Reply on RC2

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Improving the estimates of CO<span class="inline-formula">₂</span> 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 CO<span class="inline-formula">₂</span> variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO<span class="inline-formula">₂</span> in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO<span class="inline-formula">₂</span> 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 <span class="inline-formula">×</span> 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of 1<span class="inline-formula">^∘</span> (<span class="inline-formula">∼</span> 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, for the surface and column CO<span class="inline-formula">₂</span>, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to <span class="inline-formula">∼</span> 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 CO<span class="inline-formula">₂</span> variability in models over the Indian subcontinent to better understand processes regulating CO<span class="inline-formula">₂</span> sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.