the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jul 2025
Developing a Coastal Hazard Prediction System in Ice-Infested Waters, Part 1: High-Resolution Regional Wave Modeling in The Estuary and Gulf of St. Lawrence
Abstract. This study is the first of a two-part paper that summarizes the development of a prototype coastal hazard prediction system providing short-term (+48 h) forecasts of the total water level (TWL) at 50 m resolution for the province of Quebec, Eastern Canada. In this first part, the implementation of the offshore wave model component of the system, which is a regional 1 km-resolution WAVEWATCH III™(WW3) configuration for the Estuary and Gulf of St. Lawrence (EGSL), is presented and discussed. The configuration is forced by high resolution atmosphere, ocean and sea ice forecasts provided by Environment and Climate Change Canada (ECCC) and includes a state-of-the-art parameterization of wave propagation and attenuation in sea ice that has been tuned with observations from the EGSL. Performances are assessed against wave data collected over a two-year period during which the forecasting system was running operationally, and against historical storm data using a model hindcast. Results demonstrate reasonable forecast skills both for normal and extreme wave conditions during ice-free periods with errors ranging from 15 % to 31 % of the mean wave height. However, when sea ice is present, performances are drastically reduced, primarily due to inaccuracies in the predicted ice fields at spatial scales over which wave energy typically dissipates in sea ice.
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RC1: 'Comment on egusphere-2025-2168', Anonymous Referee #1, 15 Dec 2025
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AC1: 'Reply on RC1', Jeremy Baudry, 05 Mar 2026
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Thank you for the thorough review of our manuscript and the useful comments provided, it is much appreciated. Please find below our response to all questions from Reviewer 1.
This paper reports on the implementation of the wind wave component of a forecasting system for coastal hazard. As such, it is an interesting paper to read. As stated by the authors, much progress is still needed for the proper representation of the complex interactions between atmosphere, waves, currents and sea ice. It is a bit worrisome that the wave predictions were quite well off the observations and potentially not providing much guidance. One could wonder whether an ensemble approach should be used to sample the large uncertainty in the sea ice conditions (i.e. what would the wave conditions be if the sea ice conditions were to be a lot less)?
In general, we would say that in the context of an early warning system an ensemble approach is indeed more desirable than a purely deterministic approach. Knowledge of the uncertainty/spread of the prediction is an important parameter to take into account for decision-making and risk management—arguably just as important as the predicted value itself. However, we see some potential challenges associated with running ensemble simulations in this context (without mentioning, of course, the significant computational cost associated with ensemble prediction using a high-resolution model). The most critical concern is regarding how the system is perturbed to construct the ensemble. Ensemble forecasting is primarily intended to address model sensitivity to initial conditions that arise with highly non-linear equations, or uncertainties in the model’s physical parameters (which are themselves often linked to unresolved subgrid-scale processes). One must therefore sample from a distribution with a known uncertainty —and most importantly unbiased—in the initial conditions/or physical parameters to construct the ensemble members. The issue raised in our paper is not really an initial conditions problem. In fact, the data assimilation system forces the ice model to directly stick to observations derived from ice charts constructed from satellite imagery. The issue primarily comes from the ice model physics itself, which does not allow for an adequate representation of the dynamics of fragmented ice, and the creation of heterogeneous and “patchy” medium as we observed in reality at these spatial scales. It therefore seems extremely difficult to obtain an unbiased ensemble spread. To our knowledge, there are not many short-term ensemble forecasting systems for sea ice. Nevertheless, at the time we write this response, we just came across a recent article currently under review on EGUsphere that specifically addresses parameter perturbation methods (notably P*, the compressive strength of ice) to construct ensembles in the context of seasonal forecasting: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6402/. Finally, to address your question, “what would happen if there were much less ice, or even no ice at all?”, we believe this is a highly relevant question in the context of coastal hazard risk prediction and assessment. We can think as a useful “ensemble”, one composed by only two members: a control member corresponding to the current deterministic forecast, and a second member assuming completely ice-free conditions, intended to represent the worst-case scenario that could occur during a given storm.
A discussion on this point is added in the concluding section of the revised manuscript.Table 1: what is the justification for using ST3 as most WW3 these days are using ST4 or ST6?
The use of ST3 in this study does not necessarily reflect an improvement compared to ST4 and ST6, and we do not claim that ST3 performs significantly better than the other parameterizations. While we agree that nowadays the use of ST4 and ST6 is probably more widespread and seems to improve overall performances, each of these parameterizations have their strengths and weaknesses under different sea states, the use of ST6 for example have been shown to overestimate Hs under wind-wave-dominated sea states (Lin et al. 2020). The difference between ST3, ST4, and ST6 under the typical wave conditions of the St. Lawrence and for our intended application remains altogether minor. Overall, differences among these three parameterizations mainly affect the spectrum shape ( especially at the high-frequency tail of the spectrum that might affect higher-order spectral moments), but the difference remains second-order compared for example to the impact of the resolution of the wind forcing itself.A note an that point has been added in the revised manuscript advising the reader that changing the wave input-dissipation parameterization might potentially improve the results.
Lin, S., Sheng, J., & Xing, J. (2020). Performance evaluation of parameterizations for wind input and wave dissipation in the spectral wave model for the northwest Atlantic Ocean. Atmosphere-Ocean, 58(4), 258-286.
117: WW3 employs logarithmically frequencies: f(n) = r * f(n-1).
With f(1)=0.05 and f(25)=1.1 Hz would imply r=1.1375, which is a bit unusual. Is it what was used? More commonly used is r=1.1, which would make the frequency discretisation slightly less coarse and probably more appropriate for low energy, short fetch conditions. You would have had to increase the total number of the frequencies to 34 but noting that the set-up is for high resolution forecasts, it might have been relevant.
Good catch. This is an error in the manuscript. In our configuration, we indeed used r=1.1, which gives f = [0.05, 0.5].193: what is the frequency used for the calculation of the mean wave period (Tm2)? Is it consistent with what the model is using? From figures B1 and B2, it does look to me that the model and the prediction have used different frequency range when estimating Tm2. Not using the same frequency range will results in a systematic bias between the two quantities.
This is another good catch. There is indeed a discrepancy between the two ranges. Data from the AWAC have been computed over 0.055-1.0 Hz and we agree that discrepancy, especially at the high frequencies, introduces a bias. AWAC data have been re-extracted with the correct frequency band, and figures and performance statistics have been updated accordingly.215: are you sure about your definition of MBE (A1)? From Figure 7, it looks to me that biases should be negative, i.e. Observations are more often larger than Prediction. Hence while you could say that here is an underestimation of the highest waves by the model.
Good catch, thanks. All metrics have been computed according to: Observation – Predictions. Corrections have been applied in the revised manuscriptMinor correction:
Table 1: WAM Cycle 4 -> WAM Cycle 4 (ecWAM) (i.e. ST3 in WW3 5.16 is based on ECMWF modifications of the original WAM Cycle 4)Corrected
wave breaking -> bottom induced wave breaking
Thank you for the precision. We made the suggested change in the revised manuscript.119: 3h -> 3-hourly
Corrected.127: is the forecast output also 3-hourly?
HRDPS forecasts are provided hourly. This is now specified in the revised version.Figure 10: wave direction -> mean wave direction?
Corrected.Citation: https://doi.org/10.5194/egusphere-2025-2168-AC1
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AC1: 'Reply on RC1', Jeremy Baudry, 05 Mar 2026
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- 1
Review of “Developing a Coastal Hazard Prediction System in Ice-Infested Waters, Part 1: High-Resolution Regional Wave Modeling in The Estuary and Gulf of St. Lawrence”
This paper reports on the implementation of the wind wave component of a forecasting system for coastal hazard. As such, it is an interesting paper to read. As stated by the authors, much progress is still needed for the proper representation of the complex interactions between atmosphere, waves, currents and sea ice. It is a bit worrisome that the wave predictions were quite well off the observations and potentially not providing much guidance. One could wonder whether an ensemble approach should be used to sample the large uncertainty in the sea ice conditions (i.e. what would the wave conditions be if the sea ice conditions were to be a lot less)?
Some comments and questions:
Table 1: what is the justification for using ST3 as most WW3 these days are using ST4 or ST6?
117: WW3 employs logarithmically frequencies: f(n) = r * f(n-1)
with f(1)=0.05 and f(25)=1.1 Hz would imply r=1.1375, which is a bit unusual. Is it what was used? More commonly used is r=1.1, which would make the frequency discretisation slightly less coarse and probably more appropriate for low energy, short fetch conditions. You would have had to increase the total number of the frequencies to 34 but noting that the set-up is for high resolution forecasts, it might have been relevant.
193: was there any quality control applied to the observations?
193: what is the frequency used for the calculation of the mean wave period (Tm2)? Is it consistent with what the model is using? From figures B1 and B2, it does look to me that the model and the prediction have used different frequency range when estimating Tm2. Not using the same frequency range will results in a systematic bias between the two quantities.
215: are you sure about your definition of MBE (A1)? From Figure 7, it looks to me that biases should be negative, i.e. Observations are more often larger than Prediction. Hence while you could say that here is an underestimation of the highest waves by the model.
Minor correction:
Table 1:
WAM Cycle 4 -> WAM Cycle 4 (ecWAM) (i.e. ST3 in WW3 5.16 is based on ECMWF modifications of the original WAM Cycle 4)
wave breaking -> bottom induced wave breaking
119: 3h -> 3-hourly
127: is the forecast output also 3-hourly?
Figure 10: wave direction -> mean wave direction?