Abstract The climate crisis necessitates urgent action to reduce greenhouse gas (GHG) emissions with a better understanding of their regional carbon sources and sinks. However, considerable uncertainties in current multi-data modeling approaches for GHG estimations arise, in part, from errors in atmospheric tracer transport. This study investigates the impact of transport errors in mesoscale meteorological variables on atmospheric tracer distribution, which needs to be taken into account in robust carbon source-sink estimate systems. The reasonable meteorological transport errors are generated by performing ensemble simulations of the Weather Research Forecast (WRF) model by carefully choosing different model schemes and settings. The resulting meteorological ensemble fields are utilized to simulate CO2 transport distributions using the Stochastic Time Inverted Lagrangian Transport (STILT) model. The model spread in tracer simulations owing to slight differences in meteorological transport is then derived, in conjunction with meteorological (both surface and upper levels) and surface CO2 observations, to assess the expected transport accuracy in the carbon assimilation system. Our analysis reveals that slight changes in the land surface and the evolution of PBL significantly affect surface CO2. Variations in WRF transports caused a mean CO2 uncertainties of 3.2 ppm at the urban site (maximum: 24.3 ppm) and 2.8 ppm at the rural site (maximum: 10.8 ppm). Nighttime CO2 uncertainties, possibly amplified by shallow-stable PBL conditions, were three to five times higher than daytime. In urban areas, low wind speeds increase trace gas accumulation in the lower boundary layer, increasing model uncertainties. By identifying potential uncertainties arising from meteorological models and their impacts on tracers, the outcome of the present study can serve as a useful reference for the adequate representation of transport errors in inverse models. Our findings offer valuable insights for designing future carbon assimilation frameworks to ensure maximum use of observations and improve GHG source-sink estimations over India.
Mathew et al. (Wed,) studied this question.