Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (${\mathrm{XCH}}_4$) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of ${\mathrm{CH}}_4$ fluxes using the CarbonTracker Europe-${\mathrm{CH}}_4$ (CTE-${\mathrm{CH}}_4$) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where ${\mathrm{CH}}_4$ emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher ${\mathrm{CH}}_4$ emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH₄) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH₄ than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH₄ difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH₄ difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH₄ (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH₄ differences comparable to or smaller than those from ground-based remote sensing.

The increase in atmospheric greenhouse gas concentrations of CO₂ and CH₄, due to human activities, is the main driver of the observed increase in surface temperature by more than 1 °C since the pre-industrial era. At the 2015 United Nations Climate Change Conference held in Paris, most nations agreed to reduce greenhouse gas emissions to limit the increase in global surface temperature to 1.5 °C. Satellite remote sensing of CO₂ and CH₄ is now well established thanks to missions such as NASA’s OCO-2 and the Japanese GOSAT missions, which have allowed us to build a long-term record of atmospheric GHG concentrations from space. They also give us a first glimpse into CO₂ and CH₄ enhancements related to anthropogenic emission, which helps to pave the way towards the future missions aimed at a Monitoring & Verification Support (MVS) capacity for the global stock take of the Paris agreement. China plays an important role for the global carbon budget as the largest source of anthropogenic carbon emissions but also as a region of increased carbon sequestration as a result of several reforestation projects. Over the last 10 years, a series of projects on mitigation of carbon emission has been started in China, including the development of the first Chinese greenhouse gas monitoring satellite mission, TanSat, which was successfully launched on 22 December 2016. Here, we summarise the results of a collaborative project between European and Chinese teams under the framework of the Dragon-4 programme of ESA and the Ministry of Science and Technology (MOST) to characterize and evaluate the datasets from the TanSat mission by retrieval intercomparisons and ground-based validation and to apply model comparisons and surface flux inversion methods to TanSat and other CO₂ missions, with a focus on China.

Methane (${\mathrm{CH}}_4$) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (${\mathrm{XCH}}_4$). To understand and quantify the spatial differences of the seasonal cycle and trend of ${\mathrm{XCH}}_4$ in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average ${\mathrm{XCH}}_4$ seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC ${\mathrm{CH}}_4$ Proxy algorithm version 2.3.8 and RemoTeC ${\mathrm{CH}}_4$ Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT ${\mathrm{XCH}}_4$ retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of ${\mathrm{XCH}}_4$ agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of ${\mathrm{XCH}}_4$. These differences are linked to the regional ${\mathrm{CH}}_4$ sources and sinks, and call for further research.

Satellite-driven inversions provide valuable information about methane (CH₄) fluxes, but the assimilation of total column-averaged dry-air mole fractions of CH₄ (XCH₄) has been challenging. This study explores, for the first time, the potential of the new lower-tropospheric partial column (pXCH₄_LT) GOSAT data, retrieved by the Japan Aerospace Exploration Agency (JAXA), to constrain global and regional CH₄ fluxes. Using the CarbonTracker Europe-CH₄ (CTE-CH₄) atmospheric inverse model, we estimated CH₄ fluxes between 2016–2019 by assimilating the JAXA/GOSAT pXCH₄_LT and XCH₄ data and surface CH₄ observations independently of each other. The Northern Hemisphere CH₄ fluxes derived from the pXCH₄_LT data were similar to the estimates derived from the surface observations but were underestimated by about 35 Tg CH₄ yr⁻¹ (∼ 6 % of the global total) using the XCH₄ data. For the Southern Hemisphere, the estimates from both GOSAT inversions were about 15–30 Tg CH₄ yr⁻¹ higher than those derived from surface data. The evaluations against independent data from the Atmospheric Tomography Mission aircraft campaign showed good agreement in the lower-tropospheric CH₄ from the inversions using the pXCH₄_LT and surface data. However, from these inversions, the modelled north–south gradients showed significant overestimation in the upper troposphere and stratosphere, possibly due to relatively uniform inter-hemispheric OH distributions that control CH₄ sinks. Overall, we found that the use of the JAXA/GOSAT pXCH₄_LT data shows considerable potential in constraining global and regional CH₄ fluxes, advancing our understanding of the CH₄ budget.

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.