DOI: 10.57760/sciencedb.02269
title: 2010-2017 China's methane emissions inferred from an inversion of satellite and surface observations
title: 2010-2017 China's methane emissions inferred from an inversion of satellite and surface observations

description: This repository contains data for "Observed Changes in China's Methane Emissions Linked to Policy Drivers" by Zhang et al. (2022).Update: add province.zip on Mar 21, 2023, which aggregates annual sectoral methane emissions by province.1 ReferenceY. Zhang, S. Fang, J. Chen, Y. Lin, Y. Chen, R. Liang, et al., Observed Changes in China's Methane Emissions Linked to Policy Drivers, Proceedings of the National Academy of Sciences, 119 (41) e2202742119, https://doi.org/10.1073/pnas.2202742119, 2022.2 Data filesChina_inversion_Obs_*.nc tabulate satellite and surface observations that are assimilated in the inversion. "*" can be GOSAT (satellite column, https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb), CMA (the China Meteorology Administration network, surface in situ), and WDCGG (the World Data Centre for Greenhouse Gases archival, surface in situ, https://gaw.kishou.go.jp). Simulated values (GEOS-Chem, E1) are listed for comparison.China_inversion_Flux_*.nc report monthly prior and posterior sectoral methane emission fluxes on a 0.5°x0.625° grid over East Asia (60°E-145°E, 15°W-55°W) between 2010-2017. "*" represents ensemble members (see "Inversion Ensemble" below) or the ensemble mean. See also "Inversion setup" and "Emission Sectors" for more information to understand the reported flux data.China_inversion_PBU_Rice-Aquaculture.xlsx tabulate the provincial-level bottom-up (PBU) calculation of methane emissions from rice cultivation and freshwater aquaculture in China. The activity data are from the statistical yearbook and calculations are performed at the provincial level.province.zip include csv files for 2010-2017. The csv files report annual prior and posterior sectoral methane emission fluxes aggregated by province. The unit for these files is Tg CH4/year. (added on Mar 21, 2023)3 Inversion EnsembleThe ensemble includes 4 members using different prior anthropogenic emission inventories. Results for each member as well as the ensemble mean are reported in separate files.E1: EDGAR v4.3.2 for 2012 with coal in China replaced by Sheng et al., 2019E2: PKU_CH4 v2 2010–2017E3: EDGAR v5.0 2010–2015E4: CEDS v2021-04-214 Emission Sectorsoil, gas, coal, livestock, rice, landfills, wastewater, wetlands, biomass burning (BiomassBurn), termites, seeps, and other anthropogenic (OtherAnth).5 Inversion setupThe inversion is performed on 600 spatial groups of varied sizes over the East Asia domain to optimize their 2010-2017 mean, annual anomalies, and seasonal anomalies. East Asia nested version of the GEOS-Chem chemical transport model (0.5°x0.625°) is used as the forward model for the inversion. Sector attribution of posterior fluxes is based on prior sectoral fractions for individual grid cells. See the reference for more information on the methodology.6 ContactYuzhong Zhang (zhangyuzhong@westlake.edu.cn); Shuangxi Fang (fangsx@zjut.edu.cn)7 FundingNational Key Research and Development Program of China (2020YFA0607502)National Natural Science Foundation of China (42007198).
description: This repository contains data for "Observed Changes in China's Methane Emissions Linked to Policy Drivers" by Zhang et al. (2022).Update: add province.zip on Mar 21, 2023, which aggregates annual sectoral methane emissions by province.1 ReferenceY. Zhang, S. Fang, J. Chen, Y. Lin, Y. Chen, R. Liang, et al., Observed Changes in China's Methane Emissions Linked to Policy Drivers, Proceedings of the National Academy of Sciences, 119 (41) e2202742119, https://doi.org/10.1073/pnas.2202742119, 2022.2 Data filesChina_inversion_Obs_*.nc tabulate satellite and surface observations that are assimilated in the inversion. "*" can be GOSAT (satellite column, https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb), CMA (the China Meteorology Administration network, surface in situ), and WDCGG (the World Data Centre for Greenhouse Gases archival, surface in situ, https://gaw.kishou.go.jp). Simulated values (GEOS-Chem, E1) are listed for comparison.China_inversion_Flux_*.nc report monthly prior and posterior sectoral methane emission fluxes on a 0.5°x0.625° grid over East Asia (60°E-145°E, 15°W-55°W) between 2010-2017. "*" represents ensemble members (see "Inversion Ensemble" below) or the ensemble mean. See also "Inversion setup" and "Emission Sectors" for more information to understand the reported flux data.China_inversion_PBU_Rice-Aquaculture.xlsx tabulate the provincial-level bottom-up (PBU) calculation of methane emissions from rice cultivation and freshwater aquaculture in China. The activity data are from the statistical yearbook and calculations are performed at the provincial level.province.zip include csv files for 2010-2017. The csv files report annual prior and posterior sectoral methane emission fluxes aggregated by province. The unit for these files is Tg CH4/year. (added on Mar 21, 2023)3 Inversion EnsembleThe ensemble includes 4 members using different prior anthropogenic emission inventories. Results for each member as well as the ensemble mean are reported in separate files.E1: EDGAR v4.3.2 for 2012 with coal in China replaced by Sheng et al., 2019E2: PKU_CH4 v2 2010–2017E3: EDGAR v5.0 2010–2015E4: CEDS v2021-04-214 Emission Sectorsoil, gas, coal, livestock, rice, landfills, wastewater, wetlands, biomass burning (BiomassBurn), termites, seeps, and other anthropogenic (OtherAnth).5 Inversion setupThe inversion is performed on 600 spatial groups of varied sizes over the East Asia domain to optimize their 2010-2017 mean, annual anomalies, and seasonal anomalies. East Asia nested version of the GEOS-Chem chemical transport model (0.5°x0.625°) is used as the forward model for the inversion. Sector attribution of posterior fluxes is based on prior sectoral fractions for individual grid cells. See the reference for more information on the methodology.6 ContactYuzhong Zhang (zhangyuzhong@westlake.edu.cn); Shuangxi Fang (fangsx@zjut.edu.cn)7 FundingNational Key Research and Development Program of China (2020YFA0607502)National Natural Science Foundation of China (42007198).