° grid using a bilinear interpolation scheme before analysis. As recommended by the CMIP6, the period 1995–2014 is used as the reference period for the evaluation and projection. The ensemble in this study is calculated with the same weight. The statistical significance is examined by the Student’s t-test.

      A Taylor diagram (Taylor, 2001) is used to evaluate spatial distributions of temperature and precipitation over China. This diagram provides a concise statistical summary of how well a simulated pattern matches an observed pattern in terms of the spatial correlation coefficient (SCC), the root-mean-square error (RMSE), and the ratio of variances. The interannual variability of the simulations relative to the observations is assessed by the interannual variability skill score (IVS) (Gleckler et al., 2008; Scherrer, 2011), which is calculated as

      where STDm and STDo are the standard deviations of the simulation and observation, respectively. IVS is a symmetric variability statistic that is used to measure the similarity of interannual variation between the simulation and observation. A smaller IVS value indicates a better simulation of interannual variability.

      To quantitatively examine regional differences, following Zhou et al. (2014), we divide China into eight subregions: Northeast China (NEC; 39°–54°N, 119°–134°E), North China (NC; 36°–46°N, 111°–119°E), East China (EC; 27°–36°N, 116°–122°E), Central China (CC; 27°–36°N, 106°–116°E), Northwest China (NWC; 36°–46°N, 75°–111°E), Tibetan Plateau (SWC1; 27°–36°N, 77°–106°E), Southwest China (SWC2; 22°–27°N, 98°–106°E), and South China (SC; 20°–27°N, 106°–120°E) (see Fig. 1), all of which are based on administrative boundaries and societal and geographical conditions (National Report Committee, 2007).

      Figure 1.  Domains and topography (shading, units: m) of eight sub-regions in China. NEC: Northeast China; NC: North China; EC: East China; CC: Central China; NWC: Northwest China; SWC1: Tibetan Plateau; SWC2: Southwest China; SC: South China.

    3.   Evaluations
    • Figures 2af show the climatological spatial distributions of annual, winter (December to February, DJF), and summer (June to August, JJA) temperatures from observations and the ensemble simulation of all models (AMME), respectively. In general, the AMME simulated spatial patterns approximate those of the observations. However, relative to the observations, a general underestimation of annual temperature is noted over most of China in the AMME simulation. The most pronounced cold bias is located in the Tibetan Plateau (Fig. 2g). This phenomenon was also present in the CMIP3 and CMIP5 simulations as revealed by previous studies (Jiang et al., 2005; Xu and Xu, 2012a; Jiang et al., 2016). For winter (Fig. 2h) and summer (Fig. 2i) temperatures, there are notable warm biases in parts of northern China, in addition to the cold bias in the Tibetan Plateau.

      Figure 2.  Spatial distributions of (a−c) observed temperature (units: °C), (d−f) AMME simulated temperature (units: °C), and (g−i) AMME simulation biases from the observation (simulation minus observation, units: °C) for the period 1995–2014. The panels from the left to right side are for annual (ANN), winter (DJF), and summer (JJA), respectively. The black lines in (g)−(i) show the boundary of subregions. Note that the scales of color bars are different.

      For observed precipitation (Figs. 3ac), the annual, winter, and summer precipitation amounts decrease from the southeast coast to the northwest areas. These spatial patterns are captured by the AMME simulation (Figs. 3df) but with overall wet biases (Figs. 3gi). The wet bias for annual precipitation appears in most parts of northern and western China, particularly on the northern and southern flanks of the Tibetan Plateau (Fig. 3g), which was also reported for the CMIP3 and CMIP5 simulations (Jiang et al., 2005; Xu and Xu, 2012a; Jiang et al., 2016). Compared with the CMIP5, the wet bias in the CMIP6 models was observed to be smaller (Jiang et al., 2020; Zhu et al., 2020). The spatial distributions of wet biases for winter precipitation resemble that for annual precipitation, but with larger bias magnitudes (Fig. 3h). Besides the wet bias, dry biases are also notable for summer precipitation in parts of Northwest China and East China (Fig. 3i).

      Figure 3.  Spatial distributions of (a−c) observed precipitation (units: mm), (d−f) AMME simulated precipitation (units: mm), and (g−i) AMME simulation biases from the observation ((simulation minus observation)/observation, units: %) for the period 1995–2014. The panels from the left to right side are for annual (ANN), winter (DJF), and summer (JJA), respectively. The black lines in (g)−(i) show the boundary of subregions. Note that the scales of color bars are different.

      Figure 4 shows the Taylor diagrams for annual, winter, and summer temperature and precipitation over China as simulated by the 20 CMIP6 models and AMME against the observations. The azimuthal position of the model point indicates the SCC between the simulated and observed patterns. The distance from the reference point (REF) to the model point indicates the normalized RMSE of the simulation relative to the observation. The radial distance from the origin to the model point indicates the ratio of standard deviations between the simulation and observation. The overall model biases are excluded in this diagram. Clearly, the CMIP6 models show better performance for temperature than for precipitation. For temperature, regardless of whether winter, summer, or annual mean values are used, the SCCs between the simulations and observations are all greater than 0.9, the RMSEs of the simulations relative to the observations are generally below 0.5, and the ratios of variances to the observations are close to 1 for most models. These results indicate that the CMIP6 models effectively capture the climatological distributions in terms of annual, summer, and winter temperatures.

      Figure 4.  Taylor diagrams of (a) annual (ANN), (b) winter (DJF), and (c) summer (JJA) temperature (red dots; units: °C) and precipitation (blue dots; units: mm) over China for the period 1995–2014. The black dot in each panel represents AMME.

      Compared with temperature, the SCCs for precipitation over China are relatively lower and the RMSEs are relatively higher. Specifically, the SCCs and RMSEs are mainly in the range of 0.6–0.9 (still statistically significant) and 0.5–1, respectively. In addition, the ratios of variances mostly lie between 1 and 1.5. Overall, the simulations of most models are reliable for the spatial patterns of annual, summer, and winter precipitation, although the variances are overestimated.

      Figure 5 presents the IVS values of the simulations for the interannual variability of annual, winter, and summer temperature and precipitation over China. In this study, the IVS values were first calculated in each grid of China and then averaged. For temperature (Fig. 5a), the IVS values are below 1.5 for all models except for CanESM5 which shows a value of 4.0 in summer. This suggests that the CMIP6 models can reasonably reproduce the observed interannual variability of annual, winter, and summer temperature. In comparison, the model performances for the interannual variability of annual and winter temperatures are better than their performances in summer. For precipitation (Fig. 5b), though the IVS values are larger than those for temperature, the relatively low IVS values in annual mean and summer imply a reasonable reproduction of the observed interannual variability by the CMIP6 models. It also reflects the dominant contribution of summer precipitation to annual precipitation (Sui et al., 2013). There is a large range for the winter IVS values, which vary from 7.1 to 62.9 and are much larger than those of annual mean and summer. This result indicates large inconsistencies among the models and poor simulations for the interannual variability of winter precipitation.

      Figure 5.  Interannual variability skill score (IVS) of the CMIP6 models for annual (ANN), winter (DJF), and summer (JJA) (a) temperature and (b) precipitation over China. Note that the IVS for winter precipitation is divided by 10.

      According to Gleckler et al. (2008), the rankings for all models that considered the three factors of the Taylor diagram and the interannual variability skill score are summarized in Fig. 6. This figure depicts the overall performance of individual models. A smaller ranking value indicates a better performing model. The rankings for the Taylor diagram are the average of the rankings of SCC, RMSE, and ratio of variance. On the whole, the AMME outperforms its ensemble members in a comprehensive manner. For a given individual model, the performance ranks are somewhat different for different metrics. Considering the comprehensive performance for both spatial patterns and IVS, the relatively “highest-ranked” and “lowest-ranked” models are selected based on Fig. 6 and listed in Table 2. For these “highest-ranked” and “lowest-ranked” models, their comprehensive performances (arithmetic average of the rankings for Taylor Diagram and IVS) rank in the top three and bottom three among all models, respectively. Note that ACCESS-ESM1-5 and CESM2-WACCM (ACCESS-ESM1-5 and CESM2) show the same ranking for annual (summer) precipitation.

      Figure 6.  Portrait diagram of the rankings of model performance for annual (ANN), winter (DJF), and summer (JJA) (a) temperature (units: °C) and (b) precipitation (units: mm). The colors in the label bar indicate the rankings. A smaller ranking number indicates a better model performance. Columns from the left to the right side in each group show the rankings of the SCC, ratio of variances, and RMSE, mean rankings of the three factors in the Taylor diagram, and IVS rankings, respectively.

      ANNDJFJJA
      Highest ranked modelsLowest ranked models Highest ranked modelsLowest ranked models Highest ranked modelsLowest ranked models
      TasCESM2-WACCMCanESM5ACCESS-CM2CanESM5CESM2CanESM5
      GFDL-ESM4IPSL-CM6A-LRCESM2-WACCMIPSL-CM6A-LRCESM2-WACCMFGOALS-g3
      MPI-ESM1-2-HRMIROC6NorESM2-MMMIROC6NorESM2-MMINM-CM5-0
      PreEC-Earth3ACCESS-ESM1-5EC-Earth3MPI-ESM1-2-LRACCESS-CM2ACCESS-ESM1-5
      EC-Earth3-VegCESM2EC-Earth3-VegINM-CM4-8BCC-CSM2-MRCESM2
      MRI-ESM2-0CESM2-WACCMGFDL-CM4INM-CM5-0INM-CM4-8FGOALS-g3
      FGOALS-g3MPI-ESM1-2-LR

      Table 2.  Highest and lowest ranking models selected for the ensembles for annual (ANN), winter (DJF), and summer (JJA) temperature and precipitation.

      Some studies have shown that increasing the model resolution is an effective way to improve the performance of model simulations (Yao et al., 2017; Zhou et al., 2018b; Bador et al., 2020), thus we examine the relationships between the model performances and resolutions. The analyses show that the comprehensive performances of the models and their resolutions are significantly correlated. The correlation coefficients are 0.50 and 0.81 for annual and summer temperatures, respectively. The comprehensive performance of the models for winter precipitation also show a significant correlation of 0.65 with their resolutions, which is consistent with the previous finding that model resolution influences the simulation of winter precipitation in China (Gao et al., 2006; Jiang et al., 2016, 2020).

    • Figure 7 shows the spatial distributions of the biases from the “highest-ranked” model ensemble (hereafter BMME) and the “lowest-ranked” model ensemble (hereafter WMME) for annual temperature and precipitation. Compared with the AMME simulation (Fig. 2g), the cold bias over the Tibetan Plateau is reduced in the BMME simulation (Fig. 7a) and augmented in the WMME simulation (Fig. 7b). The regionally averaged BMME, AMME, and WMME biases in SWC1 are −1.3°C, −2.0°C, and −4.3°C, respectively (Fig. 8a). From a seasonal perspective, the performance of the BMME for winter temperature is better than that of the AMME and WMME simulations over SWC1, CC, EC, SC, and SWC2 (Fig. 8c). However, due to an overall warm bias, the BMME does not perform better than the AMME in simulating summer temperature but does indicate a smaller spread (Fig. 8e).

      Figure 7.  Spatial distributions of (a, c) BMME and (b, d) WMME simulation biases for annual (a, b) temperature (simulation minus observation, units: °C) and (c, d) precipitation [(simulation minus observation)/observation, units: %]. The black lines show the boundary of subregions.

      Figure 8.  Biases of the BMME, AMME, and WMME simulations for annual (ANN), winter (DJF), and summer (JJA) temperature (left panel, units: °C) and precipitation (right panel, units: %) in eight subregions of China. Boxes indicate the range of biases from the ensemble models and the black lines show the ensemble mean values. Note that the vertical scales are different.

      For annual precipitation, the wet biases in the AMME simulation (Fig. 3g) decrease in the BMME simulation (Fig. 7c) and increase in the WMME simulation (Fig. 7d). When regionally averaged, the percentage-based wet biases over NWC, SWC1, NC, and NEC are 199%, 191%, 45%, and 28% respectively for the WMME simulation. These decrease to 136%, 147%, 40%, and 32% for the AMME simulation; the wet biases further reduce to 39%, 96%, 4%, and 23% in the BMME simulation, respectively (Fig. 8b). Similar results are obtained for the simulation of winter precipitation (Fig. 8d). Nevertheless, there is no improvement in the BMME simulation for summer precipitation over subregions except for EC, NWC, and NEC when compared to the AMME and WMME simulations, although the model spread is reduced.

      In short, the BMME generally shows better performance than the AMME and WMME in reproducing the spatial patterns of annual and winter temperature and precipitation, particularly in subregions with complex terrain. Similarly, regardless of whether for annual, winter, or summer temperature (precipitation), the BMME presents the smallest IVS values, followed by the AMME and then by the WMME. The IVS values for annual, winter, and summer temperature (precipitation) over China are 0.1 (0.9), 0.2 (8.3), and 0.3 (1.0) from the BMME simulation, 0.2 (1.4), 0.3 (22.2), and 0.5 (1.0) from the AMME simulation, and 0.6 (2.4), 0.7 (43.4), and 1.1 (1.4) from the WMME simulation, respectively.

    4.   Projected Changes
    • Figure 9 displays the temporal evolution of the projected changes in annual temperature and precipitation over China under SSP245 and SSP585 from the AMME, BMME, and WMME. Similar to the CMIP5 projection (e.g., Xu and Xu, 2012a; Chen and Sun, 2013; Tian et al., 2015; Tan et al., 2016), an increasing trend toward the end of the 21st century is projected for annual temperature and precipitation, with larger increases under SSP585 than under SSP245. Relative to the reference period of 1995–2014, the increases in annual temperature (precipitation) by the end of the 21st century that are projected by the AMME are 2.7°C (17%) under SSP245 and 5.4°C (30%) under SSP585. Compared with the AMME projection, the BMME (WMME) projects a smaller (larger) increase in temperature and a larger (smaller) increase in precipitation under each scenario. At the end of the 21st century under SSP245 and SSP585, the BMME projected increases in annual temperature (precipitation) are 2.4°C and 4.8°C (24% and 45%), respectively, and those projected by the WMME are 3.3°C and 7.0°C (13% and 25%), respectively.

      Figure 9.  Time series of annual (a) temperature (units: °C) and (b) precipitation (units: %) anomalies (relative to 1995–2014) over China for the observation (green), historical simulation (black), SSP245 (red), and SSP585 (blue). Solid, dashed, and dotted lines indicate the BMME, AMME, and WMME simulations, respectively. The shadings show the AMME ensemble spread. The time series are smoothed with a 20-yr running mean filter.

      Figure 10 further illustrates the spatial distributions of the projected changes in annual temperature under SSP245 and SSP585 by the end of the 21st century. The annual temperature is projected to increase across China, with much stronger warming under SSP585. The greatest warming under SSP585 occurs at higher latitudes and higher elevations of China such as in NEC, NWC, and SWC1. In contrast, the warming is comparable across subregions under SSP245. Of particular interest, the warming magnitudes under both scenarios gradually increase from the BMME projection to the AMME projection and then to the WMME projection, which may be associated with different climate sensitivities in the models and/or regional land-atmosphere feedbacks (Zhou and Chen, 2015; Tokarska et al., 2020; Zelinka, et al., 2020). When regionally averaged over subregions (Fig. 11a), the BMME projects an increase of 2.1°C (SWC2) to 2.6°C (NC) under SSP245. The increases projected by the AMME range from 2.3°C (SWC2) to 2.9°C (NEC), which are slightly higher than the BMME projection in each subregion. Those projected by the WMME are in a range of 2.5°C (SC) to 3.7°C (SWC1), which are the highest among the three ensemble projections. Under SSP585, the greatest changes that exceed 5°C as projected by the AMME, occur in NEC (5.8°C), NWC (5.7°C), NC (5.4°C), and SWC1 (5.4°C). The magnitudes of increase over these subregions decrease to 5.2°C, 5.0°C, 4.9°C, and 4.8°C, respectively, in the BMME projection, while they increase to 7.6°C, 7.6°C, 7.1°C, and 7.4°C in the WMME projection, respectively. From a seasonal perspective, the AMME and WMME generally project larger increases in winter temperature than in summer temperature by the end of the 21st century under SSP585. The BMME projected increases in winter temperature are also larger than those of summer temperature in NEC, SWC1, SWC2, and SC, but it is reversed in NWC, NC, CC, and EC (Figs. 11b, c). In addition, the BMME projected increases in winter temperature are generally higher than the AMME projection and lower than the WMME projection (Fig. 11b); the increases in summer temperature from the BMME projection are larger than the AMME and WMME projections (Fig. 11c). Moreover, the spreads of the projections for annual, winter, and summer temperature from the BMME ensemble members are narrowed, compared with the projections from the AMME and WMME ensemble members (Figure not shown).

      Figure 10.  Projected changes in annual temperature (units: °C) under (a–c) SSP245 and (d–f) SSP585 over the period 2081–2100 relative to the reference period 1995–2014 from (a, d) BMME, (b, e) AMME, and (c, f) WMME. Black solid dots indicate those grids with statistically significant changes at the 95% level. The values in the lower-left corners represent the changes averaged over China.

      Figure 11.  Projected changes in (a) annual (ANN), (b) winter (DJF), and (c) summer (JJA) temperature (units: °C) under SSP245 and SSP585 over the period 2081–2100 relative to the reference period 1995–2014. Boxes indicate the interquartile model spread (i.e., 25th and 75th quantiles), horizontal lines indicate the AMME values, and whiskers show the AMME ensemble ranges. Black rectangles and blue solid dots represent the BMME and WMME values, respectively.

      The spatial distributions of the projected percentage changes for annual precipitation under SSP245 and SSP585 are shown in Fig. 12. By the end of the 21st century, the annual precipitation is projected to increase uniformly across the country, and the magnitude of the increase is greater under SSP585 than under SSP245. Moreover, the projected percentage increases are larger in northern China than in southern China. The largest increase is anticipated over western China due to the drier climate toward the north and northwest areas, which is consistent with the CMIP5 projection (Zhou et al., 2014). Although the three ensembles show resemblances in their spatial distributions for the projected changes, salient differences exist in the magnitudes among the projections. For instance, compared with the AMME projection under SSP585 (Fig. 12e), the percentage increases of annual precipitation over northern and western China are enhanced in the BMME projection, whereas they are reduced in the WMME projection. There are no obvious differences among the three ensembles for the precipitation projections over eastern China.

      Figure 12.  Same as in Fig. 10, but for annual precipitation (units: %).

      Figure 13a summarizes the projected percentage changes in annual precipitation over subregions. Under SSP585, annual precipitation amounts, as projected by the BMME in NWC, NC, NEC, and SWC1 (i.e., northern and western regions of China) are expected to increase by 81%, 43%, 35%, and 32% at the end of the 21st century, respectively, which are larger than those of the AMME projection (45%, 30%, 25%, 29%, respectively) and the WMME projection (33%, 21%, 20%, 29%, respectively). Note that the historical simulation of the BMME shows the smallest biases from the observation in these subregions (Fig. 8b). The case for the SSP245 scenario is generally similar but with smaller magnitudes of percentage increase. Seasonal changes in precipitation in winter (Fig. 13b) and summer (Fig. 13c), as projected from the three ensembles under SSP585, generally approximate those of annual precipitation in the above subregions. We also notice that the magnitudes of percentage increases are much more pronounced in winter than in summer. For example, in the NWC, NC, and NEC subregions, the increases of winter precipitation from the BMME projection are 262%, 145%, and 106%, compared with 28%, 30%, and 29% increases for summer, respectively. This result suggests a relatively larger contribution to annual precipitation change from the increased winter precipitation. However, because of the different climatology of seasonal precipitation, it may not necessarily reflect the absolute changes in precipitation values. A similar seasonal change but with smaller magnitudes of percentage increase is noted in CC. The opposite seasonal pattern is projected in SC, where the projected percentage increases in summer are larger than those in winter. The cases for other subregions (e.g., SWC2 and EC) are diverse for the three ensemble projections.

      Figure 13.  Same as in Fig. 11, but for precipitation (units: %). Note that the vertical scales are different.

    5.   Conclusion
    • In this study, we evaluated the performance of 20 CMIP6 models in simulating temperature and precipitation over China from the perspective of spatial pattern and interannual variability. Generally, the CMIP6 models show a good ability to capture the climatological distributions of temperature and precipitation, with better performance for temperature than for precipitation. The interannual variability of temperature and precipitation can be reasonably reproduced by most models, although poor performance is shown for that of winter precipitation. Comparative analysis conducted by Jiang et al. (2020) also indicated that the performances have improved from CMIP5 to CMIP6 for climatological temperature and precipitation, but show little improvement for their interannual variability. Based upon the comprehensive model performance regarding both spatial patterns and interannual variability, the ensembles of the “highest-ranked” models (BMME), the “lowest-ranked” models (WMME), and all models (AMME) are determined. It is worth noting that the relevant model rankings could differ for other applications (e.g., climate extremes). The differences among the aforementioned three ensembles for simulations and projections of temperature and precipitation over China are examined. The main findings are summarized below:

      (1) The BMME outperforms the AMME and WMME for the simulation of annual and winter temperature and precipitation, particularly in those subregions with complex terrain. Nevertheless, there is no salient improvement for summer temperature or precipitation over most of the subregions.

      (2) The three ensembles all project increased temperature and precipitation over China by the end of the 21st century, accompanied by larger increases under SSP585 than under SSP245. For the SSP585 scenario, the greatest warming is projected to occur at higher latitudes and at higher elevations of China (such as in NEC, NWC, and SWC1). The largest percentage-based increase in annual precipitation is anticipated in northern and western China.

      (3) The three ensembles project different magnitudes of increase in temperature and precipitation. By the end of the 21st century under SSP585, the warming of annual temperature is the lowest in the BMME projection, which increases successively in the AMME and WMME projections. The BMME projected percentage increases in annual, summer, and winter precipitation over northern and western China are larger than those projected from the AMME and WMME.

      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).

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return