Abstract
Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during 1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), and 30 models from phase 5 of CMIP (CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean (CMIP6-MME) can simulate well the spatial pattern of annual mean temperature, maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The areal-mean bias for total precipitation has been reduced from 127% (CMIP5-MME) to 79% (CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.
摘要
基于高分辨率的中国区域1961-2005年逐日观测资料,以及参与第6次耦合模式比较计划(CMIP6)的12个气候模式和第5次(CMIP5)的30个模式的结果,评估了模式对中国区域极端温度空间分布和年际变率的模拟能力。结果发现,CMIP6多模式集合平均(CMIP6-MME)能很好地模拟年平均温度、日最高气温最大值和日最低气温最小值的空间分布。但是很难再现冷夜和暖日的空间分布,且在青藏高原上存在很大的冷偏差。对极端降水的模拟性能通常低于极端温度。与CMIP5相比,CMIP6模式对中国区域极端气候的模拟能力得到了一定程度的改善。尤其是极端降水的气候态和年际变率都改善明显。比如,湿日总降水量区域平均的偏差从127% (CMIP5-MME)降低到79% (CMIP6-MME)。其中,最为显着的改善是,在CMIP5-MME中持续且普遍存在的中国南方降水的干偏差,在CMIP6-MME中显著减少。更强的上升运动和更充足的水汽输送可以解释CMIP6中干偏差的减小。青藏高原东部湿日总降水量、强降水和降水强度的湿偏差在CMIP6-MME中仍然存在,但其偏差小于CMIP5-MME。
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References
Akinsanola, A., and W. Zhou, 2019: Projections of West African summer monsoon rainfall extremes from two CORDEX models. Climate Dyn., 52, 2017–2028, https://doi.org/10.1007/s00382-018-4238-8.
Cao, J., and Coauthors, 2018: The NUIST Earth System Model (NESM) version 3: Description and preliminary evaluation. Geoscientific Model Development, 11, 2975–2993, https://doi.org/10.5194/gmd-11-2975-2018.
Chen, H. P., and J. Q. Sun, 2014: Robustness of precipitation projections in China: Comparison between CMIP5 and CMIP3 models. Atmos. Ocean. Sci. Lett., 7, 67–73, https://doi.org/10.3878/j.issn.1674-2834.13.0071.
Chen, H. P., and J. Q. Sun, 2015: Assessing model performance of climate extremes in China: An intercomparison between CMIP5 and CMIP3. Climatic Change, 129, 197–211, https://doi.org/10.1007/s10584-014-1319-5.
Chen, L., and O. W. Frauenfeld, 2014: A comprehensive evaluation of precipitation simulations over China based on CMIP5 multimodel ensemble projections. J. Geophys. Res.: Atmos., 119, 5767–5786, https://doi.org/10.1002/2013JD021190.
Chen, L., Z. G. Ma, and X. G. Fan, 2012: A comparative study of two land surface schemes in WRF model over Eastern China. Journal of Tropical Meteorology, 18, 445–456.
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.1773-1719.2014.03.011. (in Chinese with English abstract)
Committee of the Third China’s National Assessment Report on Climate Change, 2015: The Third China’s National Assessment Report on Climate Change. Science Press. (in Chinese)
Dong, S. Y., Y. Xu, B. T. Zhou, and Y. Shi, 2015: Assessment of indices of temperature extremes simulated by multiple CMIP5 models over China. Adv. Atmos. Sci., 32, 1077–1091, https://doi.org/10.1007/s00376-015-4152-5.
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-9-1377-2016.
Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 741–866.
Frich, P., L. V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. M. G. Klein Tank, and T. Peterson, 2002: Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research, 19, 193–212, https://doi.org/10.3354/cr019193.
Gao, X., Y. Shi, R. Song, F. Giorgi, Y. Wang, and D. Zhang, 2008: Reduction of future monsoon precipitation over China: Comparison between a high resolution RCM simulation and the driving GCM. Meteorol. Atmos. Phys., 100, 73–86, https://doi.org/10.1007/s00703-008-0296-5.
Gao, Y., H. J. Wang, and D. B. Jiang, 2015: An intercomparison of CMIP5 and CMIP3 models for interannual variability of summer precipitation in Pan — Asian monsoon region. International Journal of Climatology, 35, 3770–3780, https://doi.org/10.1002/joc.4245.
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)
Gusain, A., S. Ghosh, and S. Karmakar, 2020: Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmospheric Research, 232, 104680, https://doi.org/10.1016/j.atmosres.2019.104680.
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, Solomon et al., Eds., IPCC Fourth Assessment Report. Climate Change 2007, Working Group I Report “The Physical Science Basis”, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.
IPCC, 2012: Changes in climate extremes and their impacts on the natural physical environment. 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, S. I. N. Nicholls, et al., Eds., Cambridge University Press, 109–230.
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, et al., Eds., Cambridge University Press.
Jiang, D. B., Y. Zhang, and J. Q. Sun, 2009: Ensemble projection of 1–3°C warming in China. Chinese Science Bulletin, 54, 3326–3334, https://doi.org/10.1007/s11434-009-0313-1.
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, Z. H., J. Song, L. Li, W. L. Chen, Z. F. Wang, and J. Wang, 2012: Extreme climate events in China: IPCC-AR4 model evaluation and projection. Climatic Change, 110, 385–401, https://doi.org/10.1007/s10584-011-0090-0.
Jiang, Z. H., W. Li, J. J. Xu, and L. Li, 2015: Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. J. Climate, 28, 8603–8619, https://doi.org/10.1175/JCLI-D-15-0099.1.
Kawai, H., S. Yukimoto, T. Koshiro, N. Oshima, T. Tanaka, H. Yoshimura, and R. Nagasawa, 2019: Significant improvement of cloud representation in the global climate model MRI-ESM2. Geoscientific Model Development, 12, 2875–2897, https://doi.org/10.5194/gmd-12-2875-2019.
Koutroulis, A. G., M. G. Grillakis, I. K. Tsanis, and L. Papadimitriou, 2016: Evaluation of precipitation and temperature simulation performance of the CMIP3 and CMIP5 historical experiments. Climate Dyn., 47, 1881–1898, https://doi.org/10.1007/s00382-015-2938-x.
Kusunoki, S., and O. Arakawa, 2015: Are CMIP5 models better than CMIP3 models in simulating precipitation over East Asia? J. Climate, 28, 5601–5621, https://doi.org/10.1175/JCLI-D-14-00585.1.
Li, F. Y., D. Rosa, W. D. Collins, and M. F. Wehner, 2012: “Super — parameterization”: A better way to simulate regional extreme precipitation? Journal of Advances in Modeling Earth Systems, 4, M04002, https://doi.org/10.1029/2011MS000106.
Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteorol. Soc., 88, 1383–1394, https://doi.org/10.1175/BAMS-88-9-1383.
Mehran, A., A. AghaKouchak, and T. J. Phillips, 2014: Evaluation of CMIP5 continental precipitation simulations relative to satellite — based gauge — adjusted observations. J. Geophys. Res.: Atmos., 119, 1695–1707, https://doi.org/10.1002/2013JD021152.
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.
Ou, T. H., D. L. Chen, H. W. Linderholm, and J. H. Jeong, 2013: Evaluation of global climate models in simulating extreme precipitation in China. Tellus A: Dyn. Meteorol. Oceanogr., 65, 19799, https://doi.org/10.3402/tellusa.v65i0.19799.
Park, S., J. Shin, S. Kim, E. Oh, and Y. Kim, 2019: Global climate simulated by the seoul national university atmosphere model version 0 with a unified convection scheme (SAM0-UNICON). J. Climate, 32, 2917–2949, https://doi.org/10.1175/JCLI-D-18-0796.1.
Rosa, D., and W. Collins, 2013: A case study of subdaily simulated and observed continental convective precipitation: CMIP5 and multiscale global climate models comparison. Geophys. Res. Lett., 40, 5999–6003, https://doi.org/10.1002/2013GL057987.
Sillmann, J., V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res.: Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203.
Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, D. Bronaugh, and M. G. Donat, 2014: Evaluating model — simulated variability in temperature extremes using modified percentile indices. International Journal of Climatology, 34, 3304–3311, https://doi.org/10.1002/joc.3899.
Song, Y. J., F. L. Qiao, Z. Y. Song, and C. F. Jiang, 2013: Water vapor transport and cross-equatorial flow over the Asian-Australia monsoon region simulated by CMIP5 climate models. Adv. Atmos. Sci., 30, 726–738, https://doi.org/10.1077/s00376-012-2148-y.
Sperber, K. R., H. Annamalai, I. S. Kang, A. Kitoh, A. Moise, A. Turner, B. Wang, and T. Zhou, 2013: The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dyn., 41, 2711–2744, https://doi.org/10.1007/s00382-012-1607-6.
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.
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.: Atmos., 120, 4806–4824, https://doi.org/10.1002/2014JD022994.
Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res.: Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.
Voldoire, A., and Coauthors, 2019: Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11, 2177–2213, https://doi.org/10.1029/2019MS001683.
Wang, B., L. H. Zheng, D. L. Liu, F. Ji, A. Clark, and Q. Yu, 2018: Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. International Journal of Climatology, 38, 4891–4902, https://doi.org/10.1002/joc.5705.
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. (in Chinese with English abstract)
Wu, T. W., and Coauthors, 2019: The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019.
Xu, Y., X. J. Gao, and F. Giorgi, 2010: Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections. Climate Research, 41, 61–81, https://doi.org/10.3354/cr00835.
Xu, Y., X. J. Gao, F. Giorgi, B. T. Zhou, Y. Shi, J. Wu, and Y. X. Zhang, 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, https://doi.org/10.1007/s00376-017-6269-1.
Zhang, X. B., and Coauthors, 2011: Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2, 851–870, https://doi.org/10.1002/wcc.147.
Zhou, B. T., Q. Z. H. 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/10.1157/JCLI-D-13-00761.1.
Zhou, L. M., A. G. Dai, Y. J. Dai, R. S. Vose, C. Z. Zou, Y. H. Tian, and H. S. Chen, 2009: Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004. Climate Dyn., 32, 429–440, https://doi.org/10.1007/s00382-008-0387-5.
Acknowledgements
We wish to thank the three anonymous reviewers, whose valuable comments and suggestions helped us to improve our manuscript. We would like to acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP. We thank the climate modeling groups for producing and making their model outputs available. This research was supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0603804 and 2018YFC1507704) and the Natural Science Foundation of China (Grant No. 41805048).
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Article Highlights
• CMIP6 models, as with CMIP5 models, generally perform better in simulating annual mean temperature, maximum daily maximum temperature, and minimum daily minimum temperature, than in simulating extreme precipitation indices.
• The persistent dry biases in southern China in CMIP5-MME are largely reduced in CMIP6-MME.
• CMIP6 models show obvious improvements in simulating precipitation extremes compared with CMIP5 models.
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Zhu, H., Jiang, Z., Li, J. et al. Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China?. Adv. Atmos. Sci. 37, 1119–1132 (2020). https://doi.org/10.1007/s00376-020-9289-1
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DOI: https://doi.org/10.1007/s00376-020-9289-1