the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Correction of Air-Sea Heat Fluxes in the NEMO Ocean General Circulation Model Using Neural Networks
Abstract. The atmospheric forcing and the heat exchanges between the ocean and the atmosphere represent one of the major sources of uncertainty for numerical ocean reconstructions and predictions. Air-sea heat fluxes may suffer from inaccuracies in meteorological fields, sea surface variables, and bulk formulations, which have a strongly non-linear dependence on the ocean state. Here, state-dependent errors of the heat fluxes are learned by artificial neural networks (ANN) from a dataset of heat flux correction terms, derived in turn from previous sea surface temperature nudging experiments. The pre-trained model predictors include stationary fields, atmospheric forcing data, ocean state, and stratification indices. Variable importance scores emphasize the dependence of the air-sea heat flux errors on the wind forcing. The pre-trained model of heat flux correction is then used to adaptively correct the fluxes online, in a series of global ocean experiments performed with the NEMO (Nucleus for European Modelling of the Ocean) ocean general circulation model, augmented with ANN inference capabilities. Results indicate the positive impact of the correction procedure, beyond the training period, e.g., in independent observation-poor and -rich periods, leading to the same dynamical and subsurface signature as in nudging experiments. Prediction experiments also indicate the method's potential for operational forecast applications. The method may also be adopted in coupled long-term reanalyses, long-range predictions, and projections.
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RC1: 'Comment on gmd-2024-185', Anonymous Referee #1, 20 Nov 2024
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The authors propose to use artificial neural networks to correct air-sea heat fluxes in the NEMO ocean model. This is a novel approach compared to traditional bias correction techniques or climatology-based methods. It involves developing a machine learning-based state-dependent correction approach. Learning the corrections from nudging SST increments allows for online inference without relying on direct observational data in operational forecast settings or in historical periods with scarce data.
The use of artificial neural networks (ANNs) to model nonlinear relationships between atmospheric and oceanic state predictors supported by stationary predictors and heat flux errors appears remarkably effective. It would be of interest to include in the manuscript information on the performance of the neural network in predicting the nudging increments using standard metrics like R2 or normalised mean square error or error maps. It appears that the network is able to learn a substantial part of the corrections. Figure 4 provides a hint.
The method has been demonstrated to be effective in the ocean only model. Non-linear feedbacks in the coupled models could affect the effectiveness of the ANN-based corrections. Do you foresee the method to work well in the coupled models? Perhaps applicability in the context of coupled modelling/coupled reanalysis could be discussed.
Have you tested other ANN architectures like CNN? While CNN may be more difficult to apply online, it would be interesting to assess if it would be superior to the simple column model?
Citation: https://doi.org/10.5194/gmd-2024-185-RC1 -
CEC1: 'Comment on gmd-2024-185 - No compliance with the policy of the journal', Juan Antonio Añel, 28 Nov 2024
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlSeveral of the links that you mention in the "Code Availability" section of your manuscript fail to comply with the requirements of our policy. I ask you to read the policy and deposit NEMO v4.0.7 (currently stored in jussieu.fr) and the modified version, and the ANIFF module (both stored in cnr.it) in one of the suitable repositories listed in our policy. Remind to include licenses for each one of the codes you are publishing.
You must reply to this comment as soon as possible (without waiting for the end of the Discussions period) with the information for such repositories, including link and DOI, and modify the "Code Availability" section of your manuscript accordingly in potential future reviewed versions of it.
Note that if you do not fix this problem, we will have to reject your manuscript for publication because of no compliance with the policy of the journal.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/gmd-2024-185-CEC1 -
AC1: 'Reply on CEC1', Andrea Storto, 28 Nov 2024
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Dear Editor,
Thank you very much for cross-checking the manuscript's assets.
However, the request is not clear: At the end of the Code section in the pre-printed manuscript, we already report the Zenodo link https://zenodo.org/records/13380698 , which contains all the code used in the study (including ANNIF and NEMO). We have indeed chosen to indicate both the repositories (original as in NEMO, and our institutional repository) and a frozen version of the code, using Zenodo, a supported repository.
Could you please clarify if Zenodo is not OK and suggest alternative repositories?
Thank you very much,
Andrea Storto
Citation: https://doi.org/10.5194/gmd-2024-185-AC1
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AC1: 'Reply on CEC1', Andrea Storto, 28 Nov 2024
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Data sets
Material for the manuscript Andrea Storto https://doi.org/10.5281/zenodo.13380698
Model code and software
NEMO 4.0.7 Andrea Storto https://baltig.cnr.it/nemo_ismar-rm/nemo_4.0.7/-/tree/3.0?ref_type=tags
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