Articles | Volume 13, issue 5
https://doi.org/10.5194/gmd-13-2297-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-2297-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Representing model uncertainty for global atmospheric CO2 flux inversions using ECMWF-IFS-46R1
Joe R. McNorton
CORRESPONDING AUTHOR
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Nicolas Bousserez
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Anna Agustí-Panareda
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Gianpaolo Balsamo
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Margarita Choulga
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Andrew Dawson
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Richard Engelen
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Zak Kipling
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Simon Lang
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
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Cited
13 citations as recorded by crossref.
- Verifying Methane Inventories and Trends With Atmospheric Methane Data J. Worden et al. 10.1029/2023AV000871
- An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions J. McNorton et al. 10.1029/2022MS003286
- 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
- The Resolvable Scales of Regional‐Scale CO2 Transport in the Context of Imperfect Meteorology: The Predictability of CO2 in a Limited‐Area Model J. Kim et al. 10.1029/2021JD034896
- The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates J. Worden et al. 10.5194/acp-22-6811-2022
- Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother M. Steiner et al. 10.5194/acp-24-12447-2024
- National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- Global anthropogenic CO<sub>2</sub> emissions and uncertainties as a prior for Earth system modelling and data assimilation M. Choulga et al. 10.5194/essd-13-5311-2021
- Regional CO2 Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments H. Chen et al. 10.1029/2022MS003208
- Discussion on A high‐resolution bilevel skew‐tstochastic generator for assessing Saudi Arabia's wind energy resources A. Zammit‐Mangion 10.1002/env.2649
- 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
- CO<sub>2</sub> surface variability: from the stratosphere or not? M. Prather 10.5194/esd-13-703-2022
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
13 citations as recorded by crossref.
- Verifying Methane Inventories and Trends With Atmospheric Methane Data J. Worden et al. 10.1029/2023AV000871
- An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions J. McNorton et al. 10.1029/2022MS003286
- 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
- The Resolvable Scales of Regional‐Scale CO2 Transport in the Context of Imperfect Meteorology: The Predictability of CO2 in a Limited‐Area Model J. Kim et al. 10.1029/2021JD034896
- The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates J. Worden et al. 10.5194/acp-22-6811-2022
- Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother M. Steiner et al. 10.5194/acp-24-12447-2024
- National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- Global anthropogenic CO<sub>2</sub> emissions and uncertainties as a prior for Earth system modelling and data assimilation M. Choulga et al. 10.5194/essd-13-5311-2021
- Regional CO2 Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments H. Chen et al. 10.1029/2022MS003208
- Discussion on A high‐resolution bilevel skew‐tstochastic generator for assessing Saudi Arabia's wind energy resources A. Zammit‐Mangion 10.1002/env.2649
- 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
- CO<sub>2</sub> surface variability: from the stratosphere or not? M. Prather 10.5194/esd-13-703-2022
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
Latest update: 13 Dec 2024
Short summary
To infer carbon emissions from observations using atmospheric models, detailed knowledge of uncertainty is required. The uncertainties associated with models are often estimated because they are difficult to attribute. Here we use a state-of-the-art weather model to assess the impact of uncertainty in the wind fields on atmospheric concentrations of carbon dioxide. These results can be used to help quantify the uncertainty in estimated carbon emissions from atmospheric observations.
To infer carbon emissions from observations using atmospheric models, detailed knowledge of...