This is an interesting study showing how sparse sampling may affect trends and monthly means of ozone in the troposphere. We all have to live with the limitations of our observing system. This study explicitly shows some of them, and also gives some ideas about possible improvements,  by using additional parameters, or additional observations at suitable times. All of this is scientifically interesting and I commend the authors for a well written and well illustrated paper. Unfortunately, we also know that many observing systems are controlled by factors much different from the statistical properties of the observed atmospheric property, e.g. ozone. So while the study is very good and interesting, I wonder how much change, e.g. in ozone sounding frequency it could, or should, generate. Hopefully not a reduction.

Apart from this more general question, I don't really have any detailed comments. I think the authors have done a very good job,  probably with a few iterations.

This paper is a comprehensive sensitivity study of the impact of sampling rate on the accuracy and precision of inferred long-term trends. The paper’s syntax and structure are clear, although from time to time, the excessive amount of details makes it more difficult to follow.

The authors start with a high-elevation, multi-decadal surface ozone timeseries (Mauna Loa Observatory, 19.5N) known to have a high sampling frequency with very few gaps over several decades (the “perfect timeseries”), then design a number of scenarios in which only a fraction of all samples are used to compute the trends using classic multi-component LS and LAD fitting models (collocated dew point found to be the most critical proxy after deseasonalizing). The monthly mean bias, and trend bias and error are computed for all scenarios to assess which sub-sampling scenarios yield best agreement with the full-sampling results. The scientific approach is excellent and the authors try to address a very important and well-known aspect of atmospheric composition trend in general, which means that their findings are potentially applicable to a lot more studies beyond free tropospheric ozone.

If there must be one main criticism, it is only to say that the results are extremely detailed, too detailed at times, to a point that many figures could be simplified in order for the reader to extract the essential information.  For example, I suggest that the "without meteorological adjustment" figures be removed after section 3.2. Another example, is the number of time periods, ranging from 1980-2021 to 2005-2021. I think showing results for only 2 or maximum 3 periods is just enough and will avoid overloading many figures.

The next main debatable point is the choice of ozone climatology for their so-called "adaptive sampling" strategy (D). Why choose 1980-1989 and not the entire period, or the period over which trends are computed?  By choosing 1980-1989, it leaves room for a possible offset from the mean values that would be computed over the later periods, which potentially can skew the distribution of monthly mean bias on which strategy D is based upon.

Other comments are minor:

Figure 2 and related text (page 4):

There is no information on the measurement uncertainty for the MLO temperature and ozone surface instruments. Instruments yielding large measurement uncertainty are likely to produce similar inconsistency between calculated trends. The authors probably assume that the total measurement uncertainty of these instruments is small enough to be taken out of the equation. If so, please state it.

 Figure 4, lines 17-29 (note on the sampling deviation vs. sampling bias):

This sentence is not clear. What does the expression “insufficient number of samples to infer a monthly mean value” mean exactly? Please re-phrase/clarify.

Figure 7:

Too much information between top and bottom row. I suggest top row shows only “with meteorological adjustment” trends, and only for period 1990-2021. This way reader has a much faster access to the actual information to be used from this figure (seasonality).

Figure 9:

Because panel (b) shows a coverage rate of 2.2 samples/week, I would suggest to show a 2-samples/week example in panel (a) instead of Sunday (1 day/week).

Figure S15:

It would be nice here to plot the trends from ozonesonde data itself, together with the MLO subsampled data.  The consistency of these two independent datasets could be demonstrated, and would provide a direct justification to apply the methods discussed in this appear to the ozonesonde launch programs, among others. On the other end, if the two datasets do not show consistent results, this would trigger (a needed) discussion on the applicability of the method discussed in this paper, for example highlighting the possible impact of measurement uncertainty and long-term stability.

Page 14, lines 18-24: discussion on stratospheric intrusions:

Actually, stratospheric intrusions could (should?) be included among the “meteorological adjustments”. There is no scientific reasons to exclude samples underlying stratospheric intrusions and yet include samples underlying other dynamical/natural variability.