The Ability of CMIP5 Models in Capturing the Asymmetric Impact of the Spring Arctic Oscillation on the Following Winter El Niño—Southern Oscillation
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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。Abstract: Previous observational studies have demonstrated that the spring Arctic Oscillation (AO) has a significant asymmetric impact on the El Niño-Southern Oscillation (ENSO) during the following winter. In particular, the positive spring AO year can exert a notable impact on the following winter El Niño event. However, the impact of the negative spring AO on the following winter La Niña is weak. In this study, the authors examined the ability of the 30 coupled models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in reproducing the observed asymmetric impact of the spring AO on the ENSO during the following winter. The results show that out of the 30 models, only two models (i.e., CNRM-CM5 and GISS-E2-H-CC) can well capture the observed significant spring AO-ENSO relation. These two models were further employed to examine the asymmetric impact of the spring AO on the following winter ENSO. The CNRM-CM5 model could reasonably reproduce the observed asymmetric relationship between the spring AO and the winter ENSO. In particular, in the CNRM-CM5 model, the positive (negative) spring AO could (could not) lead to an El Niño-like (La Niña-like) sea surface temperature (SST) warming in the tropical central-eastern Pacific. By contrast, in the GISS-E2-H-CC model, the significant impact of the spring AO on the subsequent winter ENSO is symmetric; that is, the positive (negative) spring AO could result in significant positive (negative) SST anomalies in the tropical central-eastern Pacific during the following winter. The possible factors responsible for the asymmetric/symmetric relationship of the spring AO with the following winter ENSO in the CNRM-CM5/GISS-E2-H-CC models were further examined. For the CNRM-CM5 model, a significant anomalous cyclone and positive precipitation anomalies could be seen over the subtropical western-central North Pacific during the positive spring AO year. The positive precipitation anomalies play an important role in maintaining the westerly wind anomalies over the tropical western Pacific via Gill-type atmospheric response. The westerly wind anomalies over the tropical western Pacific further impact the following winter El Niño event by triggering eastward propagating and downwelling Kelvin waves. However, during the negative spring AO year, the associated anomalous anticyclone, and negative precipitation anomalies over the subtropical North Pacific are weak. Hence, significant easterly wind anomalies cannot be induced over the tropical western Pacific, leading to a weak connection of the negative spring AO with the following La Niña event. Hence, the CNRM-CM5 model can well reproduce the observed asymmetric impact of the spring AO on the following winter ENSO. In comparison, for the GISS-E2-H-CC model, significant positive (negative) precipitation anomalies could be seen in the subtropical western North Pacific during the positive (negative) spring AO year, which could induce clear westerly (easterly) wind anomalies in the tropical western Pacific via Gill-type atmospheric response. The resultant westerly (easterly) wind anomalies over the tropical western Pacific further contribute to the formation of the El Niño (La Niña)-like SST anomalies in the tropical central-eastern Pacific during the following winter. Hence, the spring AO has a symmetric impact on the following winter ENSO in the GISS-E2-H-CC model. Further analysis suggests that the model’s ability in capturing the observed asymmetric impact of the spring AO on the following winter ENSO may also be partly related to the model’s ability in capturing the observed spatial structure of the spring AO.
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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 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 ACCESS1-3 Centre for Australian Weather and Climate Research (CAWCR) 145×192 BCC-CSM1-1 Beijing Climate Center, China Meteorological Administration 64×128 BNU-ESM Beijing Normal University Earth system 64×128 CanESM2 Canadian Centre for Climate Modelling and Analysis 64×128 CCSM4 National Center for Atmospheric Research (NCAR) 192×288 CESM1-CAM5 National Center for Atmospheric Research (NCAR) 192×288 CESM1-FASTCHEM National Center for Atmospheric Research (NCAR) 192×288 CESM1-WACCM National Center for Atmospheric Research (NCAR) 192×288 CMCC-CM Centro Euro-Mediterraneo per I Cambiamenti Climatici 240×480 CNRM-CM5 Centre National de Recherches Meteorologiques/Centre
Europeen de Recherches et de Formation Avancee en Calcul Scientifque128×256 CSIRO-Mk3-6-0 Commonwealth Scientific and Industrial Research Organization/Queensland
Climate Change Centre of Excellence (CSIRO-QCCCE), Australia96×192 FGOALS-s2 Institute of Atmospheric Physics, Chinese Academy of Sciences 108×128 FGOALS-g2 Institute of Atmospheric Physics, Chinese Academy of Sciences 60×128 FIO-ESM The First Institution of Oceanography, SOA, Qingdao, China 64×128 GFDL-CM3 NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540) 90×144 GFDL-ESM2G NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540) 90×144 GISS-E2-H-CC NASA/GISS (Goddard Institute for Space Studies) New York, NY 90×144 GISS-E2-R-CC NASA/GISS (Goddard Institute for Space Studies) New York, NY 90×144 HadCM3 Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK 73×96 HadGEM2-AO NIMR (National Institute of Meteorological Research, Seoul, South Korea) 145×192 IPSL-CM5B-LR Institute Pierre Simon Laplace, Paris, France 96×96 IPSL-CM5A-LR IPSL (Institute Pierre Simon Laplace, Paris, France) 96×96 INMCM4 Institute for Numerical Mathematics, Moscow, Russia 120×180 MIROC4h AORI (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-CHEM AORI, NIES, and JAMSTEC 160×320 MIROC-ESM AORI, NIES, and JAMSTEC 64×128 MPI-ESM-LR Max Planck Institute for Meteorology 96×192 MPI-ESM-P Max Planck Institute for Meteorology 96×192 MRI-CGCM3 MRI (Meteorological Research Institute, Tsukuba, Japan) 160×320 NorESM1-M Norwegian Climate Centre, Norway 96×144 
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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。
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