Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution

Abstract

In recent studies, proxy XCH4 retrievals from the Japanese Greenhouse gases Observing SATellite (GOSAT) have been used to constrain top-down estimation of CH4 emissions. Still, the resulting interannual variations often show significant discrepancies over some of the most important CH4 source regions, such as China and Tropical South America, by causes yet to be determined. This study compares monthly CH4 flux estimates from two parallel assimilations of GOSAT XCH4 retrievals from 2010 to 2019 based on the same Ensemble Kalman Filter (EnKF) framework but with the global chemistry transport model (GEOS-Chem v12.5) being run at two different spatial resolutions of 4° × 5° (R4, lon × lat) and 2° × 2.5° (R2, lon × lat) to investigate the effects of resolution-related model errors on the derived long-term global and regional CH4 emission trends. We found that the mean annual global methane emission for the 2010s is 573.04 Tg yr−1 for the inversion using the R4 model, which becomes about 4.4 Tg yr−1 less (568.63 Tg yr−1) when a finer R2 model is used, though both are well within the ensemble range of the 22 top-down results (2008–17) included in the current Global Carbon Project (from 550 Tg yr−1 to 594 Tg yr−1). Compared to the R2 model, the inversion based on the R4 tends to overestimate tropical emissions (by 13.3 Tg yr which is accompanied by a general underestimation (by 8.9 Tg yr−1) in the extratropics. Such a dipole reflects differences in tropical-mid-latitude air exchange in relation to the model’s convective and advective schemes at different resolutions. The two inversions show a rather consistent long-term CH4 emission trend at the global scale and over most of the continents, suggesting that the observed rapid increase in atmospheric methane can largely be attributed to the emission growth from North Africa (1.79 Tg yr−2 for R4 and 1.29 Tg yr−2 for R2) and South America Temperate (1.08 Tg yr−2 for R4 and 1.21 Tg yr−2 for R2) during the first half of the 2010s, and from Eurasia Boreal (1.46 Tg yr−2 for R4 and 1.63 Tg yr−2 for R2) and Tropical South America (1.72 Tg yr−2 for R4 and 1.43 Tg yr−2 for R2) over 2015–19. In the meantime, emissions in Europe have shown a consistent decrease over the past decade. However, the growth rates by the two parallel inversions show significant discrepancies over Eurasia Temperate, South America Temperate, and South Africa, which are also the places where recent GOSAT inversions usually disagree with one other.

CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

“…LETKF has been used extensively in chemical data assimilation, and has benefits compared with other algorithms, most notably the ease of implementation for a wide variety of simulations. LETKF and related ensemble Kalman filter methods have been used for CO2 flux inversions [Liu et al, 2016;Kong et al, 2022], single-species studies of NO2, SO2, and NH3 emissions [Miyazaki et al, 2012a;Dai et al, 2021;van der Graaf et al, 2022], and analysis of methane emission trends [Zhu et al, 2022]. Multispecies assimilation, 4D assimilation of temporally scattered observations, and flexibility in state vector definition are easy to implement under the LETKF framework; the algorithm also provides detailed error characterization including correlations as part of the solution.…”