Canadian and Alaskan wildfire smoke particle properties, their evolution, and controlling factors, from satellite observations

Junghenn Noyes, Katherine T.; Kahn, Ralph A.; Limbacher, James A.; Li, Zhanqing

doi:https://doi.org/10.5194/acp-2021-863

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https://doi.org/10.5194/acp-2021-863

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03 Nov 2021

Review status: this preprint is currently under review for the journal ACP.

Canadian and Alaskan wildfire smoke particle properties, their evolution, and controlling factors, from satellite observations

Katherine T. Junghenn Noyes^1,2, Ralph A. Kahn³, James A. Limbacher^3,4, and Zhanqing Li^1,5 Katherine T. Junghenn Noyes et al.

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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

Received: 18 Oct 2021 – Accepted for review: 02 Nov 2021 – Discussion started: 03 Nov 2021

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.

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RC1: 'Comment on acp-2021-863', Anonymous Referee #1, 18 Nov 2021 reply

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.

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Citation: https://doi.org/10.5194/acp-2021-863-RC1

Katherine T. Junghenn Noyes et al.

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Short summary

We compare retrievals on wildfire smoke particle size, shape, and light-absorption from the MISR satellite instrument to modeling and other satellite data on land cover type, drought conditions, meteorology, and estimates of fire intensity (fire radiative power, or FRP). We find statistically significant differences in the particle properties based on burning conditions and land cover type, and interpret how changes in these properties point to specific aerosol aging mechanisms.


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