Towards monitoring the CO2 source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO2 mole fraction

• Published in: Atmospheric chemistry and physics 2022-12, Vol.22 (23), p.15287-15312
• Main Authors: Thilakan, Vishnu, Pillai, Dhanyalekshmi, Gerbig, Christoph, Galkowski, Michal, Aparnna Ravi, Thara Anna Mathew
• Format: Article
• Language: English
• Subjects: Atmospheric models; Atmospheric monitoring; Atmospheric transport; Atmospheric transport models; Carbon dioxide; Climate change; Data assimilation; Emissions; Errors; Greenhouse gases; Ground stations; Heterogeneity; Meteorology; Modelling; Monitoring; Monitoring systems; Parameterization; Regions; Representations; Resolution; Seasonal variability; Seasonal variation; Seasonal variations; Seasons; Spatial discrimination; Spatial resolution; Surface fluxes; Topography; Transport processes; Variability; Weather forecasting; Wind
• ISSN: 1680-7316; 1680-7324
• DOI: 10.5194/acp-22-15287-2022

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 make a realistic accounting of atmospheric CO2 variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO2 in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO2 in coarse models is quantified by using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) 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 1∘ (∼ 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, 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 even comparable to half of the magnitude of the seasonal variability or concentration enhancement due to hotspot emissions. 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 to take 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.