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中国降水季节性的预估

姚世博 姜大膀 范广洲

姚世博, 姜大膀, 范广洲. 中国降水季节性的预估[J]. 大气科学, 2018, 42(6): 1378-1392. doi: 10.3878/j.issn.1006-9895.1801.17219
引用本文: 姚世博, 姜大膀, 范广洲. 中国降水季节性的预估[J]. 大气科学, 2018, 42(6): 1378-1392. doi: 10.3878/j.issn.1006-9895.1801.17219
Shibo YAO, Dabang JIANG, Guangzhou FAN. Projection of Precipitation Seasonality over China[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(6): 1378-1392. doi: 10.3878/j.issn.1006-9895.1801.17219
Citation: Shibo YAO, Dabang JIANG, Guangzhou FAN. Projection of Precipitation Seasonality over China[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(6): 1378-1392. doi: 10.3878/j.issn.1006-9895.1801.17219

中国降水季节性的预估

doi: 10.3878/j.issn.1006-9895.1801.17219
基金项目: 

国家重点研发计划项目 2016YFA0600704

国家自然科学基金项目 41421004

详细信息
    作者简介:

    姚世博, 男, 1991年出生, 博士研究生, 主要从事气候变化研究。E-mail:190636012@qq.com

    通讯作者:

    姜大膀, E-mail:jiangdb@mail.iap.ac.cn

  • 中图分类号: P467

Projection of Precipitation Seasonality over China

Funds: 

National Key Research and Development Program of China 2016YFA0600704

National Natural Science Foundation of China (NSFC) 41421004

  • 摘要: 本文使用国际耦合模式比较计划第五阶段(CMIP5)中共46个全球气候模式的数值试验数据,通过评估择优选取了14个模式来预估21世纪中国各季节降水百分率及其变率。结果表明,模式集合平均能够较好地模拟各季节降水百分率及其变率,但模式与观测间、各模式间都存在一定不同,空间上西部差异较大,季节上夏季差异明显。21世纪中国降水百分率整体表现为夏季大冬季小,但存在区域性,如华南春季降水百分率大于夏季。与1986~2004年相比,中国降水百分率整体表现为在夏季显著减少,冬春季显著增加,但高原则与之相反。此外,模式对于长江中下游地区降水百分率的预估存在较大不确定性。RCP8.5情景下降水季节性变幅要大于RCP4.5情景。降水季节性的变率在四季均表现出一定的增加趋势,但21世纪早、中和末期与1986~2004年相比并无显著差异(置信水平为95%)。
  • 图  1  46个模式模拟的各季节降水百分率及其变率与观测资料的泰勒图:(a)春季;(b)夏季;(c)秋季;(d)冬季。图中参考点REF为观测资料;数字为表 1中模式序号;“clm”表示降水百分率气候态;“var”表示降水百分率的变率

    Figure  1.  Taylor diagram of seasonal precipitation proportion and its variability (the ratio of seasonal of annual precipitation and its interannual standard deviation) simulated by the 46 models compared to the observational data: (a) Spring; (b) summer; (c) autumn; (d) winter. The reference point is the observational data; the numbers denote each model's order number in Table 1; "clm" indicates the climatology of seasonal precipitation proportion; and "var" indicates the variability of seasonal precipitation proportion

    图  2  14个CMIP5模式模拟的1986~2004年各季节降水百分率气候态与观测的差异(仅给出达到95%置信水平的部分):(a)春季;(b)夏季;(c)秋季;(d)冬季

    Figure  2.  Geographical distributions of differences between the 14-model ensemble mean and observations for the climatology of seasonal precipitation proportion during 1986–2004 (only those results passing the significance test at the 95% confidence level are presented): (a) Spring; (b) summer; (c) autumn; (d) winter

    图  3  14个CMIP5模式模拟的1986~2004年各季节降水百分率气候态的模式间标准差:(a)春季;(b)夏季;(c)秋季;(d)冬季

    Figure  3.  Geographical distributions of standard deviations among 14 climate models for the climatology of seasonal precipitation proportion during 1986–2004: (a) Spring; (b) summer; (c) autumn; (d) winter

    图  4  图 3,但为降水百分率变率的标准差

    Figure  4.  Same as Fig. 3, but for the standard deviations of variability of seasonal precipitation proportion

    图  5  在RCP4.5(左列)和RCP8.5(右列)情景下,多模式集合平均预估的2006~2099年各季节降水占全年降水百分率的气候态(填色)与降水百分率变化趋势[圆圈,(单位:%(10a)-1]分布:(a、b)春季;(c、d)夏季;(e、f)秋季;(g、h)冬季。绿色(红色)实心圆表示增加(减小)趋势,且置信水平为95%

    Figure  5.  The climatology (shaded) and trends [circles, units: %(10a)-1] of seasonal precipitation proportion during 2006 to 2099 derived from multi-model ensemble mean under RCP4.5 (left panel) and RCP8.5 (right panel) scenarios. Solid circles indicate the confidence level higher than 95%, and green (red) indicates increasing (decreasing) trend: (a, b) Spring; (c, d) summer; (e, f) autumn; (g, h) winter

    图  6  在RCP4.5(左列)和RCP8.5(右列)情景下,多模式集合平均预估的21世纪末期各季节降水百分率变化的空间分布(相对于1986~2004年参考时段,仅给出通过95%置信水平的部分):(a、b)春季;(c、d)夏季;(e、f)秋季;(g、h)冬季。图中矩形代表 5个子区域范围

    Figure  6.  Spatial distributions of changes in climatology of seasonal precipitation proportion during the late 21st century derived from multi-model ensemble mean under RCP4.5 (left panel) and RCP8.5 (right panel) scenarios (relative to 1986–2004, only those results passing the significance test at the 95% confidence level are presented): (a, b) Spring; (c, d) summer; (e, f) autumn; (g, h) winter. The rectangles indicate the five subregions

    图  7  相对于1986~2004年参考时段,未来不同情景下多模式集合平均预估的各区域平均的四季降水百分率的变化。绿、红、黄、蓝实线分别表示春、夏、秋、冬四个季节;阴影区域为模式结果间的一个标准差范围;图中缩写与表 2分区相对应

    Figure  7.  Changes in seasonal precipitation proportion relative to the reference period 1986–2004 averaged over China and over individual subregions derived from multi-model ensemble mean for different scenarios. Green, red, yellow, and blue lines represent spring, summer, autumn, and winter, respectively. Shadings represent plus/minus one standard deviation among the models from the ensemble mean. The acronyms represent the regions listed in Table 2

    图  8  在RCP4.5和RCP8.5情景下,各模式以及多模式集合平均预估的各区域四季降水百分率趋势。红色表示RCP4.5情景,蓝色表示RCP8.5情景;填色为通过95%的显著性检验;图中缩写与表 2分区相对应;横坐标数字对应表 1中的模式序号,“mme”表示多模式集合平均

    Figure  8.  Trends of seasonal precipitation proportion averaged over China and over individual subregions derived from each model and multi-model ensemble mean under the RCP4.5 and RCP8.5 scenarios. Red/Blue represents the RCP4.5/RCP8.5. Results passing the test at the 95% confidence level are shaded. The acronyms represent the regions listed in Table 2. The abscissa numbers represent the models listed in Table 1 and "mme" indicates multi-model ensemble mean

    图  9  在RCP4.5(左列)和RCP8.5(右列)情景下,多模式集合平均预估的2006~2099年各季节降水占全年降水百分比的标准差(填色)和降水百分比的9年滑动标准差的变化趋势[圆圈,单位:%(10a)-1]:(a、b)春季;(c、d)夏季;(e、f)秋季;(g、h)冬季。红色(绿色)实心圆表示增加(减小)趋势,且置信水平为95%

    Figure  9.  Standard deviations (shadings) of seasonal precipitation proportion and trends of 9-year running standard deviations of seasonal precipitation proportion [circles, units: %(10a)-1] during 2006–2099 derived from multi-model ensemble means under the RCP4.5 (left panel) and RCP8.5 (right panel) scenarios: (a, b) Spring; (c, d) summer; (e, f) autumn; (g, h) winter. Results passing significance test at the 95% confidence level are marked by solid circles, and red (green) indicates increasing (decreasing) trend

    表  1  所选CMIP5气候模式的基本信息

    Table  1.   Basic information of the CMIP5 climate models

    序号 模式名称 所属国家 所属机构简称 分辨率(纬向格点数×经向格点数)
    01 ACCESS1.0 澳大利亚 CSIRO-BOM 192×145
    02** ACCESS1.3 澳大利亚 CSIRO-BOM 192×145
    03 BCC-CSM1.1 中国 BCC 128×64
    04 BCC-CSM1.1(m) 中国 BCC 320×160
    05 BNU-ESM 中国 GCESS 128×64
    06 CanESM2 加拿大 CCCma 128×64
    07** CCSM4 美国 NCAR 288×192
    08** CESM1(BGC) 美国 NSF-DOE-NCAR 288×192
    09** CESM1(CAM5) 美国 NSF-DOE-NCAR 128×192
    10* CESM1(FASTCHEM) 美国 NSF-DOE-NCAR 288×192
    11* CESM1(WACCM) 美国 NSF-DOE-NCAR 144×96
    12 CMCC-CESM 意大利 CMCC 96×48
    13 CMCC-CM 意大利 CMCC 480×240
    14 CMCC-CMS 意大利 CMCC 192×96
    15* CNRM-CM5-2 法国 CNRM-CERFACS 256×128
    16** CNRM-CM5 法国 CNRM-CERFACS 256×128
    17 CSIRO-Mk3.6.0 澳大利亚 CSIRO-QCCCE 192×96
    18 EC-EARTH 欧洲 ICHEC & SMHI 320×160
    19 FGOALS-g2 中国 LASG-CESS 128×60
    20 FIO-ESM 中国 FIO 128×64
    21 GFDL-CM2.1 美国 NOAA-GFDL 144×90
    22** GFDL-CM3 美国 NOAA-GFDL 144×90
    23 GFDL-ESM2G 美国 NOAA-GFDL 144×90
    24 GFDL-ESM2M 美国 NOAA-GFDL 144×90
    25 GISS-E2-H-CC 美国 NASA-GISS 144×90
    26 GISS-E2-H 美国 NASA-GISS 144×90
    27** GISS-E2-R-CC 美国 NASA-GISS 144×90
    28** GISS-E2-R 美国 NASA-GISS 144×90
    29* HadCM3 英国 MOHC 96×73
    30** HadGEM2-AO 韩国 NIMR-KMA 192×145
    31** HadGEM2-CC 英国 MOHC 192×145
    32** HadGEM2-ES 英国 MOHC 192×145
    33 INM-CM4 俄罗斯 INM 180×120
    34 IPSL-CM5A-LR 法国 IPSL 96×96
    35 IPSL-CM5A-MR 法国 IPSL 144×143
    36 IPSL-CM5B-LR 法国 IPSL 96×96
    37* MIROC4h 日本 MIROC 640×320
    38** MIROC5 日本 MIROC 256×128
    39 MIROC-ESM-CHEM 日本 MIROC 128×64
    40** MIROC-ESM 日本 MIROC 128×64
    41 MPI-ESM-LR 德国 MPI-M 192×96
    42 MPI-ESM-MR 德国 MPI-M 192×96
    43 MPI-ESM-P 德国 MPI-M 192×96
    44 MRI-CGCM3 日本 MRI 320×160
    45 NorESM1-ME 挪威 NCC 144×96
    46** NorESM1-M 挪威 NCC 144×96
    *标识为优选出的19个模式,**标识为最后用于预估的14个模式
    下载: 导出CSV

    表  2  中国全区及5个子区的经纬度范围

    Table  2.   The longitude and latitude ranges of whole China and five subregions

    地区名称 简称 纬度范围 经度范围
    中国全区 All 17°~55°N 72°~136°E
    内蒙 IM 37°~45°N 100°~115°E
    长江中下游 YZ 28°~35°N 110°~122°E
    华西 WC 30°~35°N 100°~110°E
    西北 NWC 35°~48°N 72°~95°E
    高原地区 SWC 26°~35°N 75°~90°E
    下载: 导出CSV
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  • 收稿日期:  2017-08-24
  • 网络出版日期:  2018-01-18
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