The northern wetland methane emission estimates have large uncertainties. Inversion models are a qualified method to estimate the methane fluxes and emissions in northern latitudes but when atmospheric observations are sparse, the models are only as good as their a priori estimates. Thus, improving a priori estimates is a competent way to reduce uncertainties and enhance emission estimates in the sparsely sampled regions. Here, we use a novel way to integrate remote sensing soil freeze/thaw (F/T) status from SMOS satellite to better capture the seasonality of methane emissions in the northern high latitude. The SMOS F/T data provide daily information of soil freezing state in the northern latitudes, and in this study, the data is used to define the cold season in the high latitudes and, thus, improve our knowledge of the seasonal cycle of biospheric methane fluxes. The SMOS F/T data is implemented to LPX-Bern DYPTOP model estimates and the modified fluxes are used as a biospheric a priori in the inversion model CarbonTracker Europe-CH${}_4$. The implementation of the SMOS F/T soil state is shown to be beneficial in improving the inversion model’s cold season biospheric flux estimates. Our results show that cold season biospheric CH${}_4$ emissions in northern high latitudes are approximately 0.60 Tg lower than previously estimated, which corresponds to 17% reduction in the cold season biospheric emissions. This reduction is partly compensated by increased anthropogenic emissions in the same area (0.23 Tg), and the results also indicates that the anthropogenic emissions could have even larger contribution in cold season than estimated here.

Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH₄ emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH₄ emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH₄ emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH₄ yr⁻¹) and the mean of the BU CH₄ emissions (17.8 (16–19) Tg CH₄ yr⁻¹) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH₄ yr⁻¹ and 24 (22–25) Tg CH₄ yr⁻¹ for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH₄ yr⁻¹ (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH₄ estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH₄ pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH₄ emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.12818506 (Petrescu et al., 2024).

Drivers of natural high-latitude biogenic methane fluxes were studied by combining atmospheric inversion modelling results of methane fluxes (CTE-CH4 model) with datasets on permafrost (ESA Permafrost CCI), climate (Köppen–Geiger classes) and wetland classes (BAWLD) and seasonality of soil freezing (ESA SMOS F/T) for the years 2011–2019. The highest emissions were found in the southern parts of the study region, while areas with continuous permafrost, tundra climate, and tundra wetlands had the lowest emissions. The magnitude of the methane flux per wetland area followed the order of permafrost zones excluding non-permafrost, continuous permafrost having the smallest flux and sporadic the largest. Fens had higher fluxes than bogs in the thaw period, but bogs had higher fluxes in the colder seasons. The freezing period when the soil status is between complete thaw and frozen contributed to annual emissions more in the warmest regions studied than in other regions. In the coldest areas, freezing period fluxes were lower and closer to wintertime values than elsewhere. Emissions during freezing periods were smaller than those during winter periods, but were of comparable magnitude in warm regions. The contribution of the thaw period to the total annual emission varied from 86% in warmest areas to 97% in the coldest areas, suggesting that the longest winter periods did not contribute significantly to the annual budget.

Accurate national methane (CH₄) emission estimates are essential for tracking progress towards climate goals. This study investigated Finnish CH₄ emissions from 2000–2021 using bottom-up and top-down approaches. We evaluated the ability of a global atmospheric inverse model CarbonTracker Europe – CH₄ to estimate CH₄ emissions within a single country. We focused on how different priors and their uncertainties affect the optimised emissions and showed that the optimised anthropogenic and natural CH₄ emissions were strongly dependent on the prior emissions. However, while the range of CH₄ estimates was large, the optimised emissions were more constrained than the bottom-up estimates. Further analysis showed that the optimisation aligned the trends of anthropogenic and natural CH₄ emissions and improved the modelled seasonal cycles of natural emissions. Comparison of atmospheric CH₄ observations with model results showed no clear preference between anthropogenic inventories (EDGAR v6 and CAMS-REG), but results using the highest natural prior (JSBACH–HIMMELI) agreed best with observations, suggesting that process-based models may underestimate CH₄ emissions from Finnish peatlands or unaccounted sources such as freshwater emissions. Additionally, using an uncertainty estimate based on a process-based model ensemble for natural CH₄ emissions seemed to be advantageous compared to the standard uncertainty definition. The average total posterior emission of the ensemble from one inverse model with different priors was similar to the average of the ensemble including different inverse models but similar priors. Thus, a single inverse model using a range of priors can be used to reliably estimate CH₄ emissions when an ensemble of different models is unavailable.

Wetland methane responses to temperature and precipitation are studied in a boreal wetland-rich region in northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5, and CLM5) are compared to multi-model means of ecosystem models and atmospheric inversions from the Global Carbon Project and upscaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, are used as priors in atmospheric inversions with Carbon Tracker Europe–CH4 (CTE-CH4) in order to find out how the assimilation of atmospheric concentration data changes the flux estimates and how this alters the interpretation of the flux responses to temperature and precipitation. Inversion moves wetland emissions of both models towards co-limitation by temperature and precipitation. Between 2000 and 2018, periods of high temperature and/or high precipitation often resulted in increased emissions. However, the dry summer of 2018 did not result in increased emissions despite the high temperatures. The process models show strong temperature and strong precipitation responses for the region (51 %–91 % of the variance explained by both). The month with the highest emissions varies from May to September among the models. However, multi-model means, inversions, and upscaled eddy covariance flux observations agree on the month of maximum emissions and are co-limited by temperature and precipitation. The setup of different emission components (peatland emissions, mineral land fluxes) has an important role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the regional representation of wet and dry mineral soils and periodic flooding which contribute to the seasonality and magnitude of methane fluxes. The realistic representation of temperature dependence of the peat soil fluxes is also important. Furthermore, it is important to use process-based descriptions for both mineral and peat soil fluxes to simulate the flux responses to climate drivers.