Articles | Volume 16, issue 16
https://doi.org/10.5194/acp-16-10399-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-10399-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
18 Aug 2016
Research article |
| 18 Aug 2016
A biogenic CO2 flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO2 analyses and forecasts
Anna Agustí-Panareda
CORRESPONDING AUTHOR
European Centre for Medium-Range Weather Forecasts, Reading, UK
Sébastien Massart
European Centre for Medium-Range Weather Forecasts, Reading, UK
Frédéric Chevallier
Laboratoire des Sciences du Climat et l'Environnement, Gif-sur-Yvette, France
Gianpaolo Balsamo
European Centre for Medium-Range Weather Forecasts, Reading, UK
Souhail Boussetta
European Centre for Medium-Range Weather Forecasts, Reading, UK
Emanuel Dutra
European Centre for Medium-Range Weather Forecasts, Reading, UK
Anton Beljaars
European Centre for Medium-Range Weather Forecasts, Reading, UK
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Cited
21 citations as recorded by crossref.
- Short-term forecasting of regional biospheric CO<sub>2</sub> fluxes in Europe using a light-use-efficiency model (VPRM, MPI-BGC version 1.2) J. Chen et al. 10.5194/gmd-13-4091-2020
- Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling A. van der Woude et al. 10.5194/essd-15-579-2023
- Evaluating high-resolution forecasts of atmospheric CO and CO<sub>2</sub> from a global prediction system during KORUS-AQ field campaign W. Tang et al. 10.5194/acp-18-11007-2018
- An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2) S. Liu et al. 10.5194/amt-13-755-2020
- Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level D. Custódio et al. 10.3390/toxics10060331
- Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems B. Weir et al. 10.5194/acp-21-9609-2021
- A global fuel characteristic model and dataset for wildfire prediction J. McNorton & F. Di Giuseppe 10.5194/bg-21-279-2024
- Improving the inter-hemispheric gradient of total column atmospheric CO<sub>2</sub> and CH<sub>4</sub> in simulations with the ECMWF semi-Lagrangian atmospheric global model A. Agusti-Panareda et al. 10.5194/gmd-10-1-2017
- Quantifying uncertainties due to chemistry modelling – evaluation of tropospheric composition simulations in the CAMS model (cycle 43R1) V. Huijnen et al. 10.5194/gmd-12-1725-2019
- COCCON Measurements of XCO2, XCH4 and XCO over Coal Mine Aggregation Areas in Shanxi, China, and Comparison to TROPOMI and CAMS Datasets Q. Tu et al. 10.3390/rs16214022
- Representing model uncertainty for global atmospheric CO<sub>2</sub> flux inversions using ECMWF-IFS-46R1 J. McNorton et al. 10.5194/gmd-13-2297-2020
- 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
- Metrology for low-cost CO<sub>2</sub> sensors applications: the case of a steady-state through-flow (SS-TF) chamber for CO<sub>2</sub> fluxes observations R. Curcoll et al. 10.5194/amt-15-2807-2022
- Evaluation of Regional CO2 Mole Fractions in the ECMWF CAMS Real‐Time Atmospheric Analysis and NOAA CarbonTracker Near‐Real‐Time Reanalysis With Airborne Observations From ACT‐America Field Campaigns H. Chen et al. 10.1029/2018JD029992
- Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020 A. Agustí-Panareda et al. 10.5194/acp-23-3829-2023
- Field measurements of trace gases and aerosols emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Niño C. Stockwell et al. 10.5194/acp-16-11711-2016
- Toward reduced transport errors in a high resolution urban CO2 inversion system A. Deng et al. 10.1525/elementa.133
- ERA5-Land: a state-of-the-art global reanalysis dataset for land applications J. Muñoz-Sabater et al. 10.5194/essd-13-4349-2021
- Quantification of Aerosol Particle Radiative Forcing Under Cloud-Free Condition During Dry Season Period Over West Africa P. Awoleye et al. 10.1007/s41810-023-00202-8
- Modelling CO<sub>2</sub> weather – why horizontal resolution matters A. Agustí-Panareda et al. 10.5194/acp-19-7347-2019
- Carbon dioxide exchange in an idealized valley M. Reif et al. 10.1016/j.envsoft.2023.105887
21 citations as recorded by crossref.
- Short-term forecasting of regional biospheric CO<sub>2</sub> fluxes in Europe using a light-use-efficiency model (VPRM, MPI-BGC version 1.2) J. Chen et al. 10.5194/gmd-13-4091-2020
- Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling A. van der Woude et al. 10.5194/essd-15-579-2023
- Evaluating high-resolution forecasts of atmospheric CO and CO<sub>2</sub> from a global prediction system during KORUS-AQ field campaign W. Tang et al. 10.5194/acp-18-11007-2018
- An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2) S. Liu et al. 10.5194/amt-13-755-2020
- Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level D. Custódio et al. 10.3390/toxics10060331
- Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems B. Weir et al. 10.5194/acp-21-9609-2021
- A global fuel characteristic model and dataset for wildfire prediction J. McNorton & F. Di Giuseppe 10.5194/bg-21-279-2024
- Improving the inter-hemispheric gradient of total column atmospheric CO<sub>2</sub> and CH<sub>4</sub> in simulations with the ECMWF semi-Lagrangian atmospheric global model A. Agusti-Panareda et al. 10.5194/gmd-10-1-2017
- Quantifying uncertainties due to chemistry modelling – evaluation of tropospheric composition simulations in the CAMS model (cycle 43R1) V. Huijnen et al. 10.5194/gmd-12-1725-2019
- COCCON Measurements of XCO2, XCH4 and XCO over Coal Mine Aggregation Areas in Shanxi, China, and Comparison to TROPOMI and CAMS Datasets Q. Tu et al. 10.3390/rs16214022
- Representing model uncertainty for global atmospheric CO<sub>2</sub> flux inversions using ECMWF-IFS-46R1 J. McNorton et al. 10.5194/gmd-13-2297-2020
- 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
- Metrology for low-cost CO<sub>2</sub> sensors applications: the case of a steady-state through-flow (SS-TF) chamber for CO<sub>2</sub> fluxes observations R. Curcoll et al. 10.5194/amt-15-2807-2022
- Evaluation of Regional CO2 Mole Fractions in the ECMWF CAMS Real‐Time Atmospheric Analysis and NOAA CarbonTracker Near‐Real‐Time Reanalysis With Airborne Observations From ACT‐America Field Campaigns H. Chen et al. 10.1029/2018JD029992
- Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020 A. Agustí-Panareda et al. 10.5194/acp-23-3829-2023
- Field measurements of trace gases and aerosols emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Niño C. Stockwell et al. 10.5194/acp-16-11711-2016
- Toward reduced transport errors in a high resolution urban CO2 inversion system A. Deng et al. 10.1525/elementa.133
- ERA5-Land: a state-of-the-art global reanalysis dataset for land applications J. Muñoz-Sabater et al. 10.5194/essd-13-4349-2021
- Quantification of Aerosol Particle Radiative Forcing Under Cloud-Free Condition During Dry Season Period Over West Africa P. Awoleye et al. 10.1007/s41810-023-00202-8
- Modelling CO<sub>2</sub> weather – why horizontal resolution matters A. Agustí-Panareda et al. 10.5194/acp-19-7347-2019
- Carbon dioxide exchange in an idealized valley M. Reif et al. 10.1016/j.envsoft.2023.105887
Latest update: 21 Nov 2024
Short summary
This paper presents a method to adjust the sinks and sources of CO2 associated with land ecosystems within a global atmospheric CO2 forecasting system in order to reduce the errors in the forecast. This is done by combining information on (1) retrospective fluxes estimated by a global flux inversion system, (2) land-use information, and (3) simulated fluxes from the model. Because the method is simple and flexible, it can easily run in real time as part of a forecasting system.
This paper presents a method to adjust the sinks and sources of CO2 associated with land...
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