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CMIP5模式对春季北极涛动影响后期冬季ENSO不对称性的模拟能力分析

郑玉琼 陈文 陈尚锋

郑玉琼, 陈文, 陈尚锋. CMIP5模式对春季北极涛动影响后期冬季ENSO不对称性的模拟能力分析[J]. 大气科学, 2020, 44(2): 435-454. doi: 10.3878/j.issn.1006-9895.1908.19124
引用本文: 郑玉琼, 陈文, 陈尚锋. CMIP5模式对春季北极涛动影响后期冬季ENSO不对称性的模拟能力分析[J]. 大气科学, 2020, 44(2): 435-454. doi: 10.3878/j.issn.1006-9895.1908.19124
ZHENG Yuqiong, CHEN Wen, CHEN Shangfeng. The Ability of CMIP5 Models in Capturing the Asymmetric Impact of the Spring Arctic Oscillation on the Following Winter El Niño—Southern Oscillation[J]. Chinese Journal of Atmospheric Sciences, 2020, 44(2): 435-454. doi: 10.3878/j.issn.1006-9895.1908.19124
Citation: ZHENG Yuqiong, CHEN Wen, CHEN Shangfeng. The Ability of CMIP5 Models in Capturing the Asymmetric Impact of the Spring Arctic Oscillation on the Following Winter El Niño—Southern Oscillation[J]. Chinese Journal of Atmospheric Sciences, 2020, 44(2): 435-454. doi: 10.3878/j.issn.1006-9895.1908.19124

CMIP5模式对春季北极涛动影响后期冬季ENSO不对称性的模拟能力分析

doi: 10.3878/j.issn.1006-9895.1908.19124
基金项目: 国家重点研发计划项目2016YFA0600604,国家自然科学基金资助项目41721004、41605050,中国科学院前沿科学重点研究项目YZDY-SSW-DQC024

The Ability of CMIP5 Models in Capturing the Asymmetric Impact of the Spring Arctic Oscillation on the Following Winter El Niño—Southern Oscillation

  • 摘要: 根据观测资料的研究指出春季北极涛动(Arctic Oscillation, AO)对随后冬季厄尔尼诺—南方涛动(El Niño-Southern Oscillation, ENSO)的影响具有明显不对称性。春季AO处于正位相时,它对随后冬季厄尔尼诺(El Niño)事件的影响显著,然而春季AO负位相对随后冬季拉尼娜(La Niña)的影响不明显。本研究分析了30个来自CMIP5的耦合模式对春季AO与随后冬季ENSO不对称性关系的模拟能力。30个CMIP5耦合模式中,只有CNRM-CM5和GISS-E2-H-CC模式能较好地抓住春季AO与冬季ENSO的联系。进一步分析这两个模式中春季AO与冬季ENSO的不对称性关系,发现CNRM-CM5模式能较好地再现春季AO与冬季ENSO的非对称关系,即春季AO正(负)位相会导致赤道中东太平洋出现El Niño(La Niña)型海表温度增暖(冷却)。然而,GISS-E2-H-CC模式的模拟结果显示,春季AO对随后冬季ENSO的影响是对称的。本文随后解释了CNRM-CM5(GISS-E2-H-CC)模式能(不能)模拟出春季AO与冬季ENSO不对称关系的原因。对于CNRM-CM5模式,在春季AO正位相年,副热带西北太平洋上空存在明显的异常气旋和正降水异常,正降水异常通过Gill型大气响应对赤道西太平洋异常西风的形成和维持起着重要作用,异常西风通过激发向东传播的暖赤道Kelvin波对随后冬季El Niño事件的发生产生显著的影响;然而,在春季AO负位相年,副热带北太平洋的异常反气旋和负降水异常较弱,导致赤道西太平洋的异常东风不明显,因此,春季AO负异常对随后冬季La Niña的影响不显著。所以,CNRM-CM5模式能够较好地抓住春季AO对随后冬季ENSO事件的非对称性影响。相比之下,对于GISS-E2-H-CC模式,春季AO正(负)位相年副热带西北太平洋上存在显著的正(负)降水异常,通过Gill型大气响应在赤道西太平洋激发出明显的异常西(东)风从而影响随后冬季的El Niño(La Niña)事件。因此,在GISS-E2-H-CC模式中,春季AO对随后冬季ENSO具有对称性影响。另外,模式捕捉春季AO对随后冬季ENSO非对称性影响的能力与模式对春季AO空间结构的模拟能力有一定的联系。
  • 图  1  1958~2005年(a)春季AO指数回归的春季SLP异常(单位:hPa),(b)标准化的春季AO指数时间序列。图a中打点区域表示SLP异常通过95%信度的显著性检验

    Figure  1.  (a) Spring SLP (sea level pressure) anomalies (units: hPa) regressed upon the spring AO (Arctic Oscillation) index, (b) normalized spring AO index during the period 1958–2005. In Fig. a, the dotted areas indicate SLP anomalies significant above the 95% confidence level

    图  2  观测结果中春季AO(a–f)正位相年和(g–l)负位相年合成的SST异常(单位:°C)在(a、g)3、4月,(b、h)5、6月,(c、i)7、8月,(d、j)9、10月,(e、k)11、12月,(f、l)1、2月的分布。打点区域表示SST异常通过90%信度水平的显著性检验

    Figure  2.  Composites of SST anomalies (units: °C) in (a, g) March, April, (b, h) May, June, (c, i) July, August, (d, j) September, October, (e, k) November, December, (f, l) January, February for (a–f) positive and (g–l) negative spring AO years from observations. The dotted areas indicate SST anomalies significant above the 90% confidence level

    图  3  观测结果中春季AO(a–f)正位相年和(g–l)负位相年合成的降水异常(阴影,单位:mm d−1)及850 hPa水平风场异常(箭头,单位:m s−1)在(a、g)3、4月,(b、h)5、6月,(c、i)7、8月,(d、j)9、10月,(e、k)11、12月,(f、l)1、2月的分布。打点区域表示降水异常通过90%信度水平的显著性检验,图中没有显示小于0.2 m s−1的纬向或者经向风异常

    Figure  3.  Composites of precipitation anomalies (shadings, units: mm d−1) and 850-hPa horizontal wind anomalies (arrows, units: m s−1) in (a, g) March, April, (b, h) May, June, (c, i) July, August, (d, j) September, October, (e, k) November, December, (f, l) January, February for (a–f) positive and (g–l) negative spring AO years from observations. The dotted areas indicate precipitation anomalies significant above the 90% confidence level. Zonal (meridional) wind anomalies less than 0.2 m s−1 are not shown

    图  4  30组CMIP5模式中春季AO指数回归的冬季(11~2月)海表温度异常(单位:°C)。打点区域表示海温异常通过90%信度水平的显著性检验

    Figure  4.  SST anomalies (units: °C) in the following winter (November, December, January, February) obtained by regression upon the normalized spring AO index in the runs of 30 models from CMIP5 historical experiment. The dotted areas indicate SST anomalies significant above the 90% confidence level

    图  5  观测、30组CMIP5模式中春季AO指数和随后冬季Niño3.4指数的相关系数。水平虚线表示相关性通过95%信度水平的显著性检验

    Figure  5.  Correlation coefficients between the spring AO index and subsequent winter Niño3.4 index from observations (OBS) and 30 CMIP5 models. The horizontal dashed line indicates the correlation significant at the 95% confidence level

    图  6  (a、b)CNRM-CM5模式和(c、d)GISS-E2-H-CC模式模拟的春季AO(a、c)正位相年和(b、d)负位相年合成的冬季SST异常(单位:°C)分布。打点区域表示SST异常通过90%信度水平的显著性检验

    Figure  6.  Composites of SST anomalies (units: °C) in winter for (a, c) positive and (b, d) negative spring AO years in (a, b) CNRM-CM5 model and (c, d) GISS-E2-H-CC model. The dotted areas indicate SST anomalies significant above the 90% confidence level

    图  7  (a、b)CNRM-CM5模式和(c、d)GISS-E2-H-CC模式模拟的春季AO(a、c)正位相年和(b、d)负位相年合成的冬季降水异常(阴影,单位:mm d−1)和850 hPa水平风场异常(箭头,单位: m s−1)分布。打点区域表示降水异常通过90%信度水平的显著性检验,图中没有显示小于0.1 m s−1的纬向或者经向风异常

    Figure  7.  Composites of precipitation anomalies (shadings, units: mm d−1) and 850-hPa horizontal wind anomalies (arrows, units: m s−1) in winter for (a, c) positive and (b, d) negative spring AO years in (a, b) CNRM-CM5 model and (c, d) GISS-E2-H-CC model. The dotted areas indicate precipitation anomalies significantly above the 90% confidence level. Zonal (meridional) wind anomalies less than 0.1 m s−1 are not shown

    图  8  (a、b)CNRM-CM5模式和(c、d)GISS-E2-H-CC模式模拟的春季AO(a、c)正位相年和(b、d)负位相年合成的春季SST异常(单位:°C)分布。打点区域表示SST异常通过90%信度水平的显著性检验

    Figure  8.  Composites of SST anomalies (units: °C) in the simultaneous spring for (a, c) positive and (b, d) negative spring AO years in (a, b) CNRM-CM5 model and (c, d) GISS-E2-H-CC model. The dotted areas indicate SST anomalies significantly above the 90% confidence level

    图  9  (a、b)CNRM-CM5模式和(c、d)GISS-E2-H-CC模式模拟的春季AO(a、c)正位相年和(b、d)负位相年合成的春季降水异常(阴影,单位:mm d−1)和850 hPa水平风场异常(箭头,单位:m s−1)分布。打点区域表示降水异常通过90%信度水平的显著性检验。图中没有显示小于0.2 m s−1的纬向或者经向风异常

    Figure  9.  Composites of precipitation anomalies (shadings, units: mm d−1) and 850-hPa horizontal wind anomalies (arrows, units: m s−1) in spring for (a, c) positive and (b, d) negative spring AO years in (a, b) CNRM-CM5 model and (c, d) GISS-E2-H-CC model. The dotted areas indicate precipitation anomalies significantly above the 90% confidence level. Zonal (meridional) wind anomalies less than 0.2 m s−1 are not shown

    图  10  CNRM-CM5模式模拟的春季AO正位相年合成的降水异常(阴影,单位:mm d−1)和850 hPa风场异常(箭头,单位:m s−1)在(a)5~6月、(b)7~8月、(c)9~10月、(d)11~12月、(e)1~2月的分布。(f–j)同(a–e),但为春季AO负位相年。(k–t)同(a–j),但为GISS-E2-H-CC模式的模拟结果。打点区域表示降水异常通过90%信度水平的显著性检验

    Figure  10.  Composites of precipitation anomalies (shadings, units: mm d−1) and 850-hPa horizontal wind anomalies (arrows, units: m s−1) in (a) May and June, (b) July and August, (c) September and October, (d) November and December, (e) January and February for positive spring AO years in CNRM-CM5 model. (f–j) As in (a–e), but for composite anomalies for negative spring AO years in the CNRM-CM5 model. (k–t) As in (a–j), but for the GISS-E2-H-CC model. The dotted areas indicate precipitation anomalies significant above the 90% confidence level

    图  11  图10,但为合成的SST异常(单位:°C)。打点区域表示SST异常通过90%信度水平的显著性检验

    Figure  11.  As in Fig. 10, but for composite SST anomalies (units: °C). The dotted areas indicate SST anomalies significant above the 90% confidence level

    图  12  CNRM-CM5模式模拟的春季AO(a、e)正位相年和(b、f)负位相年合成的春季(a、b)SLP异常(阴影,单位:hPa)和(e、f)500 hPa位势高度异常(等值线,单位:gpm)分布。(c、d)、(g、h)同(a、b)、(e、f),但为GISS-E2-H-CC模式的模拟结果。填色区域表示异常通过90%信度水平的显著性检验

    Figure  12.  Composites of (a, b) SLP anomalies (shadings, units: hPa) and (e, f) 500-hPa geopotential height anomalies (contours, units: gpm) in the simultaneous spring for (a, e) positive and (b, f) negative spring AO years in the CNRM-CM5 model. (c, d), (g, h) As in (a, b), (e, f), but for the anomalies in the GISS-E2-H-CC model. The shadings indicate anomalies significantly above the 90% confidence level

    表  1  30组CMIP5模式的相关信息

    Table  1.   Information about the 30 models in CMIP5 (Coupled Model Intercomparison Project Phase 5)

    模式名称模式所属机构水平分辨率
    ACCESS1-3Centre for Australian Weather and Climate Research (CAWCR)145×192
    BCC-CSM1-1Beijing Climate Center, China Meteorological Administration64×128
    BNU-ESMBeijing Normal University Earth system64×128
    CanESM2Canadian Centre for Climate Modelling and Analysis64×128
    CCSM4National Center for Atmospheric Research (NCAR)192×288
    CESM1-CAM5National Center for Atmospheric Research (NCAR)192×288
    CESM1-FASTCHEMNational Center for Atmospheric Research (NCAR)192×288
    CESM1-WACCMNational Center for Atmospheric Research (NCAR)192×288
    CMCC-CMCentro Euro-Mediterraneo per I Cambiamenti Climatici240×480
    CNRM-CM5Centre National de Recherches Meteorologiques/Centre
    Europeen de Recherches et de Formation Avancee en Calcul Scientifque
    128×256
    CSIRO-Mk3-6-0Commonwealth Scientific and Industrial Research Organization/Queensland
    Climate Change Centre of Excellence (CSIRO-QCCCE), Australia
    96×192
    FGOALS-s2Institute of Atmospheric Physics, Chinese Academy of Sciences108×128
    FGOALS-g2Institute of Atmospheric Physics, Chinese Academy of Sciences60×128
    FIO-ESMThe First Institution of Oceanography, SOA, Qingdao, China64×128
    GFDL-CM3NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)90×144
    GFDL-ESM2GNOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)90×144
    GISS-E2-H-CCNASA/GISS (Goddard Institute for Space Studies) New York, NY90×144
    GISS-E2-R-CCNASA/GISS (Goddard Institute for Space Studies) New York, NY90×144
    HadCM3Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK73×96
    HadGEM2-AONIMR (National Institute of Meteorological Research, Seoul, South Korea)145×192
    IPSL-CM5B-LRInstitute Pierre Simon Laplace, Paris, France96×96
    IPSL-CM5A-LRIPSL (Institute Pierre Simon Laplace, Paris, France)96×96
    INMCM4Institute for Numerical Mathematics, Moscow, Russia120×180
    MIROC4hAORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan),
    NIES (National Institute for Environmental Studies, Ibaraki, Japan),
    and JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan)
    320×640
    MIROC-ESM-CHEMAORI, NIES, and JAMSTEC160×320
    MIROC-ESMAORI, NIES, and JAMSTEC64×128
    MPI-ESM-LRMax Planck Institute for Meteorology96×192
    MPI-ESM-PMax Planck Institute for Meteorology96×192
    MRI-CGCM3MRI (Meteorological Research Institute, Tsukuba, Japan)160×320
    NorESM1-MNorwegian Climate Centre, Norway96×144
    下载: 导出CSV

    表  2  1958~2005年春季AO正位相年、负位相年和正常年

    Table  2.   Positive spring AO years, negative spring AO years, and normal years from 1958 to 2005

    春季AO正位相年春季AO负位相年正常年
    观测数据1959, 1963, 1967, 1968, 1972, 1977,
    1982, 1985, 1986, 1990, 1994, 1997,
    2002, 2003(14
    1958, 1960, 1962, 1966, 1969, 1970,
    1979, 1980, 1983, 1984, 1987, 1988,
    1991,1996, 1999,
    2005(16
    1961, 1964, 1965, 1971, 1973, 1974,
    1975, 1976, 1978, 1981, 1989, 1992,
    1993, 1995, 1998,
    2000, 2001, 2004(18
    CNRM-CM5
    模式输出结果
    1965, 1967, 1970, 1972, 1978, 1981,
    1985, 1989, 1992, 1993, 1994, 1999,
    2003(13
    1960, 1968, 1969, 1973, 1977, 1980,
    1986, 1990, 1991, 1995, 1996, 2000,
    2002, 2005(14
    1958, 1959, 1961, 1962, 1963, 1964,
    1966, 1971, 1974, 1975, 1976, 1979,
    1982, 1983, 1984, 1987, 1988, 1997,
    1998, 2001, 2004(21
    GISS-E2-H-CC
    模式输出结果
    1959, 1961, 1963, 1965, 1971, 1975,
    1980, 1982, 1985, 1991, 1997,
    2003(12
    1958, 1960, 1962, 1964, 1966, 1970,
    1973, 1974, 1979, 1983, 1987, 1988,
    1992, 1999, 2001 (15)
    1967, 1968, 1969, 1972, 1976, 1977,
    1978, 1981, 1984, 1986, 1989, 1990,
    1993, 1994, 1995, 1996, 1998, 2000,
    2002, 2004, 2005 (21)
    注:括号里的黑体数字表示年份的个数。
    下载: 导出CSV
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