Evaluating the meteorological transport model ensemble for accounting uncertainties in carbon flux estimation over India

Abstract. The existing climate change scenario calls for immediate intervention to curb rising greenhouse gas emissions. An improved understanding of the regional distributions of carbon sources and sinks under the perturbed climate system is vital for assisting the above mitigation efforts. The current uncertainties in estimation can potentially be reduced by employing a multi-data modelling system capable of representing atmospheric tracer transport adequately. This study focuses on the mesoscale transport patterns that can affect atmospheric tracer distribution and examines how well they are represented in the meteorological models employed. We investigate the capability of the Weather Research and Forecasting (WRF) model to predict meteorological fields such as temperature, humidity, wind, and planetary boundary layer height (PBLH) by comparing different model simulations with surface and vertical profile observations available at urban and rural stations, Cochin and Gadanki, and with global reanalysis data over India. Combining different model schemes and data products allows us to present a model ensemble of 11 members. Using these ensemble simulations, the impacts of changes in physics schemes, initial and boundary conditions, and spatial resolutions on meteorology and, consequently, on CO₂ mixing ratio simulations are quantified. Most simulations capture variations in temperature and moisture very well (R²> 0.75). The wind (R²> 0.75 for height above 2 km) and PBLH simulations (R²> 0.75 for daytime) are also reasonably correlated with the observations. The sensitivity to changing planetary boundary layer (PBL) schemes and land surface model (LSM) schemes on meteorological and CO₂ mixing ratio simulations is significant, thereby producing higher inter-model differences between experiments. Our analysis provides an assessment of expected CO₂ transport errors when using WRF-like models in the inverse modelling framework. We emphasise the importance of treating these errors in the carbon data assimilation system to utilize the full potential of the measurements and conclude that WRF can be utilised as a potential transport model for the regional carbon flux estimations in India.