Comparison of coupled and uncoupled models in simulating Monsoon Intraseasonal Oscillation from CMIP6
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Abstract: The monsoon intraseasonal oscillation (MISO) is the dominant variability over the Indian Ocean during the Indian summer monsoon (ISM) season and is characterized by pronounced northward propagation. Previous studies have shown that general circulation models (GCMs) still have difficulty in simulating the northward-propagating MISO, and that the role of air-sea interaction in MISO is unclear. In this study, 14 atmosphere-ocean coupled GCMs (CGCMs) and the corresponding atmosphere-only GCMs (AGCMs) are selected from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) to assess their performance in reproducing MISO and the associated vortex tilting mechanism. The results show that both CGCMs and AGCMs are able to well simulate the significant relationship between MISO and vortex tilting. However, 80% of CGCMs show better simulation skills for MISO than AGCMs in CMIP6. In AGCMs, the poor model fidelity in MISO is due to the failure simulation of vortex tilting. Moreover, it is found that failure to simulate the downward motion to the north of convection is responsible for the poor simulation of vortex tilting in AGCMs. In addition, it is observed that there is a significant relationship between the simulated sea surface temperature gradient and simulated vertical velocity shear in the meridional direction. These findings indicate that air-sea interaction may play a vital role in simulating vertical motions in tilting and MISO processes. This work offers us a specific target to improve the MISO simulation and further studies are needed to elucidate the physical processes of this air-sea interaction coupling with vortex tilting.
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Key words:
- monsoon intraseasonal oscillation (MISO) /
- model comparison /
- vortex tilting /
- CMIP6
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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 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 name Modeling center/country ACCESS-CM2 Commonwealth Scientific and Industrial Research Organization (CSIRO) and the Bureau of Meteorology (BOM)/Australia ACCESS-ESM1-5 BCC-SCM2-MR Beijing Climate Center, China Meteorological Administration/China CanESM5 Canadian Centre for Climate Modelling and Analysis (CCCMA)/Canada CESM2-WACCM-FV2 National Science Foundation, Department of Energy, NCAR/USA CESM2-FV2 National Science Foundation, Department of Energy, NCAR/USA GFDL-CM4 NOAA Geophysical Fluid Dynamics Laboratory/USA IPSL-CM6A-LR Institut Pierre-Simon Laplace (IPSL)/France MIROC6 Atmosphere and Ocean Research Institute, National Institute for Environmental Studies and Japan Agency for Marine-Earth Science and Technology/Japan MPI-ESM-1-2-HAM Max Planck Institute for Meteorology/Germany MPI-ESM1-2-HR Max Planck Institute for Meteorology/Germany MPI-ESM1-2-LR Max Planck Institute for Meteorology/Germany MRI-ESM2-0 Meteorological Research Institute (MRI)/Japan NorESM2-LM Norwegian Climate Centre/Norway -
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