Comment on acp-2022-214

Abstract

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 realistically account for atmospheric CO2 variability along with good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability of atmospheric CO2 in models for the optimal use of observations through inverse modelling. The unresolved variability of atmospheric CO2 in coarse models is quantified by using WRF-Chem simulations at a spatial resolution of 10 km × 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of one degree (~ 100 km) are considerable (median values of 1.5 ppm and 0.4 ppm for the surface and column CO2, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to ~5 ppm, which is comparable to a quarter to half of the magnitude of seasonal variability. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo observations from nine tall tower sites over India, we show that the Net Ecosystem Exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need for taking account of fine-scale CO2 variability in models over the Indian subcontinent to better understand processes regulating CO2 sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.