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Correction Based on Distribution Scaling for Precipitation Simulated by Climate Model
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摘要: 应用概率调整法订正区域气候模式系统PRECIS在SRES A1B情景下模拟的各季节全国日降水量。以第95百分位降水量为阈值，利用Γ分布分段拟合1962年12月—1972年11月的模拟值，构建传递函数，得到1991年12月—2001年11月的订正值。结果表明：全国平均日降水量空间分布的模拟改善明显，偏差百分率高于100%的格点比例从23.5%降低到1.0%；对各地区平均降水月循环的模拟结果改善，冷季降水较暖季更接近观测，提高拟合优度是改进订正方法的关键；多数地区连续干日数、连续5 d最大降水量及极端降水贡献率的空间强度、概率分布与空间相关性的订正效果显著。总体来说，该方法对模拟中国区域降水的平均态与极端降水均有明显改善，有助于气候评估工作的展开。Abstract: A statistical bias correction based on piecewise Γ distribution fitting to construct seasonal transfer function is applied to the precipitation simulated by a regional climate model PRECIS under the SRES-A1B emission scenario over China. The transfer function (TF) is derived from the control period of December 1962—November 1972, fitting the cumulative probability density function of both simulated and observed precipitation with Γ distribution. The 95th percentile precipitation is chosen to be the threshold and precipitation below and upon the threshold are fitted, respectively. When compared with wholesale fitting, this method can better fit the distributions of both small/medium precipitation and extreme precipitation. Then the TF is applied for the validation period of December 1991-November 2001. The correction strategy is based on the assumption that discrepancies between model and observation stay constant with time.Results show that PRECIS can reproduce the spatial distribution of mean and extreme precipitation, while the biases exist. The biases are larger if the topography is more complex. If the region is high or low in altitudes, the bias tends to be positive or negative, while Sichuan Basin is the exception, where large positive biases occur.The correction based on the piecewise Γ distribution fitting can well correct the spatial distribution of the mean precipitation over China, especially over the original large-bias regions, and the grids in which the bias percentages used to be larger than 100% are reduced from 23.5% down to 1.0%. Simulation of region-averaged monthly precipitation is significantly improved, especially over Southwest China and the Tibet Plateau regions. Precipitation in cold seasons is better corrected, while it has relatively larger biases in warm seasons especially in June due to a wide range of precipitation, which may bring difficulties during fitting. So, it's crucial to improve the fitting probability in warm seasons.The piecewise Γ distribution fitting correction also does a quite good job in correcting the extreme precipitation. The spatial distribution, probability density distribution and spatial correlation coefficient of consecutive dry days, the maximum 5-day precipitation amount and the contribution of extreme precipitation are corrected significantly, except for maximum 5-day preciptiation amount in East China, contribution of extreme precipitation in Northwest China and the Tibet Plateau are overcorrected. These show that the technique has the ability to correct the extreme precipitation.In general, the correction results are satisfying, which implies that the piecewise Γ distribution fitting correction is capable of improving the reproduction of both mean and extreme precipitation simulated by regional climate model PRECIS over China, which is useful for assessment research.
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图 2 订正时段观测、模拟及订正的中国区域日平均降水量的空间分布及其偏差百分率 (a) 观测值的空间分布，(b) 模拟值的空间分布，(c) 模拟值的偏差百分率，(d) 订正值的空间分布，(e) 订正值的偏差百分率
Fig. 2 Spatial distribution of observed, simulated and corrected mean precipitation and the bias percentage over China in correction period (a) spatial distribution of the observed, (b) spatial distribution of the simulated, (c) the bias percentages of the simulated, (d) spatial distribution of the corrected, (e) the bias percentages of the corrected
图 3 订正时段观测、模拟及订正的各区域平均降水量
Fig. 3 The observed, the simulated and the corrected region-averaged precipitation in correction period
图 4 订正时段中国区域连续干日数的空间分布 (a) 观测值，(b) 模拟值，(c) 订正值
Fig. 4 Spatial distribution of the observed (a), the simulated (b) and the corrected (c) consecutive dry days over China in correction period
图 5 同图 4，但为连续5 d最大降水量的空间分布
Fig. 5 The same as in Fig.4, but for the spatial distribution of maximum 5-day precipitation amount
图 6 同图 4，但为极端降水贡献率的空间分布
Fig. 6 The same as in Fig.4, but for the spatial distribution of extreme precipitation contribution
图 7 订正时段中国区域连续干日数、连续5 d最大降水量及极端降水贡献率的概率分布
Fig. 7 The observed, the simulated and the corrected probability density distributions of consecutive dry days, maximum 5-day precipitation amount and contribution of extreme precipitation over China in correction period
表 1 订正时段模拟及订正与观测的各区域平均极端指标空间相关系数
Table 1 Spatial correlation coefficient of region-averaged extreme index derived from the simulated and the corrected to the observed precipitation in correction period
地区 连续干日数 连续5 d最大降水量 极端降水贡献率 模拟值 订正值 模拟值 订正值 模拟值 订正值 东北 0.56 0.67 0.47 0.56 0.39 0.32* 华北 0.81 0.90 0.74 0.87 0.38 0.40 西北 0.64 0.94 0.76 0.92 0.22 0.28 华东 0.51 0.77 0.52 0.41* 0.05 0.20 中部 0.23 0.80 0.30 0.64 0.16 0.29 华南 0.62 0.81 0.41 0.83 0.43 0.63 西南 0.50 0.89 0.40 0.84 0.46 0.76 青藏高原 0.54 0.89 0.72 0.93 0.32 0.29* 注：*表示过订正，即订正值较模拟值更偏离观测值。 
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[1] 刘永强, 丁一汇.区域气候模拟研究.应用气象学报, 1995, 6(2):228-239. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19950236&flag=1 [2] 高学杰, 赵宗慈, 丁一汇, 等.温室效应引起的中国区域气候变化的数值模拟.第一部分:模式对中国气候模拟能力的检验.气象学报, 2003, 61(1):20-28. doi: 10.11676/qxxb2003.003 [3] 李维京, 张培群, 李清泉, 等.动力气候模式预测系统业务化及其应用.应用气象学报, 2005, 6(3):1-11. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2005S1000.htm [4] 刘一鸣, 丁一汇, 李清泉, 等.区域气候模式对中国夏季降水的10年回报试验及其评估分析.应用气象学报, 2005, 16(3):41-47. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2005S1004.htm [5] 高学杰, 徐影, 赵宗慈, 等.数值模式不同分辨率和地形对东亚降水模拟影响的试验.大气科学, 2006, 30(2):185-192. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200602000.htm [6] Yu E T, Wang H J, Sun J Q.A quick report on a dynamical downscaling simulation over china using the nested model.Atmos Oceanic Sci Lett, 2010, 3(6):325-329. doi: 10.1080/16742834.2010.11446886 [7] Dosio A, Paruolo P.Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models:Evaluation on the present climate.J Geophys Res, 2011, 116, D16106. doi: 10.1029/2011JD015934 [8] 李莉, 朱跃建.T213降水预报订正系统的建立与研究.应用气象学报, 2006, 17(2):130-134. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2006S1018.htm [9] 任宏利, 丑纪范.数值模式的预报策略和方法研究进展.地球科学进展, 2007, 22(4):376-385. http://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ200704006.htm [10] Wood A W, Leung L R, Sridhar V, et al.Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs.Climatic Change, 2004, 62(1-3):189-216. doi: 10.1023/B:CLIM.0000013685.99609.9e [11] Baigorria G A, Jones J W, Shin D W, et al.Assessing uncertainties in crop model simulations using daily bias-corrected regional climate model projections.Climate Research, 2007, 34:211-222. doi: 10.3354/cr00703 [12] Ghose S, Mujumdar P P.Climate change impact assessment:Uncertainty modeling with imprecise probability.J Geophys Res, 2009, 114, D18113. doi: 10.1029/2008JD011648 [13] 宋超辉.不同数学模型在降水量资料序列订正中适用性探讨.应用气象学报, 1998, 9(2):213-218. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19980229&flag=1 [14] 石英, 高学杰, 吴佳, 等.华北地区未来气候变化的高分辨率数值模拟.应用气象学报, 2010, 21(5):580-589. doi: 10.11898/1001-7313.20100507 [15] Piani C, Haerter J O, Coppola E.Statistical bias correction for daily precipitation in regional climate models over Europe.Theor Appl Climatol, 2010, 99(1-2):187-192. doi: 10.1007/s00704-009-0134-9 [16] Piani C, Weedon G P, Best M, et al.Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models.J Hydrol, 2010, 395(3-4):199-215. doi: 10.1016/j.jhydrol.2010.10.024 [17] Yang W, Johan A, Phil G L, et al.Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impact studies.Hydrology Research, 2010, 41(3-4):211-229. doi: 10.2166/nh.2010.004 [18] Jones R G, Noguer M, Hassell D C, et al.Generating High Resolution Climate Change Scenarios Using PRECIS.Met Office Hadley Centre, 2004. http://unfccc.int/files/national_reports/non-annex_i_natcom/cge/application/pdf/precis_flyers_cop9_rcm.pdf [19] 周林, 潘婕, 张镭, 等.气候模拟日降水量的统计误差订正分析——以上海为例.热带气象学报, 2014, 30(1):137-144. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX201401015.htm [20] 许吟隆, Richard Jones.利用ECMWF再分析数据验证PRECIS对中国区域气候的模拟能力.中国农业气象, 2004, 25(1):5-9. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200401001.htm [21] 许吟隆, 黄晓莹, 张勇, 等.PRECIS对华南地区气候模拟能力的验证.中山大学学报:自然科学版, 2007, 46(5):93-97. http://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ200705021.htm [22] 田红, 许吟隆, 林而达.温室效应引起的江淮流域气候变化预估.气候变化研究进展, 2008, 4(6):357-362. http://www.cnki.com.cn/Article/CJFDTOTAL-QHBH200806013.htm [23] 王芳栋, 许吟隆, 李涛.区域气候模式PRECIS对中国气候的长期数值模拟试验.中国农业气象, 2010, 31(3):327-332. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY201003002.htm [24] Xie P P, Chen M Y, Song Y, et al.A gauge-based analysis of daily precipitation over east Asia.J Hydrometeorology, 2007, 8:607-626. doi: 10.1175/JHM583.1 [25] 么枕生, 丁裕国.气候统计.北京:气象出版社, 1990:68-170. [26] Durbin J.Distribution Theory for Tests based on the Sample Distribution Function.Society for India and Applied Mathematics, 1973. http://www.doc88.com/p-8476144414964.html [27] Bonsal B R, Zhang X, Vincent L A, et al.Characteristics of daily and extreme temperature over Canada.American Metrological Society, 2001, 5(14):1959-1976. https://www.researchgate.net/publication/252919009_Characteristics_of_Daily_and_Extreme_Temperatures_over_Canada [28] Thom H S.A note on the Gamma distribution.Mon Wea Rev, 1958, 86(4):117-122. doi: 10.1175/1520-0493(1958)086<0117:ANOTGD>2.0.CO;2 [29] 姜彤. 适应行动: 数据及风险分析. 专题研讨一: 适应气候变化的共性问题//适应气候变化——东亚峰会成员国的战略、政策与行动. 北京: 科学出版社, 2009. [30] Ho C K, Stephenson D B, Collins M, et al.Calibration strategy:A source of additional uncertainty in climate change projections.Bull Amer Meteor Soc, 2012, 93:21-26. doi: 10.1175/2011BAMS3110.1 [31] Hawkins E, Osborne T M, Ho C K, et al.Calibration and Bias Correction of Climate Projections for Crop Modelling:An Idealised Case Study Over Europe.Agricultural and Forest Meteorology, 2012.http://dx.doi.org/10.1016/j.agrformet.2012.04.007. -
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