NEX-GDDP降尺度数据对中国极端降水指数模拟能力的评估

doi:10.12006/j.issn.1673-1719.2020.253

摘要/Abstract

摘要：

利用1986—2005年中国地面气象台站观测的格点化逐日降水数据（CN05.1）评估了NASA高分辨率降尺度逐日数据集NEX-GDDP中21个全球气候模式在0.25˚（约25 km×25 km)分辨率下对中国极端降水的模拟能力。选取年最大日降水量(RX1D)、年最大5 d降水量（RX5D)、湿日总降水量（PRCPTOT)、湿日平均降水强度（SDII)、强降水总量（R95p）和极端强降水总量（R99p）这6个强度指数,暴雨日数（R50)、强降水日数（R95T)、极端强降水日数（R99T)、最大连续湿日（CWD)、最大连续干日（CDD）这5个频率指数作为评价指数开展评估,结果表明：(1)各模式很难捕捉到极端降水指数年际变化线性趋势,表现最好的模式GFDL-ESM2G也仅有45%的指标显示出了与观测的正相关性,而且很弱。(2)各模式对于气候态均值的模拟效果较好,其中,CSIRO-MK3-6-0、NorESM1-M、MRI-CGCM3对强度指数模拟较优,inmcm4、IPSL-CM5A-MR、MIROC5对频率指数模拟较优,综合表现最优的3个模式为CSIRO-MK3-6-0,inmcm4、MRI-CGCM3。(3)综合考虑各模式对11个极端降水指数在气候态均值和年际变化线性趋势模拟能力的评估结果来看,GFDL-ESM2G、CSIRO-MK3-6-0、ACCESS1-0显示了相对较高的综合模拟能力。

关键词: NEX-GDDP, 中国, 极端降水, 模式评估

Abstract:

Taking the grid daily precipitation data (CN05.1) observed by China surface meteorological stations from 1986 to 2005 as the observation data, the performance of 21 global climate models were evaluated based on the high-resolution downscaling daily dataset NASA Earth Exchange/Global Daily Downscaled Projections (NEX-GDDP) with the resolution of 0.25° (~25 km×25 km). Six intensity indices, annual maximum daily precipitation (RX1D), the largest consecutive precipitation for five days (RX5D), total wet-day precipitation (PRCPTOT), simple daily precipitation intensity (SDII), cumulative precipitation in the 95 and 99 quantiles (R95p, R99p), and five frequency indices, heavy rain days (R50), cumulative precipitation days in the 95 and 99 quantiles (R95T, R99T), consecutive wet days (CWD), consecutive dry days (CDD), were selected for evaluation. The results show that: (1) It is difficult for models to capture the linear variation of extreme precipitation indices. Even for the best performance model, GFDL-ESM2G, only 45% of the simulated indices present the positive correlation with the observation. (2) The performance of models on the climatological means is better. CSIRO-MK3-6-0, NorESM1-M and MRI-CGCM3 have better performance on the intensity indices. The inmcm4, IPSL-CM5A-MR and MIROC5 have better performance on the frequency indices. The three best synthetical performance models are CSIRO-MK3-6-0, inmcm4 and MRI-CGCM3. (3) Considering the performance of 11 extreme precipitation indices in the climatological means and trend, GFDL-ESM2G, CSIRO-MK3-6-0 and ACCESS1-0 have relatively higher performance.

Key words: NEX-GDDP, China, Extreme precipitation, Models evaluation

王倩之, 刘凯, 汪明. NEX-GDDP降尺度数据对中国极端降水指数模拟能力的评估[J]. 气候变化研究进展, 2022, 18(1): 31-43.

WANG Qian-Zhi, LIU Kai, WANG Ming. Evaluation of extreme precipitation indices performance based on NEX-GDDP downscaling data over China[J]. Climate Change Research, 2022, 18(1): 31-43.

图/表 8

表1 21个气候模式基本信息

Table 1 Basic information of 21 climate models

表2 本研究采用的极端降水指数

Table 2 The extreme precipitation indices used in this study

图1 各模式对1986—2005年中国极端降水指数年际变化线性趋势模拟泰勒图

Fig. 1 The taylor diagram for performance of 21 models on simulating the trend of extreme precipitation indices in China from 1986 to 2005

图2 各模式对1986—2005年中国极端降水指数年际变化线性趋势模拟能力的排名注：RMSE越小、σx/σy越接近1、r越大则排名越靠前。

Fig. 2 Ranking of 21 models for their performance on simulating the trend of extreme precipitation indices in China from 1986 to 2005 (a) RMSE, (b) σx/σy, (c) r, (d) combined of the three indicators above

图3 同图1,但为气候态模拟

Fig. 3 Same as Fig. 1, but for climatological means (1986-2005)

图4 同图2,但为气候态模拟

Fig. 4 Same as Fig. 2, but for climatological means (1986-2005)

图5 观测数据与优选模式集合中国极端降水指数1986—2005年气候态结果空间分布对比注：中国台湾地区无数据。

Fig. 5 Climatological means of extreme precipitation indices from observations and optimal-model ensemble simulations in China from 1986 to 2005

图6 同图5,但为年际变化线性趋势结果

Fig. 6 Same as Fig. 5, but for trend of extreme precipitation indices

参考文献 31

[1]	Pachauri R K, Allen M R, Barros V R, et al. Climate change 2014: synthesis report [M]. Cambridge: Cambridge University Press, 2014
[2]	丁一汇, 任国玉, 石广玉, 等. 气候变化国家评估报告(I): 中国气候变化的历史和未来趋势[J]. 气候变化研究进展, 2006, 2(1): 3-8.
	Ding Y H, Ren G Y, Shi G Y, et al. National assessment report of climate change (I): climate change in China and it future trend[J]. Climate Change Research, 2006, 2(1): 3-8 (in Chinese)
[3]	Li L, Li W, Ballard T, et al. CMIP5 model simulations of Ethiopian Kiremt-season precipitation: current climate and future changes[J]. Climate Dynamics, 2016, 46(9-10): 2883-2895 doi: 10.1007/s00382-015-2737-4 URL
[4]	Toreti A, Naveau P, Zampieri M, et al. Projections of global changes in precipitation extremes from Coupled Model Intercomparison Project Phase 5 models[J]. Geophysical Research Letters, 2013, 40(18): 4887-4892 doi: 10.1002/grl.v40.18 URL
[5]	李柔珂. CMIP5模式对气候变化背景下中国地区未来气候灾害风险预估研究[D]. 兰州:兰州大学, 2017.
	Li R K. Projection of climate hazard and risk in China considering climate change by CMIP5 models[D]. Lanzhou: Lanzhou University, 2017 (in Chinese)
[6]	Taylor K E, Stouffer R J, Meehl G A. An overview of CMIP5 and the experiment design[J]. Bulletin of The American Meteorological Society, 2012, 93(4): 485-498 doi: 10.1175/BAMS-D-11-00094.1 URL
[7]	Joetzjer E, Douville H, Delire C, et al. Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3[J]. Climate Dynamics, 2013, 41(11-12): 2921-2936 doi: 10.1007/s00382-012-1644-1 URL
[8]	Zhao Z C, Li X. Impacts of global warming on climate change over East Asia as simulated by 15 GCMs [R]. Woodridge, IL: Global Warming International Center, 1997
[9]	Kharin V V, Zwiers F, Zhang X, et al. Changes in temperature and precipitation extremes in the CMIP5 ensemble[J]. Climatic Change, 2013, 119(2): 345-357 doi: 10.1007/s10584-013-0705-8 URL
[10]	姜大膀, 田芝平. 21世纪东亚季风变化: CMIP3和CMIP5模式预估结果[J]. 科学通报, 2013, 58(8): 707-716.
	Jiang D B, Tian Z P. East Asian monsoon change for the 21st century: results of CMIP3 and CMIP5 models[J]. Chinese Science Bulletin, 2013, 58(8): 707-716 (in Chinese)
[11]	陈晓晨, 徐影, 许崇海, 等. CMIP5全球气候模式对中国地区降水模拟能力的评估[J]. 气候变化研究进展, 2014, 10(3): 217-225.
	Chen X C, Xu Y, Xu C H, et al. Assessment of precipitation simulations in China by CMIP5 multi-models[J]. Climate Change Research, 2014, 10(3): 217-225 (in Chinese)
[12]	杨侃, 许吟隆, 陈晓光, 等. 全球气候模式对宁夏区域未来气候变化的情景模拟分析[J]. 气候与环境研究, 2007, 12(5): 629-637.
	Yang K, Xu Y L, Chen X G, et al. Analyses on the GCMs projection of future climate change scenarios in Ningxia[J]. Climatic and Environment Research, 2007, 12(5): 629-637 (in Chinese)
[13]	胡芩, 姜大膀, 范广洲. CMIP5全球气候模式对青藏高原地区气候模拟能力评估[J]. 大气科学, 2014, 38(5): 924-938.
	Hu Q, Jiang D B, Fan G Z. Evaluation of CMIP5 models over the Qinghai-Tibetan Plateau[J]. Chinese Journal of Atmospheric Sciences, 2014, 38(5): 924-938 (in Chinese)
[14]	翟薇, 石英. IPCC AR5全球模式对华北地区未来气候的预估[J]. 安徽农业科学, 2014, 42(23): 7928-7930.
	Zhai W, Shi Y. Climate change projection over North China simulated by the IPCC AR5 simulations[J]. Journal of Anhui Agriculture Sciences, 2014, 42(23): 7928-7930 (in Chinese)
[15]	Huang D, Yan P, Zhu J, et al. Uncertainty of global summer precipitation in the CMIP5 models: a comparison between high-resolution and low-resolution models[J]. Theoretical and Applied Climatology, 2018, 132(1-2): 55-69 doi: 10.1007/s00704-017-2078-9 URL
[16]	Huang D Q, Zhu J, Zhang Y C, et al. Uncertainties on the simulated summer precipitation over Eastern China from the CMIP5 models[J]. Journal of Geophysical Research: Atmospheres, 2013, 118(16): 9035-9047 doi: 10.1002/jgrd.50695 URL
[17]	Giorgi F, Mearns L O. Approaches to the simulation of regional climate change: a review[J]. Reviews of Geophysics, 1991, 29(2): 191-216 doi: 10.1029/90RG02636 URL
[18]	Thrasher B, Maurer E P, Mckellar C, et al. Bias correcting climate model simulated daily temperature extremes with quantile mapping[J]. Hydrology and Earth System Sciences, 2012, 16(9): 3309-3314 doi: 10.5194/hess-16-3309-2012 URL
[19]	Wood A W, Leung L R, Sridhar V, et al. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs[J]. Climatic Change, 2004, 62(1-3): 189-216 doi: 10.1023/B:CLIM.0000013685.99609.9e URL
[20]	Wood A W, Maurer E P, Kumar A, et al. Long-range experimental hydrologic forecasting for the eastern United States[J]. Journal of Geophysical Research: Atmospheres, 2002, 107(D20): ACL 6-1-ACL 6-15
[21]	NCCS (NASA Center for Climate Simulation). NASA Earth exchange global daily downscaled projections (NEX-GDDP)[R/OL]. 2020 2020-11-02. https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp
[22]	Chen H P, Sun J Q, Li H X. Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set[J]. Atmospheric and Oceanic Science Letters, 2017, 10(6): 403-410 doi: 10.1080/16742834.2017.1367625 URL
[23]	Zhao C, Gong J, Wang H, et al. Changes of temperature and precipitation extremes in a typical arid and semiarid zone: observations and multi-model ensemble projections[J]. International Journal of Climatology, 2020, 40(12): 5128-5153 doi: 10.1002/joc.v40.12 URL
[24]	Xu R, Chen Y, Chen Z. Future changes of precipitation over the Han River basin using NEX-GDDP dataset and the SVR QM method[J]. Atmosphere, 2019, 10(11): 688 doi: 10.3390/atmos10110688 URL
[25]	李金洁, 王爱慧, 郭东林, 等. 高分辨率统计降尺度数据集NEX-GDDP对中国极端温度指数模拟能力的评估[J]. 气象学报, 2019, 77(3): 579-593.
	Li J J, Wang A H, Guo D L, et al. Evaluation of extreme temperature indices over China in the NEX-GDDP simulated by high-resolution statistical downscaling models[J]. Acta Meteorologica Sinica, 2019, 77(3): 579-593 (in Chinese)
[26]	吴佳, 高学杰. 一套格点化的中国区域逐日观测资料及与其它资料的对比[J]. 地球物理学报, 2013, 56(4): 1102-1111.
	Wu J, Gao X J. A gridded daily observation dataset over China region and comparison with the other datasets[J]. Chinese Journal of Geophysics, 2013, 56(4): 1102-1111 (in Chinese)
[27]	孔祥慧, 毕训强. 利用区域气候模式对我国南方百年气温和降水的动力降尺度模拟[J]. 气候与环境研究, 2016, 21(6): 711-724.
	Kong X H, Bi X Q. Simulation of temperature and precipitation during the last 100 years over Southern China by a Regional Climate Model[J]. Climatic and Environment Research, 2016, 21(6): 711-724 (in Chinese)
[28]	王丹, 王爱慧. 1901—2013年GPCC和CRU降水资料在中国大陆的适用性评估[J]. 气候与环境研究, 2017, 22(4): 446-462.
	Wang D, Wang A H. Applicability assessment of GPCC and CRU precipitation products in China during 1901 to 2013[J]. Climatic and Environment Research, 2017, 22(4): 446-462 (in Chinese)
[29]	Zhou B, Xu Y, Wu J, et al. Changes in temperature and precipitation extreme indices over China: analysis of a high-resolution grid dataset[J]. International Journal of Climatology, 2016, 36(3): 1051-1066 doi: 10.1002/joc.4400 URL
[30]	Theil H. A rank-invariant method of linear and polynomial regression analysis[J]. Mathematisch Centrum, 1992: 345-381
[31]	Sen P K. Estimates of the regression coefficient based on Kendall’s tau[J]. Journal of the American Statistical Association, 1968, 63(324): 1379-1389 doi: 10.1080/01621459.1968.10480934 URL