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

CAS-ESM模式对欧亚大陆逐日降水特征的数值模拟:物理参数化方案和水平分辨率的影响

孔祥慧 王爱慧 毕训强 李星雨 张贺

孔祥慧, 王爱慧, 毕训强, 等. 2021. CAS-ESM模式对欧亚大陆逐日降水特征的数值模拟:物理参数化方案和水平分辨率的影响[J]. 大气科学, 45(4): 725−745 doi: 10.3878/j.issn.1006-9895.2010.20171
引用本文: 孔祥慧, 王爱慧, 毕训强, 等. 2021. CAS-ESM模式对欧亚大陆逐日降水特征的数值模拟:物理参数化方案和水平分辨率的影响[J]. 大气科学, 45(4): 725−745 doi: 10.3878/j.issn.1006-9895.2010.20171
KONG Xianghui, WANG Aihui, BI Xunqiang, et al. 2021. Simulation of Daily Precipitation Characteristics in Eurasia Using CAS-ESM Model: Sensitivity of Physical Parameterization Schemes and Horizontal Resolutions [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(4): 725−745 doi: 10.3878/j.issn.1006-9895.2010.20171
Citation: KONG Xianghui, WANG Aihui, BI Xunqiang, et al. 2021. Simulation of Daily Precipitation Characteristics in Eurasia Using CAS-ESM Model: Sensitivity of Physical Parameterization Schemes and Horizontal Resolutions [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(4): 725−745 doi: 10.3878/j.issn.1006-9895.2010.20171

CAS-ESM模式对欧亚大陆逐日降水特征的数值模拟:物理参数化方案和水平分辨率的影响

doi: 10.3878/j.issn.1006-9895.2010.20171
基金项目: 国家重点研发计划项目2016YFB0200805,国家自然科学基金项目41991282、41925021,国家重大科技基础设施项目“地球系统数值模拟装置”
详细信息
    作者简介:

    孔祥慧,女,1989年出生,博士,主要从事区域气候模式和全球气候模式的模拟、分析和极端气候变化研究。E-mail: kongxianghui@mail.iap.ac.cn

    通讯作者:

    王爱慧,E-mail: wangaihui@mail.iap.ac.cn

  • 中图分类号: P467

Simulation of Daily Precipitation Characteristics in Eurasia Using CAS-ESM Model: Sensitivity of Physical Parameterization Schemes and Horizontal Resolutions

Funds: National Key Research and Development Program of China (Grant 2016YFB0200805), National Natural Science Foundation of China (Grants 411991282, 41925021), National Key Scientific and Technological Infrastructure Project of China“Earth System Science Numerical Simulator Facility”
  • 摘要: 本文利用中国科学院大气物理研究所地球系统模式CAS-ESM和NCAR CESM中的气候系统模式开展了一系列不同物理参数化方案和水平分辨率的模拟试验,并针对欧亚大陆逐日降水特征模拟性能进行分析研究。本研究进行了四组时长为19年(1998~2016年)的AMIP(Atmospheric Model Intercomparison Project)数值积分试验:在1.9°×2.5°的低分辨率下NCAR CESM模式使用CAM5物理参数化方案组合(记为CESM),在1.4°×1.4°的低水平分辨率下CAS-ESM模式使用CAM4与CAM5两种不同物理参数化方案组合(依次记为Lcam4和Lcam5),在0.5°×0.5°的高水平分辨率下CAS-ESM模式使用CAM5物理参数化方案(记为Hcam5)。通过与GPCC(Global Precipitation Climatology Centre)、CMORPH(CPC MORPHing technique)观测资料比较,两个模式较好地再现了平均降水特征和极端降水的气候态,但模式的降水频率偏大、降水强度偏弱。CESM的大雨日数与观测较为接近,Hcam5模拟的日最大降水量与观测最接近。针对CAS-ESM模式,不同物理参数化方案和水平分辨率均对降水特征产生影响,其中提高分辨率对降水特征的模拟有显著的改进。Lcam4和Lcam5相比,Hcam5显著提高了极端降水的模拟性能。在欧亚大陆中高纬地区,Lcam4的降水频率高于Lcam5;而在中国东部,Hcam5的降水频率比Lcam5小,与GPCC偏差更小。进一步分析的结果表明,与Lcam5相比,在欧洲地区Lcam4中的大尺度降水较多,水汽输送更强。在中国东部,Hcam5中对流性降水频率比Lcam5更小,而大尺度降水和水汽输送更大,使得高分辨率的模拟试验性能提高。
  • 图  1  研究区域和(a)CESM、(b)Lcam4和Lcam5、(c)Hcam5试验的地形(填色,单位:m)。图1a中SEU、IND、SEA、NEA表示南欧、印度半岛、亚洲东南部、亚洲东北部

    Figure  1.  Model domain and topography (shadings, units: m) for experiments (a) CESM [NCAR CESM (Community Earth System Model, National Center for Atmospheric Research) with the CAM5 (Community Atmosphere Model version 5) package at a resolution of 1.9°×2.5°], (b) Lcam4 [CAS-ESM with the CAM4 (Community Atmosphere Model version 4) package at a resolution of 1.4°×1.4°] and Lcam5 (CAS-ESM with the CAM5 package at a resolution of 1.4°×1.4°), (c) Hcam5 (CAS-ESM with the CAM5 package at a resolution of 0.5°×0.5°). In Fig.1a, the red boxes denote the four sub-regions: SEU (South Europe), IND (India Peninsula), SEA (South and East Asia), NEA (North and East Asia)

    图  2  1998~2016年多年平均降水量(单位:mm d−1)分布:(a)GPCC;(b)CMORPH;(c)CESM、(d)Lcam4、(e)Lcam5、(f)Hcam5与GPCC的偏差场;(g)Lcam4、(h)Hcam5与Lcam5的差异场。图右上角的数值是区域平均值

    Figure  2.  Spatial distributions of mean precipitation (units: mm d−1) derived from (a) GPCC (Global Precipitation Centre) data, (b) CMORPH [Climate Prediction Center (CPC) MORPHing] data, the differences between schemes (c) CESM, (d) Lcam4, (e) Lcam5, (f) Hcam5 and GPCC, differences between (g) Lcam4, (h) Hcam5 and Lcam5 during 1998–2016. Area-weighted averaged value of precipitation amount or biases are shown in the upper-right of each panel

    图  3  图2,但为1998~2016年多年平均降水频率(单位:d)分布

    Figure  3.  As in Fig. 2, but for mean precipitation frequency (units: d) during 1998–2016

    图  4  图2,但为1998~2016年多年平均降水强度(单位:mm d−1)分布

    Figure  4.  As in Fig. 2, but for mean precipitation intensity (units: mm d−1) during 1998–2016

    图  5  图2,但为1998~2016年多年平均日最大降水量(Rx1day,单位:mm)分布

    Figure  5.  As in Fig. 2, but for mean maximum daily precipitation (Rx1day, units: mm) during 1998–2016

    图  6  图2,但为1998~2016年多年平均大雨日数(R25,单位:d)分布

    Figure  6.  As in Fig. 2, but for mean number of heavy rain days (R25, units: d) during 1998–2016

    图  7  1998~2016年多年平均的四个子区域(a)SEU、(b)IND、(c)SEA、(d)NEA季节平均降水量分布,(e)季节平均降水量柱状图。DJF、MAM、JJA、SON分别表示春季、夏季、秋季、冬季

    Figure  7.  Seasonal mean precipitation for (a) SEU, (b) IND, (c) SEA, (d) NEA, and (e) a histogram of seasonal mean precipitation averaged in 1998–2016. DJF, MAM, JJA, SON represent spring, summer, autumn, winter, respectively

    图  8  1998~2016年多年平均的逐日降水量(单位:mm d−1)在(a)SEU、(b)IND、(c)SEA、(d)NEA区域平均的时间序列。右侧两列数字分别表示平均值(MEAN)和标准差(STD)

    Figure  8.  Time series for daily precipitation (units: mm d−1) averaged over (a) SEU, (b) IND, (c) SEA, (d) NEA during 1998–2016. Numbers on the right side represent means (MEAN) and standard deviations (STD) for different data sets

    图  9  1998~2016年多年平均对流性降水(左)和大尺度降水(右)空间分布(单位:mm d−1):(a、b)CESM;(c、d)Lcam4;(e、f)Lcam5;(g、h)Hcam5

    Figure  9.  Spatial distributions (units: mm d−1) of mean daily convective precipitation (left) and large-scale precipitation (right) during 1998–2016: (a, b) CESM; (c, d) Lcam4; (e, f) Lcam5; (g, h) Hcam5

    图  10  1998~2016年多年平均的对流性降水(左)和大尺度降水(右)在(a)SEU、(b)IND、(c)SEA、(d)NEA区域平均的时间序列。图中数字第一行为平均值,第二行为标准差

    Figure  10.  Time series for daily convective precipitation (left) and large-scale precipitation (right) averaged over (a) SEU, (b) IND, (c) SEA, (d) NEA during 1998–2016. Numbers in the first row and second row are the means (MEAN) and standard deviations (STD) for different data sets

    图  11  1998~2016年多年平均的Lcam4、Hcam5分别与Lcam5在纬向方向(左)和经向方向(右)的水汽通量差异(单位:kg m−1 s−1):(a–d)JJA;(e–h)DJF

    Figure  11.  Spatial distributions of mean differences between Lcam4, Hcam5 and Lcam5 for zonal (left) and meridional (right) water vapor fluxes (units: kg m−1 s−1) during 1998–2016: (a–d) JJA; (e–f) DJF

    图  12  1998~2016年多年平均的Lcam4、Hcam5分别与Lcam5在纬向方向(左)和经向方向(右)的气候态水汽输送差异(单位:kg m−1 s−1):(a–d)MAM;(e–h)SON

    Figure  12.  Spatial distributions of annual mean differences between Lcam4, Hcam5 and Lcam5 for zonal (left) and meridional (right) water vapor fluxes (units: kg m−1 s−1) during 1998–2016: (a–d) MAM; (e–f) SON

    表  1  CAS-ESM模式的四组试验设置

    Table  1.   Design of four experiments for CAS-ESM (Earth System Model, Chinese Academy of Sciences)

    试验名称模式分辨率物理参数
    化方案
    积分
    步长/s
    水平方向垂直方向
    CESMNCAR CESM v1.2.21.9°×2.5°L30CAM51800
    Lcam4CAS-ESM v1.01.4°×1.4°L26CAM41200
    Lcam51.4°×1.4°L30CAM51200
    Hcam50.5°×0.5°L30CAM5900
    下载: 导出CSV

    表  2  CAM4和CAM5的物理参数化方案组合配置

    Table  2.   Description of physical parameterization schemes in CAM4 (Community Atmosphere Model version 4) and CAM5 (Community Atmosphere Model version 5)

    CAM4CAM5
    深对流方案Zhang and McFarlane方案(Zhang and McFarlane, 1995Zhang and McFarlane方案(Zhang and McFarlane, 1995
    浅对流方案Hack方案(Hack, 1994Park and Bretherton方案(Park and Bretherton, 2009
    边界层湍流方案Dry turbulence方案(Holtslag and Boville, 1993Moist turbulence 方案(Bretherton and Park, 2009
    云微物理参数化方案Prognostic sigle-moment 方案(Rasch and Kristjánsson, 1998Prognostic double-moment 方案(Morrison and Gettelman, 2008
    辐射方案CAM方案(Collins et al., 2004RRTMG方案(Neale et al., 2010
    下载: 导出CSV

    表  3  四组试验CESM、Lcam4、Lcam5、Hcam5分别与GPCC的空间相关系数(CC)及标准化方差(NSD)

    Table  3.   Spatial correlation coefficients (CC) and normalized standardized deviations (NSD) between GPCC and CESM, Lcam4, Lcam5, Hcam5

    平均降水量降水频率降水强度Rx1dayR25
    CCNSDCCNSDCCNSDCCNSDCCNSD
    CESM0.731.160.781.650.690.590.680.480.560.80
    Lcam40.580.960.741.410.600.480.570.360.310.58
    Lcam50.591.010.681.8710.570.430.440.330.160.49
    Hcam50.770.940.761.470.740.470.720.640.510.58
    下载: 导出CSV
  • [1] Bacmeister J T, Wehner M F, Neale R B, et al. 2014. Exploratory high-resolution climate simulations using the community atmosphere model (CAM) [J]. J. Climate, 27(9): 3073−3099. doi: 10.1175/JCLI-D-13-00387.1
    [2] Bretherton C S, Park S. 2009. A new moist turbulence parameterization in the community atmosphere model [J]. J. Climate, 22(12): 3422−3448. doi: 10.1175/2008JCLI2556.1
    [3] Chen D, Dai A G. 2018. Dependence of estimated precipitation frequency and intensity on data resolution [J]. Climate Dyn., 50(9): 3625−3647. doi: 10.1007/s00382-017-3830-7
    [4] Collins W D, Rasch P J, Boville B A, et al. 2004. Description of the NCAR community atmosphere model (CAM 3.0) [R]. NCAR/TN-464+STR.
    [5] Dai A G. 2006. Precipitation characteristics in eighteen coupled climate models [J]. J. Climate, 19(18): 4605−4630. doi: 10.1175/JCLI3884.1
    [6] Ding Y H, Ren G Y, Zhao Z C, et al. 2007. Detection, causes and projection of climate change over China: An overview of recent progress [J]. Adv. Atmos. Sci., 24(6): 954−971. doi: 10.1007/s00376-007-0954-4
    [7] Duan W L, Hanasaki N, Shiogama H, et al. 2019. Evaluation and future projection of Chinese precipitation extremes using large ensemble high-resolution climate simulations [J]. J. Climate, 32(8): 2169−2183. doi: 10.1175/JCLI-D-18-0465.1
    [8] Easterling D R, Evans J L, Groisman P Y, et al. 2000. Observed variability and trends in extreme climate events: A brief review [J]. Bull. Amer. Meteor. Soc., 81(3): 417−426. doi:10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2
    [9] Gates W L. 1992. An AMS continuing series: Global change—AMIP: The atmospheric model intercomparison project [J]. Bull. Amer. Meteor. Soc., 73(12): 1962−1970. doi:10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2
    [10] Hack J J. 1994. Parameterization of moist convection in the national center for atmospheric research community climate model (CCM2) [J]. J. Geophys. Res. Atmos., 99(D3): 5551−5568. doi: 10.1029/93JD03478
    [11] Han Z Y, Shi Y, Wu J, et al. 2019. Combined dynamical and statistical downscaling for high-resolution projections of multiple climate variables in the Beijing–Tianjin–Hebei region of China [J]. J. Appl. Meteor. Climatol., 58(11): 2387−2403. doi: 10.1175/JAMC-D-19-0050.1
    [12] Holtslag A A M, Boville B A. 1993. Local versus nonlocal boundary–layer diffusion in a global climate model [J]. J. Climate, 6(10): 1825−1842. doi:10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2
    [13] Houze Jr R A. 1997. Stratiform precipitation in regions of convection: A meteorological paradox? [J]. Bull. Amer. Meteor. Soc., 78(10): 2179−2196. doi:10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2
    [14] 黄荣辉, 蔡榕硕, 陈际龙, 等. 2006. 我国旱涝气候灾害的年代际变化及其与东亚气候系统变化的关系 [J]. 大气科学, 30(5): 730−743. doi: 10.3878/j.issn.1006-9895.2006.05.02

    Huang Ronghui, Cai Rongshuo, Chen Jilong, et al. 2006. Interdecaldal variations of drought and flooding disasters in China and their association with the East Asian climate system [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 30(5): 730−743. doi: 10.3878/j.issn.1006-9895.2006.05.02
    [15] Hurrell J W, Hack J J, Shea D, et al. 2008. A new sea surface temperature and sea ice boundary dataset for the community atmosphere model [J]. J. Climate, 21(19): 5145−5153. doi: 10.1175/2008JCLI2292.1
    [16] 姜大膀, 王会军, 朗咸梅. 2004. 全球变暖背景下东亚气候变化的最新情景预测 [J]. 地球物理学报, 47(4): 590−596. doi: 10.3321/j.issn:0001-5733.2004.04.007

    Jiang D B, Wang H J, Lang X M. 2004. East Asian climate change trend under global warming background [J]. Chinese J. Geophys. (in Chinese), 47(4): 590−596. doi: 10.3321/j.issn:0001-5733.2004.04.007
    [17] Joyce R J, Janowiak J E, Arkin P A, et al. 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution [J]. J. Hydrometeor., 5(3): 487−503. doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2
    [18] Kong X H, Wang A H, Bi X Q, et al. 2020. Effects of horizontal resolution on hourly precipitation in AGCM simulations [J]. J. Hydrometeor., 21(4): 643−670. doi: 10.1175/JHM-D-19-0148.1
    [19] Kutzbach J E, Guetter P J, Ruddiman W F, et al. 1989. Sensitivity of climate to late Cenozoic uplift in southern Asia and the American West: Numerical experiments [J]. J. Geophys. Res. Atmos., 94(D15): 18393−18407. doi: 10.1029/JD094iD15p18393
    [20] Li L J, Wang B, Zhou T J. 2007. Contributions of natural and anthropogenic forcings to the summer cooling over eastern China: An AGCM study [J]. Geophys. Res. Lett., 34(18): L18807. doi: 10.1029/2007GL030541
    [21] 李星雨, 毕训强, 张贺. 2018. 全球模式NCAR CESM和CAS ESM对亚洲东部夏季气候的模拟性能评估: 气候平均态和降水日变化分析 [J]. 气候与环境研究, 23(6): 645−656. doi: 10.3878/j.issn.1006-9585.2018.18050

    Li Xingyu, Bi Xunqiang, Zhang He. 2018. Evaluation of NCAR CESM and CAS ESM models for the simulation of boreal summer climate over eastern Asia: Climatological mean and diurnal cycle of precipitation [J]. Climatic and Environmental Research (in Chinese), 23(6): 645−656. doi: 10.3878/j.issn.1006-9585.2018.18050
    [22] Lu R Y. 2002. Indices of the summertime western North Pacific subtropical high [J]. Adv. Atmos. Sci., 19(6): 1004−1028. doi: 10.1007/s00376-002-0061-5
    [23] Luo F F, Wilcox L, Dong B W, et al. 2020. Projected near-term changes of temperature extremes in Europe and China under different aerosol emissions [J]. Environ. Res. Lett., 15(3): 034013. doi: 10.1088/1748-9326/ab6b34
    [24] Miao C Y, Sun Q H, Borthwick A G L, et al. 2016. Linkage between hourly precipitation events and atmospheric temperature changes over China during the warm season [J]. Sci. Rep., 6(1): 22543. doi: 10.1038/srep22543
    [25] Morrison H, Gettelman A. 2008. A new two-moment bulk stratiform cloud microphysics scheme in the community atmosphere model, version 3 (CAM3). Part I: Description and numerical tests [J]. J. Climate, 21(15): 3642−3659. doi: 10.1175/2008JCLI2105.1
    [26] Neale R B, Gettelman A, Park A, et al. 2010. Description of the NCAR community atmosphere model (CAM 5.0) [R]. Tech. Rep. NCAR/TN-486+STR.
    [27] Oleson K W, Lawrence D M, Bonan G B, et al. 2013. Technical description of version 4.5 of the Community Land Model (CLM) [R]. No. NCAR/TN-503+STR.
    [28] Park S, Bretherton C S. 2009. The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the community atmosphere model [J]. J. Climate, 22(12): 3449−3469. doi: 10.1175/2008JCLI2557.1
    [29] Pendergrass A G, Knutti R. 2018. The uneven nature of daily precipitation and its change [J]. Geophys. Res. Lett., 45(21): 11980−11988. doi: 10.1029/2018GL080298
    [30] Qian C, Zhou T J. 2014. Multidecadal variability of North China aridity and its relationship to PDO during 1900-2010 [J]. J. Climate, 27(3): 1210−1222. doi: 10.1175/JCLI-D-13-00235.1
    [31] Rasch P J, Kristjánsson J E. 1998. A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations [J]. J. Climate, 11(7): 1587−1614. doi:10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2
    [32] Rayner N A, Parker D E, Horton E B, et al. 2003. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century [J]. J. Geophys. Res. Atmos., 108(D14): 4407. doi: 10.1029/2002JD002670
    [33] Ren X J, Yang X Q, Sun X G. 2013. Zonal oscillation of western Pacific subtropical high and subseasonal SST variations during Yangtze persistent heavy rainfall events [J]. J. Climate, 26(22): 8929−8946. doi: 10.1175/JCLI-D-12-00861.1
    [34] Reynolds R W, Rayner N A, Smith T M, et al. 2002. An improved in situ and satellite SST analysis for climate [J]. J. Climate, 15(13): 1609−1625. doi:10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2
    [35] Rodó X, Baert E, Comín F A. 1997. Variations in seasonal rainfall in southern Europe during the present century: Relationships with the North Atlantic oscillation and the El Niño–southern oscillation [J]. Climate Dyn., 13(4): 275−284. doi: 10.1007/s003820050165
    [36] Schamm K, Ziese M, Becker A, et al. 2014. Global gridded precipitation over land: A description of the new GPCC first guess daily product [J]. Earth Syst. Sci. Data, 6(1): 49−60. doi: 10.5194/essd-6-49-2014
    [37] 苏宝煌, 姜大膀, 田芝平. 2018. 全球山脉隆升影响副热带干旱气候的模拟 [J]. 科学通报, 63(12): 1142−1153. doi: 10.1360/N972017-01275

    Su Baohuang, Jiang Dabang, Tian Zhiping. 2018. Numerical simulation on the impact of global mountain uplift on the subtropical arid climate [J]. Chin. Sci. Bull. (in Chinese), 63(12): 1142−1153. doi: 10.1360/N972017-01275
    [38] Sun B Y, Bi X Q. 2019. Validation for a tropical belt version of WRF: Sensitivity tests on radiation and cumulus convection parameterizations [J]. Atmos. Oceanic Sci. Lett., 12(3): 192−200. doi: 10.1080/16742834.2019.1590118
    [39] Sun Y, Solomon S, Dai A G, et al. 2006. How often does it rain? [J]. J. Climate, 19(6): 916−934. doi: 10.1175/JCLI3672.1
    [40] Trenberth K E, Dai A G, Rasmussen R M, et al. 2003. The changing character of precipitation [J]. Bull. Amer. Meteor. Soc., 84(9): 1205−1218. doi: 10.1175/BAMS-84-9-1205
    [41] Williamson D L. 2013. The effect of time steps and time-scales on parametrization suites [J]. Quart. J. Roy. Meteor. Soc., 139(671): 548−560. doi: 10.1002/qj.1992
    [42] Williamson D L, Olson J G. 2003. Dependence of aqua-planet simulations on time step [J]. Quart. J. Roy. Meteor. Soc., 129(591): 2049−2064. doi: 10.1256/qj.02.62
    [43] 吴佳, 周波涛, 徐影. 2015. 中国平均降水和极端降水对气候变暖的响应: CMIP5模式模拟评估和预估 [J]. 地球物理学报, 58(9): 3048−3060. doi: 10.6038/cjg20150903

    Wu Jia, Zhou Botao, Xu Ying. 2015. Response of precipitation and its extremes over China to warming: CMIP5 simulation and projection [J]. Chinese J. Geophys. (in Chinese), 58(9): 3048−3060. doi: 10.6038/cjg20150903
    [44] Wu C L, Liu X H, Lin Z H, et al. 2017. Exploring a variable-resolution approach for simulating regional climate in the Rocky Mountain region using the VR-CESM [J]. J. Geophys. Res. Atmos., 122(20): 10939−10965. doi: 10.1002/2017JD027008
    [45] Xie P P, Joyce R, Wu S R, et al. 2017. Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates [J]. J. Hydrometeor., 18(6): 1617−1641. doi: 10.1175/JHM-D-16-0168.1
    [46] Xie X N, Zhang H, Liu X D, et al. 2018. Role of microphysical parameterizations with droplet relative dispersion in IAP AGCM 4.1 [J]. Adv. Atmos. Sci., 35(2): 248−259. doi: 10.1007/s00376-017-7083-5
    [47] Xie J B, Zhang M H, Xie Z H, et al. 2020. An orographic-drag parametrization scheme including orographic anisotropy for all flow directions [J]. Journal of Advances in Modeling Earth Systems, 12(3): e2019MS001921. doi: 10.1029/2019MS001921
    [48] Xue F, Bi X Q, Lin Y H. 2001. Modelling the global monsoon system by IAP 9L AGCM [J]. Adv. Atmos. Sci., 18(3): 404−412. doi: 10.1007/BF02919319
    [49] 杨萍, 肖子牛, 石文静. 2017. 基于小时降水资料研究北京地区降水的精细化特征 [J]. 大气科学, 41(3): 475−489. doi: 10.3878/j.issn.1006-9895.1606.16134

    Yang Ping, Xiao Ziniu, Shi Wenjing. 2017. Fine-scale characteristics of rainfall in Beijing urban area based on a high-density autonomous weather stations (AWS) dataset [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 41(3): 475−489. doi: 10.3878/j.issn.1006-9895.1606.16134
    [50] 宇如聪, 李建, 陈昊明, 等. 2014. 中国大陆降水日变化研究进展 [J]. 气象学报, 72(5): 948−968. doi: 10.11676/qxxb2014.047

    Yu Rucong, Li Jian, Chen Haoming, et al. 2014. Progress in studies of the precipitation diurnal variation over contiguous China [J]. Acta Meteorologica Sinica (in Chinese), 72(5): 948−968. doi: 10.11676/qxxb2014.047
    [51] Yuan W H, Yu R C, Zhang M H, et al. 2013. Diurnal cycle of summer precipitation over subtropical East Asia in CAM5 [J]. J. Climate, 26(10): 3159−3172. doi: 10.1175/JCLI-D-12-00119.1
    [52] Zarzycki C M, Levy M N, Jablonowski C, et al. 2014. Aquaplanet experiments using CAM’s variable-resolution dynamical core [J]. J. Climate, 27(14): 5481−5503. doi: 10.1175/JCLI-D-14-00004.1
    [53] 曾庆存, 林朝晖. 2010. 地球系统动力学模式和模拟研究的进展 [J]. 地球科学进展, 25(1): 1−6. doi: 10.11867/j.issn.1001-8166.2010.01.0001

    Zeng Qingcun, Lin Zhaohui. 2010. Recent progress on the earth system dynamical model and its numerical simulations [J]. Adv. Earth Sci. (in Chinese), 25(1): 1−6. doi: 10.11867/j.issn.1001-8166.2010.01.0001
    [54] Zhang G J, McFarlane N A. 1995. Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model [J]. Atmos.–Ocean, 33(3): 407−446. doi: 10.1080/07055900.1995.9649539
    [55] Zhang H, Zhai P M. 2011. Temporal and spatial characteristics of extreme hourly precipitation over eastern China in the warm season [J]. Adv. Atmos. Sci., 28(5): 1177−1183. doi: 10.1007/s00376-011-0020-0
    [56] Zhang H, Zhang M H, Zeng Q C. 2013. Sensitivity of simulated climate to two atmospheric models: Interpretation of differences between dry models and moist models [J]. Mon. Wea. Rev., 141(5): 1558−1576. doi: 10.1175/MWR-D-11-00367.1
    [57] Zhou B T, Wen Q H, Xu Y, et al. 2014. Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles [J]. J. Climate, 27(17): 6591−6611. doi: 10.1175/JCLI-D-13-00761.1
    [58] Zhou B T, Wu J, Xu Y, et al. 2019. Projected changes in autumn rainfall over West China: Results from an ensemble of dynamical downscaling simulations [J]. Int. J. Climatol., 39(12): 4869−4882. doi: 10.1002/joc.6115
  • 加载中
图(12) / 表(3)
计量
  • 文章访问数:  177
  • HTML全文浏览量:  55
  • PDF下载量:  59
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-06-20
  • 录用日期:  2021-04-09
  • 网络出版日期:  2021-04-07
  • 刊出日期:  2021-07-15

目录

    /

    返回文章
    返回