Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 22, issue 23
Articles | Volume 22, issue 23
https://doi.org/10.5194/acp-22-15287-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-15287-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 23
Research article
 | 
01 Dec 2022
Research article |  | 01 Dec 2022

Towards monitoring the CO2 source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO2 mole fraction

Vishnu Thilakan, Dhanyalekshmi Pillai, Christoph Gerbig, Michal Galkowski, Aparnna Ravi, and Thara Anna Mathew

Supplement

https://doi.org/10.5194/acp-22-15287-2022-supplement

Data sets

Representation error in global model CO2 simulations over India V. Thilakan and D. Pillai https://doi.org/10.5281/zenodo.6616466

Jena CarboScope Version s10oc_v2020 Max Planck Institute for Biogeochemistry (MPI-BGC) http://www.bgc-jena.mpg.de/CarboScope/

Greenhouse Gases Flux Inversions Version v18r3 Copernicus Atmosphere Monitoring Service (CAMS)/Laboratoire des Sciences du Climat et l'Environnement (LSCE) https://apps.ecmwf.int/datasets/data/cams-ghg-inversions/

Greenhouse Gases Flux Inversions Version FT18r1 Copernicus Atmosphere Monitoring Service (CAMS)/Laboratoire des Sciences du Climat et l'Environnement (LSCE) https://apps.ecmwf.int/datasets/data/cams-ghg-inversions/

EDGAR v6.0 Greenhouse Gas Emissions M. Crippa, D. Guizzardi, M. Muntean, E. Schaaf, E. Lo Vullo, E. Solazzo, F. Monforti-Ferrario, J. Olivier, and E. Vignati http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b

Global Fire Assimilation System (GFAS) v1.2 Copernicus Atmosphere Monitoring Service (CAMS) https://apps.ecmwf.int/datasets/data/cams-gfas/

ERA5 hourly data on single levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.adbb2d47

ERA5 hourly data on pressure levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.bd0915c6

CarbonTracker CT2019B A. R. Jacobson, K. N. Schuldt, J. B. Miller, et al. https://doi.org/10.25925/20201008

Model code and software

Weather Research and Forecasting Model Version 3.9.1.1 National Center for Atmospheric Research (NCAR) https://doi.org/10.5065/D6MK6B4K

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Short summary
This paper demonstrates how we can use atmospheric observations to improve the CO2 flux estimates in India. This is achieved by improving the representation of terrain, mesoscale transport, and flux variations. We quantify the impact of the unresolved variations in the current models on optimally estimated fluxes via inverse modelling and quantify the associated flux uncertainty. We illustrate how a parameterization scheme captures this variability in the coarse models.
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