高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

QM和QDM方法对中国极端气候的高分辨率气候变化模拟的误差订正对比

童尧 韩振宇 高学杰

童尧, 韩振宇, 高学杰. 2022. QM和QDM方法对中国极端气候的高分辨率气候变化模拟的误差订正对比[J]. 气候与环境研究, 27(3): 383−396 doi: 10.3878/j.issn.1006-9585.2021.21037
引用本文: 童尧, 韩振宇, 高学杰. 2022. QM和QDM方法对中国极端气候的高分辨率气候变化模拟的误差订正对比[J]. 气候与环境研究, 27(3): 383−396 doi: 10.3878/j.issn.1006-9585.2021.21037
TONG Yao, HAN Zhenyu, GAO Xuejie. 2022. Bias Correction in Climate Extremes over China for High-Resolution Climate Change RegCM4 Simulations Using QM and QDM Methods [J]. Climatic and Environmental Research (in Chinese), 27 (3): 383−396 doi: 10.3878/j.issn.1006-9585.2021.21037
Citation: TONG Yao, HAN Zhenyu, GAO Xuejie. 2022. Bias Correction in Climate Extremes over China for High-Resolution Climate Change RegCM4 Simulations Using QM and QDM Methods [J]. Climatic and Environmental Research (in Chinese), 27 (3): 383−396 doi: 10.3878/j.issn.1006-9585.2021.21037

QM和QDM方法对中国极端气候的高分辨率气候变化模拟的误差订正对比

doi: 10.3878/j.issn.1006-9585.2021.21037
基金项目: 中国科学院战略性先导科技专项XDA20060401,国家自然科学基金41690141,华风气象传媒集团有限责任公司基础型创新研究项目CY-J2020008
详细信息
    作者简介:

    童尧,女,1990年出生,博士研究生,主要从事区域气候模式模拟与误差订正研究。E-mail: tongyao0811@126.com

    通讯作者:

    韩振宇,E-mail: hanzy@cma.gov.cn

  • 中图分类号: P467

Bias Correction in Climate Extremes over China for High-Resolution Climate Change RegCM4 Simulations Using QM and QDM Methods

Funds: Chinese Academy of Sciences Strategic Priority Program (Grant XDA20060401), National Natural Science Foundation of China (Grant 41690141), Huafeng Meteorological Media Group Essential Research Project (Grant CY-J2020008)
  • 摘要: 采用分位数映射(Quantile Mapping, QM)和delta分位数映射(Quantile Delta Mapping, QDM)两种误差订正方法对区域气候模式RegCM4在中国区域内模拟的逐日气温和降水数据进行订正。模式数据是5种不同全球气候模式驱动下的区域模式气候变化模拟结果。计算订正前后的极端气候指数进行对比分析,包括日最高气温极大值(TXx)、日最低气温极小值(TNn)、连续干旱日数(CDD)和最大日降水量(RX1day)。结果表明,5组模拟结果和其集合平均(ensR)都显示气温指数的模拟效果高于降水指数,其中对TXx模拟最好,对CDD的模拟最差;经过订正后,针对不同模式的两种订正结果都能够有效地减小模式与观测的偏差并提高了空间相关系数,且两种方法的订正效果无明显差别。对RCP4.5情景下未来变化的分析中,QM在一定程度上改变了模式模拟的未来变化幅度和空间分布特征,QDM则能够有效地保留所有极端指数的气候变化信号。从全国平均来看,除CDD外,所有指数未来都呈现增加趋势,且QDM订正结果与订正前模式模拟的变化趋势更为接近。建议在气候变化模拟的误差订正中采用QDM方法。
  • 图  1  验证期(2001~2015年)中国地区日最高气温极大值(TXx,左列)和日最低气温极小值(TNn,右列)的模式结果订正前后与观测的偏差:(a、d)ensR,(b、e)ensR_QM,(c、f)ensR_QDM。划线区域表示所有模式模拟都为正/负偏差

    Figure  1.  Bias in the simulation of the max daily maximum temperature (TXx, left panel) and min daily minimum temperature (TNn, right panel) from the observations before and after bias correction during the verification period (2001–2015) over China: (a, d) ensR; (b, e) ensR_QM; (c, f) ensR_QDM. The cross area indicates that all the simulations simulated the negative/positive bias

    图  2  同图1,但为连续干旱日数(CDD)和最大日降水量(RX1day)

    Figure  2.  Same as Fig. 1, but for the Consecutive Dry Day (CDD) and Max 1-d precipitation amount (RX1day)

    图  3  验证期(2001~2015年)误差订正前后模式在中国地区的模拟性能:(a)泰勒图;(b)空间相关系数;(c)S评分

    Figure  3.  Skills of simulations and bias correction in the extreme climate indices over China during the verification period (2001–2015): (a) Taylor diagram; (b) pattern correlation; (c) skill score

    图  4  21世纪末(2079~2098年)的TXx(左列)和TNn(右列)变化(相对于1986~2005年):(a、d)ensR;(b、e)ensR_QM;(c,f)ensR_QDM。图中左下角给出了整个中国的区域平均值且整个中国区域全部通过了95%显著性检验

    Figure  4.  Projected changes in TXx (left panel) and TNn (right panel) at the end of the 21st century (2079–2098 in relation to 1986–2005): (a, d) ensR; (b, e) ensR_QM; (c, f) ensR_QDM. The regional mean over the entire China is provided in the lower-left corner of the panels. All the changes over China are statistically significant at the 95% confidence level

    图  5  同图4,但为CDD和RX1day。打点区域表示为通过95%显著性检验

    Figure  5.  Same as Fig. 4, but for the CDD and RX1day. The dot area indicates that they are all statistically significant at the 95% confidence level

    图  6  极端指数变化的时间序列:(a)TXx;(b)TNn;(c)CDD;(d)RX1day

    Figure  6.  Time series of extreme index changes: (a) TXx; (b) TNn; (c) CDD; (d) RX1day

    表  1  验证期(2001~2015年)模式集合平均(ensemble RCM, ensR)和订正结果(QM和QDM)与观测之间的均方根误差(RMSE)和空间相关系数(COR),ensR、QM和QDM三者之间及与观测之间的显著差异的格点占全国百分比

    Table  1.   Root-mean-square error (RMSE) and spatial correlation coefficient (COR) between the simulation of ensemble RCM (ensR) and bias corrections and the observation during the verification period (2001–2015), as well as the percentage of grid points with significant differences among the observation, ensR, QM, and QDM over China

    RMSE (COR)显著差异的格点占全国百分比
    ensRQMQDMensR-OBSQM-OBSQDM-OBSensR-QMensR-QDMQM-QDM
    TXx1.76ºC(0.98)0.62ºC(1.00)0.59ºC(1.00)65%23%20%60%56%0
    TNn7.32ºC(0.91)0.79ºC(1.00)0.84ºC(1.00)79%15%15%71%71%1%
    CDD39.11 d(0.46)8.66 d(0.97)9.53 d(0.97)83%27%31%73%77%20%
    RX1day23.30%(0.77)6.88%(0.98)7.92%(0.97)76%22%18%62%64%2%
    注:COR均通过了95%信度检验。
    下载: 导出CSV

    表  2  21世纪末(2079~2098年)极端指数变化(相对于1986~2005年)在订正结果QM和QDM与未订正模拟结果ensR中的RMSE和COR以及ensR、QM和QDM三者之间显著差异(95%信度)的格点占全国百分比

    Table  2.   RMSEs and CORs of the changes (2079–2098 in relation to 1986–2005) in the extreme indices between the QM/QDM and ensR, as well as the percentage of grid points with significant differences (95%) among the ensR, QM, and QDM over China

    RMSECOR显著差异的格点占全国百分比
    QMQDMQMQDMensR和QMensR和QDMQM和QDM
    TXx0.59ºC0.20ºC0.340.8054%11%46%
    TNn1.21ºC0.34ºC0.550.9664%3%56%
    CDD6.07 d4.00 d0.560.6639%31%11%
    RX1day8.51%6.37%0.680.757%3%7%
    注:COR均通过了95%显著性检验。
    下载: 导出CSV

    表  3  全国平均时间序列的变化趋势及订正结果QM和QDM与未订正模拟结果ensR的RMSE

    Table  3.   Linear trends in future changes in the regional mean over the entire China and the RMSE between the QM/QDM and ensR

    变化趋势RMSE
    ensRQMQDMQMQDM
    TXx0.27ºC/10 a0.30ºC/10 a0.28ºC/10 a0.17ºC0.12ºC
    TNn0.36ºC/10 a0.25ºC/10 a0.33ºC/10 a0.65ºC0.27ºC
    CDD−0.22 d/10 a−0.59 d/10 a−0.43 d/10 a2.25 d1.40 d
    RX1day1.36%/10 a1.74%/10 a1.57%/10 a3.15%2.77%
    注:变化趋势均通过了95%显著性检验。
    下载: 导出CSV
  • [1] Ahmed K F, Wang G L, Silander J, et al. 2013. Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U. S. northeast [J]. Global and Planetary Change, 100: 320−332. doi: 10.1016/j.gloplacha.2012.11.003
    [2] Ashfaq M, Bowling L C, Cherkauer K, et al. 2010. Influence of climate model biases and daily-scale temperature and precipitation events on hydrological impacts assessment: A case study of the United States [J]. J. Geophys. Res.: Atmos., 115(D14): D14116. doi: 10.1029/2009JD012965
    [3] Bentsen M, Bethke I, Debernard J B, et al. 2013. The Norwegian Earth System Model, NorESM1-M-Part 1: Description and basic evaluation of the physical climate [J]. Geosci. Model Dev., 6(3): 687−720. doi: 10.5194/gmd-6-687-2013
    [4] Boé J, Terray L, Habets F, et al. 2007. Statistical and dynamical downscaling of the Seine basin climate for hydro–meteorological studies [J]. Int. J. Climatol., 27(12): 1643−1655. doi: 10.1002/joc.1602
    [5] Bürger G, Sobie S R, Cannon A J, et al. 2013. Downscaling extremes: An intercomparison of multiple methods for future climate [J]. J. Climate, 26(10): 3429−3449. doi: 10.1175/JCLI-D-12-00249.1
    [6] Cannon A J, Sobie S R, Murdock T Q. 2015. Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? [J]. J. Climate, 28(17): 6938−6959. doi: 10.1175/JCLI-D-14-00754.1
    [7] Collins W J, Bellouin N, Doutriaux-Boucher M, et al. 2011. Development and evaluation of an Earth-System model–HadGEM2 [J]. Geosci. Model Dev., 4(4): 1051−1075. doi: 10.5194/gmd-4-1051-2011
    [8] Eden J M, Widmann M, Grawe D, et al. 2012. Skill, correction, and downscaling of GCM-simulated precipitation [J]. J. Climate, 25(11): 3970−3984. doi: 10.1175/JCLI-D-11-00254.1
    [9] Gao X J, Giorgi F. 2017. Use of the RegCM system over East Asia: Review and perspectives [J]. Engineering, 3(5): 766−772. doi: 10.1016/J.ENG.2017.05.019
    [10] Gao X J, Shi Y, Giorgi F. 2016. Comparison of convective parameterizations in RegCM4 experiments over China with CLM as the land surface model [J]. Atmos. Oceanic Sci. Lett., 9(4): 246−254. doi: 10.1080/16742834.2016.1172938
    [11] Gao X J, Shi Y, Han Z Y, et al. 2017. Performance of RegCM4 over major river basins in China [J]. Adv. Atmos. Sci., 34(4): 441−455. doi: 10.1007/s00376-016-6179-7
    [12] Gao X J, Wu J, Shi Y, et al. 2018. Future changes in thermal comfort conditions over China based on multi-RegCM4 simulations [J]. Atmos. Oceanic Sci. Lett., 11(4): 291−299. doi: 10.1080/16742834.2018.1471578
    [13] Giorgi F, Coppola E, Solmon F, et al. 2012. RegCM4: Model description and preliminary tests over multiple CORDEX domains [J]. Climate Res., 52: 7−29. doi: 10.3354/cr01018
    [14] Gudmundsson L, Bremnes J B, Haugen J E, et al. 2012. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations—A comparison of methods [J]. Hydrol. Earth Syst. Sci., 16(9): 3383−3390. doi: 10.5194/hess-16-3383-2012
    [15] Gunarathna M H J P, Sakai K, Nakandakari T, et al. 2019. Sensitivity analysis of plant-and cultivar-specific parameters of APSIM–sugar model: Variation between climates and management conditions [J]. Agronomy, 9(5): 242. doi: 10.3390/agronomy9050242
    [16] 韩振宇, 高学杰, 石英, 等. 2015. 中国高精度土地覆盖数据在RegCM4/CLM模式中的引入及其对区域气候模拟影响的分析 [J]. 冰川冻土, 37(4): 857−866. doi: 10.7522/j.issn.1000-0240.2015.0095

    Han Z Y, Gao X J, Shi Y, et al. 2015. Development of Chinese high resolution land cover data for the RegCM4/CLM and its impact on regional climate simulation [J]. Journal of Glaciology and Geocryology (in Chinese), 37(4): 857−866. doi: 10.7522/j.issn.1000-0240.2015.0095
    [17] 韩振宇, 童尧, 高学杰, 等. 2018. 分位数映射法在RegCM4中国气温模拟订正中的应用 [J]. 气候变化研究进展, 14(4): 331−340. doi: 10.12006/j.issn.1673-1719.2017.156

    Han Z Y, Tong Y, Gao X J, et al. 2018. Correction based on quantile mapping for temperature simulated by the RegCM4 [J]. Climate Change Research (in Chinese), 14(4): 331−340. doi: 10.12006/j.issn.1673-1719.2017.156
    [18] Hazeleger W, Severijns C, Semmler T, et al. 2010. EC-Earth: A seamless earth–system prediction approach in action [J]. Bull. Amer. Meteor. Soc., 91(10): 1357−1364. doi: 10.1175/2010bams2877.1
    [19] Iversen T, Bentsen M, Bethke I, et al. 2013. The Norwegian Earth System Model, NorESM1-M-Part 2: Climate response and scenario projections [J]. Geoscientific Model Development, 6(2): 389−415. doi: 10.5194/GMD-6-389-2013
    [20] Jungclaus J H, Fischer N, Haak H, et al. 2013. Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model [J]. J. Adv. Model. Earth Syst., 5(2): 422−446. doi: 10.1002/JAME.20023
    [21] Li H B, Sheffield J, Wood E F. 2010. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching [J]. J. Geophys. Res. :Atmos., 115(D10): D10101. doi: 10.1029/2009JD012882
    [22] Maraun D. 2013. Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue [J]. J. Climate, 26(6): 2137−2143. doi: 10.1175/JCLI-D-12-00821.1
    [23] Moss R H, Edmonds J A, Hibbard K A, et al. 2010. The next generation of scenarios for climate change research and assessment [J]. Nature, 463(7282): 747−756. doi: 10.1038/nature08823
    [24] Ngai S T, Tangang F, Juneng L. 2017. Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method [J]. Global and Planetary Change, 149: 79−90. doi: 10.1016/j.gloplacha.2016.12.009
    [25] Olsson J, Berggren K, Olofsson M, et al. 2009. Applying climate model precipitation scenarios for urban hydrological assessment: A case study in Kalmar City, Sweden [J]. Atmos. Res., 92(3): 364−375. doi: 10.1016/j.atmosres.2009.01.015
    [26] Peng D D, Zhou T J, Zhang L X, et al. 2020. Observationally constrained projection of the reduced intensification of extreme climate events in Central Asia from 0.5 °C less global warming [J]. Clim. Dyn., 54(1): 543−560. doi: 10.1007/s00382-019-05014-6
    [27] Piani C, Weedon G P, Best M, et al. 2010. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models [J]. J. Hydrol., 395(3−4): 199−215. doi: 10.1016/j.jhydrol.2010.10.024
    [28] Reiter P, Gutjahr O, Schefczyk L, et al. 2016. Bias correction of ENSEMBLES precipitation data with focus on the effect of the length of the calibration period [J]. Meteorologische Zeitschrift, 25(1): 85−96. doi: 10.1127/metz/2015/0714
    [29] Rotstayn L D, Collier M A, Dix M R, et al. 2010. Improved simulation of Australian climate and ENSO-related rainfall variability in a global climate model with an interactive aerosol treatment [J]. Int. J. Climatol., 30(7): 1067−1088. doi: 10.1002/joc.1952
    [30] Stevens B, Giorgetta M, Esch M, et al. 2012. Atmospheric component of the MPI-M earth system model: ECHAM6 [J]. J. Adv. Model. Earth Syst., 5(2): 146−172. doi: 10.1002/JAME.20015
    [31] Sun F B, Roderick M L, Lim W H, et al. 2011. Hydroclimatic projections for the Murray–Darling Basin based on an ensemble derived from intergovernmental panel on climate change AR4 climate models [J]. Water Resources Research, 47(12): W00G02. doi: 10.1029/2010WR009829
    [32] Switanek M B, Troch P A, Castro C L, et al. 2017. Scaled distribution mapping: A bias correction method that preserves raw climate model projected changes [J]. Hydrol. Earth Syst. Sci., 21(6): 2649−2666. doi: 10.5194/hess-21-2649-2017
    [33] Taylor K E. 2001. Summarizing multiple aspects of model performance in a single diagram [J]. J. Geophys. Res. Atmos., 106(D7): 7183−7192. doi: 10.1029/2000JD900719
    [34] Teutschbein C, Seibert J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods [J]. J. Hydrol., 456–457: 12–29. doi: 10.1016/j.jhydrol.2012.05.052
    [35] 童尧, 高学杰, 韩振宇, 等. 2017. 基于RegCM4模式的中国区域日尺度降水模拟误差订正 [J]. 大气科学, 41(6): 1156−1166. doi: 10.3878/j.issn.1006-9895.1704.16275

    Tong Y, Gao X J, Han Z Y, et al. 2017. Bias correction of daffy precipitation simulated by RegCM4 model over China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 41(6): 1156−1166. doi: 10.3878/j.issn.1006-9895.1704.16275
    [36] Tong Y, Gao X J, Han Z Y, et al. 2020. Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods [J]. Climate Dyn., 57: 1425−1443. doi: 10.1007/S00382-020-05447-4
    [37] Wang L, Chen W. 2014a. A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China [J]. Int. J. Climatol., 34(6): 2059−2078. doi: 10.1002/JOC.3822
    [38] Wang L, Chen W. 2014b. Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation [J]. Atmos. Sci. Lett., 15(1): 1−6. doi: 10.1002/ASL2.454
    [39] Wang N, Wang J, Wang E L, et al. 2015. Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming [J]. Eur. J. Agron., 71: 19−33. doi: 10.1016/j.eja.2015.08.005
    [40] Wood A W, Leung L R, Sridhar V, et al. 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs [J]. Climatic Change, 62(1): 189−216. doi: 10.1023/B:CLIM.0000013685.99609.9e
    [41] 吴佳, 高学杰. 2013. 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, 56(4): 1102−1111. doi: 10.6038/cjg20130406

    Wu J, Gao X J. 2013. A gridded daily observation dataset over China region and comparison with the other datasets [J]. Chinese Journal of Geophysics (in Chinese), 56(4): 1102−1111. doi: 10.6038/cjg20130406
    [42] Wu J, Gao X J. 2020. Present day bias and future change signal of temperature over China in a series of multi-GCM driven RCM simulations [J]. Climate Dyn.,54(1–2): 1113–1130. https://doi.org/10.1007/s00382-019-05047-x
    [43] Xu Y, Gao X J, Shen Y, et al. 2009. A daily temperature dataset over China and its application in validating a RCM simulation [J]. Adv. Atmos. Sci., 26(4): 763−772. doi: 10.1007/s00376-009-9029-z
    [44] Yang W, Andréasson J, Graham L P, et al. 2010. Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies [J]. Hydrol. Res., 41(3−4): 211−229. doi: 10.2166/nh.2010.004
    [45] Zhang X B, Alexander L, Hegerl G C, et al. 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data [J]. Wiley Interdisciplinary Reviews: Climate Change, 2(6): 851−870. doi: 10.1002/wcc.147
  • 加载中
图(6) / 表(3)
计量
  • 文章访问数:  179
  • HTML全文浏览量:  34
  • PDF下载量:  33
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-02-18
  • 网络出版日期:  2021-12-05
  • 刊出日期:  2022-06-02

目录

    /

    返回文章
    返回