Abstract. The Arctic is experiencing unprecedented environmental changes with rapidly rising temperatures. Emissions of methane (CH4) – a potent greenhouse gas – may be increasing from the region, making accurate monitoring essential. The TROPOspheric Monitoring Instrument (TROPOMI) instrument offers high spatial and temporal coverage of CH4 column mole fractions. However, its data in the Arctic has historically exhibited seasonal and latitudinal biases and low-quality retrievals. A major challenge is the lack of ground-based validation data in high-latitude regions, which are used to improve satellite retrievals. This study evaluates inverse modelling to estimate CH4 emissions using TROPOMI measurements over the North Slope of Alaska. Using two retrieval products – the operational SRON product and the scientific WFMD product from the University of Bremen – we assess the alignment of derived emissions with surface measurement-derived inversions over 2018–2020 and test their robustness through sensitivity analyses. Our results show that tundra emissions from SRON inversions align more closely with surface measurement-derived emissions than WFMD inversions. Both TROPOMI-product derived emissions have anomalously low emissions in August 2018 compared to surface measurement-derived emissions, likely due to low data density resulting from high cloud cover. TROPOMI inversions provided stronger constraints on fugitive anthropogenic emissions compared to surface inversions. However, each retrieval produced different emission estimates, highlighting retrieval-dependent differences. Sensitivity tests revealed a strong prior dependence in both retrievals, raising concerns about robustness in northern high latitudes. This study highlights the importance of using multiple retrievals and rigorous sensitivity testing in high-latitude satellite inversions.

Around half of the European peatlands are drained, and in Finland, most of them are drained for forestry. Drainage degrades the soil organic matter (SOM) and lowers the soil water-table level (WTL), increasing oxygen levels in the soil. This suppresses the production and increases in-soil oxidation of methane (CH4) but enhances the decomposition of SOM, accelerating aerobic soil respiration. Consequently, emissions of CH4 from soil to the atmosphere may decrease while those of CO2 may increase. Process-based models are useful in estimating greenhouse gas emissions and sinks from large areas. In previous modelling studies, the focus has mainly been on pristine peatlands with high CH4 emissions. However, simulations of soil CH₄ and CO₂ fluxes of multiple forestry-drained peatland sites over several years that are compared with measurement data remain still scarce.We simulated the soil-atmosphere CH4 and CO2 fluxes from six Finnish forestry-drained peatlands with a process-based model, JSBACH (Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg) coupled with a peatland CH4 model, HIMMELI (HelsinkI Model of MEthane buiLd-up and emission). Our aim was to better understand and evaluate the accuracy of the predicted soil CO2 and CH4 fluxes from multiple peatland forest sites, and to identify the sources of uncertainty in the modelled fluxes. To do this, we used WTL and chamber flux data measured over 2-5 years from each site.The average modelled soil CO2 fluxes varied between 0.7 and 1.42 µmol m-2 s-1 among the sites. The model overestimated emissions in two sites and underestimated them in three sites. The mean differences between model and measurement varied from 0.05 to 2.02 µmol m-2 s-1 among all sites. There was a clear interannual variation on this. The average modelled CH4 fluxes varied between -1.09 and 3.77 nmol m-2 s-1 among the sites. The model underestimated sink or predicted occasional CH4 emission peaks in four sites. In turn, the CH4 sink was overestimated by the model in two sites. The measurements indicated all the sites being, on average, small sinks of CH4. The mean differences between modelled and measured CH4 fluxes were between 0.44 and 5.44 nmol m-2 s-1 among the sites. Generally high WTL of a site was associated with larger discrepancies between modelled and measured CH4 fluxes. The WTL was considered high for three sites (modelled WTL on average -30 – (-32) cm), and low for three sites (modelled WTL on average -42 – (-64) cm). We found that by tuning the CH4 production and oxidation parameters in the model, we can improve the prediction accuracy of the modelled CH4 fluxes.The results of this work will be useful for further model development and when aiming to estimate soil CH4 and CO2 sinks and emissions of forestry-drained peatlands.

In Finland there are tens of thousands of hectares of drained peat extraction sites (‘cutover peatlands’) in which the extraction has recently ceased, leaving an amount of peat still on site. Restoration and productive reuse of such cutover peatlands and related research on their impact have been ongoing for studying and mitigating greenhouse gas (GHG) emissions, sustaining wetland ecosystem services, and developing local economy. Such comprehensive restoration and paludicultural reuse often include rewetting, vegetation restoration (with fertilisation if necessary), solar or wind power production, and/or agricultural (including husbandry) use provided that the vegetation regenerates sufficiently. In the current EU-funded project ‘AurinkoSuo’, we use modelling tools to investigate the dynamics of carbon dioxide and methane emissions, vegetation regeneration, peat carbon pools, and net ecosystem production (NEP) during peat extraction, vegetation restoration, and reuse for photovoltaic production in cutover peatlands in southwestern Finland. We modified land surface model JSBACH for cutover peatlands, coupled it with peatland GHG model HIMMELI, and used the coupled model to simulate the aforementioned dynamics over 1996—2055. The model was parameterized using specific literature on cutover peatlands and information on our study sites, and the climate forcing inputs were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the scenario of Shared Socioeconomic Pathway (SSP) 1-2.6. The panels’ shading effects on the ground vegetation were modelled and implemented into our simulation. The simulation included different combinations of water table depths, shading effects, and biomass removal (mimicking crop harvesting or husbandry use). Our work is among the first attempts to model the GHG- and vegetation-related processes in WPG paludiculture spanning over the historical peat extraction to the future with changing climate.

Constraining methane (CH4) emissions at high spatial and temporal resolution is critical for accurate European greenhouse gas budgets and mitigation policy. We use the Community Inversion Framework to estimate monthly CH4 fluxes across Europe (2017–2022) at 0.2°×0.2°, coupling FLEXPART and assimilating observations from 46 in situ stations, including Integrated Carbon Observation System (ICOS) and non-ICOS sites. Prior emissions combine anthropogenic inventories, biomass burning estimates, wetland models, and climatological natural sources. The inversion substantially improves agreement with atmospheric observations (r2=0.87, RMSE=24.4 ppb, mean bias=-2.1 ppb), performing best at northern European stations. For the European Union countries, together with the UK, Norway, and Switzerland, we estimate total methane emissions of 23.3±2.3 Tg CH4 yr−1, which is 6.6 % higher than the prior. For these countries, we found an average anthropogenic emissions of 17.6 Tg CH4 yr−1, with a decreasing trend of 0.3 Tg yr−1. This estimate exceeds the prior by 11 %, EDGARv8 by 4 %, and UNFCCC NGHGI (2023) by 3 %, while remaining consistent with recent studies. Country-level differences are notable, with higher emissions estimated for the Netherlands and Germany, and lower for Romania and Italy. Sectoral changes mainly reflect agricultural increases, alongside reductions in northern wetlands and southern geological sources. Sensitivity tests highlight the influence of spatial spread of emissions distribution assumptions and the importance of dense observational networks for refining regional CH4 budgets.

Atmospheric inversions are widely used to evaluate and improve inventories of methane (CH4) emissions across scales from global to local, 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, using a detailed dataset of CH4 emissions described and presented here for first time.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-27) 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 mole fractions. 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 a subregional 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 shift in the trends in Western, Eastern and Southern Europe.Our results highlight the importance of improving the inversion setup, such as the treatment of lateral boundary conditions and the model representation of measurement sites, to narrow the uncertainty ranges further. The referenced dataset related to the analysis and figures are available at the ICOS portal: 10.18160/KZ63-2NDJ (Ioannidis et al., 2025).

In our changing climate and the green transition movement, it is important to verify national greenhouse gas emission inventories. By using prior emission estimates from inventories and process models as well as atmospheric greenhouse gas observations, we are able to assess emissions and their changes from different sources with inversion modeling. However, the scarcity of measuring stations affect the uncertainty of the inversion models.In this study, we assess our ability to monitor the green transition in Finland. Currently atmospheric greenhouse gas concentrations are measured in 6 locations in Finland. Including new observation towers would fill existing gaps in the observation network. We aim to see how new atmospheric concentration measurement towers with different sensor accuracies could improve greenhouse gas detection in the area. We carry out an observing system simulation experiment (OSSE), using sensitivity tests with the Community Inversion Framework (CIF) using the transport model FLEXPART with a 0.1º x 0.1º spatial resolution on a nested domain, and observing how the model reacts to changes in the prior emissions and synthetic observations.Addition of new stations in Finland could improve greenhouse gas detection and emission inventory assessment, and using lower accuracy sensors could help improve detection with a lower cost. Development of the OSSE experiment is still in progress and emissions from different source sectors, like wetlands and anthropogenic emissions will be optimized for multiple years. Our final inversion products will give improved estimates of inversion model sensitivity and show how effective a new observing system in Finland will be at detecting emissions.

Abstract. Rotational forestry (RF) is the prevailing management practice on drained peatlands in Finland, while continuous cover forestry (CCF) is increasingly studied for its potential climate benefits. We applied the process-based LandscapeDNDC model, for the first time, to simulate experimental peatland forest stands under three different managements: RF, CCF and non-managed control. Mixed-species stands of pine, spruce, and birch were initialized, with management, partial harvest of pine in CCF and clear-cut harvest of all species in RF, leading to species shifts toward spruce–birch dominance in CCF and birch seedlings in RF. The primary objective of this study was to evaluate the performance of LandscapeDNDC model in forested drained peatlands. To this aim, we quantified the differences in gas exchange and water balance originating from differences in species composition and management methods. We also implemented modification to dynamic water table (WT) calculations and improved humus pool partitioning based on soil carbon-to-nitrogen ratios. Model evaluation against field data showed strong agreement for daily net ecosystem exchange (correlation 0.84–0.88; Nash–Sutcliffe efficiency 0.66–0.75). Modeled leaf area index (LAI) closely matched site estimates before management and Sentinel-2 satellite estimated LAI afterwards. Soil moisture and WT dynamics were realistically reproduced. Methane flux patterns were accurately captured in the control and CCF stands. Moreover, the methane flux was found to be sensitive to the WT after clear-cut in the RF stand. Modeled annual carbon balances were consistent with measurements and indicated that CCF became a carbon sink more rapidly than RF. These results demonstrate that LandscapeDNDC can reliably simulate the biogeochemical and hydrological consequences of alternative peatland forest management scenarios. The model therefore provides a valuable tool for developing climate-smart management strategies on drained peat soils.

Northern high latitude wetlands are significant sources of methane, with emissions driven by seasonal soil freezing and thawing. To better understand the seasonality of northern high latitude methane emissions, we defined the melt period occurring in spring time using the remote sensing Soil Moisture and Ocean Salinity Freeze/Thaw data from 2011–2021. To estimate methane emissions in the northern high latitudes, we used the atmospheric inverse model CarbonTracker Europe-CH4. The melt period was defined for three permafrost zones and for a seasonally frozen non-permafrost region using two approaches: region-based, which considered climatological conditions of permafrost regions, and grid-based, which defines the melt period at a finer 1°×1° scale. The length and timing of the melt period varied significantly depending on the approach. The melt period generally occurred between March and June and was influenced by air temperature, with a negative correlation between the length and the mean temperature of the melt period. The longest melt period was in the non-permafrost zone and the shortest varied between the two methods. The melt period emissions were on average 1.83 Tg with the region-based approach and 0.45 Tg with the grid-based approach, the non-permafrost zone having the largest share of the emissions. They were largely dependent on the season’s length. Year-to-year variation was modest, within 15 % (region-based) and 23 % (grid-based) of average emissions, and there was also no trend during the study period. Our dual-method approach allows for robust comparison with both large-scale regional studies and localized site-level research.

Accurate estimation of critical greenhouse gas fluxes, particularly carbon dioxide (CO2) and methane (CH4), is vital for shaping effective climate change policies. Leveraging the state-of-the-art Community Inversion Framework (CIF), we estimate high-resolution emissions across Europe (-12°E to 37°E, 35°N to 73°N). Using the Lagrangian Particle Dispersion Model (FLEXPART) with ECMWF meteorological data, we calculate surface flux footprints at 0.2° × 0.2° resolution, enhancing comparisons with national inventories. Assimilating data from 40+ in-situ observations, including ICOS and non-ICOS stations, our 4-dimensional variational optimization refines prior high-resolution flux estimates. Diverse sources contribute to the total flux, including fossil fuel emissions, biomass burning, land emissions, air-sea exchange. Flux corrections enhance accuracy, yielding posterior estimates with reduced bias and heightened correlation. Major CH4 emitters (France, Germany, Italy, Spain, Poland, and the UK) collectively contribute 72% of total emissions. The EU27 + UK average is 16.47 ± 1.33 Tg CH4/yr. Posterior anthropogenic emissions reveal a regional mean reduction of > 5 gC/m2/month in summer compared to prior estimates, highlighting seasonal emission dynamics.

The global goal to mitigate climate change (CC) is to achieve net zero greenhouse gas emissions (GHGE) by 2050; the European Union (EU) aim is to cut GHGE at least by 55% already by 2030. These ambition targets require new GHGE mitigation measures across all land use sectors (LULUCF), where wetlands, as carbon (C) rich ecosystem, can effectively contribute to climate targets, biodiversity, and water-related ecosystem services. Natural peatlands accumulate C effectively due to water-logged conditions. However, they can turn into high GHG sources if they are drained, therefore there is still need to enhance knowledge regarding how and/or how much C is sequestered or released by peatlands after their restoration, as well as the socioeconomic effects. “ALFAwetlands - Restoration for the future” (www.alfawetlands.eu) is a Horizon Europe funded project (2022-2026), which is coordinated by Luke and carried out at local to EU levels with 15 partners across Europe. It’s main goal, in short, is to mitigate CC while supporting biodiversity and ecosystem services (BES) and being socially just and rewarding. This includes, e.g., increasing the knowledge about C storage and release in peatlands, specifically after restoration. While, in terms of C fluxes, focussing on peatlands, the project scope is larger and includes additionally floodplains, coastal wetlands and few artificial wetlands. ALFAwetlands will develop and indicate management alternatives for wetlands including such that have been or will be restored during this project. Measures under this project are not restricted to ecological restoration but include rehabilitation and re-vegetation action to improve ecosystem conditions (e.g., peatland forest: continuous-cover-forestry, cultivated peatlands: paludiculture). Studies are conducted in 9 Living Labs (LL’s) including 30 sites, which are located in wetlands in different parts of Europe (north-south gradient). At the local level, LL’s support and integrate interdisciplinary and multi-actor research on ecological, environmental, economic, and social issues. Experimental data from local sites are scaled-up and will be utilized e.g., by models to gain and understanding the potential impacts of upscaled wetland restoration measures. To achieve ALFAwetlands goals, 5 research workpackages are being implemented, namely: 1)improve geospatial knowledge base of wetlands, 2)co-create socially fair and rewarding pathways for wetland restoration, 3)estimate effects of restoration on GHGE and BES, with the data achieved from field experiments, 4)develop policy relevant scenarios for CC and BES, and 5)study societal impacts of wetland restoration. The project will also encourage stakeholders to utilise outputs and support their active participation in wetland management.

Methane emissions from Northern Hemisphere high-latitude wetlands are associated with large uncertainties, especially in the rapidly warming climate. Satellite observations of column-averaged methane concentrations (XCH4) in the atmosphere exhibit variability due to time-varying sources and sinks as well as atmospheric transport. In this study, we investigate how environmental variables, such as temperature, soil moisture, snow cover, and the hydroxyl radical (OH) sink of methane, explain the seasonal variability in XCH4 observed from space over Northern Hemisphere high-latitude wetland areas. We use XCH4 data obtained from the TROPOMI instrument aboard the Sentinel-5 Precursor satellite, retrieved using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) algorithm. In addition, we perform the analysis using two atmospheric inversion model configurations: one based on non-optimized prior fluxes and another using fluxes optimized with in situ atmospheric observations. The aim was to assess the consistency between satellite-based and model-based results and to explore differences in how environmental variables drive the variability in XCH4.Environmental variables are derived primarily from meteorological reanalysis datasets, with satellite-based data used for snow cover and soil freeze-thaw dynamics and modelled data used for the OH sink. Our analysis focuses on five wetland-dominated case study regions over Northern Hemisphere high latitudes, including two in Finland and three in Russian Siberia, covering the period from 2018 to 2023.Our findings reveal that environmental variables have a systematic impact on satellite-based XCH4 variability. Seasonal variability is primarily driven by the OH sink and snow, particularly the snow water equivalent, while daily variability is most strongly affected by air temperature. The results are largely consistent with local in situ studies, although the role of snow appears more pronounced in our analysis. We observe interesting differences in the environmental drivers influencing satellite-based and model-based XCH4. The posterior results after in situ data assimilation were better aligned with the satellite-based results than the prior, suggesting that, while there remains room for improvement in model priors and configurations, there is already some consistency between the modelled and observed total-column methane dynamics. However, the prior fluxes used in the model could benefit from improved snow information.Overall, our results demonstrate how satellite-based XCH4 observations can be used to study the seasonal variability in atmospheric methane over large wetland regions. The results imply that satellite observations of atmospheric composition and other Earth observations and meteorological reanalysis data can be jointly informative with respect to the processes controlling emissions in Northern Hemisphere high latitudes.

Northern wetlands are considered to be one of the most significant natural sources of methane (CH4) emissions. The default wetland CH4 emission scheme in JULES, a current state-of-art land surface model, only takes into account the CH4 emissions from inundated wetland areas in a simple manner based on soil temperature and substrate availability. In this work, a process-based peatland CH4 emission model HIMMELI was integrated with JULES, and the HIMMELI parameters were optimized with measured CH4 flux at six northern wetland sites for each site separately or multi-sites simultaneously. The simulated CH4 emission was significantly improved when using the optimized parameter values, with the bias of 54.88 mg m-2 d-1 averaged across all the studied sites in the simulation using the default parameter (DPR) values being reduced to -0.70 mg m-2 d-1 in the simulations using parameters values derived from the single site optimization (SSO) for each site. In the simulations using parameters values from the averages of single site optimization (SSO_AVG) and the multi-site optimization (MSO), the biases averaged across all the studied sites were -7.39 mg m-2 d-1 and -8.36 mg m-2 d-1, respectively. The MSO simulations demonstrated more stable root mean square error (RMSE) between the simulated and observed methane emissions than the SSO_AVG simulations over the studied sites, when the RMSEs of SSO simulations were used as reference points. To further reduce the uncertainties in the simulated CH4 emissions by the JULES-HIMMELI model, model processes related to the environment conditions (e.g. water table, soil carbon and vegetation) of wetland and northern wetland CH4 emission processes (e.g. snow and ice covering effect) are suggested to be improved in JULES and HIMMELI, respectively. This study presents a comprehensive analysis of the impact of different parameters on the CH4 emission in the JULES-HIMMELI model and obtains optimal parameter values for modelling CH4 emissions at the studied northern wetlands. These findings pave the way for accurate regional estimates of northern wetland CH4 emission.

National greenhouse gas inventories (NGHGIs) and Biennial Transparency Reports (BTRs) on emissions and removals are crucial elements of the Paris Agreement and its Global Stocktake. However, NGHGIs are subject to significant uncertainties, owing to uncertain emission factors and/or insufficient activity data, thus there is a need for their independent verification. One method to do this is through atmospheric inversions, which use atmospheric observations in a statistical optimization framework to estimate surface-to-atmosphere fluxes. This method of verification is referred to in the 2006 IPCC Guidelines on national reporting and in their 2019 refinement. However, atmospheric inversions have been hitherto considered too complex and inaccurate at national scales to be widely used for this purpose. EYE-CLIMA is a Horizon Europe project that aims to develop the atmospheric inversion methodology to a level of readiness where it can be used to support the verification of NGHGIs. The overarching goals are to: i) develop a best practice in atmospheric inverse modelling for estimating emissions at national scale, including a full assessment of the uncertainties, ii) develop the methodology on how to prepare sectorial emission estimates from atmospheric inversions and make these comparable to what is reported in NGHGIs, iii) work together with NGHGI agencies on projects piloting the EYE-CLIMA methodology of emissions verification and iv) develop international best practices for the quality control of NGHGIs. EYE-CLIMA covers CH4, N2O, 5 HFC species, SF6, and the black carbon (BC) aerosol. This presentation will focus on the set-up of the EYE-CLIMA project and provide an overview of the first results in support of NGHGI verification.

2025 started with the launch of the H-Europe project IM4CA to enhance the quantification and understanding of methane emissions and sinks. A consortium of 25 partners joins forces to investigate pressing questions about the evolution atmospheric methane levels in recent decades, to reduce the uncertainty in future projections and design efficient solutions for monitoring and mitigating emissions in and outside of Europe. It will build new measurement and modelling infrastructure for improved monitoring of the progress toward short- and long-term emission reduction targets, with a prominent role for existing and upcoming satellite missions for measuring atmospheric composition and land surface properties.The changing European methane emissions are an important focus of the project, which we keep track of with help of eastward extensions of the ICOS monitoring network in Poland and Romania. Intensive measurement campaigns in Rumania are conducted combining surface, aircraft, and total column measurements to improve the accuracy of emission quantification techniques using satellite data. The world-wide applicability of these techniques will extend the impact of our campaigns far beyond European borders.Besides changing anthropogenic emissions, climate impacts on natural sources and sinks of methane are an important focus of IM4CA also. The four-year research program will initiate new measurement infrastructure in Congo to help characterize emissions from tropical wetlands in Africa. Campaigns will be conducted in Northern Scandinavia along a transect of disappearing permafrost to investigate impacts on vegetation and methane emissions using techniques that can be applied to high-resolution satellite instruments for circumpolar emission mapping.This presentation will provide an overview of the planned activities and goals of IM4CA. The project offers a great opportunity to learn about methane in a cooperative spirit and to reach out and provide support to those who can turn knowledge about methane into climate action.

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.

Peat extraction and use of peat resources for energy purposes are ending in Finland as part of societal green transition and energy production shift. Former peat extraction areas are often restored by rewetting, but short- and long-term impacts of rewetting actions to water resources and leaching, greenhouse gas (GHG) emissions, and terrestrial and aquatic ecology are currently poorly known. For this purpose, we have established a new intensive monitoring site for Turvesuo-Miehonsuo peat extraction areas at Sanginjoki catchment, close to the City of Oulu. Our mission is to obtain a detailed understanding of hydrological, biogeochemical, and ecological impact of the rewetting of the former peat extraction site. Peat extraction at Turvesuo-Miehonsuo ended 2023, and rewetting is planned to be done in 2025/2026. Our monitoring includes: continuous high-frequency in-situ water quality and aquatic gas monitoring, eddy-covariance and chamber measurements of GHG exchanges, extensive drone surveys to measure spatial variability of rewetting impacts, hydrological monitoring of surface and groundwaters, biological monitoring of bacterial communities and algal biomass accrual in surface waters, and detailed vascular plant and bryophyte inventories. continuous high-frequency in-situ water quality and aquatic gas monitoring, eddy-covariance and chamber measurements of GHG exchanges, extensive drone surveys to measure spatial variability of rewetting impacts, hydrological monitoring of surface and groundwaters, biological monitoring of bacterial communities and algal biomass accrual in surface waters, and detailed vascular plant and bryophyte inventories. Together with the City of Oulu, we plan to establish a science nature trail for the education sector, citizens, and others interested. The aim of this presentation is to present a monitoring and measurement setup that will enable monitoring of ecosystem-level changes in a former peat production area. The new comprehensive monitoring system aims to provide accurate scientific data to support land use decisions on peatlands and to provide reference measurements for the calculation of carbon emissions from the land use sector.