the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Nov 2023
Modeling atmospheric brown carbon in the GISS ModelE Earth system model
Abstract. Brown carbon (BrC) is an absorbing organic aerosol, primarily emitted through biomass burning, that exhibits light absorption unique from both black carbon (BC) and other organic aerosols (OA). Despite many field and laboratory studies seeking to constrain BrC properties, the radiative forcing of BrC is still highly uncertain. To better understand it’s climate impact, we introduced BrC to the One-Moment Aerosol (OMA) module of the GISS ModelE Earth system model (ESM). We assessed ModelE sensitivity to primary BrC processed through a novel chemical aging scheme, as well as secondary BrC formed from biogenic volatile organic compounds (BVOCs). Initial results show BrC typically contributes a top of the atmosphere (TOA) radiative effect of 0.04 W m-2. Sensitivity tests indicate that explicitly simulating BrC (separating it from other OA), including secondary BrC, and simulating chemical bleaching of BrC all contribute distinguishable radiative effects and should be accounted for in BrC schemes. This addition of prognostic BrC to ModelE allows for greater physical and chemical complexity in OA representation with no apparent trade-off in model performance as evaluation of ModelE aerosol optical depth, with and without the BrC scheme, against AERONET and MODIS retrieval data reveals similar skill in both cases. Thus, BrC should be explicitly simulated to allow for more physically based chemical composition, which is crucial for more detailed OA study like comparisons to in-situ measurement campaigns. We include additional recommendations for BrC representation within ESMs at the end of this paper.
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RC1: 'Comment on egusphere-2023-2472', Anonymous Referee #1, 19 Dec 2023
This work introduced BrC to the One-Moment Aerosol module of the GISS ModelE Earth system model. The different part from previous similar modeling studies might be the implementation of BrC from BVOCs and the Hems et al. aging. The manuscript is in general well written. However, I have some main concerns for the authors to consider:
- It seems that all BrC in the model is assumed to be water-soluble, is it true? Many references that cited in the manuscript have suggested that water-soluble BrC might only contribute ~half of the absorption.
- The comparison of AOD/AAOD to measurements is not very helpful. Maybe it is because the author said extensive work of evaluation will be presented in a future study. The authors focused on AOD at 550nm which is not very relevant to BrC. Alternatively, I think comparison of AAOD to AERONET can be elaborated, e.g., considering AAOD at different wavelengths, which could give much more useful information.
- The authors provided their recommendations for BrC model representation in Section 5, but did not explain clearly how these recommendations are made. Due to the lack of model evaluation, it is hard to point out which mechanisms/sensitivity simulations in this work are more likely to be correct than others.
Specific comments:
- p.6, unit of kgC is used for BrC and OA emissions for BrC/OA ratio. Could you please also indicate whether kgC or kg was used when deriving BC/OA ratio in equation (1) and (5)? If you used kg for OA emission here, what is the OC/OA ratio in your calculation?
- p.7, line 192. I think this statement could be clearer. Aromatic SOA may contribute small to OA mass, but its absorption efficiency could be larger than biogenic SOA. I think talking about secondary BrC mass is meaningless as it is highly sensitive to your assumption that non-zero imaginary RI means 100% BrC.
- Table 1. I do not think one single number for the solubility and k is appropriate. According to Laskin et al., 2015 that cited here, water soluble and insoluble BrC could contribute similar mass. Even for BC and OC, models usually treat their wet scavenging based on their solubility (e.g., fresh vs aged).
- p.10, line 277. The two numbers, 150% and 20%, can bring huge uncertainties and need more discussion. In addition, it seems that the reference that cited, Zhao et al., focused on water-soluble BrC only.
- Section 2.2.4, what about the mass change due to the classic OA aging? It would affect the mass and density so also affect MAE and absorption.
- Fig 6,7,9,10-13, could you please add wavelength information to the figure title or captain?
- I am confused that throughout the manuscript, while BrC radiative effect is seemed to be focused (e.g., in eq 8), radiative forcing is also frequently used later. Please make this clearer.
- p.19, paragraph 2, how are those +/- 0.1 uncertainties calculated?
- Table 3, I am very surprised that longwave RE of dust is larger than its shortwave RE.
- The structure of Section 3 looks confusing. I suggest comparing AOD etc. with measurements first and then discussing radiative forcing.
- p.706-707, how are these suggested ranges derived? As there is no observational constraint in this work, how a “reasonable range” could be recommended?
Citation: https://doi.org/10.5194/egusphere-2023-2472-RC1 -
RC2: 'Comment on egusphere-2023-2472', Anonymous Referee #2, 27 Dec 2023
The study presents a new methodology to introduce brown carbon aerosol absorption in the GISS ModelE Earth system model. This includes browned (stronger absorbing) and bleached (weaker absorbing) organic aerosol tracers that are transformed from the primary emitted tracer depending upon the concentrations of oxidants in the atmosphere. Several sensitivity runs are performed to understand the effect of the different parameters required for modelling BrC optics in the model. Overall, the manuscript is, in general, clearly written and presents an important improved framework to previous treatment of brown carbon absorption in climate models that aids in increased accuracy of their radiative effects. However, some changes are suggested below, that the authors may consider to improve the manuscript.
Major comments:
1) The one moment aerosol (OMA) module is used to calculate the aerosol optical properties in the model. However, a description of how this module makes optical property calculations is missing from the manuscript, which makes it difficult to understand how the brown carbon aerosols were incorporated. A brief description of the module, including the aerosol mixing scheme is suggested to precede the discussion on how BrC was added to the module.
2) Lines 98-99: Wang et al. (2018) have also simulated chemical aging based on OH concentrations. So, the present study may not be the first attempt to incorporate a concentration-dependent aging scheme. Also, it would be interesting to see if such oxidant concentration-dependent aging schemes perform any differently from fixed-time aging schemes.
3) OA has been used throughout the manuscript to denote organic aerosols with units as Tg C /yr in some places (eg. line 174) and just Tg /yr in others (eg line 139). This is confusing as OA is generally used as the total organic aerosol mass and OC only the carbon mass in OA. I suggest that this distinction be made clear when it is first mentioned, and the terms used appropriately in the manuscript.
4) dark-BrC particles exhibit BC-like optical properties, but emission inventories likely identify them as OC emissions as they are thermo-optically defined. Meaning that neglecting them in the present simulations may not be insignificant. There is evidence that these particles are relatively resistant to photobleaching and possibly significant considering that the absence of photo-bleaching increases the BrC radiative effect, as demonstrated in the manuscript. Could these particles be incorporated into the present framework?
Considering these and other limitations already highlighted, some statements in the manuscript regarding the insignificance of the optical properties of BrC and BrC-to-OA emissions ratios (eg. Line 539 and 614) may need to be revisited. Especially when considering the uncertainty associated with the optical properties of brown carbon particles and their variation with aging.
Specific comments:
- Line 27: "aerosol produced from fuel and biomass burning..." - what kind of fuel?
- Line 40: "SOA from biogenic VOCs (BVOCs) are also expected to grow in importance" - why?
- Line 43: Mention which assessment report of IPCC
- Line 50: "... incomplete combustion and smoldering fires ..." - smoldering fires also have incomplete combustion, please rephrase.
- Line 121: "Carbonaceous aerosols include BC and OA, which are each separated into aerosols from industrial and BB sources" - what about other sources like transport and energy production?
- Line 133: Where are the biomass and industrial plumes emitted in the transient simulations and how are these expected to alter aerosol lifetimes?
- Line 166: MAC_BrC_550 is kept at a fixed value (1 m2/g) but this value changes with changing k_BrC (see Saleh, 2020). This may bias the emissions.
- Line 219: What are the values/ranges of parameters used to estimate n and k?
- Line 225: How does the 0.003 compare to the default OA imaginary refractive index?
- Line 229: Why is f_HM = 89%?
- Line 229: "... led to not only a good fit with n_BrC spectra..." - not clear what is meant here. Please clarify and rephrase if required. Also, what is the range of n_BrC using the Kramers-Kronig relations?
- Figure 2: This is not very useful in its present form as most of the points are clustered together. It may be revised or represented as a table.
- Line 280: How were the Mie calculations performed? Do the refractive indexes correspond to the k with 20% and 150% of the absorption?
- Line 407-409: How were these biomass burning regions selected? What kind of biomass burning is included here?
- Line 483: How did Druge et al. (2022) treat BrC-to-OA differently?
- Figure 9: Why does the only bleaching have a higher radiative effect in comparison to the moderately absorbing (bleaching + browning) case? If this is internal variability, does this mean that bleaching is the key parameter? Is bleaching the dominant process during the BrC lifetime?
- Line 576: "… total AAOD is usually dominated either by BC or dust aerosols" - what is this statement based on? If this is based on previous studies (reference), do they consider absorption by BrC?
- Line 593-602: The model overestimates AOD in most cases and underestimates AAOD - this is different from what IPCC AR6 models simulate (GliB et al., 2021), which underestimate both the properties. Why might this be happening? If AOD is being dominated by another tracer, would this be overpowering any changes in AOD caused by the introduction of BrC? There seems to be an improvement in simulating AAOD, so why is this not discussed as extensively in the manuscript?
- Line 601: "... data scatter is mostly caused by dust and BC, rather than BrC" - how was this determined?
Citation: https://doi.org/10.5194/egusphere-2023-2472-RC2 - AC1: 'Comment on egusphere-2023-2472', Maegan DeLessio, 14 Feb 2024
Status: closed
-
RC1: 'Comment on egusphere-2023-2472', Anonymous Referee #1, 19 Dec 2023
This work introduced BrC to the One-Moment Aerosol module of the GISS ModelE Earth system model. The different part from previous similar modeling studies might be the implementation of BrC from BVOCs and the Hems et al. aging. The manuscript is in general well written. However, I have some main concerns for the authors to consider:
- It seems that all BrC in the model is assumed to be water-soluble, is it true? Many references that cited in the manuscript have suggested that water-soluble BrC might only contribute ~half of the absorption.
- The comparison of AOD/AAOD to measurements is not very helpful. Maybe it is because the author said extensive work of evaluation will be presented in a future study. The authors focused on AOD at 550nm which is not very relevant to BrC. Alternatively, I think comparison of AAOD to AERONET can be elaborated, e.g., considering AAOD at different wavelengths, which could give much more useful information.
- The authors provided their recommendations for BrC model representation in Section 5, but did not explain clearly how these recommendations are made. Due to the lack of model evaluation, it is hard to point out which mechanisms/sensitivity simulations in this work are more likely to be correct than others.
Specific comments:
- p.6, unit of kgC is used for BrC and OA emissions for BrC/OA ratio. Could you please also indicate whether kgC or kg was used when deriving BC/OA ratio in equation (1) and (5)? If you used kg for OA emission here, what is the OC/OA ratio in your calculation?
- p.7, line 192. I think this statement could be clearer. Aromatic SOA may contribute small to OA mass, but its absorption efficiency could be larger than biogenic SOA. I think talking about secondary BrC mass is meaningless as it is highly sensitive to your assumption that non-zero imaginary RI means 100% BrC.
- Table 1. I do not think one single number for the solubility and k is appropriate. According to Laskin et al., 2015 that cited here, water soluble and insoluble BrC could contribute similar mass. Even for BC and OC, models usually treat their wet scavenging based on their solubility (e.g., fresh vs aged).
- p.10, line 277. The two numbers, 150% and 20%, can bring huge uncertainties and need more discussion. In addition, it seems that the reference that cited, Zhao et al., focused on water-soluble BrC only.
- Section 2.2.4, what about the mass change due to the classic OA aging? It would affect the mass and density so also affect MAE and absorption.
- Fig 6,7,9,10-13, could you please add wavelength information to the figure title or captain?
- I am confused that throughout the manuscript, while BrC radiative effect is seemed to be focused (e.g., in eq 8), radiative forcing is also frequently used later. Please make this clearer.
- p.19, paragraph 2, how are those +/- 0.1 uncertainties calculated?
- Table 3, I am very surprised that longwave RE of dust is larger than its shortwave RE.
- The structure of Section 3 looks confusing. I suggest comparing AOD etc. with measurements first and then discussing radiative forcing.
- p.706-707, how are these suggested ranges derived? As there is no observational constraint in this work, how a “reasonable range” could be recommended?
Citation: https://doi.org/10.5194/egusphere-2023-2472-RC1 -
RC2: 'Comment on egusphere-2023-2472', Anonymous Referee #2, 27 Dec 2023
The study presents a new methodology to introduce brown carbon aerosol absorption in the GISS ModelE Earth system model. This includes browned (stronger absorbing) and bleached (weaker absorbing) organic aerosol tracers that are transformed from the primary emitted tracer depending upon the concentrations of oxidants in the atmosphere. Several sensitivity runs are performed to understand the effect of the different parameters required for modelling BrC optics in the model. Overall, the manuscript is, in general, clearly written and presents an important improved framework to previous treatment of brown carbon absorption in climate models that aids in increased accuracy of their radiative effects. However, some changes are suggested below, that the authors may consider to improve the manuscript.
Major comments:
1) The one moment aerosol (OMA) module is used to calculate the aerosol optical properties in the model. However, a description of how this module makes optical property calculations is missing from the manuscript, which makes it difficult to understand how the brown carbon aerosols were incorporated. A brief description of the module, including the aerosol mixing scheme is suggested to precede the discussion on how BrC was added to the module.
2) Lines 98-99: Wang et al. (2018) have also simulated chemical aging based on OH concentrations. So, the present study may not be the first attempt to incorporate a concentration-dependent aging scheme. Also, it would be interesting to see if such oxidant concentration-dependent aging schemes perform any differently from fixed-time aging schemes.
3) OA has been used throughout the manuscript to denote organic aerosols with units as Tg C /yr in some places (eg. line 174) and just Tg /yr in others (eg line 139). This is confusing as OA is generally used as the total organic aerosol mass and OC only the carbon mass in OA. I suggest that this distinction be made clear when it is first mentioned, and the terms used appropriately in the manuscript.
4) dark-BrC particles exhibit BC-like optical properties, but emission inventories likely identify them as OC emissions as they are thermo-optically defined. Meaning that neglecting them in the present simulations may not be insignificant. There is evidence that these particles are relatively resistant to photobleaching and possibly significant considering that the absence of photo-bleaching increases the BrC radiative effect, as demonstrated in the manuscript. Could these particles be incorporated into the present framework?
Considering these and other limitations already highlighted, some statements in the manuscript regarding the insignificance of the optical properties of BrC and BrC-to-OA emissions ratios (eg. Line 539 and 614) may need to be revisited. Especially when considering the uncertainty associated with the optical properties of brown carbon particles and their variation with aging.
Specific comments:
- Line 27: "aerosol produced from fuel and biomass burning..." - what kind of fuel?
- Line 40: "SOA from biogenic VOCs (BVOCs) are also expected to grow in importance" - why?
- Line 43: Mention which assessment report of IPCC
- Line 50: "... incomplete combustion and smoldering fires ..." - smoldering fires also have incomplete combustion, please rephrase.
- Line 121: "Carbonaceous aerosols include BC and OA, which are each separated into aerosols from industrial and BB sources" - what about other sources like transport and energy production?
- Line 133: Where are the biomass and industrial plumes emitted in the transient simulations and how are these expected to alter aerosol lifetimes?
- Line 166: MAC_BrC_550 is kept at a fixed value (1 m2/g) but this value changes with changing k_BrC (see Saleh, 2020). This may bias the emissions.
- Line 219: What are the values/ranges of parameters used to estimate n and k?
- Line 225: How does the 0.003 compare to the default OA imaginary refractive index?
- Line 229: Why is f_HM = 89%?
- Line 229: "... led to not only a good fit with n_BrC spectra..." - not clear what is meant here. Please clarify and rephrase if required. Also, what is the range of n_BrC using the Kramers-Kronig relations?
- Figure 2: This is not very useful in its present form as most of the points are clustered together. It may be revised or represented as a table.
- Line 280: How were the Mie calculations performed? Do the refractive indexes correspond to the k with 20% and 150% of the absorption?
- Line 407-409: How were these biomass burning regions selected? What kind of biomass burning is included here?
- Line 483: How did Druge et al. (2022) treat BrC-to-OA differently?
- Figure 9: Why does the only bleaching have a higher radiative effect in comparison to the moderately absorbing (bleaching + browning) case? If this is internal variability, does this mean that bleaching is the key parameter? Is bleaching the dominant process during the BrC lifetime?
- Line 576: "… total AAOD is usually dominated either by BC or dust aerosols" - what is this statement based on? If this is based on previous studies (reference), do they consider absorption by BrC?
- Line 593-602: The model overestimates AOD in most cases and underestimates AAOD - this is different from what IPCC AR6 models simulate (GliB et al., 2021), which underestimate both the properties. Why might this be happening? If AOD is being dominated by another tracer, would this be overpowering any changes in AOD caused by the introduction of BrC? There seems to be an improvement in simulating AAOD, so why is this not discussed as extensively in the manuscript?
- Line 601: "... data scatter is mostly caused by dust and BC, rather than BrC" - how was this determined?
Citation: https://doi.org/10.5194/egusphere-2023-2472-RC2 - AC1: 'Comment on egusphere-2023-2472', Maegan DeLessio, 14 Feb 2024
Data sets
Modeling atmospheric brown carbon in the GISS ModelE Earth system model Maegan A. DeLessio, Kostas Tsigaridis, Susanne E. Bauer, Jacek Chowdhary, Gregory L. Schuster https://doi.org/10.5281/zenodo.8342620
Model code and software
Modeling atmospheric brown carbon in the GISS ModelE Earth system model Maegan A. DeLessio, Kostas Tsigaridis, Susanne E. Bauer, Jacek Chowdhary, Gregory L. Schuster https://doi.org/10.5281/zenodo.8342620
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