the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Aug 2024
Synergy of active and passive airborne observations for heating rates calculation during the AEROCLO-SA field campaign in Namibia
Abstract. Aerosols have important effects on both local and global climate, as well as on clouds and precipitations. We present some original results of the airborne AErosol RadiatiOn and CLOud in Southern Africa (AEROCLO-sA) field campaign led in Namibia in August and September 2017. In order to quantify the aerosols radiative impact on the Namibian regional radiative budget, we use an innovative approach that combines the OSIRIS polarimeter and lidar data to derive heating rate of the aerosols. To calculate this parameter, we use a radiative transfer code and meteorological parameters provided by dropsondes. This approach is evaluated during massive transports of biomass burning particles above clouds. We present vertical profiles of heating rates computed in the solar and thermal parts of the spectrum. Our results indicated strong positive heating rate values retrieved above clouds due to aerosols, between +2 and +5 Kelvin per day (vertically averaged). Within the smoke layer, water vapor's cooling effect through infrared radiation generally balances its warming effect from solar radiation. At the top of the layer, a stronger cooling effect of −1.5 K/day often dominates due to water vapor. In order to validate this methodology, we use irradiance measurements acquired during sounding performed with the aircraft during dedicated parts of the flights, which provides direct measurements of irradiances distribution and heating rates in function of the altitude. Finally, we discuss the possibility to apply this method to available and future spaceborne passive and active sensors.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques. Moreover, Paola Formenti is guest editor for the ACP Special Issue “New observations and related modelling studies of the aerosol–cloud–climate system in the Southeast Atlantic and southern Africa regions”. The remaining authors declare that they have no conflicts of interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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RC1: 'Comment on amt-2024-121', Anonymous Referee #1, 13 Oct 2024
Synergy of active and passive airborne observations for heating rates calculation during the AEROCLO-SA field campaign in Namibia
Mégane Ventura et al.
General comments to the Editor:
The submitted manuscript demonstrates the synergy of active LIDAR and passive polarimeter measurements for quantifying the vertical distribution of the heating rates caused by the light-absorbing, lofted layers of biomass burning aerosols over the cloud deck off the coast of Namibia, Africa. The active-passive synergistic method was applied to the airborne measurements of aerosols collected by OSIRIS polarimeter and lidar operated during the AEROCLO-sA campaign in September 2017. The radiative transfer code was used to calculate heating rates in the solar and thermal part of the spectrum. Authors find strong positive heating rate 2-5 K/day caused by aerosols lofted over clouds. The cooling effects of water vapor through infrared radiation is found to generally balance its warming effect from solar radiation. The heating rate calculations were validated against the altitude-resolved irradiance measurements taken during the sounding portion of the flights.
The scientific content presented in the paper effectively demonstrates the value and merit of the active-passive synergistic approach to quantify the aerosol absorption effects over the clouds—a seasonal phenomenon observed over several regions of the world, including the prime hotspot region of southeastern Atlantic Ocean. The methodology, sensors, datasets, results, and their interpretation/evaluation discussed in the paper look sound. The plots showing different sets of results are discussed appropriately in the text. The application of the presented synergistic method to the planned future sensors carrying lidar and polarimeter/image is also discussed towards the end of the paper.
I don’t see any major flaw or concern on this paper that can send back the manuscript to a major revision. However, I have prepared a long list of specific suggestions and questions, included in this review, for authors to consider in the revision. I would suggest authors to proofread the revised article in terms of the language and structure for even more effective presentation.
I should be available re-review the revised submission.
Thanks for the review opportunity.
Specific Comments to authors:
Abstract:
Line 12: “We present original results derived from the airborne observations acquired from the AErosol RadiatiOn and CLOud in Southern Africa (AEROCLO-sA) field campaign led in Namibia in August and September 2017”.
Line14: “…an airborne prototype..”
While abstract mentions about the use of OSIRIS measurements, radiative transfer code, and meteorological parameters, it misses mentioning what aerosol and cloud measurements were actually used in the RT model to calculate the heating rate. This should be added to the abstract.
Introduction:
Line 30: “…variable chemical, optical, and microphysical properties.”
Line 37: “The aerosol radiative forcing estimates provided by climate model over the Southeast Atlantic Ocean show large discrepancies”
Lines 45-48: These sentences can be re-written as, “ Biomass burning aerosols are primarily composed of two components: black carbon and organic or brown carbon. The former strongly absorbs radiation across a wide spectral range, whereas the latter has relatively weaker absorption in the visible part of the spectrum but exhibits strong spectral dependence of absorption at the UV wavelengths.”
Line 59: “Our study focuses…” Also these two sentences can be re-organized as, “This study focuses on quantifying the radiative impact of biomass burning aerosols and aims to estimate the profiles of atmospheric heating (or cooling) rates attributable to these particles. We use airborne measuerements acquired during the AErosols Radiation and CLOuds in southern Africa (AEROCLO-sA) campaign (Formenti et al., 2019) conducted over Namibia between 5 and 12th September 2017 with the French Falcon 20 environmental research aircraft of Safire.”
Line 65: “..such as ORACLES…”
Line 78: SSA quantitatively describes the ability of aerosols to absorb or scatter the solar radiation.
Lines 79-80: I would not call it “To a lesser extent”. Given the same state of aerosols (AOT, SSA, asymmetry parameter), the extent of the radiative forcing or heating rates is primarily determined by the brightness of the underlying clouds, which is a function of cloud optical depth. Please reconsider this statement.
Lines 81-85: Author may consider citing Jethva et al. (2024), which uses UV-VIS-NIR satellite measurements from OMI and MODIS combined with AOT measurements from HSRL-2 lidar and 4STAR sunphotometer to derive UV-VIS-NIR spectral SSA over clouds in the SEA region. Satellite retrievals of SSA agree overall with the range measured by in-situ observations, and show relatively strongest (weakest) absorption in August (October).
Jethva, H. T., Torres, O., Ferrare, R. A., Burton, S. P., Cook, A. L., Harper, D. B., Hostetler, C. A., Redemann, J., Kayetha, V., LeBlanc, S., Pistone, K., Mitchell, L., and Flynn, C. J (2024), Retrieving UV–Vis spectral singlescattering albedo of absorbing aerosols above clouds from synergy of ORACLES airborne and A-train sensors, Atmos. Meas. Tech., 17, 2335–2366, https://doi.org/10.5194/amt-17-2335-2024.
Line 86-87: Author should consider citing following two papers on aerosol radiative effects over clouds.
Meyer K. G., S. E. Platnick, L. Oreopoulos, et al. 2013. "Estimating the direct radiative effect of absorbing aerosols overlying marine boundary layer clouds in the southeast Atlantic using MODIS and CALIOP." J. Geophys. Res. Atmos. 118 (10): 4801-4815 [10.1002/jgrd.50449] [Journal Article/Letter]
Zhang, Z.*, K. Meyer, H. Yu, S. Platnick, P. Colarco, Z. Liu, and L. Oreopoulos (2016), Shortwave direct radiative effects of above-cloud aerosols over global oceans derived from 8 years of CALIOP and MODIS observations, ACP, 16(5), 2877–2900, doi:10.5194/acpd-15-26357-2015.
Line 92: Jethva et al. (2014) should be corrected to Jethva et al. (2018).
Jethva H., O. Torres, C. Ahn and et al. 2018. "A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm." Atmospheric Measurement Techniques 11 (10): 5837-5864 [10.5194/amt-11-5837-2018] [Journal Article/Letter].
Line 98: CALIOP vertical profiles of extinction, if properly constrained, can provide altitude-dependent heating rates.
Line 129: Define OSIRIS here, if it is not defined earlier in the text.
Line 125: Double dots (..).
Line 135: Some restructuring: “During the campaign, the air mass intercepted (or measured) by the on-board instruments were mainly transported from the in-land biomass burning source areas, emitting substantial amounts of carbonaceous aerosols transported over the southeastern Atlantic Ocean, as far as the Ascension Island. The airmass was then drifted to the southeast towards the Namibian coast due to the anticyclonic circulation located over South Africa”.
Low-pressure system rotates in clockwise direction in southern hemisphere. So, make sure that the cyclonic or anticyclonic movement is referenced to the southern hemisphere.
Line 149: “…with the aircraft during specific flights”. It would be desirable to mention the dates of these flights.
Table 1: Add the wavelength of the AOT measurements (865 nm) in the respective column. Similarly, ABS should be labelled as the imaginary part of the refractive index. Please correct me, if wrong. Also, the wavelength of the ABS should be included in the table and text. AOT values of > 0.4 at 865 nm translate to AOT of > 1 at 500 nm.
Line 78: Define OSIRIS the first time it was referred in the text.
Line 184-185: It is assumed here that the LNG is a downward looking lidar.
Line 189: Re-structuring: “Consequently, lidar data acquired for optically thick plumes at these wavelengths become less reliable in accurately determining the base altitude of the aerosol layer. On the other hand, the 1064-nm signal penetrates deeper into the aerosol layer due to significantly reduced attenuation, providing a better view of the depth of the aerosol layer. For this reason, we used LNG lidar data acquired at 1064 nm to accurately depict the aerosol extinction profile.”
Line 194: “the extinction aerosol optical thickness”. PLASMA resembles the 4-STAR sun photometer. Are there any major differences in the way both measure AOTs?
Line 210: “600 m above the sea level”.
Line 213: What is the swath width of the OSIRIS instrument/retrievals?
Line 229: Equation 1: This is simply the scaling of lidar extinction profile to match with the OSIRIS-retrieved AOTs. OSIRIS measured columnar AOT above the clouds, whereas the lidar measurements, according to Figure 7 (a & b), correspond to the atmospheric depth between cloud-top to 6-km. This leads to an assumption that the smoke aerosols are confined within these altitude range. Any amounts of aerosols above 6 km, therefore, are ignored in the analysis. This should be mentioned in the text.
Line 249: Which satellite/sensor total column O3 data was used here?
Line 257: Where Cp stands for…
Line 264: Optimal estimation method.
Line 266-269: This is not understood. Both OSIRIS and GAME calculations ignore particles larger than one micron (radius or diameter?). How did author accounted for the aerosol optical properties to longer wavelengths?
Line 280: MODIS cloud product of cloud effect radius histogram over the southeastern Atlantic Ocean shows peak around 12 microns, which is closer and near-consistent with the 10 microns assumed in this study. It is imperative to mention here that which cloud model is used here. Most likely, author is assuming water clouds. What droplet size distribution was assumed here? Modified GAMMA? Also, did author compare the Irradiance-based COT retrievals with those derived from OSIRIS algorithm? How well do they compare?
Line 322: “aerosol absorption optical depth of 0.03”.
Line 325-327: spectrally neutral aerosol absorption: Does author mean the spectral imaginary part of the refractive index or aerosol absorption optical depth? In either case, assuming spectral neutral behavior might not be an appropriate assumption. Biomass burning aerosols are often rich in organics (brown carbon), which exhibits strong spectral absorption at shorter wavelengths. If it is not too much of work and computation, author is suggested to assume spectrally varying aerosol absorption optical depth assuming the Absorption Angstrom Exponent in the range 2.0-2.5, a typical range for biomass burning aerosols.
It looks like the aerosol absorption of 0.03 assumed in the simulation referred to the imaginary part of the refractive index.
Line 425-430: What was the variability in the COT retrievals during spiral descent? Since COT is retrieved at each change in altitude, these numbers should be handy and mentioned here.
Could author remind here what wavelength range was considered to calculate solar and thermal heating rate, respectively?
For thermal IR range, fine mode smoke particles do not play a significant role; however, cloud properties (COT and effective radius) drive the heating/cooling rates. Does the blue curve in Figure 5 (f) use the improved cloud modeling (cloud retrievals at each change of altitude)?
Line 464: “Despite heterogeneity in COT…”
Figure 6: It is interesting that there are seemingly three layers of aerosols identified in the solar heating rate. Could author confirm this with lidar and/or PLASMA AOT profile measurements?
Line 499-500: “which are all found in all rate calculations (>10 K/day).” This is not understood.
Figure 7: About cloud formation, as mentioned in the text, at the top of aerosol layer for Sep 8th flight. Did author notice similar cloud layer during Sep 12th flight? Or the cloud layer is filtered out in the heating/cooling rates? There are drastic differences in the 1064-nm backscatter values between these two flights. Mentioning the averaged AOTs during these two flights here, for the high-altitude segments, would aid the reader in interpreting the results.
Line 507: “fairly homogeneous values of the order of 4 to 5 K/day…”
Figure 8: It appears that the plots for different days are ordered w.r.t highest to lowest heating rates. Please mention this in the text. Also, label the x-axis as heating rates for the plots shown at top. It is worth to mention the averaged COTs measured on these days in Table 3. It is assumed here that the heating rates are vertically integrated. Please clarify it in the text.
Line 578: “…observed during the September 2017 AEROCLO-sA airborne campaign”
Line 616-620: This is a good point. The COT should also play somewhat significant role in heating rates, depending upon what range of COT is observed. Also, one needs the true COT below the aerosol layer, after aerosol attenuation correction, in order to accurately assess the role of COT in both radiative effects and heating rates.
Line 621: desert dust above the clouds not on a global scale, but regional scale, i.e., Saharan dust transport over the Atlantic, dust plumes over the Arabian Sea, and Asian desert dust over the clouds along the eastward transport pathways over the Pacific.
At the end, it is an excellent demonstrative study of how the synergy of active lidar and passive measurements brings new quantitative information on the effects of light-absorbing aerosols above the clouds.
Citation: https://doi.org/10.5194/amt-2024-121-RC1 -
AC1: 'Reply on RC1', Fabien Waquet, 17 Jan 2025
We are grateful to the reviewer for his interest in our study and for his very detailed review of the article. We have done our best to take into account these suggestions for improvement and to answer the questions raised. Below are our responses, presented point by point in the supplementary PDF document.
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AC1: 'Reply on RC1', Fabien Waquet, 17 Jan 2025
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RC2: 'Comment on amt-2024-121', Anonymous Referee #2, 05 Nov 2024
Review of “Synergy of active and passive airborne observations for heating rates calculation during the AEROCLO-SA field campaign in Namibia”
Mégane Ventura et al, AMT 2024
The manuscript presents a synergistic method of determining aerosol heating rates from a Lidar and a multi-angle polarimeter, and demonstrates with airborne data from the AEROCLO-SA campaign in coastal Namibia. This is a novel approach of relevance due, in part, to forthcoming polarimeter and lidar observations. The study is approached in a reasonable manner, and a few issues with description and presentation are resolvable. I believe the manuscript is ready for final publication after minor revisions.
Specific comments
Abstract: the abstract mentions (line 21) how the methodology is validated, but not what that validation indicated. One of the challenges in this approach appeared to be horizontal variability, such that the vertical profiles/spirals have a diameter wide enough to observe significant variability.
Introduction and Conclusion: There are many examples of strangely formatted paragraphs (e.g line 67, 590, 612, 615). In some cases these seem to be accidents and the text after this point should be part of the previous paragraph. In other cases it is unclear, and makes the logical flow more difficult to follow.
Introduction (Line 105): the term ‘in situ’ here is used to refer to the irradiance measurements, I believe. I generally think of this term to mean non-remote sensing measurements such as particle counters or other instruments that assess a specific parcel of the atmosphere. Perhaps my definition is too specific, but I recommend adding ‘irradiance’ (or some other words) to this to clarify.
Figures – I feel these are out of order. The order in which they are revealed in the text doesn’t correspond to the numbering order. For example, Figure 7 is mentioned in section 2.3 before figures 2-6. I also find the figures to be overly compact and the text too small compared to that of the manuscript. I spent a lot of zooming and squinting on figures 1b, 2, 3, 4, 5, 6, and especially 8.
Figure 1: the caption doesn’t indicate where/how the cloud optical thickness is determined (or reference section 3.3.2).
Figure 2: It took me a moment to understand why the parameter values were not in order, although it is described in the caption. I think swapping the reference case to be black, with a different symbol would best illustrate the difference/importance of these values to the other data.
Figure 7: It isn’t mentioned anywhere in the text why there seems to be regular gaps in the lidar data for the Sept. 8th case. Was there an instrumental problem? I’m guessing it is due to turns in the ‘square spiral’ during vertical profiles, but if that is the case why isn’t this present in the Sept. 12th dataset?
Table 1: I eventually figured out that ABS specifically is the imaginary component of the aerosol refractive index. This needs to be more clear. In the table and in parts of the text it is labeled as ‘aerosol absorption’ but that is unclear, since it could mean an absorption coefficient, co-albedo, absorbing aerosol optical depth, etc. In fact, I would argue that the imaginary component of the aerosol refractive index shouldn’t be labeled as ‘absorption’ at all, because the amount of light absorbed depends not just on that parameter but others that define the aerosol, such as size.
Table 2: I also dislike the terminology of ‘aircraft sounding’ as that can be confused with dropsondes which you also use. I would stick to the ‘spiral descent’ terminology used elsewhere.
Section 3.1, Line 224: While I understand the necessity of assuming minimal variation of aerosol properties vertically within the aerosol layer, I am not convinced that this is in fact the case. What does the literature from other assessments of aerosol properties from AEROCLO or ORACLES or CLARIFY indicate?
Section 3.3.1. This section would benefit with a table of retrieved parameters from the optimal estimation algorithm – and identification as to which are directly retrieved parameters and indirect (I suspect SSA is this).
Section 3.4 Are high order polynomials really the best way to provide a fit to irradiance profiles? Most likely you are overfitting. If the choice of polynomial order leads to variation of 1-1.5K in the heating rate, I consider that a problem when the overall heating rate is on the order of 3-5. Consider alternative approaches – perhaps lower order polynomials, splines, or other approaches from the interpolation literature.
Figure 4d. More details on how this reconstruction happened, or pointing to the relevant section in the paper, is necessary in the figure caption description of 4d
Section 4.4.1 is very limited in its description of the Sept 12th case. I would have thought that this would be the focus of the description due to the presence of clouds at the top of the aerosol layer for most of the Sept 8th data.
Section 4.4.1 lines 513-517: is this section referring to figure 6 rather than figure 7?
I was confused why figure 8 and table 3 are not presented in chronological order. It took me a moment to realize they were sorted in terms of aerosol heating rate. That’s fine, but it should be noted. Additionally, what is the meaning of the highlight on sept 5th data in table 3?
Conclusions, line 615: could space based profiles of water vapor also be used?
Conclusions, line 626 – this is the first time AERO-AC products are mentioned (with that name, at least). Best to include a citation, spell out the acronym, etc.
Conclusions, line 629: ACCP is now called “Atmosphere Observing System (AOS)”.
Citation: https://doi.org/10.5194/amt-2024-121-RC2 - AC2: 'Reply on RC2', Fabien Waquet, 17 Jan 2025
Status: closed
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RC1: 'Comment on amt-2024-121', Anonymous Referee #1, 13 Oct 2024
Synergy of active and passive airborne observations for heating rates calculation during the AEROCLO-SA field campaign in Namibia
Mégane Ventura et al.
General comments to the Editor:
The submitted manuscript demonstrates the synergy of active LIDAR and passive polarimeter measurements for quantifying the vertical distribution of the heating rates caused by the light-absorbing, lofted layers of biomass burning aerosols over the cloud deck off the coast of Namibia, Africa. The active-passive synergistic method was applied to the airborne measurements of aerosols collected by OSIRIS polarimeter and lidar operated during the AEROCLO-sA campaign in September 2017. The radiative transfer code was used to calculate heating rates in the solar and thermal part of the spectrum. Authors find strong positive heating rate 2-5 K/day caused by aerosols lofted over clouds. The cooling effects of water vapor through infrared radiation is found to generally balance its warming effect from solar radiation. The heating rate calculations were validated against the altitude-resolved irradiance measurements taken during the sounding portion of the flights.
The scientific content presented in the paper effectively demonstrates the value and merit of the active-passive synergistic approach to quantify the aerosol absorption effects over the clouds—a seasonal phenomenon observed over several regions of the world, including the prime hotspot region of southeastern Atlantic Ocean. The methodology, sensors, datasets, results, and their interpretation/evaluation discussed in the paper look sound. The plots showing different sets of results are discussed appropriately in the text. The application of the presented synergistic method to the planned future sensors carrying lidar and polarimeter/image is also discussed towards the end of the paper.
I don’t see any major flaw or concern on this paper that can send back the manuscript to a major revision. However, I have prepared a long list of specific suggestions and questions, included in this review, for authors to consider in the revision. I would suggest authors to proofread the revised article in terms of the language and structure for even more effective presentation.
I should be available re-review the revised submission.
Thanks for the review opportunity.
Specific Comments to authors:
Abstract:
Line 12: “We present original results derived from the airborne observations acquired from the AErosol RadiatiOn and CLOud in Southern Africa (AEROCLO-sA) field campaign led in Namibia in August and September 2017”.
Line14: “…an airborne prototype..”
While abstract mentions about the use of OSIRIS measurements, radiative transfer code, and meteorological parameters, it misses mentioning what aerosol and cloud measurements were actually used in the RT model to calculate the heating rate. This should be added to the abstract.
Introduction:
Line 30: “…variable chemical, optical, and microphysical properties.”
Line 37: “The aerosol radiative forcing estimates provided by climate model over the Southeast Atlantic Ocean show large discrepancies”
Lines 45-48: These sentences can be re-written as, “ Biomass burning aerosols are primarily composed of two components: black carbon and organic or brown carbon. The former strongly absorbs radiation across a wide spectral range, whereas the latter has relatively weaker absorption in the visible part of the spectrum but exhibits strong spectral dependence of absorption at the UV wavelengths.”
Line 59: “Our study focuses…” Also these two sentences can be re-organized as, “This study focuses on quantifying the radiative impact of biomass burning aerosols and aims to estimate the profiles of atmospheric heating (or cooling) rates attributable to these particles. We use airborne measuerements acquired during the AErosols Radiation and CLOuds in southern Africa (AEROCLO-sA) campaign (Formenti et al., 2019) conducted over Namibia between 5 and 12th September 2017 with the French Falcon 20 environmental research aircraft of Safire.”
Line 65: “..such as ORACLES…”
Line 78: SSA quantitatively describes the ability of aerosols to absorb or scatter the solar radiation.
Lines 79-80: I would not call it “To a lesser extent”. Given the same state of aerosols (AOT, SSA, asymmetry parameter), the extent of the radiative forcing or heating rates is primarily determined by the brightness of the underlying clouds, which is a function of cloud optical depth. Please reconsider this statement.
Lines 81-85: Author may consider citing Jethva et al. (2024), which uses UV-VIS-NIR satellite measurements from OMI and MODIS combined with AOT measurements from HSRL-2 lidar and 4STAR sunphotometer to derive UV-VIS-NIR spectral SSA over clouds in the SEA region. Satellite retrievals of SSA agree overall with the range measured by in-situ observations, and show relatively strongest (weakest) absorption in August (October).
Jethva, H. T., Torres, O., Ferrare, R. A., Burton, S. P., Cook, A. L., Harper, D. B., Hostetler, C. A., Redemann, J., Kayetha, V., LeBlanc, S., Pistone, K., Mitchell, L., and Flynn, C. J (2024), Retrieving UV–Vis spectral singlescattering albedo of absorbing aerosols above clouds from synergy of ORACLES airborne and A-train sensors, Atmos. Meas. Tech., 17, 2335–2366, https://doi.org/10.5194/amt-17-2335-2024.
Line 86-87: Author should consider citing following two papers on aerosol radiative effects over clouds.
Meyer K. G., S. E. Platnick, L. Oreopoulos, et al. 2013. "Estimating the direct radiative effect of absorbing aerosols overlying marine boundary layer clouds in the southeast Atlantic using MODIS and CALIOP." J. Geophys. Res. Atmos. 118 (10): 4801-4815 [10.1002/jgrd.50449] [Journal Article/Letter]
Zhang, Z.*, K. Meyer, H. Yu, S. Platnick, P. Colarco, Z. Liu, and L. Oreopoulos (2016), Shortwave direct radiative effects of above-cloud aerosols over global oceans derived from 8 years of CALIOP and MODIS observations, ACP, 16(5), 2877–2900, doi:10.5194/acpd-15-26357-2015.
Line 92: Jethva et al. (2014) should be corrected to Jethva et al. (2018).
Jethva H., O. Torres, C. Ahn and et al. 2018. "A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm." Atmospheric Measurement Techniques 11 (10): 5837-5864 [10.5194/amt-11-5837-2018] [Journal Article/Letter].
Line 98: CALIOP vertical profiles of extinction, if properly constrained, can provide altitude-dependent heating rates.
Line 129: Define OSIRIS here, if it is not defined earlier in the text.
Line 125: Double dots (..).
Line 135: Some restructuring: “During the campaign, the air mass intercepted (or measured) by the on-board instruments were mainly transported from the in-land biomass burning source areas, emitting substantial amounts of carbonaceous aerosols transported over the southeastern Atlantic Ocean, as far as the Ascension Island. The airmass was then drifted to the southeast towards the Namibian coast due to the anticyclonic circulation located over South Africa”.
Low-pressure system rotates in clockwise direction in southern hemisphere. So, make sure that the cyclonic or anticyclonic movement is referenced to the southern hemisphere.
Line 149: “…with the aircraft during specific flights”. It would be desirable to mention the dates of these flights.
Table 1: Add the wavelength of the AOT measurements (865 nm) in the respective column. Similarly, ABS should be labelled as the imaginary part of the refractive index. Please correct me, if wrong. Also, the wavelength of the ABS should be included in the table and text. AOT values of > 0.4 at 865 nm translate to AOT of > 1 at 500 nm.
Line 78: Define OSIRIS the first time it was referred in the text.
Line 184-185: It is assumed here that the LNG is a downward looking lidar.
Line 189: Re-structuring: “Consequently, lidar data acquired for optically thick plumes at these wavelengths become less reliable in accurately determining the base altitude of the aerosol layer. On the other hand, the 1064-nm signal penetrates deeper into the aerosol layer due to significantly reduced attenuation, providing a better view of the depth of the aerosol layer. For this reason, we used LNG lidar data acquired at 1064 nm to accurately depict the aerosol extinction profile.”
Line 194: “the extinction aerosol optical thickness”. PLASMA resembles the 4-STAR sun photometer. Are there any major differences in the way both measure AOTs?
Line 210: “600 m above the sea level”.
Line 213: What is the swath width of the OSIRIS instrument/retrievals?
Line 229: Equation 1: This is simply the scaling of lidar extinction profile to match with the OSIRIS-retrieved AOTs. OSIRIS measured columnar AOT above the clouds, whereas the lidar measurements, according to Figure 7 (a & b), correspond to the atmospheric depth between cloud-top to 6-km. This leads to an assumption that the smoke aerosols are confined within these altitude range. Any amounts of aerosols above 6 km, therefore, are ignored in the analysis. This should be mentioned in the text.
Line 249: Which satellite/sensor total column O3 data was used here?
Line 257: Where Cp stands for…
Line 264: Optimal estimation method.
Line 266-269: This is not understood. Both OSIRIS and GAME calculations ignore particles larger than one micron (radius or diameter?). How did author accounted for the aerosol optical properties to longer wavelengths?
Line 280: MODIS cloud product of cloud effect radius histogram over the southeastern Atlantic Ocean shows peak around 12 microns, which is closer and near-consistent with the 10 microns assumed in this study. It is imperative to mention here that which cloud model is used here. Most likely, author is assuming water clouds. What droplet size distribution was assumed here? Modified GAMMA? Also, did author compare the Irradiance-based COT retrievals with those derived from OSIRIS algorithm? How well do they compare?
Line 322: “aerosol absorption optical depth of 0.03”.
Line 325-327: spectrally neutral aerosol absorption: Does author mean the spectral imaginary part of the refractive index or aerosol absorption optical depth? In either case, assuming spectral neutral behavior might not be an appropriate assumption. Biomass burning aerosols are often rich in organics (brown carbon), which exhibits strong spectral absorption at shorter wavelengths. If it is not too much of work and computation, author is suggested to assume spectrally varying aerosol absorption optical depth assuming the Absorption Angstrom Exponent in the range 2.0-2.5, a typical range for biomass burning aerosols.
It looks like the aerosol absorption of 0.03 assumed in the simulation referred to the imaginary part of the refractive index.
Line 425-430: What was the variability in the COT retrievals during spiral descent? Since COT is retrieved at each change in altitude, these numbers should be handy and mentioned here.
Could author remind here what wavelength range was considered to calculate solar and thermal heating rate, respectively?
For thermal IR range, fine mode smoke particles do not play a significant role; however, cloud properties (COT and effective radius) drive the heating/cooling rates. Does the blue curve in Figure 5 (f) use the improved cloud modeling (cloud retrievals at each change of altitude)?
Line 464: “Despite heterogeneity in COT…”
Figure 6: It is interesting that there are seemingly three layers of aerosols identified in the solar heating rate. Could author confirm this with lidar and/or PLASMA AOT profile measurements?
Line 499-500: “which are all found in all rate calculations (>10 K/day).” This is not understood.
Figure 7: About cloud formation, as mentioned in the text, at the top of aerosol layer for Sep 8th flight. Did author notice similar cloud layer during Sep 12th flight? Or the cloud layer is filtered out in the heating/cooling rates? There are drastic differences in the 1064-nm backscatter values between these two flights. Mentioning the averaged AOTs during these two flights here, for the high-altitude segments, would aid the reader in interpreting the results.
Line 507: “fairly homogeneous values of the order of 4 to 5 K/day…”
Figure 8: It appears that the plots for different days are ordered w.r.t highest to lowest heating rates. Please mention this in the text. Also, label the x-axis as heating rates for the plots shown at top. It is worth to mention the averaged COTs measured on these days in Table 3. It is assumed here that the heating rates are vertically integrated. Please clarify it in the text.
Line 578: “…observed during the September 2017 AEROCLO-sA airborne campaign”
Line 616-620: This is a good point. The COT should also play somewhat significant role in heating rates, depending upon what range of COT is observed. Also, one needs the true COT below the aerosol layer, after aerosol attenuation correction, in order to accurately assess the role of COT in both radiative effects and heating rates.
Line 621: desert dust above the clouds not on a global scale, but regional scale, i.e., Saharan dust transport over the Atlantic, dust plumes over the Arabian Sea, and Asian desert dust over the clouds along the eastward transport pathways over the Pacific.
At the end, it is an excellent demonstrative study of how the synergy of active lidar and passive measurements brings new quantitative information on the effects of light-absorbing aerosols above the clouds.
Citation: https://doi.org/10.5194/amt-2024-121-RC1 -
AC1: 'Reply on RC1', Fabien Waquet, 17 Jan 2025
We are grateful to the reviewer for his interest in our study and for his very detailed review of the article. We have done our best to take into account these suggestions for improvement and to answer the questions raised. Below are our responses, presented point by point in the supplementary PDF document.
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AC1: 'Reply on RC1', Fabien Waquet, 17 Jan 2025
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RC2: 'Comment on amt-2024-121', Anonymous Referee #2, 05 Nov 2024
Review of “Synergy of active and passive airborne observations for heating rates calculation during the AEROCLO-SA field campaign in Namibia”
Mégane Ventura et al, AMT 2024
The manuscript presents a synergistic method of determining aerosol heating rates from a Lidar and a multi-angle polarimeter, and demonstrates with airborne data from the AEROCLO-SA campaign in coastal Namibia. This is a novel approach of relevance due, in part, to forthcoming polarimeter and lidar observations. The study is approached in a reasonable manner, and a few issues with description and presentation are resolvable. I believe the manuscript is ready for final publication after minor revisions.
Specific comments
Abstract: the abstract mentions (line 21) how the methodology is validated, but not what that validation indicated. One of the challenges in this approach appeared to be horizontal variability, such that the vertical profiles/spirals have a diameter wide enough to observe significant variability.
Introduction and Conclusion: There are many examples of strangely formatted paragraphs (e.g line 67, 590, 612, 615). In some cases these seem to be accidents and the text after this point should be part of the previous paragraph. In other cases it is unclear, and makes the logical flow more difficult to follow.
Introduction (Line 105): the term ‘in situ’ here is used to refer to the irradiance measurements, I believe. I generally think of this term to mean non-remote sensing measurements such as particle counters or other instruments that assess a specific parcel of the atmosphere. Perhaps my definition is too specific, but I recommend adding ‘irradiance’ (or some other words) to this to clarify.
Figures – I feel these are out of order. The order in which they are revealed in the text doesn’t correspond to the numbering order. For example, Figure 7 is mentioned in section 2.3 before figures 2-6. I also find the figures to be overly compact and the text too small compared to that of the manuscript. I spent a lot of zooming and squinting on figures 1b, 2, 3, 4, 5, 6, and especially 8.
Figure 1: the caption doesn’t indicate where/how the cloud optical thickness is determined (or reference section 3.3.2).
Figure 2: It took me a moment to understand why the parameter values were not in order, although it is described in the caption. I think swapping the reference case to be black, with a different symbol would best illustrate the difference/importance of these values to the other data.
Figure 7: It isn’t mentioned anywhere in the text why there seems to be regular gaps in the lidar data for the Sept. 8th case. Was there an instrumental problem? I’m guessing it is due to turns in the ‘square spiral’ during vertical profiles, but if that is the case why isn’t this present in the Sept. 12th dataset?
Table 1: I eventually figured out that ABS specifically is the imaginary component of the aerosol refractive index. This needs to be more clear. In the table and in parts of the text it is labeled as ‘aerosol absorption’ but that is unclear, since it could mean an absorption coefficient, co-albedo, absorbing aerosol optical depth, etc. In fact, I would argue that the imaginary component of the aerosol refractive index shouldn’t be labeled as ‘absorption’ at all, because the amount of light absorbed depends not just on that parameter but others that define the aerosol, such as size.
Table 2: I also dislike the terminology of ‘aircraft sounding’ as that can be confused with dropsondes which you also use. I would stick to the ‘spiral descent’ terminology used elsewhere.
Section 3.1, Line 224: While I understand the necessity of assuming minimal variation of aerosol properties vertically within the aerosol layer, I am not convinced that this is in fact the case. What does the literature from other assessments of aerosol properties from AEROCLO or ORACLES or CLARIFY indicate?
Section 3.3.1. This section would benefit with a table of retrieved parameters from the optimal estimation algorithm – and identification as to which are directly retrieved parameters and indirect (I suspect SSA is this).
Section 3.4 Are high order polynomials really the best way to provide a fit to irradiance profiles? Most likely you are overfitting. If the choice of polynomial order leads to variation of 1-1.5K in the heating rate, I consider that a problem when the overall heating rate is on the order of 3-5. Consider alternative approaches – perhaps lower order polynomials, splines, or other approaches from the interpolation literature.
Figure 4d. More details on how this reconstruction happened, or pointing to the relevant section in the paper, is necessary in the figure caption description of 4d
Section 4.4.1 is very limited in its description of the Sept 12th case. I would have thought that this would be the focus of the description due to the presence of clouds at the top of the aerosol layer for most of the Sept 8th data.
Section 4.4.1 lines 513-517: is this section referring to figure 6 rather than figure 7?
I was confused why figure 8 and table 3 are not presented in chronological order. It took me a moment to realize they were sorted in terms of aerosol heating rate. That’s fine, but it should be noted. Additionally, what is the meaning of the highlight on sept 5th data in table 3?
Conclusions, line 615: could space based profiles of water vapor also be used?
Conclusions, line 626 – this is the first time AERO-AC products are mentioned (with that name, at least). Best to include a citation, spell out the acronym, etc.
Conclusions, line 629: ACCP is now called “Atmosphere Observing System (AOS)”.
Citation: https://doi.org/10.5194/amt-2024-121-RC2 - AC2: 'Reply on RC2', Fabien Waquet, 17 Jan 2025
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