Preprints
https://doi.org/10.5194/acp-2021-392
https://doi.org/10.5194/acp-2021-392

  27 May 2021

27 May 2021

Review status: a revised version of 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. The prospect of 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 make a realistic accounting of atmospheric CO2 variability. In the context of expanding atmospheric CO2 measurement networks over India, this study aims to investigate the importance of a high-resolution modelling framework to utilize these observations and to quantify the uncertainty due to the misrepresentation of fine-scale variability of CO2 in the employed model. The spatial variability of atmospheric CO2 is represented by implementing WRF-Chem at a spatial resolution of 10 km × 10 km. We utilize these high-resolution simulations for sub-grid variability calculation within the coarse model grid at a horizontal resolution of one degree (about 100 km). We show that the unresolved variability in the coarse model reaches up to a value of 10 ppm at the surface, which is considerably larger than the sampling errors, even comparable to the magnitude of mixing ratio enhancements in source regions. We find a significant impact of monsoon circulation in sub-grid variability, causing ~3 ppm average representation error between 12–14 km altitude ranges in response to the tropical easterly jet. The cyclonic storm Ockhi during November 2017 generates completely different characteristics in sub-grid variability than the rest of the period, whose influence increases the average representation error by ~1 ppm at the surface. By employing a first-order inverse modelling scheme using pseudo observations from nine tall tower sites over India and a constellation of satellite instruments, we show that the Net Ecosystem Exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 6.3 to 16.2 % of the total NEE. We illustrate an example to test the efficiency of a simple parameterization scheme during non-monsoon periods to capture the unresolved variability in the coarse models, which reduces the bias in flux estimates from 9.4 % to 2.2 %. By estimating the fine-scale variability and its impact during different seasons, we emphasise the need for implementing a high-resolution modelling framework over the Indian subcontinent to better understand processes regulating CO2 sources and sinks.

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-2021-392', Anonymous Referee #1, 17 Jun 2021
    • AC1: 'Reply on RC1 and RC2', Dhanyalekshmi Pillai, 25 Aug 2021
  • RC2: 'Comment on acp-2021-392', Anonymous Referee #2, 25 Jun 2021
    • AC1: 'Reply on RC1 and RC2', Dhanyalekshmi Pillai, 25 Aug 2021
  • EC1: 'Editor Comment on acp-2021-392', Rolf Müller, 19 Aug 2021

Vishnu Thilakan et al.

Vishnu Thilakan et al.

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Short summary
This paper demonstrates how we can make use of 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.
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