European CH4 flux distributions estimated from CTE-CH4 atmospheric inversion assimilating TROPOMI XCH4 data
Aki Tsuruta, Finnish Meteorological Institute, aki.tsuruta@fmi.fi (Presenter)
Leif Backman, Finnish Meteorological Institute, leif.backman@fmi.fi
Hannakaisa Lindqvist, Finnish Meteorological Institute, hannakaisa.lindqvist@fmi.fi
Ella Kivimäki, Finnish Meteorological Institute, ella.kivimaki@fmi.fi
Janne Hakkarainen, Finnish Meteorological Institute, janne.hakkarainen@fmi.fi
Oliver Schneising, University of Bremen, oliver.schneising@iup.physik.uni-bremen.de
Michael Buchwitz, University of Bremen, Institute of Environmental Physics (IUP), buchwitz@uni-bremen.de
Sander Houweling, Vrije Universiteit Amsterdam, s.houweling@sron.nl
Tuula Aalto, Finnish Meteorological Institute, tuula.aalto@fmi.fi
Various efforts have recently been put in to archive robust estimates and improve uncertainty estimation of global and regional greenhouse gas budgets. Communications between bottom-up and top-down communities have shown that mutual understanding of each sources and sink terms are important in reducing differences between top-down and bottom-up estimates, and collection of various model estimates, such as inversions using different observation inputs, help to quantify the uncertainties. For European methane (CH4) fluxes, the estimates for annual to long-term trends have come to more robust understanding over the recent years, and available atmospheric CH4 data for inversion has been increasing. However, there are still large uncertainty in its detailed spatial distributions and seasonal cycles that vary between estimation methods and input data used for the estimation. In this study, we examine the spatial and temporal distributions of 2018 European CH4 fluxes using the CTE-CH4 atmospheric inverse model and assimilating various CH4 data: ground-based in situ atmospheric CH4 data mainly from the ICOS network, two retrieval products of TROPOMI XCH4 (operational and WFMD). The spatial anomalies of the flux estimates will be compared against each other to examine effects of the assimilating data and identify the spatial distribution of the data constraints.
Poster: Poster_Tsuruta__78_25.pdf
Presentation Type: Poster
Session: 3.2b Flux estimates and atmospheric inversions from space-based GHG measurements
Session Date: Wednesday (6/16) 9:45 AM