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CH4 emissions from Northern Europe wetlands : compared data assimilation approaches

Monteil, Guillaume LU orcid ; Kallingal, Jalisha Theanutti LU and Scholze, Marko LU orcid (2025) In Atmospheric Chemistry and Physics 25(21). p.14251-14277
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... (More)

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, however ecosystem data assimilation for estimation CH4 emissions is relatively new. Kallingal et al. (2024a) 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 confronted CH4 emissions optimized using the GRaB-AM system to atmospheric CH4 observations and to emission estimates from the LUMIA regional atmospheric inversion system (Monteil and Scholze, 2021). We found that the two approaches lead to very consistent corrections to the prior emission estimate from natural wetlands, with roughly a halving of the annual total compared to the LPJ-GUESS prior. Our findings confirm the interest of the GRaB-AM approach to constrain the contribution of natural ecosystems to the total methane budget, which is difficult to achieve for atmospheric inversions outside regions where emissions from natural ecosystems clearly dominate the emission budget.

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publication status
published
subject
in
Atmospheric Chemistry and Physics
volume
25
issue
21
pages
27 pages
publisher
Copernicus GmbH
external identifiers
  • scopus:105023165276
ISSN
1680-7316
DOI
10.5194/acp-25-14251-2025
language
English
LU publication?
yes
id
5de745b7-ad35-40b8-a558-85c164916cee
date added to LUP
2026-01-30 15:28:24
date last changed
2026-01-30 15:29:29
@article{5de745b7-ad35-40b8-a558-85c164916cee,
  abstract     = {{<p>Atmospheric inverse modelling and ecosystem data assimilation are two complementary approaches to estimate CH<sub>4</sub> emissions. The inverse approach infers emission estimates from observed atmospheric CH<sub>4</sub> 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 CO<sub>2</sub> emissions, however ecosystem data assimilation for estimation CH<sub>4</sub> emissions is relatively new. Kallingal et al. (2024a) developed the GRaB-AM data assimilation system, which performs a parameter optimization of the LPJ-GUESS against eddy-covariance estimation of CH<sub>4</sub> 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 confronted CH<sub>4</sub> emissions optimized using the GRaB-AM system to atmospheric CH<sub>4</sub> observations and to emission estimates from the LUMIA regional atmospheric inversion system (Monteil and Scholze, 2021). We found that the two approaches lead to very consistent corrections to the prior emission estimate from natural wetlands, with roughly a halving of the annual total compared to the LPJ-GUESS prior. Our findings confirm the interest of the GRaB-AM approach to constrain the contribution of natural ecosystems to the total methane budget, which is difficult to achieve for atmospheric inversions outside regions where emissions from natural ecosystems clearly dominate the emission budget.</p>}},
  author       = {{Monteil, Guillaume and Kallingal, Jalisha Theanutti and Scholze, Marko}},
  issn         = {{1680-7316}},
  language     = {{eng}},
  number       = {{21}},
  pages        = {{14251--14277}},
  publisher    = {{Copernicus GmbH}},
  series       = {{Atmospheric Chemistry and Physics}},
  title        = {{CH<sub>4</sub> emissions from Northern Europe wetlands : compared data assimilation approaches}},
  url          = {{http://dx.doi.org/10.5194/acp-25-14251-2025}},
  doi          = {{10.5194/acp-25-14251-2025}},
  volume       = {{25}},
  year         = {{2025}},
}