Thank you both for your reviews. We have used your comments to improve our manuscript, including the creation of a supplement (attached). Specific responses to the major and minor comments of RC1 and RC2 are below:

RC1:

Major Comments:

1. In alignment with your comment and that of RC2, we have increased discussion (Lines 62-70) regarding the interannual variability of Southern African aerosol emissions and meteorological conditions over the Southeast Atlantic, with literature suggesting that it is small enough to not significantly affect our results.

Lines 62-70: There is remarkably little interannual variability in Southern African fires due to the consistency of agricultural burning practices in the region (van der Werf et al., 2010; Redemann et al., 2021). Using the 18-year (2001-2018) European Space Agency Fire Climate Change Initiative, version 5.1 (FireCCI51) dataset derived from MODIS aboard the NASA Terra satellite, Chuvieco et al. (2021) found that Southern Africa had the smallest interannual variability in burned area in the world, with a coefficient of variation < 0.5 across the region. Ryoo et al. (2021) documented the interannual variability in meteorological conditions over the Southeast Atlantic, finding that wind velocity, relative humidity, and precipitation were near climatological averages for August 2017, September 2016, and October 2018. They also found that despite sea surface temperatures during the ORACLES campaign months being 0.2 – 0.4 K above climatological averages, it did not have a systematic effect on low-level cloud fraction.

2. We have created maps of BC and OC columnar mass densities from MERRA-2 reanalysis (Fig. S1). The BC:OC ratio remains near 1:10 throughout the ORACLES campaign months, which is consistent with our finding that the subseasonal increase in SSA is not attributable to an increase in BrC concentration. August has the greatest BC and OC densities, although the maximums are compact and located over land. By September, higher BC and OC densities are more widespread across the Southeast Atlantic, followed by decreases in October, aligning with the September peak in AOD and AAOD noted by 4STAR. The BC and OC densities also shift southeastward over the course of the BBA emission season.

3. We have created maps of Fire Radiative Power (FRP) from Terra and Aqua MODIS thermal anomalies (Fig. S2). Fire counts peak in August and decrease over the course of the BBA emission season, with far fewer fires in October. The fires shift southeastward over the season, which is also visible from the movement in the FRP-weighted mean coordinates.

4. We agree that the more southerly location of ORACLES 2016 affected our results, which was the primary focus of the latitudinal analysis in our discussion section. We have further reiterated its effects in our abstract (Lines 23-24) and conclusions (Lines 470-474).

Lines 23-24: The latitudinal analysis indicates that the September decrease in SSA noted by 4STAR is affected by the southerly sampling location of ORACLES 2016 compared to the other two campaigns.

Lines 470-474: A latitudinal analysis of in situ measurements and subsampling of 4STAR sky-scans indicates that the September decrease in SSA noted by 4STAR is affected by the more southerly sampling location of ORACLES 2016 relative to the other two campaigns, rather than from a temporal evolution, such that ORACLES 2016 would have observed greater scattering if it had similarly targeted the central smoke plume from the equator, bringing the 4STAR SSA medians more in line with the subseasonal increase observed by AERONET.

5. The altitudinal restriction requires that the 4STAR sky-scans occurred below 3 km, but it does not remove any measurements above that altitude. The 4STAR sky-scans are total columnar measurements, so all above-aircraft values are included. The altitudinal restriction was selected to best ensure that the 4STAR sky-scans have a full columnar view below the smoke plume (Mitchell et al., 2025) and is the same criterion used by Pistone et al. (2019).

6. Expansion into the UV is complicated by the presence of an instrument artifact near 420 nm and stray light scattering at 360 and 380 nm. In alignment with your comment and that of RC2, we have increased discussion of the technical limitations (Lines 450-456) and slightly softened our conclusions regarding BrC.

Lines 450-456: EAE and AAE have little subseasonal variation, indicating domination by fine BC aerosols throughout the season, suggesting that the aerosol brightening is due to a change in aerosol composition through the season, rather than a change in aerosol type, although 4STAR did not directly observe the UV wavelengths at which BrC absorption is the strongest. Improving 4STAR’s ability to identify BrC absorption via expansion into the UV spectrum is currently complicated by an instrument artifact near 420 nm and stray light scattering at 360 and 380 nm (Mitchell et al., 2025), but could be overcome with the use of hyperspectral GRASP (Generalized Retrieval of Atmosphere and Surface Properties) code (Román et al., 2018), which is the subject of ongoing work.

Minor Comments:

Previous Lines 165-166: The typical ORACLES sampling speed of the NASA P-3 Orion aircraft was ~382 km per hour, resulting in an approximate 10-second spatial resolution of 1.1 km (Lines 181-183).

Previous Lines 196-202: It is correct that we are using the reduced independent sample sizes for the statistical tests, so we have reaffirmed that in the text (Line 214).

Previous Lines 245-248: We have added discussion to our introduction (Lines 86-90) and results (Lines 265-267) regarding Zhang et al. (2022), which has found greater BrC contributions over the Southeast Atlantic.

Lines 86-90: Using TEM-EDX (Transmission Electron Microscopy - Energy Dispersive X-ray spectroscopy) and an assumed BC refractive index, Zhang et al. (2022) determined that BrC could contribute 8 – 22 % of total aerosol absorption at 470 nm. However, their BrC estimate is much lower using the AAE attribution method, at only 2 – 6 % of total aerosol absorption at 470 nm, indicating that spectroscopy could reveal a greater BrC presence over the Southeast Atlantic than suggested by optical methods.

Lines 265-267: The lack of BrC contributions from our AAE analysis also aligns with the findings of Zhang et al. (2022), although their results indicate that electron microscopy would yield greater BrC contributions than from optical methods.

RC2:

Major Comments:

We have created maps of HYSPLIT back-trajectories (Fig. S3) for ORACLES research flight dates near the beginning, middle, and end of each campaign. HYSPLIT back-trajectories suggest aerosol transport from northern Angola in August, shifting southward from southern Angola in September, and even further southeastward from Botswana, Zambia, and Zimbabwe in October.

 In alignment with your comment and that of RC1, we have increased discussion (Lines 62-70) regarding the interannual variability of Southern African aerosol emissions and meteorological conditions over the Southeast Atlantic, with literature suggesting that it is small enough to not significantly affect our results.

Lines 62-70: There is remarkably little interannual variability in Southern African fires due to the consistency of agricultural burning practices in the region (van der Werf et al., 2010; Redemann et al., 2021). Using the 18-year (2001-2018) European Space Agency Fire Climate Change Initiative, version 5.1 (FireCCI51) dataset derived from MODIS aboard the NASA Terra satellite, Chuvieco et al. (2021) found that Southern Africa had the smallest interannual variability in burned area in the world, with a coefficient of variation < 0.5 across the region. Ryoo et al. (2021) documented the interannual variability in meteorological conditions over the Southeast Atlantic, finding that wind velocity, relative humidity, and precipitation were near climatological averages for August 2017, September 2016, and October 2018. They also found that despite sea surface temperatures during the ORACLES campaign months being 0.2 – 0.4 K above climatological averages, it did not have a systematic effect on low-level cloud fraction.

We have created scatterplots showing the relationships between AAE, EAE, and SSA over time (Fig. S4). We would expect an increase in AAE if the change in SSA was due to increasing BrC concentration, but instead we see a decrease in AAE over the course of the BBA emission season, displaying a population shift even further away from BrC absorption.

Expansion into the UV is complicated by the presence of an instrument artifact near 420 nm and stray light scattering at 360 and 380 nm. In alignment with your comment and that of RC1, we have increased discussion of the technical limitations (Lines 450-456).

Lines 450-456: EAE and AAE have little subseasonal variation, indicating domination by fine BC aerosols throughout the season, suggesting that the aerosol brightening is due to a change in aerosol composition through the season, rather than a change in aerosol type, although 4STAR did not directly observe the UV wavelengths at which BrC absorption is the strongest. Improving 4STAR’s ability to identify BrC absorption via expansion into the UV spectrum is currently complicated by an instrument artifact near 420 nm and stray light scattering at 360 and 380 nm (Mitchell et al., 2025), but could be overcome with the use of hyperspectral GRASP (Generalized Retrieval of Atmosphere and Surface Properties) code (Román et al., 2018), which is the subject of ongoing work.

Minor Comments:

We have corrected the acronym introduction errors.

Previous Line 146: The table is correct, as the PSAP and Neph do have slightly different RGB reporting wavelengths. We have corrected the table caption (Lines 158-159) such that the observation counts are for SSA calculated from paired PSAP absorption and Neph scattering coefficients, following the interpolation of the scattering coefficients from the Neph wavelengths (450, 550, 700 nm) to the PSAP wavelengths (470, 530, 660 nm).