The Madden-Julian Oscillation Simulated by the IAP AGCM4.0
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摘要: 基于中国科学院大气物理所大气环流模式IAP AGCM4.0总共30年(1979~2008年)的模拟结果,评估了IAP AGCM4.0模式对热带大气季节内振荡的模拟能力。分析结果表明IAP AGCM4.0模式可以在一定程度上模拟出热带大气季节内振荡的主要时空谱结构特征,在周期30~80天处存在明显的谱能量中心;模式模拟的季节内振荡东传的主要特征与观测基本一致,东移波的能量远大于西移波。基于RMM指数(All-season Real-time Multivariate MJO Index)的分析表明,模式模拟的850 hPa和200 hPa季节内尺度风场和对流活动在赤道地区的空间分布与观测基本一致。但与观测相比,模式模拟的热带大气季节内振荡的周期较短,东传速度快于观测,虚假的西传特征过强,对流活跃区域范围较小、强度较弱。就非绝热加热而言,模式模拟结果与再分析资料比较接近,但最大加热在印度洋和西太平洋地区出现的位相较晚。进一步分析表明,模式中影响对流触发的相对湿度阈值(RHc)的不同取值(RHc分别取为85%、90%、95%和100%),可以显著影响热带大气非绝热加热垂直廓线,从而影响模式对热带大气季节内振荡的模拟;当对流触发相对湿度阈值取为90%时,IAP AGCM4.0模式对热带大气季节内振荡模拟的能力相对最好,非绝热加热垂直廓线在不同位相的分布特征也与再分析资料最为接近。这说明模式对流参数化方案中不同参数的合适选取,可以改进模式对热带大气季节内振荡的模拟能力。Abstract: The performance of IAP (Institute of Atmospheric Physics) Atmospheric General Circulation Model Version 4.0 (IAP AGCM4.0) in simulating the Madden-Julian Oscillation (MJO) is examined in this paper using the 30-year model integration results during 1979-2008. It is found that the IAP AGCM4.0 can reproduce the observed wave number-frequency power spectrum of MJO to some extent, with dominant spectrum power at wavenumber 1 and periods of 30-80 days. Meanwhile, the IAP AGCM4.0 can generally reproduce the observed coherent eastward propagating signals at the intraseasonal time scale, with the power of eastward moving waves much stronger than that of the westward moving waves. The RMM (Real-time Multivariate MJO) index is further applied to evaluate the simulated MJO structure. It is found that IAP AGCM4.0 can well reproduce the observed intraseasonal signals of 850 hPa and 200 hPa zonal winds and the enhanced convection structure of MJO in the tropical regions. However, the simulated eastward propagation is generally too fast, and the simulated westward propagation is stronger than the observation. IAP AGCM4.0 also splits the intraseasonal convective anomalies into two centers straddling the equator, and produces weaker convection. The vertical profile of diabatic heating simulated by the IAP AGCM4.0 has a similar structure to the observation, but in the Indian Ocean and western Pacific Ocean, positive maximum heating occurs later than the observation in phases. Numerical experiments are conducted by using different RHc (relative humidity criterion) values of 85%, 90%, 95%, and 100% for triggering the convection. It is found that the vertical diabatic heating profiles for experiments with different RHc vary considerably, which can lead to differences in the simulated MJO features. Comparison of results further shows that both the main features of MJO and vertical diabatic heating profiles are best simulated when RHc is set to 90%. This suggests that proper specifications of the values for key parameters in the convective parameterization scheme might help improve the model capability in simulating the observed features of MJO.
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图 1 10°S~10°N平均的850 hPa纬向风逐日距平场功率谱:北半球冬半年(11~4月)(a)观测结果和(b)IAP AGCM4.0模式结果;北半球夏半年(5~10月)(c)观测结果和(d)IAP AGCM4.0模式结果。带宽为180 d-1
Figure 1. Wavenumber-frequency spectra of 10°S-10°N averaged 850-hPa zonal wind daily anomalies for (a) observations and (b) IAP AGCM4.0 simulation during boreal winter half year (November-April), (c) observations and (d) IAP AGCM4.0 simulation during boreal summer half year (May-October). The band width is 180 d-1
图 2 10°S~10°N平均的OLR逐日距平场功率谱:北半球冬半年(11~4月)(a)观测结果和(b)IAP AGCM4.0模式结果;北半球夏半年(5月~10月)(c)观测结果和(d)IAP AGCM4.0模式结果。带宽为180 d-1
Figure 2. Wavenumber-frequency spectra of 10°S-10°N averaged OLR (outgoing longwave radiation) daily anomalies for (a) observations and (b) IAP AGCM4.0 simulation during boreal winter half year (November-April), (c) observations and (d) IAP AGCM4.0 simulation during boreal summer half year (May-October). The bandwidth is 180 d-1
图 3 10°S~10°N纬向平均的850 hPa纬向风场距平与参考区域(10°S~10°N, 120°E~150°E)平均的850 hPa纬向风距平的滞后时间—经度回归分析:北半球冬半年(11~4月)(a)再分析资料和(b)IAP AGCM4.0模式结果;北半球夏半年(5月~10月)(c)再分析资料和(d)IAP AGCM4.0模式结果。850 hPa纬向风均进行了20~100 d季节内带通滤波处理
Figure 3. Lagged-time-longitude diagrams of regression coefficients between 850-hPa zonal wind anomolies (20-100 d band-pass filtered) averaged over 10°S-10°N and 850-hPa zonal wind anomolies (20-100 d band-pass filtered) averaged over the reference region (10°S-10°N, 120°E-150°E): (a) Reanalysis data and (b) IAP AGCM4.0 simulation during boreal winter half year (November-April); (c) reanalysis data and (d) IAP AGCM4.0 simulation during boreal summer half year (May-October)
图 4 15°S~15°N平均的OLR、850 hPa纬向风场(U850)和200 hPa纬向风场(U200)多元EOF的第一、第二模态的空间分布:(a)、(b)观测结果;(d)、(e)IAP AGCM4.0模式结果。所使用的变量均进行了20~100 d季节内带通滤波和标准化处理。(c)观测、(f)IAP AGCM4.0模式模拟的前两个主分量超前—滞后的相关系数
Figure 4. The first mode (EOF1) and second mode (EOF2) of EOF of 15°S-15°N averaged OLR, 850-hPa zonal winds (U850), and 200-hPa zonal winds (U200) obtained from (a), (b) observations, (d), (e) IAP AGCM4.0 simulations. All variables are normalized and 20-100 d band-pass filtered. The lagged correlation coefficients of the first two leading PCs (principal components) are shown form (c) observation and (f) IAP AGCM4.0 simulation
图 5 MJO不同位相的OLR距平(填色)和850 hPa风场距平(矢量)合成:北半球冬半年(11~4月)(a)观测结果和(b)IAP AGCM4.0模式结果;北半球夏半年(5月~10月)(c)观测结果和(d)IAP AGCM4.0模式结果。所使用的变量均进行了20~100 d季节内带通滤波处理,每图的右下角数字为合成该位相的天数
Figure 5. Composites of OLR anomalies (shaded) and 850-hPa wind anomalies (vectors) as a function of MJO phase: (a) Observation and (b) IAP AGCM4.0 simulation during boreal winter (November-April); (c) observation and (d) IAP AGCM4.0 simulation during boreal summer (May-October). All variables are 20-100 d band-pass filtered, the number of days used to generate the composite for each phase is shown at the bottom right of each panel
图 7 非绝热加热距平(单位:K d-1)在90°E(左)、120°E(中)、150°E(右)的合成分布:(a-c)MERRA再分析资料的结果;(d-f)IAP AGCM4.0模式模拟结果。MJO位相由RMM指数(Wheeler and Hendon, 2004)定义,下同。粗实线为零值线,实(虚)线代表正(负)值,等值线间隔均为0.01 K d-1
Figure 7. Composites of diabatic heating anomalies (units: K d-1) at 90°E (left), 120°E (middle), and 150°E (right) for (a-c) MERRA reanalysis data and (d-f) IAP AGCM4.0 simulations. The MJO phase is defined by the RMM (Real-time Multivariate MJO) index (Wheeler and Hendon, 2004), the same below. Heavy lines are zero contours, solid (dashed) lines indicate positive (negative) anomalies, and the contour interval is 0.01 K d-1
图 8 非绝热加热距平(单位:K d-1)在90°E(左)、120°E(中)、150°E(右)的合成分布:(a-c)MERRA再分析资料的结果;(d-l)不同RHc取值的敏感性试验的结果。(d-f)IAP4_RHc=85%试验,(g-i)IAP4_RHc=90%试验,(j-l)IAP4_RHc=95%试验。粗实线为零线,实(虚)线代表正(负)值,等值线间隔均为0.01 K d-1
Figure 8. Composites of diabatic heating anomalies (units: K d-1) at 90°E (left), 120°E (middle) and 150°E (right) for (a-c) MERRA reanalysis data, (d-f) RHc (relative humidity criterion)=85% experiment, (g-i) RHc=90% experiment, and (j-l) RHc=95% experiment with IAP AGCM4.0. Heavy lines are zero contours, solid (dashed) lines indicate positive (negative) anomalies, and the contour interval is 0.01 K d-1
图 9 北半球冬半年(11~4月)10°S~10°N平均的OLR逐日距平场功率谱:(a)观测;(b)IAP4_RHc=85%试验;(c)IAP4_RHc=90%试验;(d)IAP4_RHc=95%试验。带宽为180 d-1
Figure 9. The wavenumber-frequency spectra of 10°S-10°N averaged OLR daily anomalies during winter half year (November-April) for (a) observations, (b) RHc=85% experiment, (c) RHc=90% experiment, and (d) RHc=95% experiment with IAP AGCM4.0. The bandwidth is 180 d-1
图 11 北半球冬半年(11~4月)10°S~10°N平均的850 hPa纬向风距平与参考区域(10°S~10°N, 120°E~150°E)平均的滞后时间—经度回归分析:(a)再分析资料;(b)IAP4_RHc=85%试验;(c)IAP4_RHc=90%试验;(d)IAP4_RHc=95%试验结果。所使用的变量均进行了20~100 d季节内带通滤波处理
Figure 11. Lagged-time-longitude diagrams of regression coefficients between 850-hPa zonal wind anomolies (20-100 d band-pass filtered) averaged over 10°S-10°N and 850-hPa zonal wind anomolies (20-100 d band-pass filtered) averaged over the reference region (10°S-10°N, 120°E-150°E) during boreal winter half year (November-April): (a) Reanalysis data; (b) RHc=85% experiment; (c) RHc=90% experiment; (d) RHc=95% experiment
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[1] Arakawa A, Schubert W H. 1974. Interaction of a cumulus cloud ensemble with the large-scale environment, Part Ⅰ[J]. J. Atmos. Sci., 31 (3):674-701, doi:10.1175/1520-0469(1974)031<0674:ioacce>2.0.co;2. [2] Benedict J J, Randall D A. 2011. Impacts of idealized air-sea coupling on Madden-Julian oscillation structure in the super parameterized CAM[J]. J. Atmos. Sci., 68 (9):1990-2008, doi: 10.1175/JAS-D-11-04.1. [3] Ding R Q, Li J P, Seo K H. 2011. Estimate of the predictability of boreal summer and winter intraseasonal oscillations from observations[J]. Mon. Wea. Rev., 139 (8):2421-2438, doi: 10.1175/2011MWR3571.1. [4] 丁一汇, 梁萍. 2010.基于MJO的延伸预报[J].气象, 36 (7):111-122. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201007019.htmDing Yihui, Liang Ping. 2010. Extended range forecast basing on MJO[J]. Meteorological Monthly (in Chinese), 36 (7):111-122. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201007019.htm [5] 冯俊阳, 肖子牛. 2012.热带低频振荡的强度和相位对中国南方冬季降水的影响[J].气象, 38 (11):1355-1366. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201211007.htmFeng Junyang, Xiao Ziniu. 2012. Impact of low-frequency oscillation intensity and phases in tropics on the winter precipitation in southern China[J]. Meteorological Monthly (in Chinese), 38 (11):1355-1366. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201211007.htm [6] 冯俊阳, 肖子牛. 2013.热带大气低频振荡强度年际异常对中国东部冬季降水的影响[J].热带气象学报, 29 (4):559-569. doi: 10.3969/j.issn.1004-4965.2013.04.004Feng Junyang, Xiao Ziniu. 2013. Impact of the interannual variability of the low-frequency oscillation intensity in the tropics on the wintertime rainfall in East China[J]. Journal of Tropical Meteorology (in Chinese), 29 (4):559-569, doi: 10.3969/j.issn.1004-4965.2013.04.004. [7] Fu X H, Wang B. 2004. Differences of boreal summer intraseasonal oscillations simulated in an atmosphere-ocean coupled model and an atmosphere-only model[J]. J. Climate, 17 (6):1263-1271, doi:10.1175/1520-0442(2004)017<1263:dobsio>2.0.co;2. [8] Fu X H, Wang B, Li T, et al. 2003. Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean[J]. J. Atmos. Sci., 60 (15):1733-1753, doi:10.1175/1520-0469(2003)060<1733:cbnioa>2.0.co;2. [9] Fu X H, Wang B, Waliser D E, et al. 2007. Impact of atmosphere-ocean coupling on the predictability of monsoon intraseasonal oscillations[J]. J. Atmos. Sci., 64 (1):157-174, doi: 10.1175/JAS3830.1. [10] Gottschalck J, Wheeler M, Weickmann K, et al. 2010. A framework for assessing operational Madden-Julian oscillation forecasts:A CLIVAR MJO working group project[J]. Bull. Amer. Meteor. Soc., 91 (9):1247-1258, doi: 10.1175/2010bams2816.1. [11] He J H, Lin H, Wu Z W. 2011. Another look at influences of the Madden-Julian oscillation on the wintertime East Asian weather[J]. J. Geophys. Res., 116 (D3):D03109, doi: 10.1029/2010JD014787. [12] Hendon H H. 2000. Impact of air-sea coupling on the Madden-Julian oscillation in a general circulation model[J]. J. Atmos. Sci., 57 (24):3939-3952, doi:10.1175/1520-0469(2001)058<3939:ioasco>2.0.co;2. [13] Holloway C E, Woolnough S J, Lister G M S. 2013. The effects of explicit versus parameterized convection on the MJO in a large-domain high-resolution tropical case study. Part Ⅰ:Characterization of large-scale organization and propagation[J]. J. Atmos. Sci., 70 (5):1342-1369, doi: 10.1175/JAS-D-12-0227.1. [14] 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. [15] Inness P M, Slingo J M, Woolnough S J, et al. 2001. Organization of tropical convection in a GCM with varying vertical resolution; implications for the simulation of the Madden-Julian oscillation[J]. Climate Dyn., 17 (10):777-793, doi: 10.1007/s003820000148. [16] Itoh H. 1989. The mechanism for the scale selection of tropical intraseasonal oscillations. Part Ⅰ:Selection of wavenumber 1 and the three-scale structure[J]. J. Atmos. Sci., 46 (12):1779-1798, doi:10.1175/1520-0469 (1989)046<1779:tmftss>2.0.co;2. [17] 贾小龙. 2006. 热带大气季节内振荡的数值模拟研究[D]. 中国科学院大气物理研究所博士学位论文, 85-88.Jia Xiaolong. 2006. Numerical simulations of the tropical intraseasonal oscillation[D]. Ph. D. dissertation (in Chinese), Institute of Atmospheric Physics, Chinese Academy of Sciences, 85-88. [18] Jia X L, Chen L J, Ren F M, et al. 2011. Impacts of the MJO on winter rainfall and circulation in China[J]. Advances in Atmospheric Sciences, 28 (3):521-533, doi: 10.1007/s00376-010-9118-z. [19] Kanamitsu M, Ebisuzaki W, Woollen J, et al. 2002. NCEP-DOE AMIP-Ⅱ reanalysis (R-2)[J]. Bull. Amer. Meteor. Soc., 83 (11):1631-1643, doi: 10.1175/bams-83-11-1631. [20] Kim D, Sperber K, Stern W, et al. 2009. Application of MJO simulation diagnostics to climate models[J]. J. Climate, 22 (23):6413-6436, doi: 10.1175/2009jcli3063.1. [21] Kodama C, Yamada Y, Noda A T, et al. 2015. A 20-year climatology of a NICAM AMIP-type simulation[J]. J. Meteor. Soc. Japan, 93 (4):393-424, doi: 10.2151/jmsj.2015-024. [22] Lau W K M, Waliser D E. 2012. Intraseasonal Variability in the Atmosphere-Ocean Climate System[M]. Berlin Heidelberg:Springer, doi: 10.1007/978-3-642-13914-7. [23] Lau W K M, Waliser D E, Waliser D. 2012. Predictability and forecasting[M]//Lau W K M, Waliser D E. Intraseasonal Variability in the Atmosphere-Ocean Climate System. Berlin Heidelberg:Springer, 433-476, doi: 10.1007/978-3-642-13914-7_12. [24] 李崇银. 1983.对流凝结加热与不稳定波[J].大气科学, 7 (3):260-268. doi: 10.3878/j.issn.1006-9895.1983.03.03Li Chongyin. 1983. Convective condensation heating and unstable mode[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 7 (3):260-268, doi: 10.3878/j.issn.1006-9895.1983.03.03. [25] 李崇银. 2004.大气季节内振荡研究的新进展[J].自然科学进展, 14 (7):734-741. doi: 10.3321/j.issn:1002-008X.2004.07.003Li Chongyin. 2004. Progress on the study of intraseasonal oscillation[J]. Progress in Natural Science (in Chinese), 14 (7):734-741, doi: 10.3321/j.issn:1002-008X.2004.07.003. [26] Li C Y, Jia X L, Ling J, et al. 2009. Sensitivity of MJO simulations to diabatic heating profiles[J]. Climate Dyn., 32 (2-3):167-187, doi: 10.1007/s00382-008-0455-x. [27] 李崇银, 潘静, 宋洁. 2013. MJO研究新进展[J].大气科学, 37 (2):229-252. doi: 10.3878/j.issn.1006-9895.2012.12318Li Chongyin, Pan Jing, Song Jie. 2013. Progress on the MJO research in recent years[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 37 (2):229-252, doi: 10.3878/j.issn.1006-9895.2012.12318. [28] Liebmann B, Simth C A. 1996. Description of a complete (interpolated) outgoing longwave radiation dataset[J]. Bull. Amer. Meteor. Soc., 77 (6):1275-1277. http://citeseerx.ist.psu.edu/showciting?cid=2773917 [29] Lin J L, Kiladis G N, Mapes B E, et al. 2006. Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part Ⅰ:Convective signals[J]. J. Climate, 19 (12):2665-2690, doi: 10.1175/jcli3735.1. [30] Ling J, Li C Y, Jia X L. 2009. Impacts of cumulus momentum transport on MJO simulation[J]. Advances in Atmospheric Sciences, 26 (5):864-876, doi: 10.1007/s00376-009-8016-8. [31] Ling J, Zhang C D. 2011. Structural evolution in heating profiles of the MJO in global reanalyses and TRMM retrievals[J]. J. Climate, 24 (3):825-842, doi: 10.1175/2010jcli3826.1. [32] Madden R A, Julian P R. 1971. Detection of a 40-50 day oscillation in the zonal wind in the tropical Pacific[J]. J. Atmos. Sci., 28 (5):702-708, doi:10.1175/1520-0469(1971)028<0702:doadoi>2.0.co;2. [33] Madden R A, Julian P R. 1972. Description of global-scale circulation cells in the tropics with a 40-50 day period[J]. J. Atmos. Sci., 29 (6):1109-1123, doi:10.1175/1520-0469(1972)029<1109:dogscc>2.0.co;2. [34] Madden R A, Julian P R. 2012. Historical perspective[M]//Lau W K M, Waliser D E. Intraseasonal Variability in the Atmosphere-Ocean Climate System. Berlin Heidelberg:Springer, 1-18, doi:10.1007/3-540-27250-X_1. [35] Maloney E D, Hartmann D L. 2001. The sensitivity of intraseasonal variability in the NCAR CCM3 to changes in convective parameterization[J]. J. Climate, 14 (9):2015-2034, doi:10.1175/1520-0442(2001)014<2015:tsoivi>2.0.co;2. [36] Murakami H, Vecchi G A, Underwood S, et al. 2015. Simulation and prediction of category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model[J]. J. Climate, 28 (23):9058-9079, doi: 10.1175/jcli-d-15-0216.1. [37] Neale R B, Richter J H, Jochum M. 2008. The impact of convection on ENSO:From a delayed oscillator to a series of events[J]. J. Climate, 21 (22):5904-5924, doi: 10.1175/2008jcli2244.1. [38] Pegion K, Kirtman B P. 2008. The impact of air-sea interactions on the predictability of the tropical intraseasonal oscillation[J]. J. Climate, 21 (22):5870-5886, doi: 10.1175/2008jcli2209.1. [39] Richter J H, Rasch P J. 2008. Effects of convective momentum transport on the atmospheric circulation in the community atmosphere model, version 3[J]. J. Climate, 21 (7):1487-1499, doi: 10.1175/2007jcli1789.1. [40] Rienecker M M, Suarez M J, Gelaro R, et al. 2011. MERRA:NASA's modern-era retrospective analysis for research and applications[J]. J. Climate, 24 (14):3624-3648, doi: 10.1175/jcli-d-11-00015.1. [41] Seo K H, Wang W Q, Gottschalck J, et al. 2009. Evaluation of MJO forecast skill from several statistical and dynamical forecast models[J]. J. Climate, 22 (9):2372-2388, doi: 10.1175/2008jcli2421.1. [42] Slingo J M, Sperber K R, Boyle J S, et al. 1996. Intraseasonal oscillations in 15 atmospheric general circulation models:Results from an AMIP diagnostic subproject[J]. Climate Dyn., 12 (5):325-357, doi: 10.1007/bf00231106. [43] Su T H, Xue F, Zhang H. 2014. Simulating the intraseasonal variation of the East Asian summer monsoon by IAP AGCM4.0[J]. Advances in Atmospheric Sciences, 31 (3):570-580, doi: 10.1007/s00376-013-3029-8. [44] 孙泓川, 周广庆, 曾庆存. 2012. IAP第四代大气环流模式的耦合气候系统模式模拟性能评估[J].大气科学, 36 (2):215-233. doi: 10.3878/j.issn.1006-9895.2011.11062Sun Hongchuan, Zhou Guangqing, Zeng Qingcun. 2012. Assessments of the climate system model (CAS-ESM-C) using IAP AGCM 4 as its atmospheric component[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 36 (2):215-233, doi: 10.3878/j.issn.1006-9895.2011.11062. [45] Tiedtke M. 1989. A comprehensive mass flux scheme for cumulus parameterization in large-scale models[J]. Mon. Wea. Rev., 117 (8):1779-1800, doi:10.1175/1520-0493(1989)117<1779:acmfsf>2.0.co;2. [46] Tung W W, Gao J B, Hu J, et al. 2011. Detecting chaos in heavy-noise environments[J]. Physical Review E, 83 (4):046210, doi: 10.1103/physreve.83.046210. [47] Vitart F, Woolnough S, Balmaseda M A, et al. 2007. Monthly forecast of the Madden-Julian oscillation using a coupled GCM[J]. Mon. Wea. Rev., 135 (7):2700-2715, doi: 10.1175/mwr3415.1. [48] Waliser D, Sperber K, Hendon H, et al. 2009. MJO simulation diagnostics[J]. J. Climate, 22 (11):3006-3030, doi: 10.1175/2008jcli2731.1. [49] Waliser D E, Lau K M, Kim J H. 1999. The influence of coupled sea surface temperatures on the Madden-Julian oscillation:A model perturbation experiment[J]. J. Atmos. Sci., 56 (3):333-358, doi:10.1175/1520-0469(1999)056<0333:tiocss>2.0.co;2. [50] Waliser D E, Zhang Z Z, Lau K M, et al. 2001. Interannual sea surface temperature variability and the predictability of tropical intraseasonal variability[J]. J. Atmos. Sci., 58 (17):2596-2615, doi:10.1175/1520-0469 (2001)058<2596:isstva>2.0.co;2. [51] Wang W Q, Schlesinger M E. 1999. The dependence on convection parameterization of the tropical intraseasonal oscillation simulated by the UIUC 11-layer atmospheric GCM[J]. J. Climate, 12 (5):1423-1457, doi:10.1175/1520-0442(1999)012<1423:tdocpo>2.0.co;2. [52] Wheeler M C, Hendon H H. 2004. An all-season real-time multivariate MJO index:Development of an index for monitoring and prediction[J]. Mon. Wea. Rev., 132 (8):1917-1932, doi:10.1175/1520-0493(2004)132<1917:aarmmi>2.0.co;2. [53] Woolnough S J, Vitart F, Balmaseda M A. 2007. The role of the ocean in the Madden-Julian oscillation:Implications for MJO prediction[J]. Quart. J. Roy. Meteor. Soc., 133 (622):117-128, doi: 10.1002/qj.4. [54] Yan Z B, Lin Z H, Zhang H. 2014. The relationship between the East Asian subtropical westerly jet and summer precipitation over East Asia as simulated by the IAP AGCM4.0[J]. Atmospheric and Oceanic Science Letters, 7 (6):487-492, doi: 10.3878/AOSL20140048. [55] 晏正滨, 林朝晖, 张贺. 2015.大气环流模式IAP AGCM4.0对东亚高空副热带西风急流的模拟及偏差原因分析[J].气候与环境研究, 20 (4):393-410. doi: 10.3878/j.issn.1006-9585.2015.14095Yan Zhengbin, Lin Zhaohui, Zhang He. 2015. Evaluation and bias analysis for the performance of IAP AGCM4.0 in simulating the East Asian subtropical westerly jet[J]. Climatic and Environmental Research (in Chinese), 20 (4):393-410, doi: 10.3878/j.issn.1006-9585.2015.14095. [56] 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.1001-8166.2010.01.0001Zeng Qingcun, Lin Zhaohui. 2010. Recent progress on the earth system dynamical model and its numerical simulations[J]. Advances in Earth Science (in Chinese), 25 (1):1-6, doi: 10.11867/j.issn.1001-8166.2010.01.0001. [57] Zhang C D. 2005. Madden-Julian oscillation[J]. Rev. Geophys., 43 (2), doi: 10.1029/2004rg000158. [58] Zhang C D. 2013. Madden-Julian oscillation:Bridging weather and climate[J]. Bull. Amer. Meteor. Soc., 94 (12):1849-1870, doi: 10.1175/bams-d-12-00026.1. [59] Zhang G J, Mu M Q. 2005. Simulation of the Madden-Julian oscillation in the NCAR CCM3 using a revised Zhang-McFarlane convection parameterization scheme[J]. J. Climate, 18 (19):4046-4064, doi: 10.1175/jcli3508.1. [60] 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. [61] 张贺. 2009. 大气环流模式IAP AGCM4. 0的设计及其数值模拟[D]. 中国科学院大气物理研究所博士学位论文, 194pp.Zhang He. 2009. Development of IAP atmospheric general circulation model version 4.0 and its climate simulations[D]. Ph. D. dissertation (in Chinese), Institute of Atmospheric Physics, Chinese Academy of Sciences, 194pp. [62] 张贺, 林朝晖, 曾庆存. 2009. IAP AGCM-4动力框架的积分方案及模式检验[J].大气科学, 33 (6):1267-1285. doi: 10.3878/j.issn.1006-9895.2009.06.13Zhang He, Lin Zhaohui, Zeng Qingcun. 2009. The computational scheme and the test for dynamical framework of IAP AGCM-4[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 33 (6):1267-1285, doi:10.3878/j.issn.1006-9895. 2009.06.13. [63] 张贺, 林朝晖, 曾庆存. 2011.大气环流模式中动力框架与物理过程的相互响应[J].气候与环境研究, 16 (1):15-30. doi: 10.3878/j.issn.1006-9585.2011.01.02Zhang He, Lin Zhaohui, Zeng Qingcun. 2011. The mutual response between dynamical core and physical parameterizations in atmospheric general circulation models[J]. Climatic and Environmental Research (in Chinese), 16 (1):15-30, doi: 10.3878/j.issn.1006-9585.2011.01.02. [64] 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. [65] Zhang L N, Wang B Z, Zeng Q C. 2009. Impact of the Madden-Julian oscillation on summer rainfall in southeast China[J]. J. Climate, 22 (2):201-216, doi: 10.1175/2008jcli1959.1. [66] Zhao C B, Ren H L, Song L C, et al. 2015. Madden-Julian oscillation simulated in BCC climate models[J]. Dyn. Atmos. Oceans, 72:88-101, doi: 10.1016/j.dynatmoce.2015.10.004. -