the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Flexible ROMS-based Hybrid Coupled Model for ENSO Studies–Model Formulation and Performance Evaluation
Abstract. The El Niño and Southern Oscillation (ENSO) constitutes the most prominent interannual climate variation mode in the climate system, originating from ocean-atmosphere interactions in the tropical Pacific. Accurately modeling ENSO variation has consistently posed a great challenge, exhibiting strong model-dependent representations and simulations of ENSO. This study presents a novel Hybrid Coupled Model (HCM), denoted as HCMROMS, built upon the Regional Ocean Modeling System (ROMS) that has been widely used for regional modeling studies. For basin-wide applications to the tropical Pacific, here, the ROMS is incorporated with a statistical atmospheric model, which is based on singular value decomposition (SVD), capturing interannual relationships of atmospheric perturbations such as wind stress and freshwater flux anomalies with sea surface temperature (SST) anomalies. The model is constructed in a flexible way so that various components representing atmospheric forcing and oceanic biogeochemistry can be easily included as a module in the HCMROMS. Results demonstrate that the HCMROMS can simulate a stable quasi-three-year ENSO cycle when the interannual wind stress coupling coefficient, ατ , is set at 1.5. The HCMROMS reproduces the three-dimensional (3D) evolution of ENSO-related anomalies, revealing that the most pronounced temperature anomalies occur beneath the surface at 150 m. The interannual temperature anomaly budget highlights the dominance of the advection process in the simulated ENSO. Vertical mixing contributes negatively to ENSO anomalies, damping temperature anomalies from the surface due to the turbulent heat flux feedback. This newly developed HCMROMS is poised to serve as an efficient modeling tool for ENSO research in the future.
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Status: final response (author comments only)
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RC1: 'Comment on gmd-2024-187', Anonymous Referee #1, 09 Feb 2025
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2024-187/gmd-2024-187-RC1-supplement.pdf
- AC1: 'Reply on RC1', Yang Yu, 06 Mar 2025
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RC2: 'Comment on gmd-2024-187', Anonymous Referee #2, 24 Feb 2025
The work built a hybrid coupled model based on ROMS and a statistical atmosphere. The paper specifically present the model formulation and its performance evaluations about ENSO. Overall, the work is interesting. The developed model will be a useful for future ENSO studies.
Table 1: I can’t understand how your “complexity” is defined, as well as your degree of freedom? The definition of “variables” for dynamical models is different from that for AI. Your rating/table is misleading, and gives one a feeling that the AI models are much more complex than the CGCMs.
Fig. 1/L121: Not sure how the first SVD modes are derived? SVD needs to be performed by a pair of fields. Please explain how the three fields are used?
Fig. 9/10: The simulated ENSO cycles are too regular. One suggestion for your future experiments is to add some state-depend noise to your statistical atmospheric model.
Fig. 14(e-h): Are you presenting streamlines in the figures? If so, are they derived based on mean currents?
L471: eastward advection anomalous => anomalous eastward advection;
Also, the statements are at least incomplete. The vertical advection of anomalous warm water by mean upwelling is important contributor to the onset of El Ninos. The subsurface warming is also related to the westerly wind anomalies through their triggered downwelling Kelvin waves.
Citation: https://doi.org/10.5194/gmd-2024-187-RC2 - AC2: 'Reply on RC2', Yang Yu, 10 Mar 2025
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