the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Sep 2026
NEMO-GSL v1.0: An integrated hydrodynamic and ice-process model for a deep subarctic lake (Great Slave Lake, Canada)
Abstract. NEMO-GSL v1.0 is a three-dimensional hydrodynamic and ice configuration of Great Slave Lake (GSL), the deepest lake in North America and a central component of the Mackenzie River system, built on NEMO v5.0 with the SI3 ice module. The configuration targets conditions absent from ocean and temperate large-lake setups: weak, temperature-controlled stratification, seasonal ice cover exceeding seven months annually, and water levels governed by riverine throughflow. Simulation domain construction required a basin-wide bathymetric product, generated by Universal Kriging of digitized nautical charts, sparse soundings and crowd-sourced depths onto a 1 km grid with 81 vertical levels. Adaptations from NEMO defaults include generic length scale k–ω closure with the Canuto B stability function for weakly stratified freshwater, adjusted SI3 snow conductivity and ice albedo for freshwater ice under prolonged cover, and a modified river routine in which outflow is computed from mean lake level using monthly stage–discharge relationships, with a hydrostatic correction for the ice and snow load applied in winter. The configuration is forced by CaSR v3.2 atmospheric reanalysis and MESH-derived runoff. Evaluation over 1998–2001 against thermistor profiles at six stations, eddy-covariance evaporation, satellite-derived lake surface water temperature (LSWT) and ice phenology yields water-column RMSE of 2.08 °C, LSWT RMSE of 1.68 °C, mixed-layer depth (MLD) MAE of 6.42 m, seasonal evaporation totals within 0.25 % of observations, and ice freeze-up and break-up errors of 1 and 2 days, respectively. Forcing sensitivity experiments reveal wind forcing as the dominant control on simulated mixed layer depth. The bathymetric reconstruction methodology, ice–turbulence parameterization protocol, and river-forcing coupling strategy developed here are independent of GSL-specific inputs and are intended for reuse in other data-scarce, ice-covered lakes. Together, these developments are designed to establish a replicable workflow for subarctic lake modeling, extending NEMO’s application domain beyond the contexts of ocean and temperate large lakes.
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Status: open (until 27 Oct 2026)
- RC1: 'Comment on egusphere-2026-3559', Anonymous Referee #1, 07 Sep 2026 reply
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RC2: 'Comment on egusphere-2026-3559', Anonymous Referee #2, 28 Sep 2026
reply
This is well-meant installation of NEMO5 in an area of scarce data. Not groundbreaking but interesting nonetheless in a setting so difficult. I am impressed by the speed at which the authors were able to set up a new model (NEMO5) in a new domain (Great Slave Lake). The mixed layer depth is quite accurately represented, and SST errors are within an acceptable range. The authors do note the high sensitivity of the model to atmospheric forcing, which is a good exercise and emphasize the need for accurate atmospheric dataset in northern Canada. Well-written manuscript. A few minor questions really remain though before publication.
- The choice of TEOS-10 for the equation of state rejoices me. Can you elaborate on the choice of absolute salinity? Was it based on some measurement?
- Do you convert from conservative temperature to in-situ when comparing for SST? (if not please provide an estimate of the bias)
- k-omega is not often used in NEMO applications (though promising), could you elaborate on the reason for this choice? Was it proved better for lake applications?
- The choice of ice embedding/loading is interesting. The option of ln_ice_embd=T is still usable with no ice dynamics (as long as the ice code provides total ice+snow mass locally). However, I understand that not everything can be tested in such a quick attempt for results and output SSH would still need to be corrected (for ice loading) for lake stage-discharge relationship. Thus, the simple choice of correcting the lake water level over the whole lake with the total mass of ice makes sense in that perspective. Was this diagnostic done inline or offline?
- Lines 371,458: Semantic rabbit hole here: as I am working in a prediction center, “prediction”, “predicted” mean something very precise, that is, this is an initial value problem (we start with the best estimate of the ocean-atm-ice state) and let the model go. Your case is more of a boundary value problem (the atmospheric forcing, rivers, and given the significant spinup period used for the model). Therefore, expressions such as “overestimation” and “simulated” or “modelled” sounds more appropriate.
- Line 481: “northwestern winds”, given the Ekman transport to the northeast being reported, I think the authors meant “southeasterly winds” (meteorologists report the wind direction from its source, not its destination). Or alternatively, “winds blowing in the NW direction” would work too. Please ensure that the caption describing the wind roses reflects which definition for the direction was used.
- Line 483-484: “A net loss of water from East Arm to the West Arm is expected due to minor river inflows into the East Arm, and the only outflow being the Mackenzie River in the western part of the lake”. I am not sure which dynamic process is involved here. The river inflow and lake outlet in the West Arm creates a SSH gradient which via Coriolis can lead to the formation of an anticlockwise lake circulation, granted. However, how far this circulation can entrain the waters of the East Arm remains a bit speculative to me without deeper analysis. I speculate that the bathymetric constriction at the entrance of the East Arm may dissipate any large-scale circulation to take place there. Additionally, steric height differences may be in favor of a surface flow from west to east in summer, unless Coriolis deviates too much of it.
Citation: https://doi.org/10.5194/egusphere-2026-3559-RC2 -
RC3: 'Comment on egusphere-2026-3559', Anonymous Referee #3, 06 Oct 2026
reply
This is a well-written paper that discusses the application of NEMO to Great Slave Lake. I find the model compelling, and the metrics used are within a reasonable range (especially the evaporation rate and the ice timing). Overall, this appears to be a model that captures key features of GSL.
That said, there are several points of note that warrant further clarification:
- Why NEMO? The Authors comment on it being used for the Great Lakes, though FVCOM is a more commonly applied model in that context. Many of the stated benefits of NEMO are also available with FVCOM and its ice module, which includes ice advection, or ELCOM, which also has an ice module.
- Following this, the Authors note that ELCOM was previously used to model GSL but “was restricted to the West Arm during the ice-free periods (Leon et al., 2007).” What is the significance of that? Is there significant exchange between the West and East Arms?
- The only comparison to that work states that “Large anti-clockwise gyres form along the shores, showing circulation patterns similar to those reproduced by Leon et al. (2007) using ELCOM.” Does that mean that ELCOM would have been equally effective?
- Some of the parameterizations could be better explained. For example:
- Light penetration coefficients: Why these numbers? Why not temporally variable? Or spatially variable as noted by the changing turbidity?
- Ice Module: What were the selected ice and snow parameters?
- Selecting a mixed layer depth based on a temperature change is common practice; however, the value of 0.5 was a choice. How sensitive are the results to this value? Based on Figure 6, selecting a value of 0.1 would give a very different story.
I do believe that this paper has merit and is an interesting contribution.
Minor Comments:
- Line 82 – Define RDRS here
- Figure 1 – Highlight the simulation domain in this figure as it’s unclear how much of the different arms are included.
- Figure 2 – Most of the discussion on the bathymetry interpolation occurs within Section 3 but we need to wait until Section 4 to see the results. While I agree that the bathymetry is a significant result, consider combining Figures 2 and 4.
- Line 199 – What is the bottom friction parameterization?
- Line 295 – What interpolation scheme?
Citation: https://doi.org/10.5194/egusphere-2026-3559-RC3
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- 1
This manuscript documents the configuration of the NEMO model for Great Slave Lake, Canada. Great Slave Lake has the interesting feature in that its main body is relatively shallow, while a complex region of islands and channels is extremely deep (by lake standards).
While the authors provide some justification for why they are reporting on configuring a deep ocean model for a medium-large sized lake with typical depths similar to on-shelf (mean depth of 41m has been reported). NEMO seems, a priori, to be the wrong tool for the job, and its use seems to have little a priori justification.
It is especially problematic that the authors present the problem of using NEMO for lakes as a “done deal”. I am aware of at least four (MIT gcm, ELCOM/AEM3D, ROMS/CROCO, FVCOM) different models in the modelling community for Canadian lakes, and my guess is that the application of these models, in terms of published papers, would outnumber those using NEMO by a factor of 10 to 1. Yet the statement made in the manuscript is that Dupont reported on applying NEMO to the Great Lakes, and then the issue is left there. I think there is a lot of rebalancing to do as far as the discussion is concerned.
The references do a nice job on many aspects of the modelling exercise (e.g. in setting why Great Slave Lake is interesting to study, in laying out large Arctic issue), but they fall short when it comes to modelling (to be fair there is discussion of the Umlauf et al papers). This seems very strange for a methods paper on modelling!
The authors state that parameterizations are vital for the sharp thermoclines in likes, but then provide little detail of why NEMO’s suite of parameterizations are the way to go. Why is a k-omega model appropriate? Why is something like the KPP scheme not appropriate when the thermal response is so surface trapped (figures 6 and 7). There are relevant papers discussed (The Umlauf et al papers) and what is around lines 205-210 is good. But I would want to know more on both the choice of model and how sensitive modelling is to the precise values chosen (i.e. the background values of eddy viscosity strike me as so low they are irrelevant; most dissipation will be numerical dissipation.
What is the cutoff for this model as far as horizontal resolution? The 1 km seems like a good start, but it’s not clear to me that all dynamic phenomena that is relevant to modeling is a priori hydrostatic. And what about vertical resolution during the winter when the dynamics is presumably quite sensitive?
I was also left wondering about the overall dynamics of the lake. How does transport within the shallow main portion compare to systematic exchange with the complex, deep arms? Surely the point of a modelling exercise like this one should, at least in part, have something to say about the actual dynamics in the lake (as opposed the statistics of error). I question reporting depth averaged circulation, given that the thermal stratification reported earlier is quite shallow.
What is the effect of using a thermodynamic only ice model? There seems to be a fair amount of detail (reported around line 220) in the model, but does that detail matter if the ice does not move? How sensitive is the model to the parameters?
What is known about the accuracy of the forcing product? Are there weather stations one can ground truth against? I get that you have to use reanalysis, but a bit more information would be useful on how good one expects it to be.
A different category of criticism is the over-reliance on RMSE. Of course RMSE is how one starts to measure error, but surely one cannot just stop there. Take Figure 6 for example; there are clear spatiotemporal differences that, at least to the eye, seem far more important than an overall RMSE value. I think this needs some discussion.
In summary, this is the start of something good. A lot of work was done, and the model will clearly be good for something. But the modelling context needs to be improved, and a subset of the above critiques need to be addressed (the authors can’t do everything, and I assume they will make judicious choices governed by what is doable).
Small points:
The manuscript does an admirable job describing the steps taken to produce a bathymetry, but the result (Fig. 4) is separated from the discussion by a large chunk of the article.
I question the need for an acronym for Great Slave Lake. GSL is generic sounding and the location is unique and deserves to be written out in full.
The wind rose in Fig 14 is pretty small at present. Could you fill the lower right space with the wind rose and put the legend in the space above?
The axis and axis label fonts in general are faint and small. I would increase size and perhaps make them bold.