the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jun 2025
An inter-comparison of inverse models for estimating European CH4 emissions
Abstract. Atmospheric inversions are widely used to evaluate and improve inventories of methane (CH4) emissions on scales ranging from global to national and beyond, combining observations with atmospheric transport models. This study uses the dense network of in situ stations of the Integrated Carbon Observation System (ICOS) to explore how well in situ data can constrain European CH4 emissions. Following the concept of inter-comparison studies of the atmospheric tracer transport model inter-comparison Project (TransCom), a CH4 inverse inter-comparison modeling study has been performed, focusing on Europe for the period 2006–2018. The aim is to investigate the capability of inverse models to deliver consistent flux estimates at the national scale and evaluate trends in emission inventories.
Study participants were asked to perform inverse modelling computations using a common database of a priori CH4 emissions and in-situ observations as specified in a protocol. The participants submitted their best estimates of CH4 emissions for the 27 European Union (EU) member states, the United Kingdom (UK), Switzerland, and Norway. Results were collected from 9 different inverse modelling systems, using 7 different global and regional transport models. The range of outcomes allows us to assess posterior emission uncertainty, accounting for transport model uncertainty and inversion design decisions, including a priori emission and model-data mismatch uncertainty.
This paper presents inversion results covering 15 years, that are used to investigate the seasonality and trends of CH4 emissions. The different inversion systems show a range of a posteriori emission adjustments, pointing to factors that should receive further attention in the design of inversions such as optimising background concentrations. Most inverse models increase the seasonal cycle amplitude, by up to 400 Gg month-1, with the largest adjustments to the a priori emissions in Western and Eastern Europe. This might be due to underestimation of emissions from wetlands during summer or the importance of seasonality in other microbial sources, such as landfills and waste water treatment plants. In Northern Europe, absolute flux adjustments are comparatively small, which could imply that the emission magnitude is relatively well captured by the a priori, though the lower station density could contribute also.
Across Europe, the inverse models yield a similar decreasing trend in CH4 emissions compared to the a priori emissions (-12.3 % instead of -9.1 %) from 2006 to 2018. While both the a priori and the a posteriori trend for the EU-27 are statistically significant from zero, their difference is not. On subregion scale, the differences between a posteriori and a priori trends are more statistically significant over regions with more in-situ measurement sites, such as over Western and Southern Europe.
Uncertainties in the a priori anthropogenic emissions, such as in the agriculture sector (cows, manure), or waste sector (microbial CH4 emissions), but also in the a priori natural emissions, e.g. wetlands, might be responsible for the discrepancies between the a priori and a posteriori emission trends in Western and Southern Europe. Our results highlight the importance of improving details in the inversion setup, such as the treatment of lateral boundary conditions and the model representation of measurement sites, to narrow the uncertainty ranges further.
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RC1: 'Comment on essd-2025-235', Anonymous Referee #1, 15 Jul 2025
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This study presents an interesting comparison of nine inverse models for estimating national CH4 emissions across Europe from 2005 to 2018. The topic is cutting-edge and highly relevant for evaluating the effectiveness of nationwide greenhouse gas reduction efforts, as well as assessing the accuracy of bottom-up emission inventories. The paper is well-structured and scientifically sound in most parts; however, several key concerns should be addressed before considering acceptance in ESSD. I recommend a major revision based on the following specific comments:
Data resolution and coverage:
The prior CH4 emissions used in this study vary in resolution and temporal coverage (Table 1). For instance, the Peatlands category has a daily resolution, while other sources are provided at monthly or annual resolutions. The authors should justify their selection of these datasets and explain how missing time periods (e.g., 2005–2020 vs. 2005–2018) were handled.
Model validation:
For the validation of prior and posterior CH4 estimates against observations, only six out of nine inverse models are presented in Figure 5. Why were the remaining three models excluded?
CTDAS-WRF model performance (Line 230):
The authors attribute the poorer performance of the CTDAS-WRF model to discrepancies with observations during winter and fall. Does this discrepancy also apply to other inverse models? Beyond meteorological variability, what other factors (e.g., transport errors) might contribute?
Seasonal cycle (Figure 6):
One inverse model exhibits abnormal seasonality compared to others, particularly in Western Europe (August–December). What explains this large variability? This likely offsets the posterior seasonality, especially in August. A more detailed explanation is needed here.
In Southern Europe, the sharp decline in mean posterior emissions from August to November appears driven by one outlier model, potentially biasing the seasonality interpretation.
Regarding Northern Europe (Lines 255–260), the authors attribute enhanced prior CH4 emissions to wetlands in summer. Could seasonal variations in CH₄ sinks such as ●OH also play a role?
For the JSBACH-HIMMELI model, the authors acknowledge underestimation of river and lake emissions. Are coastal wetland emissions well-captured in this model?
Lines 260–265: The authors note that uncertainties in temperature and precipitation limit wetland CH₄ emission estimates. However, precipitation is a poor proxy for wetland emissions compared to inundation (see https://doi.org/10.1029/2020GB006890 and https://doi.org/10.1038/s43247-025-02438-3). A discussion of these hydrological indicators would strengthen the analysis.
Suggestion: Replace "missing processes" (Line 264) with "missing/simplified processes" to account for parametrization simplifications in bottom-up models.
Lines 279–281: The deduction that wetland emissions may decrease in Southern/Eastern Europe due to reduced precipitation is problematic. Precipitation is a weak predictor of wetland CH4 emissions due to time lags (runoff, microbial decomposition). Inundation or GRACE terrestrial water storage data would be more appropriate. The argument for future projections based on precipitation should be reconsidered.
Lines 296–299: The seasonal variability in Western Europe is linked to agricultural and fossil fuel emissions. Is the agricultural sector large enough to drive summer seasonality? Source-resolved posterior analyses could clarify this.
Interannual trends (Figure 7):
Why do negative emissions appear? Are these CH4 emission anomalies? Clarify how anomalies were calculated. The figure caption and y-axis title need revision for accuracy.
Underestimated wetland emissions (Line 370):
A quantitative estimate (e.g., "underestimated by ~20%") would strengthen the discussion.
Background concentration influence (Line 386):
The impact of background concentration determination on posterior estimates is underdiscussed and should be expanded.
Study implications:
The broader implications of this work—particularly strategies to reduce inter-model discrepancies—should be discussed.
Minor Corrections:
Figure 3 caption: Inconsistent with subplot order.
Line 328: "EU 27, where they a show similar strong negative trend" correct the typo to "EU-27, where they show a similarly strong negative trend."
Citation: https://doi.org/10.5194/essd-2025-235-RC1 -
RC2: 'Comment on essd-2025-235', Anonymous Referee #2, 28 Jul 2025
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Ioannidis et al. performed a CH4 inverse inter-comparison modeling (MIP) study to estimate European CH4 emissions. With a suite of inverse models that use different transport models and have different model resolutions, designs of state vectors, and data assimilation techniques, they investigate differences in their estimated emission magnitudes, spatial patterns, seasonal variations, and trends. Given that atmosphere-based estimates are considered as an important tool to assess the accuracy of national greenhouse gas inventory reporting and their external uncertainties are often hard to quantify by using one modeling system alone, such an inter-comparison modeling study provides important insights on how well the atmospheric observations can be used to quantify European CH4 emissions. The current manuscript is well organized. It well describes the MIP protocols, the participating modeling systems, and their obtained results. It can be further improved by some revision.
- The authors discussed similarities and differences in the adjustments of the posterior estimates relative to the common prior based on different inversion results (e.g. Fig 2). Although such information is useful, it is also important to know, with the posterior adjustments, whether posterior emissions show similar spatial patterns as the prior. Therefore, besides Fig. 2, maps that show posterior emissions, as well as some discussion on the posterior spatial patterns, could be useful to add.
- The authors evaluated the performance of inverse models with observation used in the optimization and independent data. It is obvious that the performance varies quite a bit with the ICONDA model standing out as the best in near all the statistics. This is useful to know. However, it would be more beneficial for the community if the authors can provide specific insights on why ICONDA performs the best. Is it due to more accurate transport simulations, or their data assimilation techniques, or the optimization of their boundary values, or the suitability of their specified error covariance parameters? Although the authors mentioned about the importance of atmospheric transport modeling, they rarely mention the importance of the error covariance parameters at all. To me, the relatively poorer performance of the CTDAS-WRF may relate to the possibility of an overfitting of their observations.
- Base versus test runs. It is nice to see the authors conducted both base versus test runs to assess whether the posterior emission estimates can be improved with additional sites. However, it is very hard to compare the performance of the base versus test runs in their current presentation. Please consider to add the summary statistics for both base and test runs into the same barcharts. For example, merge Figs. 4 and D1 and merge Figs 5 and E1. Also, the authors only mentioned the summary statistics in those figures without much discussion. Please add discussion on the test runs.
- For the results discussing the seasonal cycle and trend. Consider add additional lines summarizing the posterior results averaged among the best performing models (e.g. ICONDA, NTLB, and another one?). It would be interesting to know if they will get the same seasonal cycle or trend by only using the best performing models. Also, consider to add some discussion on these results too.
- In the trend section, it would be insightful to know what drives the declining trend in CH4 emissions over eastern and southern Europe.
Other:
Line 151: “mg m-2 hr-1” - this is the unit used for the maps, not for the trends and seasonal cycles.
Fig. 1 – label the country names. This would be useful for readers to link the country-based similarities or differences to specific areas in the maps.
Fig. 3 – please indicate where the additional sites are considered
Citation: https://doi.org/10.5194/essd-2025-235-RC2
Data sets
CH4 inversion results for Europe E. Ioannidis et al. https://doi.org/10.18160/KZ63-2NDJ
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