the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Apr 2025
All-Sky Direct Aerosol Radiative Effects Estimated from Integrated A-Train Satellite Measurements
Abstract. Improved satellite-derived observations of the Direct Aerosol Radiative Effects (DARE) remain essential to reduce the uncertainty in the impact of aerosol on solar radiation. We develop a framework to compute DARE at the top of the Earth’s atmosphere, in the short-wave part of the electromagnetic spectrum and in all-sky conditions along the track of the A-Train constellation of satellites. We use combined state-of-the-art aerosol and cloud properties from satellite sensors Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS). We also use a global reanalysis from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) to provide vertical distribution of aerosol properties and atmospheric conditions. Diurnal mean satellite DARE values range from -25 (cooling) to 40 W⋅m-2 (warming) over the Southeast Atlantic during three days from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) aircraft campaign. These three days also show agreement between our satellite DARE and co-located airborne Solar Spectral Flux Radiometer (SSFR) measurements. This paper constitutes the first step before applying our algorithm to many more years of combined satellite and model data over many regions of the world. The goal is to ultimately assess the order of importance of atmospheric parameters in the calculation of DARE for specific aerosol and cloud regimes. This will inform future missions where, when and how accurately the retrievals should be performed to reduce all-sky DARE uncertainties.
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RC1: 'Comment on egusphere-2025-1403', Anonymous Referee #1, 12 May 2025
This manuscript presents a novel study aimed at developing improved satellite-derived estimates of Direct Aerosol Radiative Effects (DARE). This work introduces a new method for computing DARE by leveraging satellite sensors CALIOP and MODIS combined with MERRA-2 vertical distributions of aerosol properties and atmospheric conditions. With the advantage of active remote sensing, this approach is capable of performing all-sky retrievals of SW aerosol radiative effects useful for comparisons with dedicated field studies such as the ORACLES aircraft campaign. Overall, this manuscript is well-written, and the figures are clearly presented. Below are a few suggestions that may help further improve the manuscript:
Major Comments:
- The current notation for the DARE calculations (i.e., DAREs and DAREp, as defined) are challenging to read. These subscripts are fairly difficult to distinguish within the text. To enhance clarity and reader comprehension, it could be helpful to explore alternative naming conventions. For example, labeling them DARE_obs (for the method utilizing observations) and DARE_param (for the parameterized method) might be more intuitive.
- As the paper describes very well, the Southeast Atlantic presents an excellent natural laboratory for studying DARE and validating against ORACLES data, particularly given the variable atmospheric conditions. To strengthen the broader significance of this work, it would be helpful to elaborate on the potential for extending the findings beyond this region. Specifically, a discussion of the factors that might influence the transferability of these results to other parts of the globe would be particularly insightful.
Minor Comments:
- Lines 178-195: While well-structured and informative, this paragraph is difficult to read. I would recommend shorter sentences with fewer em dashes to improve clarity.
- Line 188: It can be quite challenging to distinguish a “~’ and a “-” in the text. As an alternative, “ ~-7 to ~-1” can be rewritten as “approximately -7 to -1”.
- Figure 5: The legend currently needs improvement to enhance readability. The overlay of data points on the legend makes it difficult to discern the labels. Additionally, the initial word of each legend (e.g. Cases, CALIOP, MODIS, etc.) are distracting and could be revised for improved clarity.
- Figure 6: The near 1:1 agreement in DARE calculations observed on 8/13/2017, suggests atmospheric conditions that are notably different from the two preceding cases. This is an interesting finding. Could you elaborate on the specific atmospheric conditions that might explain this unique 8/13/2017 scenario within the figure's discussion?
- Figure 7: The bottom subplot displays flux difference values, but some data points appear to extend beyond the figure's axes. Are these outlying data points of lesser significance to the overall analysis?
Citation: https://doi.org/10.5194/egusphere-2025-1403-RC1 -
RC2: 'Comment on egusphere-2025-1403', Anonymous Referee #2, 23 May 2025
Review Summary
This paper by Kacenelenbogen et al. introduces a novel framework (DARES - Direct Aerosol Radiative Effects Semi observational) to estimate all-sky Direct Aerosol Radiative Effects at the Top-Of-Atmosphere (TOA) in the shortwave spectrum. The primary goal is to reduce uncertainties in aerosol radiative effects by integrating state-of-the-art satellite observations from the A-Train constellation (primarily CALIOP for aerosol profiles/AOD and MODIS for cloud properties) with MERRA-2 reanalysis data (for aerosol intensive properties, vertical distribution, below-cloud aerosols, and atmospheric conditions).
The DARES methodology involves categorizing atmospheric conditions along the CALIOP track into four scenarios based on the presence and characteristics (thick, thin, broken, uniform) of single-layer, low-level warm liquid clouds. Radiative transfer calculations (using RRTMG-SW) are performed for each 1km segment to determine DARE. The paper details the input parameters, including various CALIOP AOD products, MODIS cloud properties (uncorrected for overlying aerosol in the presented results), and MERRA-2 derived aerosol optical properties and atmospheric profiles. Uncertainties in DARES are also estimated.
The framework is initially applied and validated over the Southeast Atlantic for three specific days during the NASA ORACLES aircraft campaign. The results show:
- Diurnal mean all-sky DARES values ranging from -25 W⋅m⁻² (cooling) to +40 W⋅m⁻² (warming).
- Highly positive DARE is associated with high Aerosol Optical Depth (AOD) above clouds with high Cloud Optical Thickness (COT), while negative DARE is linked to high AOD in clear skies.
- A significant portion of the observed scenes were classified as aerosols above thick, homogeneous clouds, though a substantial number of profiles remained unassigned due to complex cloud conditions or data inconsistencies.
The DARE estimates are assessed through two methods:
- Comparison with DAREP, a parametrization derived from ORACLES airborne data for cloudy conditions, showing good agreement (R² = 0.87-0.99).
- Comparison of DARES related upward fluxes with collocated airborne Solar Spectral Flux Radiometer (SSFR) measurements, also demonstrating good agreement (R² = 0.94-0.95 for broadband fluxes).
The authors discuss limitations, such as the reliance on MERRA-2 for certain aerosol properties and the simplified diurnal cycle. Future work aims to:
- Improve MERRA-2 integration and leverage new satellite missions (e.g., EarthCARE, PACE, AOS).
- Incorporate observed diurnal aerosol and cloud cycles using geostationary satellite data.
- Expand the framework to include multi-layer cloud scenarios.
- Apply the DARES algorithm globally over multiple years to assess the regional importance of various atmospheric parameters in DARE calculations, ultimately informing future satellite missions and improving climate models.
Two main DARE calculation methods are mentioned:
- DARES (Semi-Observational): The primary focus of the study. It uses the RRTMG-SW radiative transfer model with inputs derived from a combination of CALIOP, MODIS, and MERRA-2. The methodology includes:
- Utilizing various CALIOP AOD products, including standard, depolarization ratio (DR), and Ocean Derived Column Optical Depths (ODCOD) methods, corrected for stratospheric aerosols.
- Employing MODIS cloud products (both uncorrected and corrected for overlying aerosols) for properties like Cloud Optical Thickness (COT) and Cloud Effective Radius (CER).
- Using MERRA-2 for aerosol intensive properties (Single Scattering Albedo - SSA, Asymmetry Parameter -ASY), spectral extinction shapes, and aerosol characteristics below clouds. CALIOP AOD at 532nm is used to scale MERRA-2 aerosol extinction profiles.
- Initial theoretical calculations (DARET) were performed to understand DARE sensitivities to aerosol types and vertical distribution over clouds.
- DAREP (Parametrized): Based on Cochrane et al. (2021), used for evaluation purposes.
The paper presents a comprehensive framework for deriving semi-observational all-sky DARE. It is initially applied and validated over the Southeast Atlantic for three days during the NASA ORACLES aircraft campaign, with results showing agreement with airborne measurements. The authors state this work is a foundational step towards applying the algorithm globally over many years to assess the importance of various atmospheric parameters in DARE calculations, ultimately aiming to inform future satellite missions.
Notes
- Section 2.1.3 (DARES for Four Atmospheric Scenarios):
- S1: Aerosol above/below a single LWLC that is thick and uniform.
- S2: Aerosol above/below a single LWLC that is thick and broken.
- S3: Aerosol above/below a single LWLC that is thin and possibly broken.
- S4: Aerosol in (possibly cloud-contaminated) clear skies (LWLC small or not present).
- DARE cloudy (S1, S2, S3 combined - Eq. 3)
- DARE non-cloudy (S4 - Eq. 4)
- DARE all-sky (S1-S4 combined - Eq. 5)
- The atmosphere along the satellite track is categorized into four scenarios (S1-S4) to evaluate DARE, after filtering out profiles with clouds above 3 km (except for a single low warm liquid cloud - LWLC).
- Table 3 details the specific CALIOP and MODIS criteria (e.g., cloud layer counts, optical thickness, cloud mask confidence) used to define these scenarios and the cloud characteristics (thick, thin, uniform, broken).
- MODIS Cloud Fraction (CF) is assumed to be 1 for S1-S3 and 0 for S4 to be consistent with MODIS COT retrieval assumptions.
- Section 2.1.4 (DARES Uncertainties):
- DARES uncertainty is estimated by varying key input parameters (AOD, CWP, SSA, ASY, surface albedo) according to their respective uncertainties (detailed in Table 4).
- Specific uncertainty values are cited: 0.11 for CALIOP ODAOD, pixel-level for MODIS Cloud CWP, 0.05 for MERRA-2 SSA, 0.02 for MERRA-2 ASY, and 0.01 for surface albedo.
- Uncertainties for other CALIOP AOD products (CALIOP ACAOD_standard, CALIOP AOD_standard, CALIOP ACAOD_DR) are computed over a 20km stretch using a weighted mean approach (Eq. 6-7).
- The paper discusses sensitivity analyses (Table A3, A4, A5, Fig. A4, A5) regarding: extinction coefficient thresholds, including below-cloud aerosols, extending aerosol top height, using different AOD "versions" (V1, V2, V3), and using corrected vs. uncorrected cloud properties.
- The main results presented in Section 3 use AOD version 2 and uncorrected cloud properties, as the impact of cloud correction was found to be minimal for the low AODs in the case studies.
- Section 2.2 (Parametrized DAREP Calculations):
- DAREP, a parametrized DARE calculation from Cochrane et al. (2021), is used for evaluation.
- This parametrization links instantaneous DARE to AOD and underlying surface albedo for typical biomass burning aerosols above stratocumulus (or clear sky) in the Southeast Atlantic ORACLES region.
- It has a reported uncertainty of 20% and circumvents the need for full radiative transfer calculations but is specific to the ORACLES study conditions.
- Section 3 (Results - Initial):
- Three ORACLES case studies (Sept 18 & 20, 2016; Aug 13, 2017) offshore from Namibia are described (Fig. 2). These days featured an omnipresent stratocumulus deck with varying aerosol plumes.
- CALIOP tracks were collocated with ER-2 or P3 aircraft flights. SSFR data from the P3 aircraft will be used for DARES evaluation.
- Figure 3 shows the distribution of S1-S4 scenarios for these days, with S1 (aerosol above thick, uniform cloud) being dominant among assigned scenarios. A significant portion of profiles (26-48%) are unassigned ("N/A") due to complex cloud conditions, data inconsistencies, or missing retrievals.
- Figure 4 presents Probability Distribution Functions (PDFs), and Table 5 shows mean values for aerosol properties (AOD, SSA, ASY, EAE), cloud properties (COT, CWP, CER), and diurnal mean DARES for the three case days.
- All-sky 24h DARES ranges from -25 to 40 W⋅m⁻². Aug 13, 2017, shows the lowest mean all-sky DARE (4.0 W⋅m⁻²), attributed to more clear-sky cases and higher clear-sky AOD.
- Mean 24h cloudy (S1-S3) DARES values are ~8-15 W⋅m⁻², higher than some previous studies, attributed to differences in period, domain, and methodology. The highest cloudy DARE (Sept 20, 2016) corresponds to the highest AOD above clouds (0.6).
- Figure 5 illustrates the spatial evolution of input parameters and DARES along the CALIOP track for Aug 13, 2017, showing strong DARE variability linked to changes in cloud thickness (COT, CWP) under relatively constant AOD and SSA.
- Figure A8 (appendix, described in text) shows DARES negativity increasing with AOD in clear skies, and DARES positivity increasing with AOD above clouds and with COT for similar AODs.
- Section 3.3 (Assessment of DARES):
- 3.3.1 Comparison with DAREP: The semi-observational DARES (instantaneous, for cloudy scenarios S1-S3) is compared against DAREP, a parametrization developed from ORACLES data. Results (Figure 6, Table 6 Part 1) show good agreement (R² = 0.87-0.99, slope = 0.80-0.99, RMSE = 19-31% of mean DARES). DAREP sometimes overestimates DARES at high AOD, potentially due to DAREP's single aerosol layer assumption or MERRA-2 SSA/ASY biases in DARES. Deviations are also noted for inhomogeneous clouds with low AOD.
- 3.3.2 Comparison with Airborne SSFR Measurements: DARES-related upward fluxes (the component calculated by RRTMG with aerosols present) are compared with collocated airborne SSFR spectral irradiance measurements for the 08/13/2017 case (N=51 points after filtering). SSFR data is integrated into RRTMG broadband channels. Table 6 Part 2 shows good agreement (R² = 0.94-0.95, RMSE = 9-17% of mean DARES-related flux). Figure 7 illustrates that larger discrepancies occur with increased satellite-aircraft distance and in thinner/broken cloud conditions, while closer collocations in thicker cloud/higher AOD scenes show better agreement.
- Section 4 (Discussions and Future Work):
- MERRA-2 Improvements: The paper discusses current limitations in using MERRA-2 aerosol profiles at face value. Future improvements include selecting MERRA-2 levels with the strongest aerosol signal or inferring below-cloud properties from nearby clear-sky observations. The potential of new satellite missions (EarthCARE, PACE, AOS) to provide better observational constraints on aerosol vertical/spectral properties and reduce unassigned scenarios is highlighted.
- Diurnal Cycle: The current 24h DARES only varies Solar Zenith Angles (SZA), assuming constant aerosol/cloud properties. A planned enhancement is to incorporate observed diurnal aerosol and cloud variations using geostationary satellite data.
- Multi-Cloud Scenarios: To achieve a more comprehensive all-sky DARE, the authors plan to add scenarios with clouds overlying the primary Low Warm Liquid Clouds (LWLCs), potentially using C3M (CALIPSO-CloudSat-CERES-MODIS) derived cloud properties and MERRA-2 aerosols. This is expected to reduce the number of unassigned cases and will be evaluated with data like CAMP2Ex.
- Parameter Importance: A key future goal is to apply the DARES framework to multiple years of data over various regions to assess the relative importance of key aerosol, cloud, and surface parameters in DARE calculations for different atmospheric regimes.
- Section 5 (Conclusion):
- The paper successfully developed a framework to compute Top-Of-Atmosphere (TOA) Shortwave (SW) all-sky DARE by combining CALIOP, MODIS, and MERRA-2 data along the CALIOP track for four atmospheric scenarios involving single-layer, low-level liquid clouds.
- Analysis of three ORACLES case studies shows diurnal DARE values from -25 to 40 W⋅m⁻², with positive DARE linked to high AOD above thick clouds and negative DARE to high AOD in clear skies. A significant portion of profiles were classified as aerosols above thick, homogeneous clouds, but many also remained unassigned.
- The semi-observational DARES showed good agreement with both the DAREP parametrization (for cloudy conditions) and direct airborne SSFR flux measurements.
Questions and Comments
- Clarity of CALIOP AOD Product Usage in DARES: Table 2 mentions CALIOP AOD at 532nm is a combination of CALIOP ACAOD_standard, CALIOP ACAOD_DR, CALIOP AOD_standard, and CALIOP ODAOD. The paper details these products, but the specific logic or conditions under which each is chosen or how they are "combined" for a single AOD input to DARES could be more explicit in this section.
- DAREP Methodology Details: While DARES is detailed, DAREP (used for evaluation) is only cited. A brief summary of how it contrasts with DARES in handling key parameters (especially clouds and aerosol vertical distribution) might be beneficial for context within this paper.
- MERRA-2 SSA Bias: The paper acknowledges that MERRA-2/GEOS SSA tends to be higher than in-situ measurements (underestimating absorption), particularly for biomass burning aerosols prevalent in the ORACLES study region. The impact of this known bias on the DARES results, especially for positive DARE over clouds, might be important to discuss.
- Extinction Coefficient Thresholds: The paper sets extinction coefficient thresholds for CALIOP (0.07 km⁻¹) and MERRA-2 (0.014 km⁻¹) to define "aerosol-free" conditions. The MERRA-2 threshold is scaled from the CALIOP one based on average layer thickness. The sensitivity of DARE results to these threshold choices could be relevant.
- Regarding Table 2, Footnote (1): Could you clarify the entry "O2 mass density = 0.0 kg m3"? Does this imply that O2 is not a variable input in the radiative transfer model, being part of the standard atmospheric profile, rather than having zero density?
- Corrected vs. Uncorrected MODIS Cloud Properties: Section 2.1.1 mentions that using corrected MODIS cloud properties (accounting for above-cloud aerosols) can sometimes worsen agreement with other CER measurements depending on the spectral channel. Which version (corrected or uncorrected) is predominantly used for the DARES results presented for the ORACLES case studies, and what was the rationale for this choice?
- Aerosol Information Below Clouds: For aerosols below clouds, MERRA-2 is used for extensive and intensive properties. How are situations handled where CALIOP detects clouds, but MERRA-2 shows no significant aerosol below them, or vice-versa? Is there a priority system or a check for consistency?
- Definition of "Thick, Thin, and/or Broken" Clouds: Section 2.1.3 (referenced in Table 2) will describe atmospheric scenarios including "thick, thin and/ or broken liquid cloud." What specific CALIOP vfm and MODIS Cloud criteria are used to classify clouds into these categories for the DARES calculations?
- DAREP Applicability: Section 2.2 emphasizes DAREP is specific to ORACLES conditions. For the broader goal of assessing DARE globally, are there plans to develop similar parameterizations for other regions/aerosol types, or will the full DARES framework always be the primary tool?
- Future Work - C3M Data (Section 4): The mention of C3M data (which relies on CloudSat) for future multi-cloud scenarios is relevant. While CloudSat's operational mode has changed and it's no longer in the A-Train, historical C3M data is extensive. For ongoing and future analyses, alternatives or updated multi-sensor products might be needed if relying on contemporaneous data with new missions like EarthCARE. This is more of a consideration than an error.
- The offset in DAREP vs. DARES is notably higher for 09/20/2016 (8.3 W⋅m⁻²) compared to other days. The paper suggests DAREP overestimation for high AOD on 09/18/2016. Does the larger offset on 09/20/2016 (which had the highest AODs) also primarily point to DAREP's single aerosol layer assumption or MERRA-2 SSA/ASY issues, or are there other potential contributors to this larger systematic difference on that specific day?
- What are the anticipated major challenges in merging geostationary satellite data (which typically has coarser spatial resolution, different viewing geometries, and potentially different retrieval algorithms/sensitivities for aerosol and cloud properties) with the nadir-viewing, high-resolution Lidar/Imager data from A-Train/EarthCARE for consistent diurnal DARES calculations?
- Regarding the use of C3M data for multi-cloud scenarios: Given the changes in CloudSat's operational status, how does this impact the strategy for incorporating multi-layer cloud properties, especially for DARE calculations intended to span "multiple years" beyond the prime A-Train era? Will this rely more on the historical C3M dataset, or are there alternative/future multi-sensor cloud products (perhaps involving EarthCARE itself) that are being considered?
Citation: https://doi.org/10.5194/egusphere-2025-1403-RC2 -
AC1: 'Comment on egusphere-2025-1403', Meloe Kacenelenbogen, 01 Jul 2025
We are grateful for both referees’ thoughtful reviews as addressing them has strengthened our paper. We have addressed all of them carefully as described below.
Kindest Regards,
Meloë Kacenelenbogen on behalf of all co-authors.
Referee #1
This manuscript presents a novel study aimed at developing improved satellite-derived estimates of Direct Aerosol Radiative Effects (DARE). This work introduces a new method for computing DARE by leveraging satellite sensors CALIOP and MODIS combined with MERRA-2 vertical distributions of aerosol properties and atmospheric conditions. With the advantage of active remote sensing, this approach is capable of performing all-sky retrievals of SW aerosol radiative effects useful for comparisons with dedicated field studies such as the ORACLES aircraft campaign. Overall, this manuscript is well-written, and the figures are clearly presented. Below are a few suggestions that may help further improve the manuscript:
Major Comments:
- The current notation for the DARE calculations (i.e., DAREs and DAREp, as defined) are challenging to read. These subscripts are fairly difficult to distinguish within the text. To enhance clarity and reader comprehension, it could be helpful to explore alternative naming conventions. For example, labeling them DARE_obs (for the method utilizing observations) and DARE_param (for the parameterized method) might be more intuitive.
We have changed DARES into DARE_obs, DAREP into DARE_param and DARET into DARE_theo throughout the manuscript (includes text, tables and figures)
- As the paper describes very well, the Southeast Atlantic presents an excellent natural laboratory for studying DARE and validating against ORACLES data, particularly given the variable atmospheric conditions. To strengthen the broader significance of this work, it would be helpful to elaborate on the potential for extending the findings beyond this region. Specifically, a discussion of the factors that might influence the transferability of these results to other parts of the globe would be particularly insightful.
In the discussion section, it now reads:
“We plan to apply our DARE_obs calculations to multiple years of combined satellite and model data over different regions of the world. The most important factors influencing the transferability of our method to regions of the globe outside the Southeast Atlantic are (i) different Earth’s surfaces (i.e., ocean vs. different land types) and (ii) different horizontal, vertical and temporal distributions of aerosol and cloud types and amounts. Our method requires aerosols in cloud-free skies, and above and below single thick, thin and/ or broken low warm liquid clouds. Kacenelenbogen et al. (2019) define six major global aerosol “hotspots” over single thick low warm liquid clouds (i.e., different aerosol regimes above the same type of clouds) in the northeast Pacific, southeast Pacific, tropical Atlantic, southeast Atlantic, Indian ocean, offshore from western Australia and northwest Pacific (see their Fig. 6; and Table 2 for a list of studies over these regions). According to Fig. 7d of Kacenelenbogen et al. (2019), the region of Southeast Atlantic (this paper) shows the highest mean annual percentage of high AOD values above clouds compared to the five other regions. Note that we also plan to apply our DARE_obs calculations to regions that show different cloud regimes in addition to different aerosol regimes (e.g., the Southeast Atlantic, the tropical Atlantic, and a region encompassing the latter two representing the transition between these two regimes). We then plan to use this larger DARE_obs dataset for different atmospheric scenarios, over specific regions of the world and linked to key cloud, aerosol and surface input parameters to assess the order of importance of these parameters in DARE_obs calculations for specific aerosol and cloud regimes.”
Minor Comments:
- Lines 178-195: While well-structured and informative, this paragraph is difficult to read. I would recommend shorter sentences with fewer em dashes to improve clarity.
- Line 188: It can be quite challenging to distinguish a “~’ and a “-” in the text. As an alternative, “ ~-7 to ~-1” can be rewritten as “approximately -7 to -1”.
We modified the manuscript accordingly. It now reads:
“Like Table 2 for DARE_obs and DARE_param, Table A1 in the appendix lists the input parameters to our DARE_theo calculations. DARE_theo is computed for two types of single low warm liquid clouds (i.e., COT=1, CER=12 and CWP=8 vs. COT=10, CER=12 and CWP=80) and varying vertical distributions of RRTMG “build-in” aerosol types (see Fig. A1) while keeping cloud heights, AOD, ASY, atmospheric composition, weather and ocean surface BRDF constant (see thirty-two canonical cases illustrated in panels a, b, c, and d of Fig. A2 where we vary the order and amount of two aerosol types over clouds in the vertical). No matter which type and which vertical distribution of aerosol above cloud is considered, DARE_theo values are lower when aerosols are present above a cloud of COT equal to 1 (cases (e-b) and (e-d)), compared to a COT equal to 10 (cases (e-a) and (e-c) in Fig. A2). This is illustrated by changes of approximatively -7 to -1 W⋅m-2 for (e-b) and (e-d) vs. approximatively 9 to 24 W⋅m-2 for (e-a) and (e-c) of Fig. A2. We also record lower DARE_theo values when adding more scattering aerosols (i.e., “continental” aerosol type) to already absorbing aerosols (i.e., “urban” aerosol type). In effect, DARE_theo values drop from approximatively 24 to 14 W⋅m-2 when aerosols are more scattering above a cloud of COT equal 10 (see C1-C4 in (e-a) vs. C5-C8 in (e-a) of Fig. A2). And DARE_theo values drop from approximatively -1 to -5 W⋅m-2 when aerosols are more scattering above a cloud of COT equal 1 (see C1-C4 in (e-b) vs. C5-C8 in (e-b) of Fig. A2). In conclusion, the variability of these DARE_theo calculations confirm, as expected, that our semi-observational DARE_obs calculations need to account for the vertical order and location of aerosol types and aerosol amount.”
- Figure 5: The legend currently needs improvement to enhance readability. The overlay of data points on the legend makes it difficult to discern the labels. Additionally, the initial word of each legend (e.g. Cases, CALIOP, MODIS, etc.) are distracting and could be revised for improved clarity.
We modified figure 5, 7, A6 and A7 accordingly. Legends are now clarified and visible.
- Figure 6: The near 1:1 agreement in DARE calculations observed on 8/13/2017, suggests atmospheric conditions that are notably different from the two preceding cases. This is an interesting finding. Could you elaborate on the specific atmospheric conditions that might explain this unique 8/13/2017 scenario within the figure's discussion?
The paper now reads:
“When evaluating our semi-observational DARE_obs with coincident parametrized DARE_param over all types of clouds (i.e., S1, S2 and S3 in Table 3) and for our three case studies, we find a generally satisfying agreement (R2=0.87 to 0.99, slope=0.80 to 0.99, offset =0.37 to 8.30, N=619 to 1067 in (1) Table 6). We posit that the slight differences between DARE_obs and DARE_param (see, for example, the mean cloudy DARE_param and DARE_obs values in panel (1) of Table 6) pertain to how they are computed. On the one hand, we assume MERRA-2's vertical distribution of SSA for the DARE_obs calculations, even though the SSA magnitude lies outside the observed SSA variability during ORACLES (i.e., as seen in Fig. 4b in Cochrane et al. (2021), the peak of the in-situ SSA values measured at 532 nm is between 0.85 and 0.86). By invoking this assumption, we can either overestimate DARE_obs if the MERRA-2 SSA value is too low or underestimate DARE_obs if the MERRA-2 SSA value is too high. For example, when computing DARE_theo (see Fig. A2), we record lower DARE_theo values (by ~10 W m-2) when adding more scattering aerosols (i.e., “continental”) to already absorbing aerosols (i.e., “urban”) over a thick cloud (COT=10). A second example is seen on 09/20/2016, where the two data points showing high AOD values above clouds (in yellow) and causing an offset in the DARE_param vs. DARE_obs regression line (~8 in Table 6) are likely due to an underestimation of MERRA-2 SSA, which in turn causes an overestimation of DARE_obs compared to DARE_param. On the other hand, while DARE_param is computed using the same AOD and cloud microphysical properties as DARE_obs, the DARE_param framework was developed specifically for aerosols above homogeneous cloud conditions (i.e., S1) and thus might not apply as well to broken and/ or thin clouds (i.e., S2 and S3). The various amounts of S1, S2 and S3 cases during our three case studies (illustrated in Fig. 3) likely influence the DARE_param accuracy. We also note a distinctive feature in Fig. 6 on 09/18/2016 away from the 1:1 line for low AOD and CALIOP cloud fractions below 1 (black crosses). This feature is very likely due to cloud inhomogeneities paired with low AOD values”
- Figure 7: The bottom subplot displays flux difference values, but some data points appear to extend beyond the figure's axes. Are these outlying data points of lesser significance to the overall analysis?
We’ve added this to the text:
“For increased visibility and because the spatial satellite-aircraft colocation is deteriorated from ~9.2ºS to 7.9ºS in latitude (and hence the data is of lesser significance to the overall analysis), we allow a few data points in panel (e) to extend beyond the figure’s axes”
Referee #2
Questions and Comments
- Clarity of CALIOP AOD Product Usage in DARES: Table 2 mentions CALIOP AOD at 532nm is a combination of CALIOP ACAOD_standard, CALIOP ACAOD_DR, CALIOP AOD_standard, and CALIOP ODAOD. The paper details these products, but the specific logic or conditions under which each is chosen or how they are "combined" for a single AOD input to DARES could be more explicit in this section.
We’ve added:
(1) In section 2.1.1:
“Table A3 describes how CALIOP AOD is chosen to be equal to CALIOPACAOD_standard, CALIOPACAOD_DR, CALIOPAOD_standard and/ orCALIOPODAOD (see Table 1) in different atmospheric scenarios (i.e., clear skies, or among thick and/or thin clouds present).”
(2) In Table 2:
“Table A3 describes how CALIOP AOD is chosen to be CALIOPACAOD_standard, CALIOPACAOD_DR, CALIOPAOD_standard and/ orCALIOPODAOD in different atmospheric scenarios.”
- DAREP Methodology Details: While DARES is detailed, DAREP (used for evaluation) is only cited. A brief summary of how it contrasts with DARES in handling key parameters (especially clouds and aerosol vertical distribution) might be beneficial for context within this paper.
We’ve added:
(1) In the legend of Table 2:
Two different DARE calculations (i.e., semi-observational DARE_obs described in section 2.1, and parametrized DARE_param described in section 2.2)
(2) Under Table 2:
“The parametrization that allows us to compute DARE_param is described in section 2.2. It builds on a method that systematically links aircraft observations of SSFR-linked spectral fluxes to aerosol optical thickness and other parameters using nine cases from the 2016 and 2017 ORACLES campaigns. This observationally driven link is expressed by a parametrization of the shortwave broadband DARE in terms of the mid-visible AOD and scene albedo.”
- MERRA-2 SSA Bias: The paper acknowledges that MERRA-2/GEOS SSA tends to be higher than in-situ measurements (underestimating absorption), particularly for biomass burning aerosols prevalent in the ORACLES study region. The impact of this known bias on the DARES results, especially for positive DARE over clouds, might be important to discuss.
We’ve added in section 2.1.1:
“We expect, according to the DARE_theo calculations illustrated in Fig. A2, that a high bias in the MERRA-2 estimated SSA, if not compensated by other factors, would cause a low bias in DARE_obs calculations (see, for example, lower DARE_theo values in (e-a) for C5-C8 where SSA is higher compared to higher DARE_theo values in (e-a) for C1-C4 where SSA is lower).”
- Extinction Coefficient Thresholds: The paper sets extinction coefficient thresholds for CALIOP (0.07 km⁻¹) and MERRA-2 (0.014 km⁻¹) to define "aerosol-free" conditions. The MERRA-2 threshold is scaled from the CALIOP one based on average layer thickness. The sensitivity of DARE results to these threshold choices could be relevant.
We’ve clarified Table A3 – It now reads “we have assessed the effects of these factors in the calculation of DARE_obs” and one of them is (E-1) i.e., “Apply threshold on extinction”
We’ve added in section 2.1.1:
“In section 2.1.4, we demonstrate that adding or removing such a threshold on the aerosol extinction coefficient leads to insignificant differences in mean instant DARE_obs values (up to 0.8 W.m-2) for all three case studies.”
We’ve added in section 2.1.4:
“Regarding categories (E-1), (E-2) and (E-3), the effects add up to a small N=10 1km-data points in Table A4 and lead to a small difference in mean instant all-sky (S1-S4) DARE_obs of maximum ~1.6 W⋅m-2.”
- Regarding Table 2, Footnote (1): Could you clarify the entry "O2 mass density = 0.0 kg m3"? Does this imply that O2 is not a variable input in the radiative transfer model, being part of the standard atmospheric profile, rather than having zero density?
We have added in the legend of Table 2:
“O2 mass density, which is also a required input to RRTMG, is assumed to be 0.0 kg m3.”
- Corrected vs. Uncorrected MODIS Cloud Properties: Section 2.1.1 mentions that using corrected MODIS cloud properties (accounting for above-cloud aerosols) can sometimes worsen agreement with other CER measurements depending on the spectral channel. Which version (corrected or uncorrected) is predominantly used for the DARES results presented for the ORACLES case studies, and what was the rationale for this choice?
We’ve added in section 2.1.1:
“In section 2.1.4, we demonstrate that correcting cloud properties for aerosol above them leads to insignificant differences in mean instant DARE_obs values (up to 4 W.m-2) for all three case studies.”
We’ve also added in section 2.1.1:
“In the end, the effects of (E-1) through (E-6) all lead to small differences in DARE_obs below a threshold of 6 W.m-2, which represents the accuracy of total fluxes in overcast conditions when comparing RRTMG-SW with other radiative transfer schemes (such as RRTM-SW).”
- Aerosol Information Below Clouds: For aerosols below clouds, MERRA-2 is used for extensive and intensive properties. How are situations handled where CALIOP detects clouds, but MERRA-2 shows no significant aerosol below them, or vice-versa? Is there a priority system or a check for consistency?
We’ve added in section 2.1.2:
“As described in Table 2, on the one hand, MODIS and CALIOP satellites are used to detect and characterize clouds, define aerosol height, and provide aerosol extinction coefficients above clouds and in non-cloudy skies. MERRA-2, on the other hand, is used to define aerosol top and base heights below clouds and provide the vertical distribution of spectral ASY, SSA, and extinction coefficient above, below clouds and in non-cloudy skies, along with information about atmospheric composition, weather, and ocean surface winds. We emphasize that we use MERRA-2 aerosol and atmospheric data regardless of any MERRA-2 simulated clouds (i.e., we do not use MERRA-2 cloud simulations in any way), nor do we assess cloud agreement between MERRA-2 and satellite observations in this paper.”
- Definition of "Thick, Thin, and/or Broken" Clouds: Section 2.1.3 (referenced in Table 2) will describe atmospheric scenarios including "thick, thin and/ or broken liquid cloud." What specific CALIOP vfm and MODIS Cloud criteria are used to classify clouds into these categories for the DARES calculations?
In Table 2, we’ve added:
“Table 3 lists which satellite-derived criteria are used to define four atmospheric scenarios”
In Table 3, CALIOP and MODIS are now replaced by CALIOPVFM and MODISCloud
- DAREP Applicability: Section 2.2 emphasizes DAREP is specific to ORACLES conditions. For the broader goal of assessing DARE globally, are there plans to develop similar parameterizations for other regions/aerosol types, or will the full DARES framework always be the primary tool?
We’ve added in section 2.2:
“We emphasize that this parametrization only represents the relationship between DARE and aerosol and cloud properties as sampled over the ORACLES study region and during the ORACLES timeframe. Outside of this framework (i.e., other regions of the globe and other seasons), different aerosol and cloud types can alter the DARE to cloud and aerosol relationship. To our knowledge, there are no current plans to extend the parameterization behind DARE_param to other times and regions of the globe. Consequently, we will not be able to assess global DARE_obs results in future studies using DARE_param.”
- Future Work - C3M Data (Section 4): The mention of C3M data (which relies on CloudSat) for future multi-cloud scenarios is relevant. While CloudSat's operational mode has changed and it's no longer in the A-Train, historical C3M data is extensive. For ongoing and future analyses, alternatives or updated multi-sensor products might be needed if relying on contemporaneous data with new missions like EarthCARE. This is more of a consideration than an error.
And
- Regarding the use of C3M data for multi-cloud scenarios: Given the changes in CloudSat's operational status, how does this impact the strategy for incorporating multi-layer cloud properties, especially for DARE calculations intended to span "multiple years" beyond the prime A-Train era? Will this rely more on the historical C3M dataset, or are there alternative/future multi-sensor cloud products (perhaps involving EarthCARE itself) that are being considered?
We’ve modified this sentence in the discussion:
“We envision this additional scenario to use (i) the CALIPSO-CloudSat-CERES-MODIS (CCCM or C3M) (Kato et al., 2010, 2011) derived cloud heights and cloud microphysical properties or equivalent EarthCARE-derived product (e.g., as in Table 1 of Mason et al., (2024)) and (ii) MERRA-2 simulated aerosol extensive and intensive properties.”
- The offset in DAREP vs. DARES is notably higher for 09/20/2016 (8.3 W⋅m⁻²) compared to other days. The paper suggests DAREP overestimation for high AOD on 09/18/2016. Does the larger offset on 09/20/2016 (which had the highest AODs) also primarily point to DAREP's single aerosol layer assumption or MERRA-2 SSA/ASY issues, or are there other potential contributors to this larger systematic difference on that specific day?
We’ve added in section 3.3.1:
“When evaluating our semi-observational DARE_obs with coincident parametrized DARE_param over all types of clouds (i.e., S1, S2 and S3 in Table 3) and for our three case studies, we find a generally satisfying agreement (R2=0.87 to 0.99, slope=0.80 to 0.99, offset =0.37 to 8.30, N=619 to 1067 in (1) Table 6). We posit that the slight differences between DARE_obs and DARE_param (see, for example, the mean cloudy DARE_param and DARE_obs values in panel (1) of Table 6) pertain to how they are computed. On the one hand, we assume MERRA-2's vertical distribution of SSA for the DARE_obs calculations, even though the SSA magnitude lies outside the observed SSA variability during ORACLES (i.e., as seen in Fig. 4b in Cochrane et al. (2021), the peak of the in-situ SSA values measured at 532 nm is between 0.85 and 0.86). By invoking this assumption, we can either overestimate DARE_obs if the MERRA-2 SSA value is too low or underestimate DARE_obs if the MERRA-2 SSA value is too high. For example, when computing DARE_theo (see Fig. A2), we record lower DARE_theo values (by ~10 W m-2) when adding more scattering aerosols (i.e., “continental”) to already absorbing aerosols (i.e., “urban”) over a thick cloud (COT=10). A second example is seen on 09/20/2016, where the two data points showing high AOD values above clouds (in yellow) and causing an offset in the DARE_param vs. DARE_obs regression line (~8 in Table 6) are likely due to an underestimation of MERRA-2 SSA, which in turn causes an overestimation of DARE_obs compared to DARE_param. On the other hand, while DARE_param is computed using the same AOD and cloud microphysical properties as DARE_obs, the DARE_param framework was developed specifically for aerosols above homogeneous cloud conditions (i.e., S1) and thus might not apply as well to broken and/ or thin clouds (i.e., S2 and S3). The various amounts of S1, S2 and S3 cases during our three case studies (illustrated in Fig. 3) likely influence the DARE_param accuracy. We also note a distinctive feature in Fig. 6 on 09/18/2016 away from the 1:1 line for low AOD and CALIOP cloud fractions below 1 (black crosses). This feature is very likely due to cloud inhomogeneities paired with low AOD values.”
- What are the anticipated major challenges in merging geostationary satellite data (which typically has coarser spatial resolution, different viewing geometries, and potentially different retrieval algorithms/sensitivities for aerosol and cloud properties) with the nadir-viewing, high-resolution Lidar/Imager data from A-Train/EarthCARE for consistent diurnal DARES calculations?
We’ve added in the discussion:
“We note that aerosol and cloud retrievals from GEO satellites are in an earlier stage of development and less well-validated compared to their Low Earth Orbit (LEO) satellite counterparts. GEO aerosol and cloud retrievals are also currently often tied to specific GEO imagers and thus less global than their LEO counterparts. GEO AOD generally shows good agreement with ground-based AERONET AOD (e.g., low RMSE (0.12–0.17) in the case of the GEO Ocean Color Imager (GOCI) AOD over East Asia in Choi et al. (2019)) but have unique bias patterns related to the surface-reflectance assumptions in their retrieval algorithms (e.g., negative bias of 0.04 in GOCI AOD in Choi et al. (2019)). Recent improvements in algorithms consist in correcting surface reflectance, cloud masking and/ or fusing data from LEO and GEO imagers (e.g., Su et al. (2020), Zhang et al. (2020), Kim et al. (2020), and Choi et al. (2019)). In some cases, GEO AOD, although often biased, was shown to reproduce the AERONET AOD diurnal cycle (e.g., over Asia, on a daily average, GOCI AOD shows a diurnal variation of +20% to −30 % in inland sites according to Lennartson et al. (2018)).”
Choi, M., Lim, H., Kim, J., Lee, S., Eck, T. F., Holben, B. N., Garay, M. J., Hyer, E. J., Saide, P. E., and Liu, H.: Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign, Atmos. Meas. Tech., 12, 4619–4641, https://doi.org/10.5194/amt-12-4619-2019, 2019.
Kim, Jhoon, et al. "New era of air quality monitoring from space: Geostationary Environment Monitoring Spectrometer (GEMS)." Bulletin of the American Meteorological Society 101.1 (2020): E1-E22.
Lennartson, E. M., Wang, J., Gu, J., Castro Garcia, L., Ge, C., Gao, M., Choi, M., Saide, P. E., Carmichael, G. R., Kim, J., and Janz, S. J.: Diurnal variation of aerosol optical depth and PM2.5 in South Korea: a synthesis from AERONET, satellite (GOCI), KORUS-AQ observation, and the WRF-Chem model, Atmos. Chem. Phys., 18, 15125–15144, https://doi.org/10.5194/acp-18-15125-2018, 2018.
Su, Tianning, et al. "Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8." Remote Sensing of Environment 251 (2020): 112093.
Zhang, H., Kondragunta, S., Laszlo, I., and Zhou, M.: Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm, Atmos. Meas. Tech., 13, 5955–5975, https://doi.org/10.5194/amt-13-5955-2020, 2020.
Citation: https://doi.org/10.5194/egusphere-2025-1403-AC1
Status: closed
-
RC1: 'Comment on egusphere-2025-1403', Anonymous Referee #1, 12 May 2025
This manuscript presents a novel study aimed at developing improved satellite-derived estimates of Direct Aerosol Radiative Effects (DARE). This work introduces a new method for computing DARE by leveraging satellite sensors CALIOP and MODIS combined with MERRA-2 vertical distributions of aerosol properties and atmospheric conditions. With the advantage of active remote sensing, this approach is capable of performing all-sky retrievals of SW aerosol radiative effects useful for comparisons with dedicated field studies such as the ORACLES aircraft campaign. Overall, this manuscript is well-written, and the figures are clearly presented. Below are a few suggestions that may help further improve the manuscript:
Major Comments:
- The current notation for the DARE calculations (i.e., DAREs and DAREp, as defined) are challenging to read. These subscripts are fairly difficult to distinguish within the text. To enhance clarity and reader comprehension, it could be helpful to explore alternative naming conventions. For example, labeling them DARE_obs (for the method utilizing observations) and DARE_param (for the parameterized method) might be more intuitive.
- As the paper describes very well, the Southeast Atlantic presents an excellent natural laboratory for studying DARE and validating against ORACLES data, particularly given the variable atmospheric conditions. To strengthen the broader significance of this work, it would be helpful to elaborate on the potential for extending the findings beyond this region. Specifically, a discussion of the factors that might influence the transferability of these results to other parts of the globe would be particularly insightful.
Minor Comments:
- Lines 178-195: While well-structured and informative, this paragraph is difficult to read. I would recommend shorter sentences with fewer em dashes to improve clarity.
- Line 188: It can be quite challenging to distinguish a “~’ and a “-” in the text. As an alternative, “ ~-7 to ~-1” can be rewritten as “approximately -7 to -1”.
- Figure 5: The legend currently needs improvement to enhance readability. The overlay of data points on the legend makes it difficult to discern the labels. Additionally, the initial word of each legend (e.g. Cases, CALIOP, MODIS, etc.) are distracting and could be revised for improved clarity.
- Figure 6: The near 1:1 agreement in DARE calculations observed on 8/13/2017, suggests atmospheric conditions that are notably different from the two preceding cases. This is an interesting finding. Could you elaborate on the specific atmospheric conditions that might explain this unique 8/13/2017 scenario within the figure's discussion?
- Figure 7: The bottom subplot displays flux difference values, but some data points appear to extend beyond the figure's axes. Are these outlying data points of lesser significance to the overall analysis?
Citation: https://doi.org/10.5194/egusphere-2025-1403-RC1 -
RC2: 'Comment on egusphere-2025-1403', Anonymous Referee #2, 23 May 2025
Review Summary
This paper by Kacenelenbogen et al. introduces a novel framework (DARES - Direct Aerosol Radiative Effects Semi observational) to estimate all-sky Direct Aerosol Radiative Effects at the Top-Of-Atmosphere (TOA) in the shortwave spectrum. The primary goal is to reduce uncertainties in aerosol radiative effects by integrating state-of-the-art satellite observations from the A-Train constellation (primarily CALIOP for aerosol profiles/AOD and MODIS for cloud properties) with MERRA-2 reanalysis data (for aerosol intensive properties, vertical distribution, below-cloud aerosols, and atmospheric conditions).
The DARES methodology involves categorizing atmospheric conditions along the CALIOP track into four scenarios based on the presence and characteristics (thick, thin, broken, uniform) of single-layer, low-level warm liquid clouds. Radiative transfer calculations (using RRTMG-SW) are performed for each 1km segment to determine DARE. The paper details the input parameters, including various CALIOP AOD products, MODIS cloud properties (uncorrected for overlying aerosol in the presented results), and MERRA-2 derived aerosol optical properties and atmospheric profiles. Uncertainties in DARES are also estimated.
The framework is initially applied and validated over the Southeast Atlantic for three specific days during the NASA ORACLES aircraft campaign. The results show:
- Diurnal mean all-sky DARES values ranging from -25 W⋅m⁻² (cooling) to +40 W⋅m⁻² (warming).
- Highly positive DARE is associated with high Aerosol Optical Depth (AOD) above clouds with high Cloud Optical Thickness (COT), while negative DARE is linked to high AOD in clear skies.
- A significant portion of the observed scenes were classified as aerosols above thick, homogeneous clouds, though a substantial number of profiles remained unassigned due to complex cloud conditions or data inconsistencies.
The DARE estimates are assessed through two methods:
- Comparison with DAREP, a parametrization derived from ORACLES airborne data for cloudy conditions, showing good agreement (R² = 0.87-0.99).
- Comparison of DARES related upward fluxes with collocated airborne Solar Spectral Flux Radiometer (SSFR) measurements, also demonstrating good agreement (R² = 0.94-0.95 for broadband fluxes).
The authors discuss limitations, such as the reliance on MERRA-2 for certain aerosol properties and the simplified diurnal cycle. Future work aims to:
- Improve MERRA-2 integration and leverage new satellite missions (e.g., EarthCARE, PACE, AOS).
- Incorporate observed diurnal aerosol and cloud cycles using geostationary satellite data.
- Expand the framework to include multi-layer cloud scenarios.
- Apply the DARES algorithm globally over multiple years to assess the regional importance of various atmospheric parameters in DARE calculations, ultimately informing future satellite missions and improving climate models.
Two main DARE calculation methods are mentioned:
- DARES (Semi-Observational): The primary focus of the study. It uses the RRTMG-SW radiative transfer model with inputs derived from a combination of CALIOP, MODIS, and MERRA-2. The methodology includes:
- Utilizing various CALIOP AOD products, including standard, depolarization ratio (DR), and Ocean Derived Column Optical Depths (ODCOD) methods, corrected for stratospheric aerosols.
- Employing MODIS cloud products (both uncorrected and corrected for overlying aerosols) for properties like Cloud Optical Thickness (COT) and Cloud Effective Radius (CER).
- Using MERRA-2 for aerosol intensive properties (Single Scattering Albedo - SSA, Asymmetry Parameter -ASY), spectral extinction shapes, and aerosol characteristics below clouds. CALIOP AOD at 532nm is used to scale MERRA-2 aerosol extinction profiles.
- Initial theoretical calculations (DARET) were performed to understand DARE sensitivities to aerosol types and vertical distribution over clouds.
- DAREP (Parametrized): Based on Cochrane et al. (2021), used for evaluation purposes.
The paper presents a comprehensive framework for deriving semi-observational all-sky DARE. It is initially applied and validated over the Southeast Atlantic for three days during the NASA ORACLES aircraft campaign, with results showing agreement with airborne measurements. The authors state this work is a foundational step towards applying the algorithm globally over many years to assess the importance of various atmospheric parameters in DARE calculations, ultimately aiming to inform future satellite missions.
Notes
- Section 2.1.3 (DARES for Four Atmospheric Scenarios):
- S1: Aerosol above/below a single LWLC that is thick and uniform.
- S2: Aerosol above/below a single LWLC that is thick and broken.
- S3: Aerosol above/below a single LWLC that is thin and possibly broken.
- S4: Aerosol in (possibly cloud-contaminated) clear skies (LWLC small or not present).
- DARE cloudy (S1, S2, S3 combined - Eq. 3)
- DARE non-cloudy (S4 - Eq. 4)
- DARE all-sky (S1-S4 combined - Eq. 5)
- The atmosphere along the satellite track is categorized into four scenarios (S1-S4) to evaluate DARE, after filtering out profiles with clouds above 3 km (except for a single low warm liquid cloud - LWLC).
- Table 3 details the specific CALIOP and MODIS criteria (e.g., cloud layer counts, optical thickness, cloud mask confidence) used to define these scenarios and the cloud characteristics (thick, thin, uniform, broken).
- MODIS Cloud Fraction (CF) is assumed to be 1 for S1-S3 and 0 for S4 to be consistent with MODIS COT retrieval assumptions.
- Section 2.1.4 (DARES Uncertainties):
- DARES uncertainty is estimated by varying key input parameters (AOD, CWP, SSA, ASY, surface albedo) according to their respective uncertainties (detailed in Table 4).
- Specific uncertainty values are cited: 0.11 for CALIOP ODAOD, pixel-level for MODIS Cloud CWP, 0.05 for MERRA-2 SSA, 0.02 for MERRA-2 ASY, and 0.01 for surface albedo.
- Uncertainties for other CALIOP AOD products (CALIOP ACAOD_standard, CALIOP AOD_standard, CALIOP ACAOD_DR) are computed over a 20km stretch using a weighted mean approach (Eq. 6-7).
- The paper discusses sensitivity analyses (Table A3, A4, A5, Fig. A4, A5) regarding: extinction coefficient thresholds, including below-cloud aerosols, extending aerosol top height, using different AOD "versions" (V1, V2, V3), and using corrected vs. uncorrected cloud properties.
- The main results presented in Section 3 use AOD version 2 and uncorrected cloud properties, as the impact of cloud correction was found to be minimal for the low AODs in the case studies.
- Section 2.2 (Parametrized DAREP Calculations):
- DAREP, a parametrized DARE calculation from Cochrane et al. (2021), is used for evaluation.
- This parametrization links instantaneous DARE to AOD and underlying surface albedo for typical biomass burning aerosols above stratocumulus (or clear sky) in the Southeast Atlantic ORACLES region.
- It has a reported uncertainty of 20% and circumvents the need for full radiative transfer calculations but is specific to the ORACLES study conditions.
- Section 3 (Results - Initial):
- Three ORACLES case studies (Sept 18 & 20, 2016; Aug 13, 2017) offshore from Namibia are described (Fig. 2). These days featured an omnipresent stratocumulus deck with varying aerosol plumes.
- CALIOP tracks were collocated with ER-2 or P3 aircraft flights. SSFR data from the P3 aircraft will be used for DARES evaluation.
- Figure 3 shows the distribution of S1-S4 scenarios for these days, with S1 (aerosol above thick, uniform cloud) being dominant among assigned scenarios. A significant portion of profiles (26-48%) are unassigned ("N/A") due to complex cloud conditions, data inconsistencies, or missing retrievals.
- Figure 4 presents Probability Distribution Functions (PDFs), and Table 5 shows mean values for aerosol properties (AOD, SSA, ASY, EAE), cloud properties (COT, CWP, CER), and diurnal mean DARES for the three case days.
- All-sky 24h DARES ranges from -25 to 40 W⋅m⁻². Aug 13, 2017, shows the lowest mean all-sky DARE (4.0 W⋅m⁻²), attributed to more clear-sky cases and higher clear-sky AOD.
- Mean 24h cloudy (S1-S3) DARES values are ~8-15 W⋅m⁻², higher than some previous studies, attributed to differences in period, domain, and methodology. The highest cloudy DARE (Sept 20, 2016) corresponds to the highest AOD above clouds (0.6).
- Figure 5 illustrates the spatial evolution of input parameters and DARES along the CALIOP track for Aug 13, 2017, showing strong DARE variability linked to changes in cloud thickness (COT, CWP) under relatively constant AOD and SSA.
- Figure A8 (appendix, described in text) shows DARES negativity increasing with AOD in clear skies, and DARES positivity increasing with AOD above clouds and with COT for similar AODs.
- Section 3.3 (Assessment of DARES):
- 3.3.1 Comparison with DAREP: The semi-observational DARES (instantaneous, for cloudy scenarios S1-S3) is compared against DAREP, a parametrization developed from ORACLES data. Results (Figure 6, Table 6 Part 1) show good agreement (R² = 0.87-0.99, slope = 0.80-0.99, RMSE = 19-31% of mean DARES). DAREP sometimes overestimates DARES at high AOD, potentially due to DAREP's single aerosol layer assumption or MERRA-2 SSA/ASY biases in DARES. Deviations are also noted for inhomogeneous clouds with low AOD.
- 3.3.2 Comparison with Airborne SSFR Measurements: DARES-related upward fluxes (the component calculated by RRTMG with aerosols present) are compared with collocated airborne SSFR spectral irradiance measurements for the 08/13/2017 case (N=51 points after filtering). SSFR data is integrated into RRTMG broadband channels. Table 6 Part 2 shows good agreement (R² = 0.94-0.95, RMSE = 9-17% of mean DARES-related flux). Figure 7 illustrates that larger discrepancies occur with increased satellite-aircraft distance and in thinner/broken cloud conditions, while closer collocations in thicker cloud/higher AOD scenes show better agreement.
- Section 4 (Discussions and Future Work):
- MERRA-2 Improvements: The paper discusses current limitations in using MERRA-2 aerosol profiles at face value. Future improvements include selecting MERRA-2 levels with the strongest aerosol signal or inferring below-cloud properties from nearby clear-sky observations. The potential of new satellite missions (EarthCARE, PACE, AOS) to provide better observational constraints on aerosol vertical/spectral properties and reduce unassigned scenarios is highlighted.
- Diurnal Cycle: The current 24h DARES only varies Solar Zenith Angles (SZA), assuming constant aerosol/cloud properties. A planned enhancement is to incorporate observed diurnal aerosol and cloud variations using geostationary satellite data.
- Multi-Cloud Scenarios: To achieve a more comprehensive all-sky DARE, the authors plan to add scenarios with clouds overlying the primary Low Warm Liquid Clouds (LWLCs), potentially using C3M (CALIPSO-CloudSat-CERES-MODIS) derived cloud properties and MERRA-2 aerosols. This is expected to reduce the number of unassigned cases and will be evaluated with data like CAMP2Ex.
- Parameter Importance: A key future goal is to apply the DARES framework to multiple years of data over various regions to assess the relative importance of key aerosol, cloud, and surface parameters in DARE calculations for different atmospheric regimes.
- Section 5 (Conclusion):
- The paper successfully developed a framework to compute Top-Of-Atmosphere (TOA) Shortwave (SW) all-sky DARE by combining CALIOP, MODIS, and MERRA-2 data along the CALIOP track for four atmospheric scenarios involving single-layer, low-level liquid clouds.
- Analysis of three ORACLES case studies shows diurnal DARE values from -25 to 40 W⋅m⁻², with positive DARE linked to high AOD above thick clouds and negative DARE to high AOD in clear skies. A significant portion of profiles were classified as aerosols above thick, homogeneous clouds, but many also remained unassigned.
- The semi-observational DARES showed good agreement with both the DAREP parametrization (for cloudy conditions) and direct airborne SSFR flux measurements.
Questions and Comments
- Clarity of CALIOP AOD Product Usage in DARES: Table 2 mentions CALIOP AOD at 532nm is a combination of CALIOP ACAOD_standard, CALIOP ACAOD_DR, CALIOP AOD_standard, and CALIOP ODAOD. The paper details these products, but the specific logic or conditions under which each is chosen or how they are "combined" for a single AOD input to DARES could be more explicit in this section.
- DAREP Methodology Details: While DARES is detailed, DAREP (used for evaluation) is only cited. A brief summary of how it contrasts with DARES in handling key parameters (especially clouds and aerosol vertical distribution) might be beneficial for context within this paper.
- MERRA-2 SSA Bias: The paper acknowledges that MERRA-2/GEOS SSA tends to be higher than in-situ measurements (underestimating absorption), particularly for biomass burning aerosols prevalent in the ORACLES study region. The impact of this known bias on the DARES results, especially for positive DARE over clouds, might be important to discuss.
- Extinction Coefficient Thresholds: The paper sets extinction coefficient thresholds for CALIOP (0.07 km⁻¹) and MERRA-2 (0.014 km⁻¹) to define "aerosol-free" conditions. The MERRA-2 threshold is scaled from the CALIOP one based on average layer thickness. The sensitivity of DARE results to these threshold choices could be relevant.
- Regarding Table 2, Footnote (1): Could you clarify the entry "O2 mass density = 0.0 kg m3"? Does this imply that O2 is not a variable input in the radiative transfer model, being part of the standard atmospheric profile, rather than having zero density?
- Corrected vs. Uncorrected MODIS Cloud Properties: Section 2.1.1 mentions that using corrected MODIS cloud properties (accounting for above-cloud aerosols) can sometimes worsen agreement with other CER measurements depending on the spectral channel. Which version (corrected or uncorrected) is predominantly used for the DARES results presented for the ORACLES case studies, and what was the rationale for this choice?
- Aerosol Information Below Clouds: For aerosols below clouds, MERRA-2 is used for extensive and intensive properties. How are situations handled where CALIOP detects clouds, but MERRA-2 shows no significant aerosol below them, or vice-versa? Is there a priority system or a check for consistency?
- Definition of "Thick, Thin, and/or Broken" Clouds: Section 2.1.3 (referenced in Table 2) will describe atmospheric scenarios including "thick, thin and/ or broken liquid cloud." What specific CALIOP vfm and MODIS Cloud criteria are used to classify clouds into these categories for the DARES calculations?
- DAREP Applicability: Section 2.2 emphasizes DAREP is specific to ORACLES conditions. For the broader goal of assessing DARE globally, are there plans to develop similar parameterizations for other regions/aerosol types, or will the full DARES framework always be the primary tool?
- Future Work - C3M Data (Section 4): The mention of C3M data (which relies on CloudSat) for future multi-cloud scenarios is relevant. While CloudSat's operational mode has changed and it's no longer in the A-Train, historical C3M data is extensive. For ongoing and future analyses, alternatives or updated multi-sensor products might be needed if relying on contemporaneous data with new missions like EarthCARE. This is more of a consideration than an error.
- The offset in DAREP vs. DARES is notably higher for 09/20/2016 (8.3 W⋅m⁻²) compared to other days. The paper suggests DAREP overestimation for high AOD on 09/18/2016. Does the larger offset on 09/20/2016 (which had the highest AODs) also primarily point to DAREP's single aerosol layer assumption or MERRA-2 SSA/ASY issues, or are there other potential contributors to this larger systematic difference on that specific day?
- What are the anticipated major challenges in merging geostationary satellite data (which typically has coarser spatial resolution, different viewing geometries, and potentially different retrieval algorithms/sensitivities for aerosol and cloud properties) with the nadir-viewing, high-resolution Lidar/Imager data from A-Train/EarthCARE for consistent diurnal DARES calculations?
- Regarding the use of C3M data for multi-cloud scenarios: Given the changes in CloudSat's operational status, how does this impact the strategy for incorporating multi-layer cloud properties, especially for DARE calculations intended to span "multiple years" beyond the prime A-Train era? Will this rely more on the historical C3M dataset, or are there alternative/future multi-sensor cloud products (perhaps involving EarthCARE itself) that are being considered?
Citation: https://doi.org/10.5194/egusphere-2025-1403-RC2 -
AC1: 'Comment on egusphere-2025-1403', Meloe Kacenelenbogen, 01 Jul 2025
We are grateful for both referees’ thoughtful reviews as addressing them has strengthened our paper. We have addressed all of them carefully as described below.
Kindest Regards,
Meloë Kacenelenbogen on behalf of all co-authors.
Referee #1
This manuscript presents a novel study aimed at developing improved satellite-derived estimates of Direct Aerosol Radiative Effects (DARE). This work introduces a new method for computing DARE by leveraging satellite sensors CALIOP and MODIS combined with MERRA-2 vertical distributions of aerosol properties and atmospheric conditions. With the advantage of active remote sensing, this approach is capable of performing all-sky retrievals of SW aerosol radiative effects useful for comparisons with dedicated field studies such as the ORACLES aircraft campaign. Overall, this manuscript is well-written, and the figures are clearly presented. Below are a few suggestions that may help further improve the manuscript:
Major Comments:
- The current notation for the DARE calculations (i.e., DAREs and DAREp, as defined) are challenging to read. These subscripts are fairly difficult to distinguish within the text. To enhance clarity and reader comprehension, it could be helpful to explore alternative naming conventions. For example, labeling them DARE_obs (for the method utilizing observations) and DARE_param (for the parameterized method) might be more intuitive.
We have changed DARES into DARE_obs, DAREP into DARE_param and DARET into DARE_theo throughout the manuscript (includes text, tables and figures)
- As the paper describes very well, the Southeast Atlantic presents an excellent natural laboratory for studying DARE and validating against ORACLES data, particularly given the variable atmospheric conditions. To strengthen the broader significance of this work, it would be helpful to elaborate on the potential for extending the findings beyond this region. Specifically, a discussion of the factors that might influence the transferability of these results to other parts of the globe would be particularly insightful.
In the discussion section, it now reads:
“We plan to apply our DARE_obs calculations to multiple years of combined satellite and model data over different regions of the world. The most important factors influencing the transferability of our method to regions of the globe outside the Southeast Atlantic are (i) different Earth’s surfaces (i.e., ocean vs. different land types) and (ii) different horizontal, vertical and temporal distributions of aerosol and cloud types and amounts. Our method requires aerosols in cloud-free skies, and above and below single thick, thin and/ or broken low warm liquid clouds. Kacenelenbogen et al. (2019) define six major global aerosol “hotspots” over single thick low warm liquid clouds (i.e., different aerosol regimes above the same type of clouds) in the northeast Pacific, southeast Pacific, tropical Atlantic, southeast Atlantic, Indian ocean, offshore from western Australia and northwest Pacific (see their Fig. 6; and Table 2 for a list of studies over these regions). According to Fig. 7d of Kacenelenbogen et al. (2019), the region of Southeast Atlantic (this paper) shows the highest mean annual percentage of high AOD values above clouds compared to the five other regions. Note that we also plan to apply our DARE_obs calculations to regions that show different cloud regimes in addition to different aerosol regimes (e.g., the Southeast Atlantic, the tropical Atlantic, and a region encompassing the latter two representing the transition between these two regimes). We then plan to use this larger DARE_obs dataset for different atmospheric scenarios, over specific regions of the world and linked to key cloud, aerosol and surface input parameters to assess the order of importance of these parameters in DARE_obs calculations for specific aerosol and cloud regimes.”
Minor Comments:
- Lines 178-195: While well-structured and informative, this paragraph is difficult to read. I would recommend shorter sentences with fewer em dashes to improve clarity.
- Line 188: It can be quite challenging to distinguish a “~’ and a “-” in the text. As an alternative, “ ~-7 to ~-1” can be rewritten as “approximately -7 to -1”.
We modified the manuscript accordingly. It now reads:
“Like Table 2 for DARE_obs and DARE_param, Table A1 in the appendix lists the input parameters to our DARE_theo calculations. DARE_theo is computed for two types of single low warm liquid clouds (i.e., COT=1, CER=12 and CWP=8 vs. COT=10, CER=12 and CWP=80) and varying vertical distributions of RRTMG “build-in” aerosol types (see Fig. A1) while keeping cloud heights, AOD, ASY, atmospheric composition, weather and ocean surface BRDF constant (see thirty-two canonical cases illustrated in panels a, b, c, and d of Fig. A2 where we vary the order and amount of two aerosol types over clouds in the vertical). No matter which type and which vertical distribution of aerosol above cloud is considered, DARE_theo values are lower when aerosols are present above a cloud of COT equal to 1 (cases (e-b) and (e-d)), compared to a COT equal to 10 (cases (e-a) and (e-c) in Fig. A2). This is illustrated by changes of approximatively -7 to -1 W⋅m-2 for (e-b) and (e-d) vs. approximatively 9 to 24 W⋅m-2 for (e-a) and (e-c) of Fig. A2. We also record lower DARE_theo values when adding more scattering aerosols (i.e., “continental” aerosol type) to already absorbing aerosols (i.e., “urban” aerosol type). In effect, DARE_theo values drop from approximatively 24 to 14 W⋅m-2 when aerosols are more scattering above a cloud of COT equal 10 (see C1-C4 in (e-a) vs. C5-C8 in (e-a) of Fig. A2). And DARE_theo values drop from approximatively -1 to -5 W⋅m-2 when aerosols are more scattering above a cloud of COT equal 1 (see C1-C4 in (e-b) vs. C5-C8 in (e-b) of Fig. A2). In conclusion, the variability of these DARE_theo calculations confirm, as expected, that our semi-observational DARE_obs calculations need to account for the vertical order and location of aerosol types and aerosol amount.”
- Figure 5: The legend currently needs improvement to enhance readability. The overlay of data points on the legend makes it difficult to discern the labels. Additionally, the initial word of each legend (e.g. Cases, CALIOP, MODIS, etc.) are distracting and could be revised for improved clarity.
We modified figure 5, 7, A6 and A7 accordingly. Legends are now clarified and visible.
- Figure 6: The near 1:1 agreement in DARE calculations observed on 8/13/2017, suggests atmospheric conditions that are notably different from the two preceding cases. This is an interesting finding. Could you elaborate on the specific atmospheric conditions that might explain this unique 8/13/2017 scenario within the figure's discussion?
The paper now reads:
“When evaluating our semi-observational DARE_obs with coincident parametrized DARE_param over all types of clouds (i.e., S1, S2 and S3 in Table 3) and for our three case studies, we find a generally satisfying agreement (R2=0.87 to 0.99, slope=0.80 to 0.99, offset =0.37 to 8.30, N=619 to 1067 in (1) Table 6). We posit that the slight differences between DARE_obs and DARE_param (see, for example, the mean cloudy DARE_param and DARE_obs values in panel (1) of Table 6) pertain to how they are computed. On the one hand, we assume MERRA-2's vertical distribution of SSA for the DARE_obs calculations, even though the SSA magnitude lies outside the observed SSA variability during ORACLES (i.e., as seen in Fig. 4b in Cochrane et al. (2021), the peak of the in-situ SSA values measured at 532 nm is between 0.85 and 0.86). By invoking this assumption, we can either overestimate DARE_obs if the MERRA-2 SSA value is too low or underestimate DARE_obs if the MERRA-2 SSA value is too high. For example, when computing DARE_theo (see Fig. A2), we record lower DARE_theo values (by ~10 W m-2) when adding more scattering aerosols (i.e., “continental”) to already absorbing aerosols (i.e., “urban”) over a thick cloud (COT=10). A second example is seen on 09/20/2016, where the two data points showing high AOD values above clouds (in yellow) and causing an offset in the DARE_param vs. DARE_obs regression line (~8 in Table 6) are likely due to an underestimation of MERRA-2 SSA, which in turn causes an overestimation of DARE_obs compared to DARE_param. On the other hand, while DARE_param is computed using the same AOD and cloud microphysical properties as DARE_obs, the DARE_param framework was developed specifically for aerosols above homogeneous cloud conditions (i.e., S1) and thus might not apply as well to broken and/ or thin clouds (i.e., S2 and S3). The various amounts of S1, S2 and S3 cases during our three case studies (illustrated in Fig. 3) likely influence the DARE_param accuracy. We also note a distinctive feature in Fig. 6 on 09/18/2016 away from the 1:1 line for low AOD and CALIOP cloud fractions below 1 (black crosses). This feature is very likely due to cloud inhomogeneities paired with low AOD values”
- Figure 7: The bottom subplot displays flux difference values, but some data points appear to extend beyond the figure's axes. Are these outlying data points of lesser significance to the overall analysis?
We’ve added this to the text:
“For increased visibility and because the spatial satellite-aircraft colocation is deteriorated from ~9.2ºS to 7.9ºS in latitude (and hence the data is of lesser significance to the overall analysis), we allow a few data points in panel (e) to extend beyond the figure’s axes”
Referee #2
Questions and Comments
- Clarity of CALIOP AOD Product Usage in DARES: Table 2 mentions CALIOP AOD at 532nm is a combination of CALIOP ACAOD_standard, CALIOP ACAOD_DR, CALIOP AOD_standard, and CALIOP ODAOD. The paper details these products, but the specific logic or conditions under which each is chosen or how they are "combined" for a single AOD input to DARES could be more explicit in this section.
We’ve added:
(1) In section 2.1.1:
“Table A3 describes how CALIOP AOD is chosen to be equal to CALIOPACAOD_standard, CALIOPACAOD_DR, CALIOPAOD_standard and/ orCALIOPODAOD (see Table 1) in different atmospheric scenarios (i.e., clear skies, or among thick and/or thin clouds present).”
(2) In Table 2:
“Table A3 describes how CALIOP AOD is chosen to be CALIOPACAOD_standard, CALIOPACAOD_DR, CALIOPAOD_standard and/ orCALIOPODAOD in different atmospheric scenarios.”
- DAREP Methodology Details: While DARES is detailed, DAREP (used for evaluation) is only cited. A brief summary of how it contrasts with DARES in handling key parameters (especially clouds and aerosol vertical distribution) might be beneficial for context within this paper.
We’ve added:
(1) In the legend of Table 2:
Two different DARE calculations (i.e., semi-observational DARE_obs described in section 2.1, and parametrized DARE_param described in section 2.2)
(2) Under Table 2:
“The parametrization that allows us to compute DARE_param is described in section 2.2. It builds on a method that systematically links aircraft observations of SSFR-linked spectral fluxes to aerosol optical thickness and other parameters using nine cases from the 2016 and 2017 ORACLES campaigns. This observationally driven link is expressed by a parametrization of the shortwave broadband DARE in terms of the mid-visible AOD and scene albedo.”
- MERRA-2 SSA Bias: The paper acknowledges that MERRA-2/GEOS SSA tends to be higher than in-situ measurements (underestimating absorption), particularly for biomass burning aerosols prevalent in the ORACLES study region. The impact of this known bias on the DARES results, especially for positive DARE over clouds, might be important to discuss.
We’ve added in section 2.1.1:
“We expect, according to the DARE_theo calculations illustrated in Fig. A2, that a high bias in the MERRA-2 estimated SSA, if not compensated by other factors, would cause a low bias in DARE_obs calculations (see, for example, lower DARE_theo values in (e-a) for C5-C8 where SSA is higher compared to higher DARE_theo values in (e-a) for C1-C4 where SSA is lower).”
- Extinction Coefficient Thresholds: The paper sets extinction coefficient thresholds for CALIOP (0.07 km⁻¹) and MERRA-2 (0.014 km⁻¹) to define "aerosol-free" conditions. The MERRA-2 threshold is scaled from the CALIOP one based on average layer thickness. The sensitivity of DARE results to these threshold choices could be relevant.
We’ve clarified Table A3 – It now reads “we have assessed the effects of these factors in the calculation of DARE_obs” and one of them is (E-1) i.e., “Apply threshold on extinction”
We’ve added in section 2.1.1:
“In section 2.1.4, we demonstrate that adding or removing such a threshold on the aerosol extinction coefficient leads to insignificant differences in mean instant DARE_obs values (up to 0.8 W.m-2) for all three case studies.”
We’ve added in section 2.1.4:
“Regarding categories (E-1), (E-2) and (E-3), the effects add up to a small N=10 1km-data points in Table A4 and lead to a small difference in mean instant all-sky (S1-S4) DARE_obs of maximum ~1.6 W⋅m-2.”
- Regarding Table 2, Footnote (1): Could you clarify the entry "O2 mass density = 0.0 kg m3"? Does this imply that O2 is not a variable input in the radiative transfer model, being part of the standard atmospheric profile, rather than having zero density?
We have added in the legend of Table 2:
“O2 mass density, which is also a required input to RRTMG, is assumed to be 0.0 kg m3.”
- Corrected vs. Uncorrected MODIS Cloud Properties: Section 2.1.1 mentions that using corrected MODIS cloud properties (accounting for above-cloud aerosols) can sometimes worsen agreement with other CER measurements depending on the spectral channel. Which version (corrected or uncorrected) is predominantly used for the DARES results presented for the ORACLES case studies, and what was the rationale for this choice?
We’ve added in section 2.1.1:
“In section 2.1.4, we demonstrate that correcting cloud properties for aerosol above them leads to insignificant differences in mean instant DARE_obs values (up to 4 W.m-2) for all three case studies.”
We’ve also added in section 2.1.1:
“In the end, the effects of (E-1) through (E-6) all lead to small differences in DARE_obs below a threshold of 6 W.m-2, which represents the accuracy of total fluxes in overcast conditions when comparing RRTMG-SW with other radiative transfer schemes (such as RRTM-SW).”
- Aerosol Information Below Clouds: For aerosols below clouds, MERRA-2 is used for extensive and intensive properties. How are situations handled where CALIOP detects clouds, but MERRA-2 shows no significant aerosol below them, or vice-versa? Is there a priority system or a check for consistency?
We’ve added in section 2.1.2:
“As described in Table 2, on the one hand, MODIS and CALIOP satellites are used to detect and characterize clouds, define aerosol height, and provide aerosol extinction coefficients above clouds and in non-cloudy skies. MERRA-2, on the other hand, is used to define aerosol top and base heights below clouds and provide the vertical distribution of spectral ASY, SSA, and extinction coefficient above, below clouds and in non-cloudy skies, along with information about atmospheric composition, weather, and ocean surface winds. We emphasize that we use MERRA-2 aerosol and atmospheric data regardless of any MERRA-2 simulated clouds (i.e., we do not use MERRA-2 cloud simulations in any way), nor do we assess cloud agreement between MERRA-2 and satellite observations in this paper.”
- Definition of "Thick, Thin, and/or Broken" Clouds: Section 2.1.3 (referenced in Table 2) will describe atmospheric scenarios including "thick, thin and/ or broken liquid cloud." What specific CALIOP vfm and MODIS Cloud criteria are used to classify clouds into these categories for the DARES calculations?
In Table 2, we’ve added:
“Table 3 lists which satellite-derived criteria are used to define four atmospheric scenarios”
In Table 3, CALIOP and MODIS are now replaced by CALIOPVFM and MODISCloud
- DAREP Applicability: Section 2.2 emphasizes DAREP is specific to ORACLES conditions. For the broader goal of assessing DARE globally, are there plans to develop similar parameterizations for other regions/aerosol types, or will the full DARES framework always be the primary tool?
We’ve added in section 2.2:
“We emphasize that this parametrization only represents the relationship between DARE and aerosol and cloud properties as sampled over the ORACLES study region and during the ORACLES timeframe. Outside of this framework (i.e., other regions of the globe and other seasons), different aerosol and cloud types can alter the DARE to cloud and aerosol relationship. To our knowledge, there are no current plans to extend the parameterization behind DARE_param to other times and regions of the globe. Consequently, we will not be able to assess global DARE_obs results in future studies using DARE_param.”
- Future Work - C3M Data (Section 4): The mention of C3M data (which relies on CloudSat) for future multi-cloud scenarios is relevant. While CloudSat's operational mode has changed and it's no longer in the A-Train, historical C3M data is extensive. For ongoing and future analyses, alternatives or updated multi-sensor products might be needed if relying on contemporaneous data with new missions like EarthCARE. This is more of a consideration than an error.
And
- Regarding the use of C3M data for multi-cloud scenarios: Given the changes in CloudSat's operational status, how does this impact the strategy for incorporating multi-layer cloud properties, especially for DARE calculations intended to span "multiple years" beyond the prime A-Train era? Will this rely more on the historical C3M dataset, or are there alternative/future multi-sensor cloud products (perhaps involving EarthCARE itself) that are being considered?
We’ve modified this sentence in the discussion:
“We envision this additional scenario to use (i) the CALIPSO-CloudSat-CERES-MODIS (CCCM or C3M) (Kato et al., 2010, 2011) derived cloud heights and cloud microphysical properties or equivalent EarthCARE-derived product (e.g., as in Table 1 of Mason et al., (2024)) and (ii) MERRA-2 simulated aerosol extensive and intensive properties.”
- The offset in DAREP vs. DARES is notably higher for 09/20/2016 (8.3 W⋅m⁻²) compared to other days. The paper suggests DAREP overestimation for high AOD on 09/18/2016. Does the larger offset on 09/20/2016 (which had the highest AODs) also primarily point to DAREP's single aerosol layer assumption or MERRA-2 SSA/ASY issues, or are there other potential contributors to this larger systematic difference on that specific day?
We’ve added in section 3.3.1:
“When evaluating our semi-observational DARE_obs with coincident parametrized DARE_param over all types of clouds (i.e., S1, S2 and S3 in Table 3) and for our three case studies, we find a generally satisfying agreement (R2=0.87 to 0.99, slope=0.80 to 0.99, offset =0.37 to 8.30, N=619 to 1067 in (1) Table 6). We posit that the slight differences between DARE_obs and DARE_param (see, for example, the mean cloudy DARE_param and DARE_obs values in panel (1) of Table 6) pertain to how they are computed. On the one hand, we assume MERRA-2's vertical distribution of SSA for the DARE_obs calculations, even though the SSA magnitude lies outside the observed SSA variability during ORACLES (i.e., as seen in Fig. 4b in Cochrane et al. (2021), the peak of the in-situ SSA values measured at 532 nm is between 0.85 and 0.86). By invoking this assumption, we can either overestimate DARE_obs if the MERRA-2 SSA value is too low or underestimate DARE_obs if the MERRA-2 SSA value is too high. For example, when computing DARE_theo (see Fig. A2), we record lower DARE_theo values (by ~10 W m-2) when adding more scattering aerosols (i.e., “continental”) to already absorbing aerosols (i.e., “urban”) over a thick cloud (COT=10). A second example is seen on 09/20/2016, where the two data points showing high AOD values above clouds (in yellow) and causing an offset in the DARE_param vs. DARE_obs regression line (~8 in Table 6) are likely due to an underestimation of MERRA-2 SSA, which in turn causes an overestimation of DARE_obs compared to DARE_param. On the other hand, while DARE_param is computed using the same AOD and cloud microphysical properties as DARE_obs, the DARE_param framework was developed specifically for aerosols above homogeneous cloud conditions (i.e., S1) and thus might not apply as well to broken and/ or thin clouds (i.e., S2 and S3). The various amounts of S1, S2 and S3 cases during our three case studies (illustrated in Fig. 3) likely influence the DARE_param accuracy. We also note a distinctive feature in Fig. 6 on 09/18/2016 away from the 1:1 line for low AOD and CALIOP cloud fractions below 1 (black crosses). This feature is very likely due to cloud inhomogeneities paired with low AOD values.”
- What are the anticipated major challenges in merging geostationary satellite data (which typically has coarser spatial resolution, different viewing geometries, and potentially different retrieval algorithms/sensitivities for aerosol and cloud properties) with the nadir-viewing, high-resolution Lidar/Imager data from A-Train/EarthCARE for consistent diurnal DARES calculations?
We’ve added in the discussion:
“We note that aerosol and cloud retrievals from GEO satellites are in an earlier stage of development and less well-validated compared to their Low Earth Orbit (LEO) satellite counterparts. GEO aerosol and cloud retrievals are also currently often tied to specific GEO imagers and thus less global than their LEO counterparts. GEO AOD generally shows good agreement with ground-based AERONET AOD (e.g., low RMSE (0.12–0.17) in the case of the GEO Ocean Color Imager (GOCI) AOD over East Asia in Choi et al. (2019)) but have unique bias patterns related to the surface-reflectance assumptions in their retrieval algorithms (e.g., negative bias of 0.04 in GOCI AOD in Choi et al. (2019)). Recent improvements in algorithms consist in correcting surface reflectance, cloud masking and/ or fusing data from LEO and GEO imagers (e.g., Su et al. (2020), Zhang et al. (2020), Kim et al. (2020), and Choi et al. (2019)). In some cases, GEO AOD, although often biased, was shown to reproduce the AERONET AOD diurnal cycle (e.g., over Asia, on a daily average, GOCI AOD shows a diurnal variation of +20% to −30 % in inland sites according to Lennartson et al. (2018)).”
Choi, M., Lim, H., Kim, J., Lee, S., Eck, T. F., Holben, B. N., Garay, M. J., Hyer, E. J., Saide, P. E., and Liu, H.: Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign, Atmos. Meas. Tech., 12, 4619–4641, https://doi.org/10.5194/amt-12-4619-2019, 2019.
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Lennartson, E. M., Wang, J., Gu, J., Castro Garcia, L., Ge, C., Gao, M., Choi, M., Saide, P. E., Carmichael, G. R., Kim, J., and Janz, S. J.: Diurnal variation of aerosol optical depth and PM2.5 in South Korea: a synthesis from AERONET, satellite (GOCI), KORUS-AQ observation, and the WRF-Chem model, Atmos. Chem. Phys., 18, 15125–15144, https://doi.org/10.5194/acp-18-15125-2018, 2018.
Su, Tianning, et al. "Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8." Remote Sensing of Environment 251 (2020): 112093.
Zhang, H., Kondragunta, S., Laszlo, I., and Zhou, M.: Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm, Atmos. Meas. Tech., 13, 5955–5975, https://doi.org/10.5194/amt-13-5955-2020, 2020.
Citation: https://doi.org/10.5194/egusphere-2025-1403-AC1
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