Hieronymus presents the sea level simulator v1.0 following the first official public release of the source code. The sea level simulator aims at providing the joint probability of mean sea level rise and sea level extremes which is crucial for coastal planning and future adaptation. This tool could be particularly useful for engineering purpose and decision-making.

The manuscript acts as a technical reference publication as stated by the author. The framework behind the sea level simulator is not new and studies using previous versions of it have previously been published (Hieronymus, 2021; Hieronymus and Kalén, 2022). This version offers some updates such as new mean sea level projections (based on Fox-Kemper et al., 2021) and the implementation of uncertainty in the GEV parameters for the extreme sea levels.

The second part of the paper focus on a particular site and shows how the model behaves based on different parameter choices. More importantly, the manuscript discusses what one can infer from the model outputs. For example, which physical process is the dominant at different time horizons; this has direct impact when designing new structures and their planning periods.

The code is written in Matlab and released under MIT licence. Having no access to a Matlab licence, I have not tested the code directly, but the Gitlab is easily accessible. Users could potentially adapt and change the routines thanks to the use of Matlab. The different functions seem relatively straightforward to read though some users might find difficult to follow due to the lack of comments and descriptions inside each Matlab file.

GMD seems to be a good fit for the manuscript considering the author aim to publish a reference technical publication on the sea level simulator. Considering the previous two peer-reviewed papers on the framework, the comments below are mostly minor but would make the paper easier to digest by readers.

General Comments:

• A significant part of the paper looks at what one can infer from the simulator (is the extreme level or SLR level the key driver, at which time horizon, etc), but it’s not mentioned in the abstract. I think it is a very interesting capability and a key aspect of the simulator as it will be part of the decision-process a user might look at. It should be a bit more highlighted.
• As this paper should be viewed as a technical documentation, I think it would highly benefit from an extended Figure 2 where each step is associated with a sub-figure showing the raw time serie data, the GEV fits for extreme levels, the SLR scenarios, the sampling etc. It would really guide the reader. (I would also use more the word components / modules instead of nodes).
• “An example probability range is shown in Tab. 1” line 138; it might be good to have some guidelines for a user in how to choose these probabilities. As shown in the second part of the paper, they do have an impact.
• I wonder if having another site with a potentially different behaviours or with different probability for the scenarios could be useful for a reader.
• More extensive comments in the source would be beneficial to the users (mainly in emulator_ringhals_par.m, example_plot.m which seems to be the drivers); the associated set of functions are much more commented.
• The figures could be improved for better readability: none have a grid, the labels, ticks, and legends have generally small font size. I also believe showing the y-axis in term of return period would be more intuitive to a user / reader. This could be done on the right side with a twin axis for an example. The return period is stated in the text but shown in figure would be appreciated: “For example, a frequency of 10−4 means that one in ten thousand planning periods contain a sea level of this height.”

Specific Comments:

• Line 80: “very close to the ideal”; does the model keep its scaling skills for 16, 32 cores?
• Figure 2 caption: “is goes”; remove “is”
• Line 100: “that reliably captures high frequency sea level variability”; maybe precise for the reader what “high-frequency” means so they do plug the right data to the model.
• Line 127-128: “These two scenarios have also low confidence projections, where the contribution from the Greenland and Antarctic ice-sheets to sea level rise is taken from some of the highest projections in the published scientific literature.” A citation would be helpful here.
• Line 145-147: I find this sentence not very clear.
• Line 157: it could be useful to precise which other distributions have been tested to know what does and doesn’t work.
• Line 161: “This script is easily edited for use with different mean sea level projections”; can easily be edited ?
• Line 179: “For example, a frequency of 10−4 mean that one in ten thousand planning periods contain a sea level of this height.” Replace mean by means
• Line 183: maybe more “sea” than “ocean”.
• Line 186: It would be good to reference here some previous studies when discussing the stratification in the North Sea / Baltic Sea regions. A recent study I found that could help finding the most appropriate references
  
  https://esd.copernicus.org/articles/13/373/2022/
• Figure 8: what is the colorbar for? or is it lost in the middle of the dark dots.
• Figure 8: I might misread the figure but why the extreme levels impact reduces with longer sea level maximum for different length of planning periods? Why not constant.

The use of Mathlab is a bit problematic as it is not really open access but I presume this is allowed according to GMD policies.

My major concern is that the paper assumes the previous two papers of the author have to be read in order to fully appreciate the current manuscript. This can be overcome.