the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Aug 2024
Modeling actinic flux and photolysis frequencies in dense biomass burning plumes
Abstract. Biomass-burning (BB) affects air quality and climate by releasing large amounts of gaseous and particulate pollutants into the atmosphere. Photochemical processing during daylight transforms these emissions, influencing their overall environmental impact. Accurately quantifying the photochemical drivers, namely actinic flux and photolysis frequencies, is crucial to constrain this chemistry. However, the complex radiative transfer within BB plumes presents a significant challenge for both direct observations and numerical models.
This study introduces an expanded version of the 1D VLIDORT-QS radiative transfer (RT) model, named VLIDORT for PhotoChemistry (VPC). VPC is designed for photochemical and remote sensing applications, particularly in BB plumes and other complex scenarios. To validate VPC and investigate photochemical conditions within BB plumes, the model was used to simulate spatial distributions of actinic fluxes and photolysis frequencies for the Shady wildfire (Idaho, US, 2019), based on plume composition data from the NOAA/NASA FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) campaign.
Comparison between modeling results and observations by the UCAR CAFS (Charged-coupled device Actinic Flux Spectro-radiometer) yield a modeling accuracy of 10–20 %. Systematic biases between model and observations are within 2 %, indicating that the uncertainties are most likely due to variability in the input data caused by the inhomogeneity of the plume as well as 3D RT effects not captured in the model. Random uncertainties are largest in the ultra-violet (UV) spectral range, where they are dominated by uncertainties in the plume particle size distribution and brown carbon (BrC) absorptive properties.
The modeled actinic fluxes show a decrease from the plume top to bottom of the plume with a strong spectral dependence caused by BrC absorption, which darkens the plume towards shorter wavelengths. In the visible (Vis) spectral range, actinic fluxes above the plume are enhanced by up to 60 %. In contrast, in the UV, actinic fluxes above the plume are not affected or even reduced by up to 10 %. Strong reductions exceeding an order of magnitude in and below the plume occur for both spectral ranges but are more pronounced in the UV.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(9125 KB) - Metadata XML
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Supplement
(6750 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2353', Anonymous Referee #1, 23 Sep 2024
Review of “Modeling actinic flux and photolysis frequencies in dense biomass burning plumes” by Tirpitz et al.
This paper describes an evaluation of simulated actinic fluxes and photolysis rates from a 1-D radiative transfer model using in situ observations of a biomass burning plume from FIREX-AQ. The paper is well organized, easy to follow, and each section contains an adequate description of the approach and interpretation of the results. The methodology of linking the measurements to the model is sound. An interesting aspect of the papers is a discussion of the issues associated with 3-D radiative effects that are often neglected by chemical transport models. It was also good to see the authors perform a sensitivity study to examine the impact of varying input aerosol properties as a function of plume age, which help understand which measurement uncertainties might be the most important. In general, the results of this study are very useful for 3-D chemical transport modelers since errors in the aerosol lifecycle arise from many parts of the model. By constraining the 1-D RT radiative transfer model, the authors provide an estimate of the possible uncertainties arising solely from that module. They show that the errors are relatively small, despite the large variations of BB plumes and that their methodology that has to weave together data from many instruments. These errors are relatively small compared to other errors in aerosol models, so that chemical transport modelers can focus efforts on other sources of errors in their models.
I recommend this paper for publication after minor revisions, based on my comments below.
Specific Comments:
Line 52. In addition to spatio-temporal coverage, such measurements are often not collected on all trace gas or aerosol aircraft missions which limits the amount of data and types of conditions that are available to evaluate models. This point might be useful to include.
Line 80: A similar issue arises for high resolution cloud simulations where 3-D radiative effects become important and have been observed near the edges of clouds. If the resolution of a model is coarse, the 3-D radiative effects become subgrid scale processes that are often ignored (and may be small anyway).
Line 95-101: What is missing here is that validating 1-D RT models, such as the one in this study, using the best input data is needed to understand uncertainties introduced into parameterization of 3-D model predictions. The 3-D model predictions are not constrained, so that errors can come from many sources. If the predicted actinic fluxes are not correct in a 3-D model simulation, most likely the errors arise from simulated aerosol number, mass, composition and assumptions in optical parameters (e.g. brown carbon), rather than from the 1-D RT model formulation.
Line 126: What type of aerosol model is assumed? Or does it matter? Since this is an off-line calculation, it is just about specifying input? To many readers an “aerosol model” implies some sort of prognostic treatment of aerosols, so how that phrase is used can be confusing. Bulk, modal, and sectional models treat the aerosol size distribution differently, and models have different representations of mixing state that affect aerosol optical properties. Some additional discussion is needed. It looks like some other discussion is included in Section 2.2.2, but think this should be mentioned here as well.
Line 171: Since particles not are treated as coated as an option, should one assume the aerosol composition is treated as an internal mixture?
Lines 325-332: Is it important to account for ambient aerosol water for this case? If so, how was that done. It looks like measurements were made for low RH conditions that may differ from ambient conditions. Neglecting aerosol water (if present) would adversely affect aerosol optical property calculations.
Line 381: It is understandable to average the measurements over some period to reduce noise, but the authors average some variables and not others. Why not average all of them? I also wonder if some noise might be due to very small shifts in the time measurements of the individual instruments (i.e., time stamp on one instrument may not exactly match another instrument), which can be very important in plumes with strong horizontal variations. One could quickly check at the BB plume edge whether measurements line up in time.
Line 392: It makes sense to use outside the plume observations for the background aerosol, but then the authors use a refractive index from the literature. Why not use a refractive index that may be more representative of the aerosol conditions outside of the plume?
Section 6: One remaining topic that could be discussed is whether there it is valuable to examine other FIREX flights. I am thinking of more complex situations in which clouds may be present. Do the authors think that examining one case is sufficient to evaluate the model? Another topic that could be discussed is the implications for 3-D chemical transport models. Unless the active fire area is very large, BB plumes near their sources are not likely to be represented adequately by the coarse resolution of chemical transport models. The models will overly smooth these plumes, complicating how one evaluates computed photolysis rates and actinic fluxes with FIREX-AQ data.
Citation: https://doi.org/10.5194/egusphere-2024-2353-RC1 - AC1: 'Reply on RC1', Jan-Lukas Tirpitz, 19 Nov 2024
- AC3: 'Markup file of revised manuscript (addition to AC1)', Jan-Lukas Tirpitz, 19 Nov 2024
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RC2: 'Comment on egusphere-2024-2353', I. Pérez, 23 Sep 2024
This paper is focused on the model validation of atmosphere optical properties such as actinic flux or photolysis frequencies for some chemical reactions. An expanded version of the ID VLIDORT-QS radiative transfer model is used and its added features are described together with inputs and outputs. Experimental data were provided by the 2019 NOAA/NASA FIREX-AQ measurement campaign. In particular, data from the “Shady Fire” on July 25, 2019 in Idaho were used. A noticeable good agreement between calculated values and measurements was obtained. Consequently, the paper is a quite detailed analysis of this topic, which merits to be published in Atmospheric Chemistry and Physics after the introduction of the following minor changes.
The data origin is introduced by the authors. However, summary additional information about the 90 plumes in this database could be provided. In particular, the authors should explain the reasons for selecting the “Shady Fire” against the rest. They should indicate that this plume is representative enough of usual atmospheric conditions. Moreover, the authors could explain the atmospheric variables during this fire, such as wind speed and direction, temperature, synoptic pattern, …
Since the variables modelled are quite specific, the authors could increase the number of possible readers with the introduction of possible simple applications of this model. Moreover, the authors could indicate if this model could be used in the future by other researchers with a web-based application.
L. 247. A smoothing kernel is used. The authors could explain the used window in this kernel and its calculation procedure or the reason for such window.
Minor remarks.
L. 118. Suppress one parenthesis.
L. 935. Revise this reference.
Supplement
L. 10. “to the” is repeated.
Figure S4. Introduce colour scale.
Citation: https://doi.org/10.5194/egusphere-2024-2353-RC2 -
AC2: 'Reply on RC2', Jan-Lukas Tirpitz, 19 Nov 2024
We combined the answers to both reviews in a single document. Please find it appended in the answer to reviewer 1 ( AC1: 'Reply on RC1')
Citation: https://doi.org/10.5194/egusphere-2024-2353-AC2
-
AC2: 'Reply on RC2', Jan-Lukas Tirpitz, 19 Nov 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2353', Anonymous Referee #1, 23 Sep 2024
Review of “Modeling actinic flux and photolysis frequencies in dense biomass burning plumes” by Tirpitz et al.
This paper describes an evaluation of simulated actinic fluxes and photolysis rates from a 1-D radiative transfer model using in situ observations of a biomass burning plume from FIREX-AQ. The paper is well organized, easy to follow, and each section contains an adequate description of the approach and interpretation of the results. The methodology of linking the measurements to the model is sound. An interesting aspect of the papers is a discussion of the issues associated with 3-D radiative effects that are often neglected by chemical transport models. It was also good to see the authors perform a sensitivity study to examine the impact of varying input aerosol properties as a function of plume age, which help understand which measurement uncertainties might be the most important. In general, the results of this study are very useful for 3-D chemical transport modelers since errors in the aerosol lifecycle arise from many parts of the model. By constraining the 1-D RT radiative transfer model, the authors provide an estimate of the possible uncertainties arising solely from that module. They show that the errors are relatively small, despite the large variations of BB plumes and that their methodology that has to weave together data from many instruments. These errors are relatively small compared to other errors in aerosol models, so that chemical transport modelers can focus efforts on other sources of errors in their models.
I recommend this paper for publication after minor revisions, based on my comments below.
Specific Comments:
Line 52. In addition to spatio-temporal coverage, such measurements are often not collected on all trace gas or aerosol aircraft missions which limits the amount of data and types of conditions that are available to evaluate models. This point might be useful to include.
Line 80: A similar issue arises for high resolution cloud simulations where 3-D radiative effects become important and have been observed near the edges of clouds. If the resolution of a model is coarse, the 3-D radiative effects become subgrid scale processes that are often ignored (and may be small anyway).
Line 95-101: What is missing here is that validating 1-D RT models, such as the one in this study, using the best input data is needed to understand uncertainties introduced into parameterization of 3-D model predictions. The 3-D model predictions are not constrained, so that errors can come from many sources. If the predicted actinic fluxes are not correct in a 3-D model simulation, most likely the errors arise from simulated aerosol number, mass, composition and assumptions in optical parameters (e.g. brown carbon), rather than from the 1-D RT model formulation.
Line 126: What type of aerosol model is assumed? Or does it matter? Since this is an off-line calculation, it is just about specifying input? To many readers an “aerosol model” implies some sort of prognostic treatment of aerosols, so how that phrase is used can be confusing. Bulk, modal, and sectional models treat the aerosol size distribution differently, and models have different representations of mixing state that affect aerosol optical properties. Some additional discussion is needed. It looks like some other discussion is included in Section 2.2.2, but think this should be mentioned here as well.
Line 171: Since particles not are treated as coated as an option, should one assume the aerosol composition is treated as an internal mixture?
Lines 325-332: Is it important to account for ambient aerosol water for this case? If so, how was that done. It looks like measurements were made for low RH conditions that may differ from ambient conditions. Neglecting aerosol water (if present) would adversely affect aerosol optical property calculations.
Line 381: It is understandable to average the measurements over some period to reduce noise, but the authors average some variables and not others. Why not average all of them? I also wonder if some noise might be due to very small shifts in the time measurements of the individual instruments (i.e., time stamp on one instrument may not exactly match another instrument), which can be very important in plumes with strong horizontal variations. One could quickly check at the BB plume edge whether measurements line up in time.
Line 392: It makes sense to use outside the plume observations for the background aerosol, but then the authors use a refractive index from the literature. Why not use a refractive index that may be more representative of the aerosol conditions outside of the plume?
Section 6: One remaining topic that could be discussed is whether there it is valuable to examine other FIREX flights. I am thinking of more complex situations in which clouds may be present. Do the authors think that examining one case is sufficient to evaluate the model? Another topic that could be discussed is the implications for 3-D chemical transport models. Unless the active fire area is very large, BB plumes near their sources are not likely to be represented adequately by the coarse resolution of chemical transport models. The models will overly smooth these plumes, complicating how one evaluates computed photolysis rates and actinic fluxes with FIREX-AQ data.
Citation: https://doi.org/10.5194/egusphere-2024-2353-RC1 - AC1: 'Reply on RC1', Jan-Lukas Tirpitz, 19 Nov 2024
- AC3: 'Markup file of revised manuscript (addition to AC1)', Jan-Lukas Tirpitz, 19 Nov 2024
-
RC2: 'Comment on egusphere-2024-2353', I. Pérez, 23 Sep 2024
This paper is focused on the model validation of atmosphere optical properties such as actinic flux or photolysis frequencies for some chemical reactions. An expanded version of the ID VLIDORT-QS radiative transfer model is used and its added features are described together with inputs and outputs. Experimental data were provided by the 2019 NOAA/NASA FIREX-AQ measurement campaign. In particular, data from the “Shady Fire” on July 25, 2019 in Idaho were used. A noticeable good agreement between calculated values and measurements was obtained. Consequently, the paper is a quite detailed analysis of this topic, which merits to be published in Atmospheric Chemistry and Physics after the introduction of the following minor changes.
The data origin is introduced by the authors. However, summary additional information about the 90 plumes in this database could be provided. In particular, the authors should explain the reasons for selecting the “Shady Fire” against the rest. They should indicate that this plume is representative enough of usual atmospheric conditions. Moreover, the authors could explain the atmospheric variables during this fire, such as wind speed and direction, temperature, synoptic pattern, …
Since the variables modelled are quite specific, the authors could increase the number of possible readers with the introduction of possible simple applications of this model. Moreover, the authors could indicate if this model could be used in the future by other researchers with a web-based application.
L. 247. A smoothing kernel is used. The authors could explain the used window in this kernel and its calculation procedure or the reason for such window.
Minor remarks.
L. 118. Suppress one parenthesis.
L. 935. Revise this reference.
Supplement
L. 10. “to the” is repeated.
Figure S4. Introduce colour scale.
Citation: https://doi.org/10.5194/egusphere-2024-2353-RC2 -
AC2: 'Reply on RC2', Jan-Lukas Tirpitz, 19 Nov 2024
We combined the answers to both reviews in a single document. Please find it appended in the answer to reviewer 1 ( AC1: 'Reply on RC1')
Citation: https://doi.org/10.5194/egusphere-2024-2353-AC2
-
AC2: 'Reply on RC2', Jan-Lukas Tirpitz, 19 Nov 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Modeling actinic flux and photolysis frequencies in dense biomass burning plumes - Data asset Jan-Lukas Tirpitz, Santo Fedele Colosimo, Nathaniel Brockway, Robert Spurr, Matthew Christi, Samuel Hall, Kirk Ullmann, Johnathan Hair, Taylor Shingler, Rodney Weber, Jack Dibb, Richard Moore, Elizabeth Wiggins, Vijay Natraj, Nicolas Theys, and Jochen Stutz https://doi.org/10.5281/zenodo.12802618
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Jan-Lukas Tirpitz
Santo Fedele Colosimo
Nathaniel Brockway
Robert Spurr
Matt Christi
Samuel Hall
Kirk Ullmann
Johnathan Hair
Taylor Shingler
Rodney Weber
Jack Dibb
Richard Moore
Elizabeth Wiggins
Vijay Natraj
Nicolas Theys
Jochen Stutz
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(9125 KB) - Metadata XML
-
Supplement
(6750 KB) - BibTeX
- EndNote
- Final revised paper