The authors thank the reviewer for the overall very positive assessment as well as for highlighting of inconsistencies, ambiguities and the occasional lack of clarity. 



Overall, we are convinced that all wording criticized for lacking clarity can be rephrased and/or extended to improve the readability of the text, following the guidance of the review.



Following the review, we looked into a possible seasonal bias of the CTD profiles and compared them to vertically interpolated profiles from the mooring records, which provide year-round data. We find that CTD profiles are able to represent the variability of temperature and salinity profiles revealed by the moored instruments. The seasonal bias in availability of CTD profiles (mostly data measured in austral summer) therefore does not translate to a seasonal bias in our dissipation rate estimates. We will point this out in the revised manuscript. However, during this analysis we found 5 (previously undetected) CTD profiles, which deviate suspiciously far from an average profile, that they were deemed physically implausible, and were therefore removed from the analysis. We find that this correction to play a minor role for the derived dissipation.

We agree with the reviewer’s statement on the finestructure parameterization. The latter indeed provides complete dissipation rate profiles, any assumption of a background value is not necessary. We assured ourselves, that the actual averaging into bins is done correctly without any false background assumptions. The mistake is therefore solely in the text and not in the calculation, and we are grateful to the reviewer for spotting this. We will remove the erroneous statement.



Following the guidance, we will add the information about the vertical resolution of the LADCP to the text. Additionally, will add a clearer explanation of what the given length scales mean, namely the integration limits in the finestructure method. This will be extended by an added paragraph in the discussion, where we compare our choice of integration limits with the choices in comparable literature. 



During this peer-review, we found our integration limits for the shear spectra to be relatively narrow compared to other studies. Therefore, we tested the effect of an increase of the range of wave numbers in the integration from (0,1,2,3) to (0,1,2,…,7,8) in order to be more consistent with previous applications.  

The new integration limits of the shear-based finestructure parameterization lead to a slightly improved agreement of shear- and strain-based results. The corresponding figure D1 will be updated accordingly in the revised manuscript. The average shear-to-strain variance ratio R_w value changes from 7.9±10.3 to 7.8±9.2. This therefore does not impact our decision to use the literature value of R_w = 7 for the strain-based formulation. In summary, because the change to the shear integration limits does not affect the strain-based calculations, our interpretation, and discussion of the results remains unchanged.

We thank the reviewer for the detailed and constructive assessment of our paper, and are very grateful for the positive feedback regarding the quality of our work. In the following, we will address the main comments as well as some of the specific questions and suggestions. A detailed point-to-point response to all comments will be provided with the revised manuscript.

In the revised text, we aim to better emphasize the scientific significance of our work, especially of our newly developed method for quantifying wave-induced dissipation rates and our contribution to an improved understanding of deep water export from the Weddell Sea.

Main comments:

In the current version, Figure 6 only shows one vertical slice through the gravity current. For the revised version, we will add an additional subfigure to allow for a method comparison as well as a result comparison along the transect. We will also better clarify how the data presented in Figure 6 was computed. The corresponding description in the text will be updated

Applicability of the finestructure parameterization

The finescale parameterization assumes (a) that all observed variability is associated with internal gravity waves and, subsequently, (b) that a stratification exists (N^2/= 0) for internal gravity waves (IGWs) to exist in. The validity of these assumptions is not always given in our study region, and we agree with the reviewer that this needs to be discussed in greater detail. In the revised manuscript, we will address both, the effect of the nearly homogeneous bottom layer (BL) and the applicability of the method in the interfacial layer in the text and mark the regions accordingly in the figures 3, 4, 5 and 6. 

The BL is defined as having almost zero stratification, and finescale parameterization is consequently not applicable here. But we observed a maximum height of the BL of around 60m, which is much smaller than the length of 187m of the lowest vertical segment of the finestructure parameterization. Therefore, the BL can affect the results of the bottom-most bins, but does not invalidate them completely. Due to the variability of the BL height, we avoid shifting the bins vertically to keep the segments comparable. In the revised version of the figures, we pla to hatch the bottom-most bins, to communicate their difference from the upper bins.

The question of the applicability of the finestructure parameterization in the interfacial layer (IL) is less clear. The IL likely contains turbulent processes not caused by internal waves. To mitigate their effect, we consider in the integration for strain variance only the resolved length scales associated with waves. The potential case of turbulence from other sources being misidentified as wave-induced will be discussed in more detail.  

At our end of the discussion, we see a careful use of the finestructure method in the IL as justified, as long as the caveats are explicitly described.

Uncertainty of the Thorpe scale approach

We looked more into the limitations and resolution of the Thorpe scale. We investigated the measured Thorpe scales by plotting the corresponding histogram and fitting a lognormal distribution to it. As expected from the vertical resolution, the histogram is truncated at O(1m). Although Thorpe scales of few centimeters are physically possible, we expect these missing small scales to only contribute little to the overall turbulence pattern, as we resolve the large majority of the expected Thorpe scales. 

The CTD measurements are accurate to 0.0005 °C in temperature and 0.002 g/kg in salinity, which results in a density resolution of similar magnitude of O(10^-3). To check if the depth or density resolution limits our results, we will use the parameter R (Stansfield et al., 2001), a smooth vertical density profile and the fixed vertical resolution of 1m. This results in the minimum value of density resolution, from where it would be the limiting factor, which we can compare to our estimated density resolution value. 

To differentiate between physical overturning and noise, a rejection criterion is already used, based on a critical value of the overturn ratio developed in Gargett and Garner, 2008. (doi:10.1175/2008JTECHO541.1). We argue that the additional application of the Galbraith and Kelly test to further describe the limits of the Thorpe scale analysis would exceed the scope of this work. The discussion section of the Thorpe scale method will be extended with the above. We continue our discussion on this topic at the relevant specific comment about the chosen density noise level for the Thorpe scale approach. 

Time-dependence of dissipation rates

We share the interest of the reviewer to look into potential temporal changes in the dissipation rates over the years. But mean dissipation rates for each expedition along the transect are not easily comparable, as the expeditions differ in their coverage and resolution of the continental slope. Making sure the resulting time series is as unbiased as possible exceeds the scope of this work. The temporal changes of dissipation rate estimates (longer trends, interannual and seasonal variability) is part of our ongoing work and will be dealt with in a follow-up paper.

Specific comments:

Line 136 Parameters in the Multitaper analysis

On the question if the parameter P=10 in the multitaper method is similar to what is usually applied, one has to consider that the multitaper analysis has 3 parameters which are not necessarily set by the data. The window length N in units of data points, the time-half-bandwidth product P, and the amount of Slepian tapers k. The time-half-bandwidth product is often called NW to reflect its origin as a product of window length N and the half-bandwidth W. 

While non-oceanographic literature (Thomson1982, Cokelaer2017) recommend NW values between 2.5 and 4 (with a corresponding choice of 2*NW or 2*NW-1 slepian tapers), the applications to marine data we found use more varied parameter values. LeBoyer2021 et al. use for their multitaper analysis a “window length [...] chosen to be the integer number of inertial periods nearest to 30 days”, together with k=3 slepian tapers. They do not give values for the chosen half-bandwidth or time-half-bandwidth product. If we applied this condition to our measurements, the inertial period at 64° S of 13.33 hours would lead to a window length N of 30 days / 13.33 hours ≈ 54 data points. 

Instead, we use a window length of the complete length of the velocity data (N = O(5*10^3), with P=NW=10 and k = 2P-1 = 19 slepian tapers. These parameters are very close to the parameters of a time-half-bandwidth product of 8, with 15 slepian tapers, Chave et al, 2019 use to resolve infragravity waves and tidal frequencies in deep ocean pressure records. The general problem persists that although multitaper is not a trivial method, its defining parameters are regularly not given completely in the main text of published literature and the corresponding research code is not easily accessible. To increase reproducibility without requiring a detailed look into our published research code, we will give all parameters of our multitaper analysis in an additional paragraph in the methods section. 

Thorpe scales Line 118, 345-346: 

For the computation of the Thorpe scales, we previously used a value for the density noise of 3e-4 kg/m^3. With a density resolution of O(10^-3) (see above), we see this now as slightly too low. We increased it to 5e-4 kg/m^3, but still below the density differences we can accurately resolve. This results in that previously accepted overturns, yielding in low dissipation rates of around 3*10^-10, are now reclassified as spurious and replaced by the background dissipation of 1*10^-10. This especially happens in the open water column towards the east of the transect. The numerical values given in the text are corrected to describe the updated results. The interpretation of the Thorpe scale dissipation rates remains unchanged, as the values change only minimally. 

Because the outlier in the dissipation rates from the Thorpe scale approach is measured in depths around 3000m deep in the ocean, it is unlikely that wind forcing could be a physical cause of this. Additionally, profiles from the same expedition do not show segments of dissipation rate this strongly enhanced. The outlier was traced back to a single diagnosed overturn of multiple hundred meter lengths, which was not automatically rejected by the internal quality control in the Thorpe scale algorithm. We removed the large overturn as non-physical, but kept the measurements from the same profile closer to the seafloor. In the text, we will add a sentence documenting the outlier removal. 

Energy distribution in vertical modes, Line 315-316:

 The fact that higher vertical modes more likely lead to locally dissipated energy, while lower modes are more likely to spatially transport energy is used several times throughout the paper. We supported that in the text by a reference to Falahat et al. 2014. More previous work to this topic is for example summarised in the introduction of de Lavergne, 2019. In short, the distribution of locally available energy among vertical modes is based on time scales of nonlinear interactions. For example, in the often dominant process of parametric subharmonic instability (short PSI), the decay time decreases strongly with vertical mode numbers (Olbers et al., 2020, Fig. 13). 

In the revised text, we will reference the assumption we make more extensively by adding the 2 mentioned papers as references, as well as a short explanatory sentence. 

WSBW definition Line 323-324:

 The definition of Weddell Sea Bottom Water is not completely unanimous, as two established definitions still coexist: as bottom-near water below a certain potential temperature (Foster1976, Orsi1999, Nicholls2009), most recently < -0.7 °C (Vernet2019, Gordon2020), or water of neutral density > 28.40 kg m^-3 (NaveiraGarabato2002b, Meredith2008, Dotto2014, Llanillo2023). We decided to use the newer classification employing neutral density, among other reasons because it automatically excludes very cold surface waters. We will extend the definition of Weddell Sea Bottom Water in the text with a short explanation.

Quantitative Averaging, Line 375-376

The dissipation rate in each region description was previously determined by averaging qualitatively ‘by eye’. For the revised version, we split the transect into 5 regions (shelf, interfacial layer, bottom layer, open ocean, air/no data) and calculate an arithmetic mean for each. Because of the strong horizontal changes (even inside a single region), this reduction to (a maximum of) 4 values each for Thorpe and Finestructure is suited best for a summary at the end of the results sections or for the conclusion. The parameterization based on velocity time series does not have the necessary resolution for meaningful averages in each region.

Waterman et al., 2014, Line 464:

We thank the reviewer for the recommendation of Waterman et al. 2014, which we missed in our literature research. They find for the Southern Ocean that bottom-near dissipation rates, predicted by the finescale parameterization, systematically overestimated microstructure estimations, especially at locations of large near-bottom flow speeds. We will add a paragraph to discuss our results in relation to the findings of Waterman et al.