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IAP AGCM4.0模式对热带大气季节内振荡的模拟评估

林朝晖 王坤 肖子牛 张贺 詹艳玲

林朝晖, 王坤, 肖子牛, 张贺, 詹艳玲. IAP AGCM4.0模式对热带大气季节内振荡的模拟评估[J]. 气候与环境研究, 2017, 22(2): 115-133. doi: 10.3878/j.issn.1006-9585.2016.16085
引用本文: 林朝晖, 王坤, 肖子牛, 张贺, 詹艳玲. IAP AGCM4.0模式对热带大气季节内振荡的模拟评估[J]. 气候与环境研究, 2017, 22(2): 115-133. doi: 10.3878/j.issn.1006-9585.2016.16085
Zhaohui LIN, Kun WANG, Ziniu XIAO, He ZHANG, Yanling ZHAN. The Madden-Julian Oscillation Simulated by the IAP AGCM4.0[J]. Climatic and Environmental Research, 2017, 22(2): 115-133. doi: 10.3878/j.issn.1006-9585.2016.16085
Citation: Zhaohui LIN, Kun WANG, Ziniu XIAO, He ZHANG, Yanling ZHAN. The Madden-Julian Oscillation Simulated by the IAP AGCM4.0[J]. Climatic and Environmental Research, 2017, 22(2): 115-133. doi: 10.3878/j.issn.1006-9585.2016.16085

IAP AGCM4.0模式对热带大气季节内振荡的模拟评估

doi: 10.3878/j.issn.1006-9585.2016.16085
基金项目: 

公益性行业(气象)科研专项重点项目 GYHY201406021

国家自然科学基金项目 41575095

国家自然科学基金项目 41175073

国家自然科学基金项目 41105050

国家重点研发计划项目 2016YFC0402702

中国科学院前沿科学重点研究项目 QYZDB-SSW-DQC017

详细信息
    作者简介:

    林朝晖, 男, 1968年出生, 研究员, 主要从事气候模式研发及模拟和预测研究。E-mail:lzh@mail.iap.ac.cn

  • 中图分类号: P435

The Madden-Julian Oscillation Simulated by the IAP AGCM4.0

Funds: 

Special Scientific Research Fund of Meteorological Public Welfare Profession of China GYHY201406021

Natural Science Foundation of China 41575095

Natural Science Foundation of China 41175073

Natural Science Foundation of China 41105050

National Key Research and Development Program 2016YFC0402702

Key Project of Chinese Academy of Sciences QYZDB-SSW-DQC017

  • 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。
  • 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  6  MJO活跃位相对应的区域(15°S~15°N, 60°E~180°)平均的非绝热加热垂直廓线。实线为MERRA再分析资料的结果,虚线为IAP AGCM4.0的模拟结果

    Figure  6.  The diabatic heating profiles averaged over (15°S-15°N, 60°E-180°) from MERRA reanalysis data (solid line) and IAP AGCM4.0 simulation (dashed line) during the strong phase of MJO

    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  10  图 9,但为北半球夏半年(5~10月)的OLR逐日距平场功率谱

    Figure  10.  As in Fig. 9, but for OLR daily anomalies during the summer half year (May-October)

    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

    图  12  图 11,但为北半球夏半年(5~10月)的时间滞后—经度回归分析

    Figure  12.  As in Fig. 11, but for lagged-time-longitude diagrams of regression coefficients during boreal summer half year (May-October)

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  • 收稿日期:  2016-04-25
  • 网络出版日期:  2016-08-08
  • 刊出日期:  2017-03-20

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