the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Dec 2024
CH4 emissions from Northern Europe wetlands: compared data assimilation approaches
Abstract. Atmospheric inverse modelling and ecosystem data assimilation are two complementary approaches to estimate CH4 emissions. The inverse approach infers emission estimates from observed atmospheric CH4 mixing ratio, which provide robust large scale constraints on total methane emissions, but with poor spatial and process resolution. On the other hand, in the ecosystem data assimilation approach, the fit of an ecosystem model (e.g. a Dynamic Global Vegetation Model, DGVM) to eddy-covariance (EC) flux measurements is used to optimize model parameters, leading to more realistic emission estimates.
Coupled data assimilation frameworks capable of assimilating both atmospheric and ecosystem observations have been shown to work for estimating CO2 emissions (e.g. Rayner et al. (2005)), however ecosystem data assimilation for estimation CH4 emissions is relatively new. Kallingal et al. (2024) developed the GRaB-AM data assimilation system, which performs a parameter optimization of the LPJ-GUESS against eddy-covariance estimation of CH4 emissions. The optimization improves the fit to EC data, but the validity of the estimate at large scale remained to be tested.
In this study, we used the LUMIA regional atmospheric inversion system (Monteil and Scholze, 2021) to confront wetland emissions from the GRaB-AM approach to atmospheric CH4 measurements in Europe. We then perform inversions using the information from GRaB-AM as prior. This let us infer a refined estimate for wetland emissions in Nordic Europe, and to explore the potential for a fully coupled data assimilation framework.
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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
(2648 KB)
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Supplement
(2496 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
(2648 KB) - Metadata XML
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Supplement
(2496 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-2024-3122', Anonymous Referee #1, 10 Jan 2025
- AC1: 'Reply on RC1', Guillaume Monteil, 03 Aug 2025
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RC2: 'Comment on egusphere-2024-3122', Anonymous Referee #2, 10 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3122/egusphere-2024-3122-RC2-supplement.pdf
- AC1: 'Reply on RC1', Guillaume Monteil, 03 Aug 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3122', Anonymous Referee #1, 10 Jan 2025
This manuscript assessed the methane emissions from Northern Europe, focusing on Nordic wetland emissions, using data assimilation approaches. The authors used atmospheric transport model and dynamic global vegetation model, each of which were constrained by atmospheric concentration and terrestrial flux data. They found that methane emissions from Northern Europe was overestimated in existing emission inventory and wetland model. Also, they found remaining large uncertainties in the background data and numerical models.
General comments
This study addressed the regional budget of methane, an important greenhouse gas, using contemporary data assimilation approaches. Although several results were unrealistic (e.g., negative wetland emissions and winter emissions) due to biases in data and models, this study gave a progress in the research field. As a result of this study, much lower Nordic wetland emissions were estimated. This was a bit surprising for me, because I thought that the wetlands in this area have been most intensively investigated by biogeochemical studies.
The manuscript is well prepared, but I offer reorganization for the Results section. It contains several methodological statements and discussions (see my minor comments). Finally, my recommendation is ‘minor revision’ before being accepted for publication.
Minor comments
Line 1: Abstract gives methodological overview but lacks statement about the outcomes (what did you found) of this study.
Line 117: LUMIA (Monteil and Scholze, 2021)
Line 126: 33°N (not S?)
Line 145: What do you mean for “quantitatively equivalent results”?
Line 227–231: This part should be moved to Method
Table 2: At several stations (hpb, hun, mhd, pal), two records are shown. What is the difference (e.g., in-situ or flask)?
Line 249–254: This part is not about result but about methodology.
Line 290: May (not may).
Line 352–357: This part may be moved to Discussion.
Line 359: Perhaps, a gridded wetland methane flux data generated from in-situ observations by using machine learning algorithms (e.g., UpCH4, McNicol et al., 2023) can be used for the comparison with gridded model-estimated flux.
Line 413: (LUMIA) is (not s) ?
Reference
McNicol, G., et al.: Upscaling wetland methane emissions from the FLUXNET-CH4 eddy covariance network (UpCH4 v1.0): Model development, network assessment, and budget comparison, AGU Advances, 4, e2023AV00956, 10.1029/2023AV000956, 2023.
Citation: https://doi.org/10.5194/egusphere-2024-3122-RC1 - AC1: 'Reply on RC1', Guillaume Monteil, 03 Aug 2025
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RC2: 'Comment on egusphere-2024-3122', Anonymous Referee #2, 10 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3122/egusphere-2024-3122-RC2-supplement.pdf
- AC1: 'Reply on RC1', Guillaume Monteil, 03 Aug 2025
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- 1
Discussed
Guillaume Monteil
Jalisha Theanutti Kallingal
Marko Scholze
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2648 KB) - Metadata XML
-
Supplement
(2496 KB) - BibTeX
- EndNote
- Final revised paper
This manuscript assessed the methane emissions from Northern Europe, focusing on Nordic wetland emissions, using data assimilation approaches. The authors used atmospheric transport model and dynamic global vegetation model, each of which were constrained by atmospheric concentration and terrestrial flux data. They found that methane emissions from Northern Europe was overestimated in existing emission inventory and wetland model. Also, they found remaining large uncertainties in the background data and numerical models.
General comments
This study addressed the regional budget of methane, an important greenhouse gas, using contemporary data assimilation approaches. Although several results were unrealistic (e.g., negative wetland emissions and winter emissions) due to biases in data and models, this study gave a progress in the research field. As a result of this study, much lower Nordic wetland emissions were estimated. This was a bit surprising for me, because I thought that the wetlands in this area have been most intensively investigated by biogeochemical studies.
The manuscript is well prepared, but I offer reorganization for the Results section. It contains several methodological statements and discussions (see my minor comments). Finally, my recommendation is ‘minor revision’ before being accepted for publication.
Minor comments
Line 1: Abstract gives methodological overview but lacks statement about the outcomes (what did you found) of this study.
Line 117: LUMIA (Monteil and Scholze, 2021)
Line 126: 33°N (not S?)
Line 145: What do you mean for “quantitatively equivalent results”?
Line 227–231: This part should be moved to Method
Table 2: At several stations (hpb, hun, mhd, pal), two records are shown. What is the difference (e.g., in-situ or flask)?
Line 249–254: This part is not about result but about methodology.
Line 290: May (not may).
Line 352–357: This part may be moved to Discussion.
Line 359: Perhaps, a gridded wetland methane flux data generated from in-situ observations by using machine learning algorithms (e.g., UpCH4, McNicol et al., 2023) can be used for the comparison with gridded model-estimated flux.
Line 413: (LUMIA) is (not s) ?
Reference
McNicol, G., et al.: Upscaling wetland methane emissions from the FLUXNET-CH4 eddy covariance network (UpCH4 v1.0): Model development, network assessment, and budget comparison, AGU Advances, 4, e2023AV00956, 10.1029/2023AV000956, 2023.