High-resolution inverse modelling of European CH₄ emissions using the novel FLEXPART-COSMO TM5 4DVAR inverse modelling system

Abstract

Abstract. We present a novel high-resolution inverse modelling system (“FLEXVAR”) based on FLEXPART-COSMO back trajectories driven by COSMO meteorological fields at 7 km×7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (“baselines”) consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (“FLExKF”) system and with TM5-4DVAR inversions at 1∘×1∘ resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km×7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1∘×1∘. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

Global Atmospheric δ13CH4 and CH4 Trends for 2000–2020 from the Atmospheric Transport Model TM5 Using CH4 from Carbon Tracker Europe–CH4 Inversions

“…The inversions were unable to reduce fossil-based emissions enough, nor could they increase the agricultural emissions enough after 2012, but this can be explained by the inversion model's ability when the prior fluxes used are estimated wrongly, e.g., in magnitude or in spatial distribution. The TM5 results indicate that wetland CH 4 emissions should be optimized separately, as previously carried out by (e.g., [30,31]), and not together with agricultural emissions, as was carried out in this study. Furthermore, the TM5 results indicate that the contributions from biogenic sources to recent CH 4 trends need to be larger, which is consistent with Basu et al [62].…”

“…Previous CH 4 inversion studies divide emissions into anthropogenic, biospheric, and other categories (e.g., [30,31]). The category of anthropogenic emissions include all CH 4 emissions from human activity, while biospheric emissions include emissions from wetlands and soil sinks.…”

Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region

“…reflectance in NIR (757-774nm) and SWIR (2305-2385nm) (Kozicka et al, 2023). Initially, retrievals based on TROPOMI had a high spatial resolution of 7 km x 3.5 km (along-track x acrosstrack, Bergamaschi et al, 2022). From August 2019 to the present, the resolution has been 145 increased to 5.5 km × 3.5 km (Sagar et al, 2022).…”

If Some Critical Regions Achieve Carbon Neutrality, How Will the Global Atmospheric CO2 Concentration Change?

“…The global chemistry Transport Model, version 5 (TM5) [25], is a two-way nested, three-dimensional atmospheric chemical transport model. This offline chemical transport model requires meteorological data, initial CO 2 concentration, and CO 2 flux fields [26]. The model simulates the process of CO 2 atmospheric transport and outputs the concentration of CO 2 in the atmosphere.…”