the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Jun 2024
Satellite quantification of methane emissions from South American countries: A high-resolution inversion of TROPOMI and GOSAT observations
Abstract. We use 2021 TROPOMI and GOSAT satellite observations of atmospheric methane in an analytical inversion to quantify national methane emissions from South America at up to 25 km × 25 km resolution. From the inversion, we derive optimal posterior estimates of methane emissions correcting the national anthropogenic emission inventories reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) and taken here as prior estimates. We also evaluate two alternative wetland emission inventories (WetCHARTs and LPJ-wsl) as prior estimates. Our best posterior estimates for wetland emissions are consistent with previous inventories for the Amazon but lower for the Pantanal and higher for the Parana. Our best posterior estimate of South American anthropogenic emissions is 48 (41–56) Tg a-1, where numbers in parentheses are the range from our inversion ensemble. This is 55 % higher than UNFCCC reports and is dominated by livestock (65 % of anthropogenic total). We find that TROPOMI and GOSAT observations can effectively optimize and separate national emissions by sector for 10 of the 13 countries and territories in the region, 7 of which account for 93 % of continental anthropogenic emissions: Brazil (19 (16–23) Tg a−1), Argentina (9.2 (7.9–11) Tg a−1 ), Venezuela (7.0 (5.5-9.9) Tg a−1), Colombia (5.0 (4.4–6.7) Tg a−1), Peru (2.4 (1.6–3.9) Tg a−1), Bolivia (0.96 (0.66–1.2) Tg a−1), and Paraguay (0.93 (0.88 – 1.0) Tg a−1). Our estimates align with UNFCCC reports for Brazil, Bolivia, and Paraguay, but are significantly higher for other countries. Emissions in all countries are dominated by livestock (mainly enteric fermentation) except for oil/gas in Venezuela and landfills in Peru. Methane intensities from the oil/gas industry are high in Venezuela (33 %), Colombia (6.5 %) and Argentina (5.9 %). Country-average emission factors for enteric fermentation from cattle in UNFCCC reports are in the range 46–60 kg head-1 a-1, close to the IPCC Tier 1 estimate which is mostly based on data from Brazil. Our inversion yields cattle enteric fermentation emission factors consistent with the UNFCCC reports for Brazil and Bolivia but a factor of two higher for other countries. The discrepancy for Argentina can be corrected by using IPCC Tier 2 emission estimates accounting for high milk production.
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CC1: 'Comment on egusphere-2024-1763 submitted by South American scientists', M. Cazorla, 26 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1763/egusphere-2024-1763-CC1-supplement.zip
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AC1: 'Reply on CC1', Sarah Hancock, 27 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1763/egusphere-2024-1763-AC1-supplement.pdf
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AC1: 'Reply on CC1', Sarah Hancock, 27 Jul 2024
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RC1: 'Comment on egusphere-2024-1763', Anonymous Referee #1, 31 Jul 2024
Hancock et al. present a comprehensive study that integrates satellite observations of CH4 with an atmospheric transport model and prior emissions estimates to derive an optimal set of methane emissions for South American countries. This analysis further disaggregates the optimized emissions by sector and country, enhancing the atmospheric constraint on South American methane emissions. The topic is significant, and well-aligned with the scope of ACP. I am not an expert on inverse modeling but rather on in-situ measurements so I can’t comment deeply on the mathematical aspects of the inversion method and learnt a lot. Below, I outline a few questions and suggestions.
Line 93: what is the retrieval success rate specifically for South America?
Regarding Fig. 1: How would the seasonal variability of methane concentration affect these emission estimates? A spatial plot of the total number of samplings in each season during 2021 might be useful to include in the supplement. Is it possible to extend this inverse modeling setup to estimate total methane emissions on a monthly scale?
Line 115: "There are few observations over the mountainous Andes, affecting much of Chile and Peru, so the inversion for those countries relies significantly on glint observations offshore and on observations of transported methane." How does this affect the uncertainties in estimating emissions for this area?
How does the inverse model handle temporal variability in emissions? While the model optimizes the overall magnitude and spatial patterns in emissions, does it also optimize seasonal or year-to-year variability? In other words, does the model assume that the temporal distribution of emissions is known or fixed according to the prior temporal distribution?
Line 206: "We use 600 Gaussian functions as state vector elements to balance aggregation and smoothing errors." While a reference is provided, a brief explanation of why 600 Gaussian functions are used would be helpful.
Regarding Fig. 3: The ratios of the posterior/prior emissions in Fig. 3c show values close to zero or over 2 in many areas (e.g., Bolivia and Argentina). Does this imply that the inversion zeroed or doubled emissions? If so, are the resulting emissions reasonable?
Given the goal of the paper is to "evaluate the national inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC) under the Paris Agreement and to identify opportunities to improve countries' bottom-up reporting methods," including model-data comparisons against independent CH4 observations is crucial for evaluating the inverse model. While there is a comparison with aircraft measurements in the Bolivian Amazon region, this is not enough. A supplement showing the bias of methane concentration in the prior and posterior run relative to in-situ observations in South America would be very informative.
Minor Point: Line 98: TCCON is first mentioned here. It should be referred to as the "Total Carbon Column Observing Network (TCCON)." Additionally, providing a brief explanation of TCCON would help readers unfamiliar with this field understand its purpose.
Citation: https://doi.org/10.5194/egusphere-2024-1763-RC1 -
RC2: 'Comment on egusphere-2024-1763', Anonymous Referee #2, 02 Aug 2024
This paper presents a comprehensive analysis of methane emissions across South America. By employing high-resolution satellite data from TROPOMI and GOSAT, the authors present a comprehensive and spatially detailed estimation of methane sources, mainly anthropogenic sources, which constitutes a significant contribution to the comprehension of regional methane budgets. However, there are a few aspects of the paper that could be addressed to yield a more robust and comprehensive analysis.
The integration of data from two complementary satellite instruments helps to improve estimates through the use of inverse modelling. Nevertheless, a validation with independent CH4 observations available in South America would be beneficial for this study. For instance, the authors could undertake a comparison of the posterior mole fraction with data obtained from the ATTO tower in Brazil and vertical profiles in the Amazon region.
With regard to the regional budget, it would be beneficial to include a comparison with other previously published top-down estimates of total methane emissions for each country, such as those included in the Global Methane Budget.
The authors could additionally provide insight into the implications of their findings for policymaking strategies. This would be beneficial, as they evaluated national anthropogenic emissions inventories reported by individual countries to the UNFCCC. In particular, the authors could elucidate how these data could help local governments to mitigate methane emissions.
Specific comments are provided below.
Line 20: The term "correcting" may suggest that top-down estimates are inherently more accurate than bottom-up estimates, whereas both approaches are subject to their own sets of uncertainties. To prevent any potential misinterpretations, it would be helpful to use a term such as "adjusting" or "reconciling" when discussing the comparison or combination of these estimates. It is also important to discuss the limitations of both methods, top-down and bottom-up estimates, in the paper.
Lines 118-119: state that satellite observations are distributed throughout the year, but are most dense during the southern hemisphere dry season (June-September). How the lower density of observations during the wet season in comparison with the dry season could affect the posterior estimates. This is particularly relevant given that this region has extensive wetland areas, where the highest emissions are expected during the wet season.
Figure 1 illustrates the annual mean 2021 dry-column methane mixing ratios (XCH4) after subtraction of background, clearly demonstrating the absence of TROPOMI data in the Amazon region, which is compensated by GOSAT observations. However, an examination of the plot of the number of observations shows that there are fewer GOSAT observations during the months of April to June in comparison with other months. Please describe the extent of data coverage for South America during this period, with particular attention to the Amazon region.It would be beneficial to have a map as supplementary material that includes both TROPOMI and GOSAT dry-column methane mixing ratios (XCH4) for the initial period of the year, particularly April to June.
Lines 196-200: What is the lifetime of methane considering all the sinks, including oxidation by hydroxyl (OH) radicals and tropospheric chlorine (Cl), oxidation in the stratosphere, and uptake by soils?
Line 328: “Most of that increase is from anthropogenic emissions”. Does this imply that the prior estimated wetland emissions for the South American region are consistent with the atmospheric measurements? Alternatively, could the posterior wetland fluxes be more dependent on the prior estimates due to the limited observations in the Amazon region (which has larger methane emissions, as illustrated in Figure 2), as reflected in the low averaging kernel sensitivities? It would be beneficial to conduct a comparison with independent atmospheric observations to evaluate the posterior estimates.
Citation: https://doi.org/10.5194/egusphere-2024-1763-RC2 -
EC1: 'Comment on egusphere-2024-1763', Eduardo Landulfo, 06 Aug 2024
Before the authors make a final reply, I would like to raise the issue about adding new authors and/or giving acknowlegdments for some feedback from the local scientific community.
Basically, there are two paths to choose :
1 - Adding new authors - The following points hsould be taken care of
a) the authors should make clear in their response why an author should be added
b) all co-authors have to approve the addition of the co-author (all co-authors can simply send an informal email to the editorial office)
c) the handling editor also has to approve the addition. You may want to add an editor comment in the discussion along those lines.
d) The authors should make sure that the added co-author is also mentioned in the 'author contribution section'This way, we can ensure transparency on the reasons why the author list was changed.
OR
2 - Inclusion of a statement on inclusion in global research - At the end of the manuscript (before the "ackowledgement section") , the authors could state what has benn mentioned in their comment and why it was difficult to include the local scientists - If any help in the wording is need EGU editorial staff can help with.
Citation: https://doi.org/10.5194/egusphere-2024-1763-EC1
Status: closed
-
CC1: 'Comment on egusphere-2024-1763 submitted by South American scientists', M. Cazorla, 26 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1763/egusphere-2024-1763-CC1-supplement.zip
-
AC1: 'Reply on CC1', Sarah Hancock, 27 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1763/egusphere-2024-1763-AC1-supplement.pdf
-
AC1: 'Reply on CC1', Sarah Hancock, 27 Jul 2024
-
RC1: 'Comment on egusphere-2024-1763', Anonymous Referee #1, 31 Jul 2024
Hancock et al. present a comprehensive study that integrates satellite observations of CH4 with an atmospheric transport model and prior emissions estimates to derive an optimal set of methane emissions for South American countries. This analysis further disaggregates the optimized emissions by sector and country, enhancing the atmospheric constraint on South American methane emissions. The topic is significant, and well-aligned with the scope of ACP. I am not an expert on inverse modeling but rather on in-situ measurements so I can’t comment deeply on the mathematical aspects of the inversion method and learnt a lot. Below, I outline a few questions and suggestions.
Line 93: what is the retrieval success rate specifically for South America?
Regarding Fig. 1: How would the seasonal variability of methane concentration affect these emission estimates? A spatial plot of the total number of samplings in each season during 2021 might be useful to include in the supplement. Is it possible to extend this inverse modeling setup to estimate total methane emissions on a monthly scale?
Line 115: "There are few observations over the mountainous Andes, affecting much of Chile and Peru, so the inversion for those countries relies significantly on glint observations offshore and on observations of transported methane." How does this affect the uncertainties in estimating emissions for this area?
How does the inverse model handle temporal variability in emissions? While the model optimizes the overall magnitude and spatial patterns in emissions, does it also optimize seasonal or year-to-year variability? In other words, does the model assume that the temporal distribution of emissions is known or fixed according to the prior temporal distribution?
Line 206: "We use 600 Gaussian functions as state vector elements to balance aggregation and smoothing errors." While a reference is provided, a brief explanation of why 600 Gaussian functions are used would be helpful.
Regarding Fig. 3: The ratios of the posterior/prior emissions in Fig. 3c show values close to zero or over 2 in many areas (e.g., Bolivia and Argentina). Does this imply that the inversion zeroed or doubled emissions? If so, are the resulting emissions reasonable?
Given the goal of the paper is to "evaluate the national inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC) under the Paris Agreement and to identify opportunities to improve countries' bottom-up reporting methods," including model-data comparisons against independent CH4 observations is crucial for evaluating the inverse model. While there is a comparison with aircraft measurements in the Bolivian Amazon region, this is not enough. A supplement showing the bias of methane concentration in the prior and posterior run relative to in-situ observations in South America would be very informative.
Minor Point: Line 98: TCCON is first mentioned here. It should be referred to as the "Total Carbon Column Observing Network (TCCON)." Additionally, providing a brief explanation of TCCON would help readers unfamiliar with this field understand its purpose.
Citation: https://doi.org/10.5194/egusphere-2024-1763-RC1 -
RC2: 'Comment on egusphere-2024-1763', Anonymous Referee #2, 02 Aug 2024
This paper presents a comprehensive analysis of methane emissions across South America. By employing high-resolution satellite data from TROPOMI and GOSAT, the authors present a comprehensive and spatially detailed estimation of methane sources, mainly anthropogenic sources, which constitutes a significant contribution to the comprehension of regional methane budgets. However, there are a few aspects of the paper that could be addressed to yield a more robust and comprehensive analysis.
The integration of data from two complementary satellite instruments helps to improve estimates through the use of inverse modelling. Nevertheless, a validation with independent CH4 observations available in South America would be beneficial for this study. For instance, the authors could undertake a comparison of the posterior mole fraction with data obtained from the ATTO tower in Brazil and vertical profiles in the Amazon region.
With regard to the regional budget, it would be beneficial to include a comparison with other previously published top-down estimates of total methane emissions for each country, such as those included in the Global Methane Budget.
The authors could additionally provide insight into the implications of their findings for policymaking strategies. This would be beneficial, as they evaluated national anthropogenic emissions inventories reported by individual countries to the UNFCCC. In particular, the authors could elucidate how these data could help local governments to mitigate methane emissions.
Specific comments are provided below.
Line 20: The term "correcting" may suggest that top-down estimates are inherently more accurate than bottom-up estimates, whereas both approaches are subject to their own sets of uncertainties. To prevent any potential misinterpretations, it would be helpful to use a term such as "adjusting" or "reconciling" when discussing the comparison or combination of these estimates. It is also important to discuss the limitations of both methods, top-down and bottom-up estimates, in the paper.
Lines 118-119: state that satellite observations are distributed throughout the year, but are most dense during the southern hemisphere dry season (June-September). How the lower density of observations during the wet season in comparison with the dry season could affect the posterior estimates. This is particularly relevant given that this region has extensive wetland areas, where the highest emissions are expected during the wet season.
Figure 1 illustrates the annual mean 2021 dry-column methane mixing ratios (XCH4) after subtraction of background, clearly demonstrating the absence of TROPOMI data in the Amazon region, which is compensated by GOSAT observations. However, an examination of the plot of the number of observations shows that there are fewer GOSAT observations during the months of April to June in comparison with other months. Please describe the extent of data coverage for South America during this period, with particular attention to the Amazon region.It would be beneficial to have a map as supplementary material that includes both TROPOMI and GOSAT dry-column methane mixing ratios (XCH4) for the initial period of the year, particularly April to June.
Lines 196-200: What is the lifetime of methane considering all the sinks, including oxidation by hydroxyl (OH) radicals and tropospheric chlorine (Cl), oxidation in the stratosphere, and uptake by soils?
Line 328: “Most of that increase is from anthropogenic emissions”. Does this imply that the prior estimated wetland emissions for the South American region are consistent with the atmospheric measurements? Alternatively, could the posterior wetland fluxes be more dependent on the prior estimates due to the limited observations in the Amazon region (which has larger methane emissions, as illustrated in Figure 2), as reflected in the low averaging kernel sensitivities? It would be beneficial to conduct a comparison with independent atmospheric observations to evaluate the posterior estimates.
Citation: https://doi.org/10.5194/egusphere-2024-1763-RC2 -
EC1: 'Comment on egusphere-2024-1763', Eduardo Landulfo, 06 Aug 2024
Before the authors make a final reply, I would like to raise the issue about adding new authors and/or giving acknowlegdments for some feedback from the local scientific community.
Basically, there are two paths to choose :
1 - Adding new authors - The following points hsould be taken care of
a) the authors should make clear in their response why an author should be added
b) all co-authors have to approve the addition of the co-author (all co-authors can simply send an informal email to the editorial office)
c) the handling editor also has to approve the addition. You may want to add an editor comment in the discussion along those lines.
d) The authors should make sure that the added co-author is also mentioned in the 'author contribution section'This way, we can ensure transparency on the reasons why the author list was changed.
OR
2 - Inclusion of a statement on inclusion in global research - At the end of the manuscript (before the "ackowledgement section") , the authors could state what has benn mentioned in their comment and why it was difficult to include the local scientists - If any help in the wording is need EGU editorial staff can help with.
Citation: https://doi.org/10.5194/egusphere-2024-1763-EC1
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