DOI: 10.57760/sciencedb.02328
title: 2010-2018 global methane emissions inferred from GOSAT satellite observations
title: 2010-2018 global methane emissions inferred from GOSAT satellite observations

description: The dataset includes gridded methane emission fluxes from a global inversion of GOSAT methane (CH4) observations by Zhang et al. (2021) (https://doi.org/10.5194/acp-21-3643-2021). Monthly prior and posterior sectoral emissions from 2010-2018 are reported on a 4x5 degree grid. Source sectors include anthropogenic (oil exploitation, gas exploitation, coal mining, livestock, rice cultivation, wastewater treatment, landfills, and other anthropogenic sources) and natural (wetlands, geological seeps, termites, and biomass burning). The total methane flux is also reported. Usually small differences exist between the total methane flux and the sum of sectoral fluxes, which is due to the soil absorption flux.The monthly gridded sectoral emission flux product presented in this dataset are derived from an inversion by Zhang et al. (2021). Original inversion results (including posterior scaling factors, error covariance, and averaging kernel matrix) are archived at https://doi.org/10.5281/zenodo.4052518 Although monthly gridded sectoral emissions are reported here, this does not mean that the inversion can resolve emissions for each 4x5 degree grid cell by sector on a monthly basis. See the description below and Zhang et al. (2021) for the inversion configuration. The main purpose of creating this dataset is to facilitate follow-up transport model simulations and analysis. A brief description of the inversion configuration: The inversion is performed with the GEOS-Chem model for the period of 2010-2018 with GOSAT CO2-proxy CH4 retrievals from Parker and Boesch (2020). The inversion optimizes mean and trend of non-wetland emissions, monthly wetland emissions for 14 subcontinental regions, and annual hemispheric OH concentrations (methane loss rates). Sector attribution of posterior non-wetland emissions in a 4x5 degree grid cell is done on the basis of the relative contribution of a sector to prior emissions in the grid cell. Detailed description of the methodology can be found in Zhang et al. (2021).
description: The dataset includes gridded methane emission fluxes from a global inversion of GOSAT methane (CH4) observations by Zhang et al. (2021) (https://doi.org/10.5194/acp-21-3643-2021). Monthly prior and posterior sectoral emissions from 2010-2018 are reported on a 4x5 degree grid. Source sectors include anthropogenic (oil exploitation, gas exploitation, coal mining, livestock, rice cultivation, wastewater treatment, landfills, and other anthropogenic sources) and natural (wetlands, geological seeps, termites, and biomass burning). The total methane flux is also reported. Usually small differences exist between the total methane flux and the sum of sectoral fluxes, which is due to the soil absorption flux.The monthly gridded sectoral emission flux product presented in this dataset are derived from an inversion by Zhang et al. (2021). Original inversion results (including posterior scaling factors, error covariance, and averaging kernel matrix) are archived at https://doi.org/10.5281/zenodo.4052518 Although monthly gridded sectoral emissions are reported here, this does not mean that the inversion can resolve emissions for each 4x5 degree grid cell by sector on a monthly basis. See the description below and Zhang et al. (2021) for the inversion configuration. The main purpose of creating this dataset is to facilitate follow-up transport model simulations and analysis. A brief description of the inversion configuration: The inversion is performed with the GEOS-Chem model for the period of 2010-2018 with GOSAT CO2-proxy CH4 retrievals from Parker and Boesch (2020). The inversion optimizes mean and trend of non-wetland emissions, monthly wetland emissions for 14 subcontinental regions, and annual hemispheric OH concentrations (methane loss rates). Sector attribution of posterior non-wetland emissions in a 4x5 degree grid cell is done on the basis of the relative contribution of a sector to prior emissions in the grid cell. Detailed description of the methodology can be found in Zhang et al. (2021).