Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling system

Abstract

Devising effective national-level climate action plans requires a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India's current carbon source-sink estimates are uncertain. This study uses a high-resolution Lagrangian transport model to examine the potential of available CO2 observations over India for inverse estimation of regional carbon fluxes. We use four different sites in India that vary in the measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2gppm) and intra-seasonal (2 to 12gppm) variability. Our modelling framework, a high-resolution Weather Research and Forecasting Model combined with the Stochastic Time-Inverted Lagrangian Transport model (WRF-STILT), performs better in simulating seasonal (R2Combining double low line0.50 to 0.96) and diurnal (R2Combining double low line0.96) variability (for the Mohali station) of observed CO2 than the current-generation global models (CarboScope, CarbonTracker and ECMWF EGG4). The seasonal CO2 concentration variability in Mohali, associated with crop residue burning, is largely underestimated by the models. WRF-STILT captures the seasonal biospheric variability over Nainital better than the global models but underestimates the strength of the CO2 uptake by crops. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10gppm), endorsing the need for accounting for emission fluxes, especially for non-background sites. Our study highlights the possibility of using the CO2 observations from these Indian stations for deducing carbon flux information at regional (Nainital) and suburban to urban (Mohali, Shadnagar and Nagpur) scales with the help of a high-resolution model. On accounting for observed variability in CO2, the global carbon data assimilation system can benefit from the measurements from the Indian subcontinent.