Preprints
https://doi.org/10.5194/acp-2022-214
https://doi.org/10.5194/acp-2022-214
 
24 Jun 2022
24 Jun 2022
Status: this preprint is currently under review for the journal ACP.

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

Vishnu Thilakan1,4, Dhanyalekshmi Pillai1,4, Christoph Gerbig2, Michal Galkowski2,3, Aparnna Ravi1,4, and Thara Anna Mathew1 Vishnu Thilakan et al.
  • 1Indian Institute of Science Education and Research Bhopal (IISERB), Bhopal, India
  • 2Max Planck Institute for Biogeochemistry, Jena, Germany
  • 3AGH University of Science and Technology, Kraków, Poland
  • 4Max Planck Partner Group (IISERB), Max Planck Society, Munich, Germany

Abstract. Improving the estimates of CO2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that realistically account for atmospheric CO2 variability along with good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability of atmospheric CO2 in models for the optimal use of observations through inverse modelling. The unresolved variability of atmospheric CO2 in coarse models is quantified by using WRF-Chem simulations at a spatial resolution of 10 km × 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of one degree (~ 100 km) are considerable (median values of 1.5 ppm and 0.4 ppm for the surface and column CO2, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to ~5 ppm, which is comparable to a quarter to half of the magnitude of seasonal variability. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo observations from nine tall tower sites over India, we show that the Net Ecosystem Exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need for taking account of fine-scale CO2 variability in models over the Indian subcontinent to better understand processes regulating CO2 sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.

Vishnu Thilakan et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-214', Anonymous Referee #2, 16 Jul 2022
  • RC2: 'Comment on acp-2022-214', Anonymous Referee #1, 19 Jul 2022

Vishnu Thilakan et al.

Vishnu Thilakan et al.

Viewed

Total article views: 311 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
243 56 12 311 20 3 5
  • HTML: 243
  • PDF: 56
  • XML: 12
  • Total: 311
  • Supplement: 20
  • BibTeX: 3
  • EndNote: 5
Views and downloads (calculated since 24 Jun 2022)
Cumulative views and downloads (calculated since 24 Jun 2022)

Viewed (geographical distribution)

Total article views: 298 (including HTML, PDF, and XML) Thereof 298 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Aug 2022
Download
Short summary
This paper demonstrates how we can use atmospheric observations to improve the CO2 flux estimates of India. This is achieved by improving the representation of terrain, mesoscale transport and flux variations. We quantify the impact of 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.
Altmetrics