How Critical Is the Accuracy of the Atmospheric Transport Modeling to Improve the Urban CO2 Emission in India?—A Lagrangian‐Based Approach

Abstract

AbstractAnthropogenic CO2 emission reduction strategies depend on how well we track emission enhancements at the urban scale. The estimation system, based on inverse modeling, relies on our knowledge of atmospheric transport and prior flux distributions. Hence, the analysis framework must account for uncertainties associated with each component in order to interpret the variations in observed CO2. Using an ensemble of simulations, we quantify the uncertainties in simulating anthropogenic CO2 mixing ratio enhancements at 15 locations in India. Differences in the representation of transport mechanisms and prior emission in the forward model induce a consistently large model spread of 62.2% and 41.9%, respectively, in the simulated mixing ratio over cities. The analysis reports an average uncertainty of 2.2 ppm with a maximum of 8 ppm for representing diurnally averaged anthropogenic CO2 enhancement. Diurnal variations in emissions and transport induce a rectification effect in those enhancements. The outcome of this study can thus inform future atmospheric CO2 inversion modeling at an urban scale on the expected forward model uncertainties, which are the essential components in the Bayesian inversion framework, typically lacking in the Indian region. The first‐order inversion experiments show that the change in the transport model induces significant uncertainty (up to 84.9%) in anthropogenic CO2 flux estimation at the national scale. Hence, the confidence level of inverse‐based emission estimation in India depends considerably on the accuracy of atmospheric transport modeling.