the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jun 2022
Sixteen years of MOPITT satellite data strongly constrain Amazon CO fire emissions
Abstract. Despite consensus on the overall downward trend in Amazon forest loss in the previous decade, estimates of yearly carbon emissions from deforestation still vary widely. Estimated carbon emissions are currently often based on data from local logging activity reports, changes in remotely sensed biomass as well as remote detection of fire hotspots, and burned area. Here, we use sixteen years of satellite-derived carbon monoxide (CO) columns to constrain fire CO emissions from the Amazon basin between 2003 and 2018. Through data assimilation, we produce 3-daily maps of fire CO emissions over the Amazon that we verified to be consistent with a long-term monitoring program of aircraft CO profiles over five sites in the Amazon. Our new product independently confirms a long-term decrease of 54 % in deforestation-related CO emissions over the study period. Interannual variability is large, with known anomalously dry years showing a more than fourfold increase in basin-wide fire emissions. At the level of individual Brazilian states, we find that both soil moisture anomalies and human ignitions determine fire activity, suggesting that future carbon release from fires depends on drought intensity as much as on continued forest protection. Our study shows that the atmospheric composition perspective on deforestation is a valuable additional monitoring instrument that complements existing bottom-up and remote sensing methods for land-use change. Extension of such a perspective to an operational framework is timely considering the observed increased fire intensity in the Amazon basin in 2019–2021.
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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
(2891 KB)
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Supplement
(474 KB)
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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
(2891 KB) - Metadata XML
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Supplement
(474 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-2022-450', Anonymous Referee #1, 15 Aug 2022
Naus et al. use 16 years MOPITT CO data from 2003-2018 to constrain CO emissions from fires over the Amazon. The authors optimize prior CO emissions from bottom-up fire emissions inventories (e.g., GFAS) and find that they agree more closely with aircraft observations. Overall, the manuscript is well-written and comprehensive; I only have some minor comments.
Line-by-Line Comments:
Line 2. By how much do these estimates vary by?
Line 8. A fourfold increase over wet years?
Line 5. I’m confused whether “3-daily” refers to 3 times per day or every 3 days. Please make this clear here.
Line 24-25. “albedo changes” needs to be corrected to burned area or surface reflectance. GFED (van der Werf et al., 2017) is primarily based on the MODIS burned area product. Burned area classification is derived from changes in surface reflectance. The references the authors list refer to quantification of fire emissions rather than pure monitoring of fires. For the latter, the authors should cite papers that describe the active fire and burned area products, such as Giglio et al. (2016) and Giglio et al. (2018). I’m not sure what the authors mean by the products being “partly related.” Related in what way? Being able to serve as the basis for emissions estimates?
Line 71. Please state the spatial resolution of the ERA-Interim reanalysis product.
Line 77. The authors say GFASv1.2 is provided at 0.5° spatial resolution here, but GFASv1.2 is provided at 0.1° spatial resolution.
Line 88. “three-day” and “three-daily” are used interchangeably. Stick with one or the other.
Line 90. Why a 0.03 Tg threshold specifically? Is this a statistical cutoff?
Line 95. Why were the GFAS emissions outside the domain not averaged? How much does the interannual variability of the emissions outside the domain influence the results?
Line 110. There’s no need to spell out MOPITT again.
Line 115. Please explain why a factor of the square root of 50 is chosen.
Line 163-171. The authors should quantify the interannual variability, e.g. standard deviation. In general, the authors should be more quantitative in describing their results.
Figure 4. The black circles in 4a shows how much CO is added to the fires, but a scale/legend is needed here.
Figure 5. It’s hard to see the purple “Observed” line in the top panel. Since the difference between “Observed” and “Simulated” are shown in the bottom panel, just showing the “Observed” line in the top panel might be a better approach.
Citation: https://doi.org/10.5194/egusphere-2022-450-RC1 - AC1: 'Reply on RC1', Stijn Naus, 05 Oct 2022
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RC2: 'Comment on egusphere-2022-450', Anonymous Referee #2, 24 Aug 2022
This is an interesting and well done study. I have the following recommendations that I wish the authors to address:
Main Comments
Line 5: Why don’t you use 2003-2021 in your analysis, especially since you say on Line 12 that 2019-2021 are interesting years?
Line 45: In this paragraph, you are trying to say what’s new about your work as compared to other studies in the literature. Your topic sentence seems to address the novelty of your work (i.e., data assimilation), but then you say in the next sentence that others have done this as well. That is, you only give a few sentences about the previous work which only raise questions about the novelty of your work. Your new aspect seems to be that you are looking at a longer time period than in other studies. If this is true, this is a weak justification unless there is something unique about the additional years. MOPITT data have been around for a very long time and many studies have been done, so I strongly recommend that you expand discussion on these previous studies and clearly articulate how your work is new.
Note: Upon reading further, I see that you devote Section 3.3.2 to Zheng et al. (2019). This makes it even more important for you to clearly differentiate between Zheng et al. and your work in the introduction.
Line 110 & Line 151: Why not assimilate satellite retrievals over the whole globe? Is it simply because such an inversion would be computationally expensive as suggested in the paragraph beginning on Line 146? It seems that it would make more sense to do the assimilation for the whole globe so that your background CO will be more realistic, especially since your OH and CO production from methane/VOC oxidation are both static (Line 102-108). What are the implications for your study by not accounting for the background trend in CO over your study period?
Minor Comments
Line 5: “3-daily” is unclear. Replace “3-daily” with “3-day average”. I think that’s what you mean.
Line 62: You said in the introduction that the model framework is comparable to other setups. Please clarify if the other studies used the same setup or some of the same components.
Line 125: Again, why just assimilate in situ observations when you have satellite retrievals?
Line 316: You didn’t mention how MOPITT’s averaging kernels may introduce uncertainty in the inversion.
Citation: https://doi.org/10.5194/egusphere-2022-450-RC2 - AC2: 'Reply on RC2', Stijn Naus, 05 Oct 2022
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-450', Anonymous Referee #1, 15 Aug 2022
Naus et al. use 16 years MOPITT CO data from 2003-2018 to constrain CO emissions from fires over the Amazon. The authors optimize prior CO emissions from bottom-up fire emissions inventories (e.g., GFAS) and find that they agree more closely with aircraft observations. Overall, the manuscript is well-written and comprehensive; I only have some minor comments.
Line-by-Line Comments:
Line 2. By how much do these estimates vary by?
Line 8. A fourfold increase over wet years?
Line 5. I’m confused whether “3-daily” refers to 3 times per day or every 3 days. Please make this clear here.
Line 24-25. “albedo changes” needs to be corrected to burned area or surface reflectance. GFED (van der Werf et al., 2017) is primarily based on the MODIS burned area product. Burned area classification is derived from changes in surface reflectance. The references the authors list refer to quantification of fire emissions rather than pure monitoring of fires. For the latter, the authors should cite papers that describe the active fire and burned area products, such as Giglio et al. (2016) and Giglio et al. (2018). I’m not sure what the authors mean by the products being “partly related.” Related in what way? Being able to serve as the basis for emissions estimates?
Line 71. Please state the spatial resolution of the ERA-Interim reanalysis product.
Line 77. The authors say GFASv1.2 is provided at 0.5° spatial resolution here, but GFASv1.2 is provided at 0.1° spatial resolution.
Line 88. “three-day” and “three-daily” are used interchangeably. Stick with one or the other.
Line 90. Why a 0.03 Tg threshold specifically? Is this a statistical cutoff?
Line 95. Why were the GFAS emissions outside the domain not averaged? How much does the interannual variability of the emissions outside the domain influence the results?
Line 110. There’s no need to spell out MOPITT again.
Line 115. Please explain why a factor of the square root of 50 is chosen.
Line 163-171. The authors should quantify the interannual variability, e.g. standard deviation. In general, the authors should be more quantitative in describing their results.
Figure 4. The black circles in 4a shows how much CO is added to the fires, but a scale/legend is needed here.
Figure 5. It’s hard to see the purple “Observed” line in the top panel. Since the difference between “Observed” and “Simulated” are shown in the bottom panel, just showing the “Observed” line in the top panel might be a better approach.
Citation: https://doi.org/10.5194/egusphere-2022-450-RC1 - AC1: 'Reply on RC1', Stijn Naus, 05 Oct 2022
-
RC2: 'Comment on egusphere-2022-450', Anonymous Referee #2, 24 Aug 2022
This is an interesting and well done study. I have the following recommendations that I wish the authors to address:
Main Comments
Line 5: Why don’t you use 2003-2021 in your analysis, especially since you say on Line 12 that 2019-2021 are interesting years?
Line 45: In this paragraph, you are trying to say what’s new about your work as compared to other studies in the literature. Your topic sentence seems to address the novelty of your work (i.e., data assimilation), but then you say in the next sentence that others have done this as well. That is, you only give a few sentences about the previous work which only raise questions about the novelty of your work. Your new aspect seems to be that you are looking at a longer time period than in other studies. If this is true, this is a weak justification unless there is something unique about the additional years. MOPITT data have been around for a very long time and many studies have been done, so I strongly recommend that you expand discussion on these previous studies and clearly articulate how your work is new.
Note: Upon reading further, I see that you devote Section 3.3.2 to Zheng et al. (2019). This makes it even more important for you to clearly differentiate between Zheng et al. and your work in the introduction.
Line 110 & Line 151: Why not assimilate satellite retrievals over the whole globe? Is it simply because such an inversion would be computationally expensive as suggested in the paragraph beginning on Line 146? It seems that it would make more sense to do the assimilation for the whole globe so that your background CO will be more realistic, especially since your OH and CO production from methane/VOC oxidation are both static (Line 102-108). What are the implications for your study by not accounting for the background trend in CO over your study period?
Minor Comments
Line 5: “3-daily” is unclear. Replace “3-daily” with “3-day average”. I think that’s what you mean.
Line 62: You said in the introduction that the model framework is comparable to other setups. Please clarify if the other studies used the same setup or some of the same components.
Line 125: Again, why just assimilate in situ observations when you have satellite retrievals?
Line 316: You didn’t mention how MOPITT’s averaging kernels may introduce uncertainty in the inversion.
Citation: https://doi.org/10.5194/egusphere-2022-450-RC2 - AC2: 'Reply on RC2', Stijn Naus, 05 Oct 2022
Peer review completion
Journal article(s) based on this preprint
Data sets
Optimized CO fire emissions Stijn Naus, Lucas G. Domingues, Maarten Krol, Ingrid T. Luijkx, Luciana V. Gatti, John B. Miller, Emanuel Gloor, Sourish Basu, Caio Correia, Gerbrand Koren, Helen M. Worden, Johannes Flemming, Gabrielle Pétron, and Wouter Peters https://doi.org/10.6084/m9.figshare.14294492
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Lucas G. Domingues
Maarten Krol
Ingrid T. Luijkx
Luciana V. Gatti
John B. Miller
Emanuel Gloor
Sourish Basu
Caio Correia
Gerbrand Koren
Helen M. Worden
Johannes Flemming
Gabrielle Pétron
Wouter Peters
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2891 KB) - Metadata XML
-
Supplement
(474 KB) - BibTeX
- EndNote
- Final revised paper