Comparison of coupled and uncoupled models in simulating Monsoon Intraseasonal Oscillation from CMIP6

Baosheng Li Dake Chen Tao Lian Jianhuang Qin

Baosheng Li, Dake Chen, Tao Lian, Jianhuang Qin. Comparison of coupled and uncoupled models in simulating Monsoon Intraseasonal Oscillation from CMIP6[J]. Acta Oceanologica Sinica, 2022, 41(10): 100-108. doi: 10.1007/s13131-022-2011-7
Citation: Baosheng Li, Dake Chen, Tao Lian, Jianhuang Qin. Comparison of coupled and uncoupled models in simulating Monsoon Intraseasonal Oscillation from CMIP6[J]. Acta Oceanologica Sinica, 2022, 41(10): 100-108. doi: 10.1007/s13131-022-2011-7

doi: 10.1007/s13131-022-2011-7

Comparison of coupled and uncoupled models in simulating Monsoon Intraseasonal Oscillation from CMIP6

Funds: The Zhejiang Provincial Natural Science Foundation of China under contract No. LR19D060001; the Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources, under contract No. JB2206; the China Postdoctoral Science Foundation under contract Nos 2022M711010 and 2021M703792; the National Natural Science Foundation of China under contract No. 42106003.
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  • Figure  1.  Regional mean intraseasonal rainfall within 5°–15°N, 80°–95°E during the ISM season from 1998 to 2014. Day 0s for the composite analysis are marked with red dots. Day 0 is the day when the regional mean intraseasonal rainfall reaches its local maximum, representing one significant MISO event.

    Figure  2.  Hovmöller diagrams of composite intraseasonal rainfall from observations (a), and the multi-model mean of CGCMs (b) and AGCMs (c), and model skills for northward-propagating MISO in CMIP6 CGCMs (black bars) and AGCMs (gray bars) (d). The rainfall anomaly is averaged between 80°E and 95°E. In a−c, all dotted areas are significant at the 99% confidence level. The pattern correlation coefficient is referred to as the model skill, and denotes the relationship between the simulated MISO in each model and the observed MISO based on TRMM. The red line in d denotes the estimation criterion of 0.75.

    Figure  3.  Hovmöller diagrams of composite intraseasonal vortex tilting based on observations (a), and on the multi-model mean of CGCMs (b) and AGCMs (c), and model skills for vortex tilting in CMIP6 CGCMs (black bars) and AGCMs (gray bars) (d). The vortex tilting anomaly is integrated from 850 hPa to 200 hPa and averaged over 80°–95°E. In a−c, all dotted areas are significant at the 99% confidence level. The pattern correlation coefficient is referred to as the model skill, and denotes the relationship between the simulated vortex tilting in each model and that observed using ERA5. The red line in d denotes the estimation criterion of 0.75.

    Figure  4.  Scatter plots of model skills for MISO simulation and vortex tilting in CMIP6 CGCMs (a) and AGCMs (b). The x- and y-axes are the pattern correlation coefficients (PCCs) in Figs 2d and 3d, respectively. The correlation coefficients of simulation skill between MISO and vortex tilting are shown in the top corner.

    Figure  5.  Mean vertical shear of background zonal winds $ {\partial \bar{u}}/{\partial p} $ (a–c) and Hovmöller diagrams of composite intraseasonal ${\partial \mathit{\omega }}/{\partial y}$ during MISO events (d–f). a and d. From ERA5, b and e. from CGCMs, c and f. from AGCMs. ${\partial \mathit{\omega }}/{\partial y}$ is integrated from 850 hPa to 200 hPa and averaged over 80°–95°E. All dotted areas are significant at the 99% confidence level. The boxes in a−c denote the active MISO domain.

    Figure  6.  Scatter plots of model skills between vortex tilting simulation and $ {\partial \bar{u}}/{\partial p} $ (a, b), ${\partial \mathit{\omega }}/{\partial y}$ (c, d) in CGCMs (left column) and AGCMs (right column) from the CMIP6. The pattern correlation coefficients between the observed pattern in ERA5 and that in models are referred to as model skills. The correlation coefficients of simulation skill between vortex tilting and the specific processes ($ {\partial \bar{u}}/{\partial p} $ and ${\partial \mathit{\omega }}/{\partial y}$) are shown in the top corner.

    Figure  7.  Composite intraseasonal vertical velocity for the vertical-meridional domain for Day −5 from ERA5 (a), and CGCMs (b), and AGCMs (c) from the CMIP6. The anomaly is averaged within 80°–95°E, and has units of Pa/s. All dotted areas are significant at the 99% confidence level.

    Figure  8.  Scatter plot of simulations for the intraseasonal SST meridional gradient (${\partial {\rm{SST}}}/{\partial y}$) and the ${\partial \mathit{\omega }}/{\partial y}$ based on Day 0s from CGCMs. The ${\partial{\rm{SST}}}/{\partial y}$ and ${\partial \mathit{\omega }}/{\partial y}$ are defined as the regional mean differences of the anomalies between the region of (10°−20°N; 80°−95°E) and that of (0°−10°N; 80°−95°E). The correlation coefficient of simulations between ${\partial {\rm{SST}}}/{\partial y}$ and ${\partial \mathit{\omega }}/{\partial y}$ is shown in the top corner. The red line shows the least squares regression of the markers.

    Table  1.   CMIP6 models used in this study

    Model nameModeling center/country
    ACCESS-CM2Commonwealth Scientific and Industrial Research Organization (CSIRO) and the Bureau of Meteorology (BOM)/Australia
    ACCESS-ESM1-5
    BCC-SCM2-MRBeijing Climate Center, China Meteorological Administration/China
    CanESM5Canadian Centre for Climate Modelling and Analysis (CCCMA)/Canada
    CESM2-WACCM-FV2National Science Foundation, Department of Energy, NCAR/USA
    CESM2-FV2 National Science Foundation, Department of Energy, NCAR/USA
    GFDL-CM4NOAA Geophysical Fluid Dynamics Laboratory/USA
    IPSL-CM6A-LRInstitut Pierre-Simon Laplace (IPSL)/France
    MIROC6Atmosphere and Ocean Research Institute, National Institute for Environmental Studies and Japan Agency for Marine-Earth Science and Technology/Japan
    MPI-ESM-1-2-HAMMax Planck Institute for Meteorology/Germany
    MPI-ESM1-2-HRMax Planck Institute for Meteorology/Germany
    MPI-ESM1-2-LRMax Planck Institute for Meteorology/Germany
    MRI-ESM2-0Meteorological Research Institute (MRI)/Japan
    NorESM2-LMNorwegian Climate Centre/Norway
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出版历程
  • 收稿日期:  2022-01-23
  • 录用日期:  2022-02-25
  • 网络出版日期:  2022-07-06
  • 刊出日期:  2022-10-27

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