CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes

Abstract

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 (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) 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 CH4 emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH4 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.

Increasing Methane Emissions and Widespread Cold‐Season Release From High‐Arctic Regions Detected Through Atmospheric Measurements

“…Previous inversion studies over Siberia have mainly focused on larger regions (Thompson et al., 2017; Tsuruta et al., 2023; Wittig et al., 2023), for the purpose of pan‐Arctic quantification, and therefore do not focus specifically on high latitude dry tundra regions. Other previous studies have also focused on boreal wetland regions such as the Hudson Bay Lowlands or West Siberian Lowlands (Thompson et al., 2017), which exhibit very different soil methane dynamics to our high‐Arctic dry tundra regions.…”

“…Emissions can also be inferred from atmospheric mole fractions through inverse modeling, which has the benefit over directly using mole fraction enhancements (as in Sweeney et al (2016)), in that it takes into account more complex meteorological information such as changes in planetary boundary layer height and wind speed. Arctic methane emissions derived from inverse methods (Berchet et al, 2015;Ishizawa et al, 2019;Tenkanen et al, 2021;Thompson et al, 2017;Tsuruta et al, 2023;Wittig et al, 2023) have primarily focused on large-scale regions, such as entire countries or highly inundated regions such as the West Siberian Lowlands or Hudson Bay Lowlands. Additionally, these studies have not focused on late-season emissions from the high Arctic, a region that contains a large proportion of dry tundra (less than 20% inundation), which has the potential to emit significantly during the cold season (Treat et al, 2018;Zona et al, 2016).…”

Permafrost Carbon: Progress on Understanding Stocks and Fluxes Across Northern Terrestrial Ecosystems

“…However, models are rapidly evolving. For example, airborne and satellite data are being more extensively used to define the prior estimates for inversions (Byrne et al, 2023;Tsuruta et al, 2023). While promising, satellite observations based on optical remote sensing still have some limitations for application during polar winter and with persistent cloud cover.…”

Detecting Methane Emissions from Space in India: analysis using EMIT and Sentinel-5P TROPOMI datasets

“…Several papers from different authors used the same similar approach to retrieve xCH4 (Cusworth et al, 2022;De Foy et al, 2023;Etminan et al, 2016;O. P. Hasekamp & Landgraf, 2002;Lauvaux, Giron, Mazzolini, Shindell, et al, 2022;Lorente et al, 2022;Schneising et al, 2019;Tsuruta et al, 2019Tsuruta et al, , 2023Wang et al, 2019;Zhang et al, 2021).…”