This study collates 3 glider campaigns in the Ross Sea to provide rolling 3-day estimates of oxygen-derived NCP and optical-backscatter POC export rates. The study supports prior classification of the Ross Sea as high productivity, low export; and highlights high-short term (ie. sub-weekly) variability of NCP. I present below a list of comments that I hope will help strengthen the message and clarify points of discussion for the reader.

Several assumptions are necessary – particularly in regions such as the Ross Sea with limited in situ sampling to support glider observations - to make such estimates, which always leads to questions around the accuracy of such estimates, the authors do the best possible in these conditions to provide quantitative metrics of NCP and export. Although the accuracy of the numbers can be brought into question, it is the patterns and interplay between NCP, POC and export which are revealing. I find the main strengths/key results of the paper to be [1] the use of a fixed criteria (within 90% of peak chlorophyll) to define a coherent bloom period which could be investigated interannually, [2] that the system is dominated by changes in dO2/dt (although I have questions about that, see next paragraph), and that [3] high biomass does not mean high NCP or export (although lines 265-267 seem to contradict this).

My main issue with the manuscript is that the authors use the term “temporal variability” as a catch-all, which can lead to confusion within the text. It is also used as a justification to ignore spatial variability. I acknowledge that the advection term is low, but this does not negate the existence of spatial variability: crossing fronts and filaments may lead to rapid changes in dO2/dt and NCP (ie. meltwater filaments are cold and fresh, leading to generally greater O2 concentrations). These are prevalent in regions of ice melt/formation like the Ross Sea. Some physical diagnostic figures would help alleviate these concerns. A timeseries of surface mixed layer [1] density and [2] oxygen solubility would reveal [1] sharp fronts which may help explain peaks in productivity (mid December??) and [2] biases in NCP due to changing water masses such as meltwater filaments. Likewise, if in the scatter plots, no obvious pattern appeared between NCP and d(solubility)/dt then it would lend further credibility to the dominance of biological processes in regulating dO2/dt.

When using the term temporal variability, please clarify whether you mean at the 3-day level, the bloom level, or the interannual level.

L58: Publications not accessible to the reader should not be cited. Hopefully it’s out by publication time.

L65-68: This statement might be strengthened by more quantitative metrics. How significant is the difference in export efficiency?

L70 and 82: “spanning 2010-2023” is somewhat misleading, maybe better to list specific years for the 3 deployments?

Methods - calibration: were the same fluorescence/chlorophyll ratios used for the whole deployments? Can you elaborate on expected consistency across Phaeocystis and diatom communities?

L114: How accurate is the Morel fit with non-mid-latitude communities? Could this be evaluated using ship-based PAR profiles to provide an error estimate?

L120: What Kz value is used and why?

L122: It would be worth stating that using DAC as an estimate of upper 100m currents when gliders dive deep likely leads to a significant underestimation of upper ocean velocities. Note that I do not think this impacts the study significantly as F_adv is an order of magnitude smaller than NCP.

L134-5: It may be worth specifying that these are assumed minimal at the 100m boundary.

Fig 3. I really struggle to tell the colours apart.

L151-154: The convention used here is confusing and it is not immediately clear to me what export potential represents. This warrants further clarification. As I read it, if we assume a scenario with no NCP and a decrease in surface POC (eg. due to an export event) then we have ‘export* = 0 – (+ve dPOC/dt)’ which leads to a negative export potential. Please explain in more detail.

L165: One would assume that inter-sensor differences are minimal after calibration to Chl and POC samples anyways?

L179-180: Can it be described as “substantially higher” with such a standard deviation?  Would it be beneficial to show the standard error of the mean as well to highlight that your means are indeed significantly different?

L186: Could a change in dO2/dt not also be due to crossing a front? Entering a meltwater filament (both colder and fresher) for example could lead to a significant increase in O2 simply due to greater solubility.

L225: I agree that averaged values over the study period are likely dominated by temporal evolution of the bloom – however I disagree that temporal variability is. I do not think we can rule out that much of the variability observed between successive 3-day segments could be due to spatial variability. I suggest reformulating the statement to highlight that over the study period, spatial variability will be averaged over, revealing the long term temporal evolution of the bloom.

L254-255: I agree 100% with this paragraph up to the last statement – maybe as I’m not sure of what temporal variability you refer to. Do you mean the variability exhibited between subsequent 3-day estimates? If so, I’m not sure I agree. Highly variable data (evidenced by high standard deviation) does not imply that that variability impacts the mean. Rather, undersampling of a highly variable system could lead to an error when estimating the mean (hinted at by, for example, a large standard error of the mean). Without further investigating the influence of spatial variability on dO2/dt, I am not convinced that the high variability observed is purely due to rapid changes in NCP. My perception of the real strength of the paper here is the large number of NCP and dPOC/dt estimates which help us identify robust and trustworthy mean values of NCP and dPOC/dt by averaging over any spatial and short term temporal variability, across a well-defined bloom period (within 90% of peak chl). The high variability is the issue that makes prior approaches (eg. incubations) questionable. Could you clarify what you mean by that last sentence in the paragraph?

L261: What is the proposed mechanism for the link between low dPOC/dt and variable mixed layer depth?

L270: Is the big increase in dPOC/dt linked to the inclusion of the earlier or the later data?

L271: I do not understand how sub-sampling the same population would lead to a different mean considering all terms involved have a linear impact on NCP. Can you explain how you expect 5-day intervals to lead to a different mean to 3-day intervals?

L270-276: I do not follow the argument made here. I do not understand how subsampling would lead to a different mean; my understanding is the difference in values is due to inclusion of the earlier and later portions of the season. So it is how one defines the bloom and the study period (ie. 90% of max chl) which is important. From what I understand of the study, this simply shows that we have different phases across a season, but that the core of the bloom (ie. 90% of max chl) is slightly autotrophic.

L270-276: I further do not understand how 3-day vs 5-day intervals would change any estimates related to climate change (L273). I feel that throughout the discussion, it would help the reader to be very clear about what scale of temporal variability you refer to (3-day, within the bloom, across a season, interannually).

L277-281: Would it be beneficial to fit a martin curve to POC profiles and report the e-folding term as a measure of retention/export efficiency?

L283-286: Fluorescence to chlorophyll ratios and optical backscatter to POC would be expected to change significantly as the community changes. I understand that in the absence of in situ samples it is not possible to assess and calibrate on a per-community basis, but I think it would be important to highlight the assumption made in the calibration and what the effect could be on results.

L287: I still do not understand what is meant by the influence of temporal variability on seasonal means; as this is a recurring point and an element presented as a key takeaway, I would really encourage you to clarify this further.

L295-296: This statement would be strengthened by the inclusion of physical diagnostics.

L306-315: I would encourage you to wait until Portela et al. is published before final publication of this manuscript.

L321: How does your study relate to sea-ice?? This seems rather a result of Portela et al. which is not available to the reader.

L331: Further up (L265-267) you essentially make the statement that export efficiency and POC removal occurs more when there is high biomass – but L331 you state they are uncoupled. I assume this is a question of time scale again (seasonal vs weekly). This should be clarified further.

This manuscript uses several seasonal glider data sets over a few years to derive carbon and export budgets for the Ross Sea in Antarctica.  The gliders collected high resolution data sets that are impressive.  The main science finding is that net community production (NCP) appears to be decoupled from phytoplankton biomass and export production.  The authors also argue for the need for high resolution data to resolve the dynamics in biogeochemistry. The paper is interesting, and the study is conducted in a very important location in terms of Earth’s biogeochemistry.  There are few things that I think the paper would benefit from.

The main findings of the study are based series of derived parameters.  For the derived parameters it would be nice to have some discussion of the sensitivity of the derived parameters.  It would be nice have some idea how sensitive are the findings to many of the implicit assumptions in the deriving these products?

The findings of NCP being decoupled from export flux and that the Ross Sea is a high productivity and low export zone is interesting.  While referenced in the introduction that this has been seen before but the reasons for this are never not explicitly explored. I think that the discussion of what processes underlie this phenomena, even if it was hypothesized, would be of great interest.  Currently the discussion reads a bit like a data report and think more synthesis would be of wide interest.  The data is solid, but I think some synthesis would increase interest in this work.