High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India

Accurate estimation of the flux of the most important greenhouse gas, carbon dioxide (CO2), plays a decisive role in designing the policies to reduce the atmospheric CO2 burden to combat climate change. Here we integrate recent (2017–2020) in-situ measurements with a high-resolution particle dispersion model for optimising CO2 flux over peninsular India based on a top-down approach. Using weekly observations of atmospheric boundary layer CO2 from three distinct locations representing peninsular India, the total surface fluxes of CO2 are optimised based on the fluxes from fossil fuel, wildfire, terrestrial biosphere and ocean. Our Bayesian mode inversion using the Lagrangian Particle Dispersion Model FLEXPART suggests that the peninsular Indian region is a slightly stronger source of CO2 (3.34 TgC yr−1) than what is synthesised in the fossil fuel and ecosystem exchanges based on independent estimations. On seasonal scale, the flux corrections to the prior fluxes over peninsular India are 4.68, 6.53, −2.28 and 4.41 TgC yr−1during winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The CO2 measurements from the stations attempted in the present inversion experiments could capture the footprints of peninsular India, giving better constraints to fluxes in the inversion. However, the data gap, pristineness of the samples, and large variabilities in the observations call for sustained monitoring for a longer term to further reduce the uncertainty of the estimated flux. •Surface CO2 fluxes over peninsular India are optimised using Bayesian inverse method.•2017–2020 weekly CO2 ground observations are combined with FLEXPART model simulation.•The peninsular India is found slightly stronger source of CO2 than the prior estimate.