Review of manuscript "Estimating Seasonal Global Sea Surface Chlorophyll-a with Resource-Efficient Neural Networks" by Martinez-Balbontin et al., submitted to Biogeosciences

This article presents an interesting data-driven approach to predict chlorophyll-a (chl-a) fields on a near-global scale based on seasonal forecasts of some oceanic physical properties, which offers an alternative tool to mechanistic biogeochemical models. Previous efforts in data-driven global chl-a reconstruction from oceanic physical properties have primarily targeted long-term retrospective analyses. In contrast, this study explores shorter-term seasonal forecasts of chl-a and argues that the results compare favorably with biogeochemical models. The topic is timely, the scientific question is original and clearly formulated, and the overall structure of the manuscript is easy to follow. However, I think several points would require further elaboration and justification to make the manuscript more rigorous and convincing.

General comments

1) Choice of predictors. The rationale for selecting only four predictors deserves further justification. While they are relevant, some may contain redundancy and some processes are not explicitly considered. It would help to discuss assumptions about neglected drivers and how this may influence the spatial and temporal variability of results. For instance, light availability is a key driver, particularly at high latitudes and in some tropical regions (see for instance Fig3A-B of Racault et al., 2017). Although SST may correlate with PAR seasonally, this relationship does not hold consistently at inter-annual timescales. Why was this predictor not included, for example? Is the potential improvement in performance considered negligible compared to the gain in terms of computing time for model training?

2) Temporal resolution. The use of 5-day data for training, while the final application relies on monthly inputs, is not fully explained. Clarifying the advantages and limitations of this choice would help readers understand whether it may contribute to the underestimation of inter-annual variability.

3) Reproducibility and robustness. Certain technical details are not sufficiently specified to ensure the reproducibility of the experiments (e.g., learning rate, number of training epochs, criteria applied to stop training, etc) and to convince readers of the robustness of the approach. While possible overfitting is mentioned to explain the network's weaker ability to predict inter-annual variability than seasonal one, giving more details regarding any regularization techniques used to monitor and limit this would strengthen confidence in the robustness of the approach.

4) Seasonal vs inter-annual variability. The manuscript could be strengthened by further analyses and discussion on how the data-driven approach performs relative to mechanistic models in representing inter-annual variability. This variability is more difficult to reproduce but highly relevant for societal applications (e.g., ENSO impacts), whereas reproducing the climatological seasonal cycle is comparatively less challenging.

5) Machine learning positioning. Clarifying the distinction between the efficiency of the overall data-driven framework versus the neural network architecture itself would avoid confusion. Six million parameters can be seen relatively large compared with some published CNNs, and the choice of U-Net over simpler alternatives could be justified more explicitly in terms of added value.

Specific comments

Title : I would have rephrased the title into something like : « Forecasting Seasonal Global Sea Surface Chlorophyll-a with a lightweight data-driven approach » to emphasize the forecasting dimension and the efficiency of the overall method  (vs. the efficiency of the architecture itself).

L4 : I would recommend a sentence like : « We propose a data-driven resource-efficient alternative : a neural architecture based on the U-Net that reconstructs surface, [..] from four physical predictors »

L15 : I think « long-term » is not appropriate here, I would recommend « seasonal »

L69-70 : « While advances in computational power and data availability have enabled the development of more complex architectures, the simplicity and efficiency of the U-Net makes it an effective and resource-efficient choice for this task ». Although the U-Net architecture may appear simpler than more recent transformer-based models, it is still more complex than basic CNNs with fewer parameters that have been used in some previous studies. While I am convinced that a Unet is well suited for this application, I would have justified this choice differently, for example by emphasizing its ability to better capture different spatial scales.

L77 : « The hyperparameters [...] were optimized using random search ». Please provide a detailed description of all hyperparameters used and clarify on which dataset they were tuned: was optimization based on the training period (1998–2017) or the validation period (2017–2023)? If the latter, how did the authors account for potential overfitting? Why was an entirely independent time period not used to assess the model’s generalization performance more objectively? Could the authors also provide the training and validation loss curves? Finally, did the authors check that the model's learning was stable from one run to the next?

L85 : « To simplify the predictive task, which consists of using a 6-month forecast of the physics to predict chl-a for the same time period, we trained twelve dedicated neural networks, each corresponding to the starting month of the forecast ». The choice seems to increase methodological complexity while reducing the training data available for each model, potentially favoring overfitting. I would have clarified how this risk was assessed and justified this strategy more explicitly, including its potential advantages and drawbacks. Have the authors compared reconstruction performance over the six-month period when using a single model trained across the whole dataset versus the proposed approach based on twelve separate models?

L90-92 : « For context, […], has between 20 and 30 million ». I would have removed that sentence. In Ansari et al. (2022), some of the Unets mentioned have almost six times fewer parameters. If a comparison in terms of parameters is to be made, I think it would be more relevant to compare them with other deep learning models published on the topic of Chl reconstruction.

L104 : « Since remotely sensed chl-a is limited by sunlight availability, we focus on the -60° to 80° latitudes ». Spatial coverage is not symmetric in latitude, suggesting factors beyond sunlight, e.g., cloud-dependent pixel availability (higher in the Southern Ocean). I would have justified the footprint selection more objectively, for example using a minimum pixel density.

L117-L119 : « The SEAS5 forecast [..]. To avoid these from biasing […], and then subtracting this difference from SEAS5 before using it as input ». I am not sure I understand the rationale of this approach. When applying the model to future forecasts, the corresponding GLORYS12 reanalyses will not be available—how will this be addressed? Is only a fixed annual climatology subtracted?

L165 : « the ACC decreases with lead time due to the increased uncertainty in the forecasted physics». For Figure 6b (6-month lead-time ACC), have the authors checked whether the poorly predicted (blue) areas correspond to regions where the seasonal forecasts of the four physical predictors are less accurate, based on literature or error maps?

In the discussion (L235-237), the use of biogeochemical variables as predictors is mentioned. Can’t these forecasts carry larger errors than the physical fields, and couldn’t their inclusion risk degrading six-month forecast quality?

FIG 7 : Have the authors tried plotting seasonal chl‑a forecasts initialized for several months using SEAS5, in the same way as Figure 5, across the different regions? Providing these outputs in the Supplementary Material could help assess spatio-temporal heterogeneity in model performance over multiple lead-time months.

L169-170 : « These figures demonstrate that the neural network is able to capture the seasonal dynamics of the data across regions, regardless of the input physics. » This statement could be complemented by a discussion on interannual variations. ACC metrics for the different regions could support this discussion.

FIG8 & 9: Figure 8 is currently under-described; a more detailed analysis is recommended to strengthen the argument. For Figure 9, adding ACC curves would provide a metric specific to interannual variations and show their evolution over time.

L190 : « a simple neural network » : I would remove the term « simple ». Similarly, L217, I would either remove « shallow », or insist on the fact that this is a lighter approach than a classical mechanistic model. L246 : I would recommend removing « lightweight »

L249 : « These predictions maintain low error and high correlation to observations across different regions and lead-times ». I would qualify that statement, as this may not be the case with regard to interannual variability.

Technical corrections

L26: even ‘when’ limited instead of « even if limited » ?

L44 : « variables » or « predictors » instead of « parameters » 

L60 : « available forecast of these as input » may be replaced by « available forecasts of those former variables as input» for clarity ? 

L83 : « the physical ocean data was » → the physical ocean data were 

L91 : « Ronneberger et al. »→ please precise the date of the reference.

L94-97 : Please precise the initial spatio-temporal resolution of the different used dataset, as well as the final resolution used in this study. If pre-processing was made (resampling, etc), please precise it.

L110 : « it uses the IFS cycle 43r1 for the atmosphere, NEM0 for the ocean, and LIM2 for sea ice ». Please precise the acronym and what it corresponds to for non numerical modeling readers.

Fg2 : Perhaps I have misunderstood, but why not have six reconstructed time series, with lead times 1-6 (see lead time of 6 shown in Figure 6) instead of five in this diagram ?

Fig 3 : it would be great to have the lat/lon axis plotted on the maps.

FIG 7 : I would recommend adding on the Figure some quantitative correlation metrics between the different time series to better compare performance between areas.

References

Racault, M. F., Sathyendranath, S., Brewin, R. J., Raitsos, D. E., Jackson, T., & Platt, T. (2017). Impact of El Niño variability on oceanic phytoplankton. Frontiers in Marine Science, 4,133