Articles | Volume 13, issue 11
https://doi.org/10.5194/essd-13-5311-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5311-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
17 Nov 2021
Data description paper |
| 17 Nov 2021
| 17 Nov 2021
Global anthropogenic CO2 emissions and uncertainties as a prior for Earth system modelling and data assimilation
Margarita Choulga
CORRESPONDING AUTHOR
Research Department, European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, RG2 9AX, United Kingdom
Greet Janssens-Maenhout
Joint Research Centre (JRC), European Commission, Ispra, 21027, Italy
Ingrid Super
Department of Climate, Air and Sustainability, TNO, Utrecht, 3584 CB, the Netherlands
Efisio Solazzo
Joint Research Centre (JRC), European Commission, Ispra, 21027, Italy
Anna Agusti-Panareda
Research Department, European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, RG2 9AX, United Kingdom
Gianpaolo Balsamo
Research Department, European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, RG2 9AX, United Kingdom
Nicolas Bousserez
Research Department, European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, RG2 9AX, United Kingdom
Monica Crippa
Joint Research Centre (JRC), European Commission, Ispra, 21027, Italy
Hugo Denier van der Gon
Department of Climate, Air and Sustainability, TNO, Utrecht, 3584 CB, the Netherlands
Richard Engelen
Research Department, European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, RG2 9AX, United Kingdom
Diego Guizzardi
Joint Research Centre (JRC), European Commission, Ispra, 21027, Italy
Jeroen Kuenen
Department of Climate, Air and Sustainability, TNO, Utrecht, 3584 CB, the Netherlands
Joe McNorton
Research Department, European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, RG2 9AX, United Kingdom
Gabriel Oreggioni
Joint Research Centre (JRC), European Commission, Ispra, 21027, Italy
Antoon Visschedijk
Department of Climate, Air and Sustainability, TNO, Utrecht, 3584 CB, the Netherlands
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14 citations as recorded by crossref.
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- Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. 10.5194/essd-15-3597-2023
- Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement A. Agustí-Panareda et al. 10.1038/s41597-022-01228-2
- Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2) P. Ciais et al. 10.5194/gmd-15-1289-2022
- Emission trends of air pollutants and CO2 in China from 2005 to 2021 S. Li et al. 10.5194/essd-15-2279-2023
- A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals L. Zhang et al. 10.3390/rs17030447
- Improved estimates of net ecosystem exchanges in mega-countries using GOSAT and OCO-2 observations L. Zhang et al. 10.1038/s43247-024-01910-w
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. 10.1007/s11783-025-1922-x
- Anthropogenic Emission Scenarios over Europe with the WRF-CHIMERE-v2020 Models: Impact of Duration and Intensity of Reductions on Surface Concentrations during the Winter of 2015 A. Cholakian et al. 10.3390/atmos14020224
- India leads in emission intensity per GDP: Insights from the gridded emission inventory for residential, road transport, and energy sectors V. Velamuri et al. 10.1016/j.jes.2024.10.015
- Deep neural network for investment decision planning on low-carbon transition in power grid M. Wang et al. 10.1093/ijlct/ctae094
- Food systems are responsible for a third of global anthropogenic GHG emissions M. Crippa et al. 10.1038/s43016-021-00225-9
- The CO2 Human Emissions (CHE) Project: First Steps Towards a European Operational Capacity to Monitor Anthropogenic CO2 Emissions G. Balsamo et al. 10.3389/frsen.2021.707247
- Impact of Prior Terrestrial Carbon Fluxes on Simulations of Atmospheric CO2 Concentrations Y. Fu et al. 10.1029/2021JD034794
11 citations as recorded by crossref.
- CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories R. Urraca et al. 10.5194/essd-16-501-2024
- Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. 10.5194/essd-15-3597-2023
- Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement A. Agustí-Panareda et al. 10.1038/s41597-022-01228-2
- Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2) P. Ciais et al. 10.5194/gmd-15-1289-2022
- Emission trends of air pollutants and CO2 in China from 2005 to 2021 S. Li et al. 10.5194/essd-15-2279-2023
- A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals L. Zhang et al. 10.3390/rs17030447
- Improved estimates of net ecosystem exchanges in mega-countries using GOSAT and OCO-2 observations L. Zhang et al. 10.1038/s43247-024-01910-w
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. 10.1007/s11783-025-1922-x
- Anthropogenic Emission Scenarios over Europe with the WRF-CHIMERE-v2020 Models: Impact of Duration and Intensity of Reductions on Surface Concentrations during the Winter of 2015 A. Cholakian et al. 10.3390/atmos14020224
- India leads in emission intensity per GDP: Insights from the gridded emission inventory for residential, road transport, and energy sectors V. Velamuri et al. 10.1016/j.jes.2024.10.015
- Deep neural network for investment decision planning on low-carbon transition in power grid M. Wang et al. 10.1093/ijlct/ctae094
3 citations as recorded by crossref.
- Food systems are responsible for a third of global anthropogenic GHG emissions M. Crippa et al. 10.1038/s43016-021-00225-9
- The CO2 Human Emissions (CHE) Project: First Steps Towards a European Operational Capacity to Monitor Anthropogenic CO2 Emissions G. Balsamo et al. 10.3389/frsen.2021.707247
- Impact of Prior Terrestrial Carbon Fluxes on Simulations of Atmospheric CO2 Concentrations Y. Fu et al. 10.1029/2021JD034794
Latest update: 21 Feb 2025
Short summary
People worry that growing man-made carbon dioxide (CO2) concentrations lead to climate change. Global models, use of observations, and datasets can help us better understand behaviour of CO2. Here a tool to compute uncertainty in man-made CO2 sources per country per year and month is presented. An example of all sources separated into seven groups (intensive and average energy, industry, humans, ground and air transport, others) is presented. Results will be used to predict CO2 concentrations.
People worry that growing man-made carbon dioxide (CO2) concentrations lead to climate change....
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