the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Canadian and Alaskan wildfire smoke particle properties, their evolution, and controlling factors, from satellite observations
- 1Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
- 2Universities Space Research Association, NASA Postdoctoral Program, Columbia, MD 21046, USA
- 3Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
- 4Department of Meteorology and Atmospheric Science, the Pennsylvania State University, State College, PA 168026, USA
- 5Earth System Science Interdisciplinary Center, College Park, MD 20740, USA
- 1Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
- 2Universities Space Research Association, NASA Postdoctoral Program, Columbia, MD 21046, USA
- 3Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
- 4Department of Meteorology and Atmospheric Science, the Pennsylvania State University, State College, PA 168026, USA
- 5Earth System Science Interdisciplinary Center, College Park, MD 20740, USA
Abstract. The optical and chemical properties of biomass burning (BB) smoke particles greatly affect the impact wildfires have on climate and air quality. Previous work has demonstrated some links between smoke properties and factors such as fuel type and meteorology. However, the factors controlling BB particle speciation at emission are not adequately understood, nor are those driving particle aging during atmospheric transport. As such, modeling wildfire smoke impacts on climate and air quality remains challenging. The potential to provide robust, statistical characterizations of BB particles based on ecosystem type and ambient environmental conditions with remote sensing data is investigated here. Space-based Multi-angle Imaging Spectrometer (MISR) observations, combined with the MISR Research Aerosol (RA) algorithm and the MISR Interactive Explorer (MINX) tool, are used to retrieve smoke plume aerosol optical depth (AOD), and to provide constraints on plume vertical extent, smoke age, and particle size, shape, and light-absorption properties, and absorption spectral dependence. These tools are applied to numerous wildfire plumes in Canada and Alaska, across a range of conditions, to create a regional inventory of BB particle-type temporal and spatial distribution. We then statistically compare these results with satellite measurements of fire radiative power (FRP) and land cover characteristics, as well as short-term climate, meteorological, and drought information from MERRA-2 reanalysis and the North American Drought Monitor. We find statistically significant differences in the retrieved smoke properties based on land cover type, with fires in forests producing the thickest plumes containing the largest, brightest particles, and fires in savannas and grasslands exhibiting the opposite. Additionally, the inferred dominant aging mechanisms and the timescales over which they occur vary systematically between land types. This work demonstrates the potential of remote sensing to constrain BB particle properties and the mechanisms governing their evolution over entire ecosystems. It also begins to realize this potential, as a means of improving regional and global climate and air quality modeling in a rapidly changing world.
- Preprint
(8043 KB) -
Supplement
(1446 KB) - BibTeX
- EndNote
Katherine T. Junghenn Noyes et al.
Status: closed
-
RC1: 'Comment on acp-2021-863', Anonymous Referee #1, 18 Nov 2021
This paper uses MISR observations to determine aerosol properties as a function of land cover type. They assess how these properties change with plume age and also relate their results to other fire and ecosystem factors such as fire radiative power and drought. The authors are able to identify statistical differences in satellite retrieved aerosol properties that depend on the fuel source.
This is a well written paper - a pleasure to read. I have a couple minor science questions and then a few technical suggestions.
Science questions
Line 201 - Isn't deposition a function of particle size? I would expect AOD and REPS to decrease with deposition as the larger particles settle out first.
Table 1 - is the humped SSA dependence in Table 1 expected? For example, Figure 1 in Samset et al. (2018) (https://link.springer.com/article/10.1007/s40641-018-0091-4) suggests either increasing or decreasing SSA as a function of wavelength, with both BC and BrC increasing with increasing wavelength and dust decreasing with increasing wavelength.
Figures 8&9 - the arrows to guide the eye - how are the arrow directions defined? is there a quantitative determination? For example, qualitatively looking at the plots I would consider drawing increasing arrows on figures 9a, 9b and 9f.
Technical corrections
Line 44 - cite Petzold et al. (2013) www.atmos-chem-phys.net/13/8365/2013/. I like the BlS and BrS designation!
Line 87 - perhaps cite Kleinman https://doi.org/10.5194/acp-20-13319-2020 here
Line 105 - 'Models are uncertain...' Models don't have thoughts...could this perhaps be rephrased?
Line 118 - 'Research Aerosol (RA) retrieval algorithm' I personally would have used RA2: 'Research Aerosol retrieval algorithm (RA2)'. Because later, e.g., line 127 if reading aloud - 'the RA successfully mapped' reads as 'the Research Aerosol successfully mapped' would sound better as 'the RA2 successfully mapped' i.e., 'the Research Aerosol retrieval algorithm successfully mapped', but no worries if RA is already accepted nomenclature
Table 2 caption - AGL is only mentioned in the footnotes, so move AGL definition to the footnotes too?
Line 289 MERRA is used several times before being defined (e.g., on 137, 282, 287).
Figure 1 - Perhaps indicated the territories since they are referenced in the first paragraph of the results section?
Line 348-349 - Table 3 defines the land cover types so don't need to repeat definitions here.
Line 363 - spell out woody to be consistent with spelling out other biomes in previous sentence.
Figure 2 and related discussion - Plumes in FT are the same as plumes above PBL described in Table 2 so perhaps it makes sense to change Table 2 to call them plumes in FT instead of 'above PBL'. The footnote to Table 2 could stay the same.
Figure 2 - Some numbers in Table 2 don't quite match up with Figure 2 - for example Table 2 suggests 33.8% plumes in FT in 2016, while sum for those plumes is 32% in Figure 2. Difference due to rounding?
Line 410 - the only typo I found! there is an extra 'a' at the end of this line
Line 471-473 - This sentence could be clarified - possibly also split into two sentences.
Line 484 - '... some plumes containing as much as 40% non-spherical...' Perhaps quantify 'some' or change 'some' to 'a very few plumes contain...' as several places (line 480 and line 485) indicate very few plumes have much in the way of non-spherical particles but then 40% is a lot!
Line 487 - I assume that the attribution of lower SAE to more non-spherical particles in F plumes is due to the fact that these non-spherical particles are large (re=1.21 um), rather than their non-sphericity?
Line 623 'there have been no other large scale regional studies' I guess this depends how large scale and regional studies are defined? For example, Kleinman et al (2020) https://doi.org/10.5194/acp-20-13319-2020 uses in-situ airborne observations to look at particle property changes due to aerosol aging in smoke plumes in the Pacific Northwest region.
-
RC2: 'Comment on acp-2021-863', Anonymous Referee #2, 14 Dec 2021
The paper presents an analysis of retrieved smoke particle properties made from the MISR space-based instrument aboard the NASA Terra spacecraft. Smoke plumes are identified over Alaska and Canada in the MISR data and correlated with (where available) fire radiative power measurements from the MODIS instrument onboard the same platform. Information on the smoke plume altitude is retrieved from the MISR stereo camera capabilities. Smoke plume altitude and particle properties from the MISR research algorithm (RA) retrievals are correlated with IGBP land types. Aerosol properties and loading are also correlated with drought indices.
The main conclusions of the paper are:
- Forest fire plumes tend to produce higher aerosol optical depth (AOD) and larger and less absorbing particles than fires from grasslands
- Aging of smoke particle is different between grassland fires and forest and woody fires, with the latter group exhibiting evident particle growth and a more rapid transition toward lower absorption in particles
- Drought is also a factor in smoke particle properties, with woody fires especially exhibiting a tendency toward decreased absorption in the particles as drought index is more severe.
The MISR instrument and products provide invaluable information on the distributions and properties of aerosols, and I think this paper (and subsequent analyses in other regions) could make a significant contribution to our understanding and characterizing the properties of smoke aerosols and their evolution near fire sources. That said, I am unclear on some implications of the retrieval assumptions that I think warrant further discussion, so I am suggesting major revisions.
Major Comments
I am unclear on how actually the plumes are selected from the totality of the MISR dataset. It isn’t clearly articulated the criteria. Evidently 50% of the selected plumes are from the MISR plume height archive. Where are the other 50% from? Is the selection done manually? How? Line 230 states “Well-defined plumes…were favored for this analysis.” What constitutes well-defined (AOD, something else)? Is it a requirement that it is a smoke plume versus something else? How are you certain you are selecting smoke cases? Are fire anomalies required? I think this needs to be in section 2.2, which rather than providing the information on case selection the title suggests seems more to be about the characteristics of the cases.
The main concern I have is that I don’t know how to judge the robustness of the retrieval results regarding particle composition presented. My understanding of an algorithm like MISR’s RA is that a “best” mixture is determined from its closeness to the observed spectral and angular data. I’m not clear on the orthogonality of the individual components in Table 1, and neither am I clear on how a mixture of these components that minimizes the cost function compares to another mixture that almost minimizes the cost function. Is there degeneracy in the results that admits a different solution? Is it significantly different? Some further presentation of such an error analysis would be important here in order enhance the confidence in the results presented.
I find the arrows in Figures 8 and 9 not very well justified and suggest they be omitted. If the point is there are clear trends then just plot the line fits with the appropriate statistics, otherwise I think the interpretation is a bit forced.
Minor Comments
Line 54-55: I think a comment here on pyroCb would be useful to add, to acknowledge the growing interest in this type of fire.
Table 1: For the utility of modelers, please include in the supplement further information on the particle properties summarized here. For example, the mode radius and width and the refractive indices at the MISR channels.
Line 91-96: This construct is unclear. Do you mean in the end that you consider new particle formation and condensation/hygroscopic growth as the distinct aging mechanisms?
Line 103: “Most current transport and climate models…” I think it is more accurate to say that most current climate models do not at present incorporate brown carbon at all. The treatment of smoke as being a mixture of black carbon, organic (white) carbon, and sulfates leads to I think what you mean by “BlS” in this paper.
Line 234: You mean the lower 48 here, as Alaska is part of your domain and is part of the US.
Line 293: 0.667 degrees is inconsistent with 0.625 degrees a few lines earlier. I believe the MERRA-2 fields are uniformly available on the 0.625 x 0.5 degree grid.
Line 327: “event” not “even”
Line 449: should be “compared to G”
Line 449/450: “More on particle properties in subsequent sections” seems a bit informal. Maybe “More information on particle properties is presented in subsequent sections.”
Line 461: Add pointer to Table 4 in this first sentence.
Line 467: Suggest starting a new paragraph with “To help interpret ANG…”
Line 476: Add reference to Figure 3 in this sentence that begins “As expected…”
Line 480: Add reference to Table 4 here again.
Line 587: I think “BlS” is meant instead of “BrS”
- AC1: 'Final Author Comments on acp-2021-863', Katherine Junghenn Noyes, 07 Feb 2022
Status: closed
-
RC1: 'Comment on acp-2021-863', Anonymous Referee #1, 18 Nov 2021
This paper uses MISR observations to determine aerosol properties as a function of land cover type. They assess how these properties change with plume age and also relate their results to other fire and ecosystem factors such as fire radiative power and drought. The authors are able to identify statistical differences in satellite retrieved aerosol properties that depend on the fuel source.
This is a well written paper - a pleasure to read. I have a couple minor science questions and then a few technical suggestions.
Science questions
Line 201 - Isn't deposition a function of particle size? I would expect AOD and REPS to decrease with deposition as the larger particles settle out first.
Table 1 - is the humped SSA dependence in Table 1 expected? For example, Figure 1 in Samset et al. (2018) (https://link.springer.com/article/10.1007/s40641-018-0091-4) suggests either increasing or decreasing SSA as a function of wavelength, with both BC and BrC increasing with increasing wavelength and dust decreasing with increasing wavelength.
Figures 8&9 - the arrows to guide the eye - how are the arrow directions defined? is there a quantitative determination? For example, qualitatively looking at the plots I would consider drawing increasing arrows on figures 9a, 9b and 9f.
Technical corrections
Line 44 - cite Petzold et al. (2013) www.atmos-chem-phys.net/13/8365/2013/. I like the BlS and BrS designation!
Line 87 - perhaps cite Kleinman https://doi.org/10.5194/acp-20-13319-2020 here
Line 105 - 'Models are uncertain...' Models don't have thoughts...could this perhaps be rephrased?
Line 118 - 'Research Aerosol (RA) retrieval algorithm' I personally would have used RA2: 'Research Aerosol retrieval algorithm (RA2)'. Because later, e.g., line 127 if reading aloud - 'the RA successfully mapped' reads as 'the Research Aerosol successfully mapped' would sound better as 'the RA2 successfully mapped' i.e., 'the Research Aerosol retrieval algorithm successfully mapped', but no worries if RA is already accepted nomenclature
Table 2 caption - AGL is only mentioned in the footnotes, so move AGL definition to the footnotes too?
Line 289 MERRA is used several times before being defined (e.g., on 137, 282, 287).
Figure 1 - Perhaps indicated the territories since they are referenced in the first paragraph of the results section?
Line 348-349 - Table 3 defines the land cover types so don't need to repeat definitions here.
Line 363 - spell out woody to be consistent with spelling out other biomes in previous sentence.
Figure 2 and related discussion - Plumes in FT are the same as plumes above PBL described in Table 2 so perhaps it makes sense to change Table 2 to call them plumes in FT instead of 'above PBL'. The footnote to Table 2 could stay the same.
Figure 2 - Some numbers in Table 2 don't quite match up with Figure 2 - for example Table 2 suggests 33.8% plumes in FT in 2016, while sum for those plumes is 32% in Figure 2. Difference due to rounding?
Line 410 - the only typo I found! there is an extra 'a' at the end of this line
Line 471-473 - This sentence could be clarified - possibly also split into two sentences.
Line 484 - '... some plumes containing as much as 40% non-spherical...' Perhaps quantify 'some' or change 'some' to 'a very few plumes contain...' as several places (line 480 and line 485) indicate very few plumes have much in the way of non-spherical particles but then 40% is a lot!
Line 487 - I assume that the attribution of lower SAE to more non-spherical particles in F plumes is due to the fact that these non-spherical particles are large (re=1.21 um), rather than their non-sphericity?
Line 623 'there have been no other large scale regional studies' I guess this depends how large scale and regional studies are defined? For example, Kleinman et al (2020) https://doi.org/10.5194/acp-20-13319-2020 uses in-situ airborne observations to look at particle property changes due to aerosol aging in smoke plumes in the Pacific Northwest region.
-
RC2: 'Comment on acp-2021-863', Anonymous Referee #2, 14 Dec 2021
The paper presents an analysis of retrieved smoke particle properties made from the MISR space-based instrument aboard the NASA Terra spacecraft. Smoke plumes are identified over Alaska and Canada in the MISR data and correlated with (where available) fire radiative power measurements from the MODIS instrument onboard the same platform. Information on the smoke plume altitude is retrieved from the MISR stereo camera capabilities. Smoke plume altitude and particle properties from the MISR research algorithm (RA) retrievals are correlated with IGBP land types. Aerosol properties and loading are also correlated with drought indices.
The main conclusions of the paper are:
- Forest fire plumes tend to produce higher aerosol optical depth (AOD) and larger and less absorbing particles than fires from grasslands
- Aging of smoke particle is different between grassland fires and forest and woody fires, with the latter group exhibiting evident particle growth and a more rapid transition toward lower absorption in particles
- Drought is also a factor in smoke particle properties, with woody fires especially exhibiting a tendency toward decreased absorption in the particles as drought index is more severe.
The MISR instrument and products provide invaluable information on the distributions and properties of aerosols, and I think this paper (and subsequent analyses in other regions) could make a significant contribution to our understanding and characterizing the properties of smoke aerosols and their evolution near fire sources. That said, I am unclear on some implications of the retrieval assumptions that I think warrant further discussion, so I am suggesting major revisions.
Major Comments
I am unclear on how actually the plumes are selected from the totality of the MISR dataset. It isn’t clearly articulated the criteria. Evidently 50% of the selected plumes are from the MISR plume height archive. Where are the other 50% from? Is the selection done manually? How? Line 230 states “Well-defined plumes…were favored for this analysis.” What constitutes well-defined (AOD, something else)? Is it a requirement that it is a smoke plume versus something else? How are you certain you are selecting smoke cases? Are fire anomalies required? I think this needs to be in section 2.2, which rather than providing the information on case selection the title suggests seems more to be about the characteristics of the cases.
The main concern I have is that I don’t know how to judge the robustness of the retrieval results regarding particle composition presented. My understanding of an algorithm like MISR’s RA is that a “best” mixture is determined from its closeness to the observed spectral and angular data. I’m not clear on the orthogonality of the individual components in Table 1, and neither am I clear on how a mixture of these components that minimizes the cost function compares to another mixture that almost minimizes the cost function. Is there degeneracy in the results that admits a different solution? Is it significantly different? Some further presentation of such an error analysis would be important here in order enhance the confidence in the results presented.
I find the arrows in Figures 8 and 9 not very well justified and suggest they be omitted. If the point is there are clear trends then just plot the line fits with the appropriate statistics, otherwise I think the interpretation is a bit forced.
Minor Comments
Line 54-55: I think a comment here on pyroCb would be useful to add, to acknowledge the growing interest in this type of fire.
Table 1: For the utility of modelers, please include in the supplement further information on the particle properties summarized here. For example, the mode radius and width and the refractive indices at the MISR channels.
Line 91-96: This construct is unclear. Do you mean in the end that you consider new particle formation and condensation/hygroscopic growth as the distinct aging mechanisms?
Line 103: “Most current transport and climate models…” I think it is more accurate to say that most current climate models do not at present incorporate brown carbon at all. The treatment of smoke as being a mixture of black carbon, organic (white) carbon, and sulfates leads to I think what you mean by “BlS” in this paper.
Line 234: You mean the lower 48 here, as Alaska is part of your domain and is part of the US.
Line 293: 0.667 degrees is inconsistent with 0.625 degrees a few lines earlier. I believe the MERRA-2 fields are uniformly available on the 0.625 x 0.5 degree grid.
Line 327: “event” not “even”
Line 449: should be “compared to G”
Line 449/450: “More on particle properties in subsequent sections” seems a bit informal. Maybe “More information on particle properties is presented in subsequent sections.”
Line 461: Add pointer to Table 4 in this first sentence.
Line 467: Suggest starting a new paragraph with “To help interpret ANG…”
Line 476: Add reference to Figure 3 in this sentence that begins “As expected…”
Line 480: Add reference to Table 4 here again.
Line 587: I think “BlS” is meant instead of “BrS”
- AC1: 'Final Author Comments on acp-2021-863', Katherine Junghenn Noyes, 07 Feb 2022
Katherine T. Junghenn Noyes et al.
Katherine T. Junghenn Noyes et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 479 | 140 | 19 | 638 | 29 | 6 | 7 |
- HTML: 479
- PDF: 140
- XML: 19
- Total: 638
- Supplement: 29
- BibTeX: 6
- EndNote: 7
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1