Using Atmospheric Inverse Modelling of Methane Budgets with Copernicus Land Water and Wetness Data to Detect Land Use-Related Emissions
2023
Tenkanen, Maria K. | Tsuruta, Aki | Tyystjärvi, Vilna | Törmä, Markus | Autio, Iida | Haakana, Markus | Tuomainen, Tarja | Leppänen, Antti | Markkanen, Tiina | Raivonen, Maarit | Niinistö, Sini | Arslan, Ali Nadir | Aalto, Tuula | Ilmatieteen laitos | Finnish Meteorological Institute | 0000-0002-5867-6295 | 0000-0002-9197-3005 | 0000-0002-1175-5463 | 0000-0003-4169-8225 | 0000-0003-1256-0163 | 0000-0002-3264-7947
Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector is expected to be a net sink. However, accurate estimates of LULUCF-related GHG emissions, such as methane (CH4), remain challenging. We estimated LULUCF-related CH4 emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH4 emissions estimated by an inversion model. According to our inversion model, most of Finland’s CH4 emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH4. Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH4 emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH4 emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories.
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