The manuscript presents stratospheric aerosol observation from the MAESTRO, with comparison to SAGE and OSIRIS. Such comparison for MAESTRO have, to my knowledge, not been published before. The instrument provides data at high latitudes - regions where SAGE III-ISS does not cover. The manuscript is well-written and it is easy to follow the presentation and discussions. Comparing stratospheric aerosol observations among different instruments is important for providing more reliable data to the research community and to compile stratospheric aerosol climatologies for simulations. I would like to see more discussion about reasons behind the discrepancy between the datasets. 

Regarding the comparison of AOD and aerosol extinction coefficients, it is not unexpected to see large differences in data from different satellite instruments. Do you have any estimate on the impact of patchiness in aerosol concentrations on the MAESTRO observations? To retrieve aerosol data one must assume homogeneous conditions throughout the entire line-of-sight, as far as I understand it. 

The instrument’s line of sight (semi-horizontal) requires a transformation algorithm that computes the conditions at the tangent point. In patchy conditions, the tangent altitude of the retrieved aerosol ext coef. will be lower than the real altitude of aerosol layers. Do you have an estimate on how large this displacement may be? If so, how does this relate to the same phenomenon for the SAGE instruments? It should lead to spread in the data of your comparisons.

Solar occultation instruments cannot quantify aerosol extinction coefficients when the AOD in the line-of-sight is high, leading to a low bias in the data. Is this limit for MAESTRO the same as SAGE’s, and if not, does that have an impact on discrepancies in the AOD comparisons of the two instruments? Do they have similar challenges with missing data, leading to similar underestimates of the AOD when sampling optically dense aerosol? Is this why there is a factor of 2 difference between the instruments after wildfire or volcanic events, and why it has a low bias in general?: 

•    L196 “…MAESTRO underestimates peak extinction values after major volcanic eruptions and wildfires by a factor of 2 or more…”

•    L213: “…MAESTRO extinction at shorter wavelengths has a low bias of 40-80% compared to SAGE III nearly everywhere in the lower stratosphere except right above the tropical tropopause region…”

•    L248 “…corresponds to a relative underestimation of 32% by MAESTRO…”

•    L406: "…The difference in spectral response of MAESTRO measurements, especially following major events may be a limiting factor in accurately characterizing stratospheric aerosol particle size information with MAESTRO….”. 

This is important to know for a user, it is important for the reader to understand during which conditions the data may be useful, and it is important to know this when compiling an aerosol climatology like CREST or GloSSAC. 

How was bias from ice-clouds treated? Data within 2-3 km of the tropopause may be affected by ice clouds to a large degree, suggesting the need to screen for signals from ice-crystals within at least 2-3 km above the tropopause + some kilometer extra (depending on instruments vertical resolution). 

Figure 7. Which latitudes and times are shown here? Is it latitude binned monthly mean data? Part of the spread in data may come from periods with patchy conditions. Have you tried excluding the first 2-4 months after volcanic eruptions or wildfires when fitting the data to see if these effects may cause large spread in your’ comparison? 

L302: I understand why you do not fit linearly, but why use a power law? Is it the best possible fit to use in this case?

This manuscript is well described to the application study of ACE-MAESTRO for stratospheric aerosol distribution. Although the analysis for stratospheric aerosol is not so complicated, this research topic is rare. For this reason, the manuscript is valuable for this scientific community.

However, some background and methodology parts are unclear. For detail:

1) In lines 81-84: This manuscript aims to evaluate the climatological stratospheric aerosol studies using MAESTRO. The MAESTRO data is weel known dataset and observation record is long. But, it is not clear that the previous studies did not use the MAESTRO data for climate record. Could you explain the reason? In addition, do you have some additional working for MAESTRO data to use the climate record?

2) Section 2.1.1: For the MAESTRO algorithm, some unclear points are existed. For example, the manuscript is not clearly explained why the MAESTRO data uses the pressure and temperature profiles as the algorithm input. In addition, for the P and T profiles, the ACE-FTS v3.5/3.6 is the reference data for MAESTRO in lines 103-104. Is the vertical resolution of P and T profiles enough to  get the lapse rate tropopause height?

In addition, the multi-wavelength of aerosol extinction coefficient was retrieved by the MAESTRO. I wonder how this wavelength is determined. 

3) Section 2.1: In this study, several satellites are used, and these satellites' specifications are quiet different. So, I suggest that the author make table for summarizing the specification of satellites.

4) Section 2.2: All the satellites in this study are solar-occulation observation. Therefore, the horizontal coverage is too wide to define the specific location. To define the reference location of the satellite datasets, how to be define? (center of the light optical path? or the lowest point?) 

In lines 148, could you explain the details for the significant gaps over the extra-tropics around winter months?

5) Section 2.3: Many of the detailed data explanation and variable definitions are included in this section. So, I suggest that this section will be moved before the section 2.2.

6) Section 3.2: This section is too simple. The manuscript is only explained the statistical number and qualitative figure explanation. Could you explain more detailed explanation inlcuding the regional characteristics of the stratopheric aerosol linked to the wildfire and volcanic events?

7)Figure 6: I suggest that the manuscript has to include the detailed explanation of this figure, such as the reason of difference between two products.

8) Section 4.1 (Line 302): Please explain the detailed reason to use the 'y = ax^b' function.

The manuscript assesses the aerosol extinction retrieval (v3.13) in the stratosphere and related products by the MAESTRO visible spectrometer onboard SCISAT. This sounds very valuable and interesting as the community needs more independent datasets for monitoring of aerosol properties, providing more comprehensive instrument comparisons and for building long-term records. The manuscript is well-written and clear. I think this study deserves publication in AMT after the following points are clarified.

I would suggest the authors indicate in which conditions (background stratospheric content, high aerosol loadings, seasons, etc.) and for which type of aerosol climatology MAESTRO dataset can be useful for the community.

In section 2.1 describing the individual datasets, I would suggest to give more quantitative information about the biases when available in the literature comparing various spaceborne observations of stratospheric aerosol extinction in different latitudinal bands and/or aerosol loadings (e.g. OSIRIS vs SAGEII, SAGEIII vs OMPS). This will allow the reader to better grasp the relevance of MAESTRO data through comparisons with more typically used records. This may be summarized in a table.

Also, what about including OMPS as there is a product from University of Saskatchewan of high interest for the scientific community?

To minimize the impact of the outliers and of high altitude clouds (especially in the tropics), what about considering SAOD from tropopause altitude + 1 km?

The choice of monthly-binning is relevant to feed stratospheric aerosol databases for climate modelling purposes. I guess you tried to bin over shorter temporal grids (e.g. weekly) to examine whether the short-term aerosol variability is consistent in MAESTRO data. Why and do you have an idea about the spread of SAOD and AE values at the weekly or 2-weekly scale?

Figure 2: some gaps can be observed in MAESTRO time series during volcanic periods especially around 2010. What is the explanation? Is it due to some saturation effect that could depend on the amplitude of each event?

Do you have any idea about the impact of spatial sampling biases in the average of MAESTRO data? Does this vary with seasons and years? From Figure 1 it seems that the limited spatial coverage in tropical latitudes could largely affect the robustness of zonal means there.

Figure 6: I suggest the evolution of AE between MAESTRO and SAGEIII to be plotted at 50°N and 50°S (e.g. AE vs time) where both datasets overlap. It is quite difficult to see the difference with the colors used in the figure. The signal seems to remain high and quite steady over the years following the eruption in MAESTRO whereas it shows more variability in SAGEIII (unless I am mistaken by the color scale). The 12-km altitude is very close to the tropopause and I am wondering if unscreened clouds can be part of the explanation. For SAGEIII do we have any idea about the enhancement in AE for 2020 where no particular volcanic/fire event occurred?

Section 4.2: the correct quantification of Rayleigh scattering is apparently an important parameter in MAESTRO data variability. Could any offline calculation of Rayleigh scattering along MAESTRO line of sight using a raytracing model (including atmospheric refraction) rather than incorporating measurements bt ACE-FTS be helpful to reduce the effect?

L21, 278: Ulawun instead of Ulawan.

L346: MAESTRO instead of MAETRO.

Some references are missing. Please check throughout the manuscript e.g. Kovilakam et al. (2023), Malinina et al. (2018), Randall et al. (2001), Sofieva et al. (2022, 2024), Thomason et aL. (2007).

For instance:

Mahesh Kovilakam, Larry Thomason, and Travis Knepp, SAGE III/ISS aerosol/cloud categorization and its impact on GloSSAC, Atmos. Meas. Tech., 16, 2709–2731, 2023, https://doi.org/10.5194/amt-16-2709-2023

Elizaveta Malinina, Alexei Rozanov, Vladimir Rozanov, Patricia Liebing, Heinrich Bovensmann, and John P. Burrows, Aerosol particle size distribution in the stratosphere retrieved from SCIAMACHY limb measurements, Atmos. Meas. Tech., 11, 2085–2100, https://doi.org/10.5194/amt-11-2085-2018, 2018

In the reference list, the Vernier et al. (2011a) is not cited in the manuscript.