An offline constrained data assimilation technique for aerosols: Improving GCM simulations over South Asia using observations from two satellite sensors

Abstract

Aerosol properties simulated by general circulation models (GCMs) exhibit large uncertainties due to biases in model processes and inaccuracies in aerosol emission inputs. In this work, we propose an offline, constrained optimization based procedure to improve these simulations by assimilating them with observational data. The proposed approach explicitly incorporates the non-negativity constraint on the aerosol optical depth (AOD) which is a key metric to quantify aerosol distributions. The resulting optimization problem is quadratic programming in nature and can be easily solved by available optimization routines. The utility of the approach is demonstrated by performing offline assimilation of GCM simulated aerosol optical properties and radiative forcing over South Asia (40-120 E, 5-40 N), with satellite AOD measurements from two sensors, namely Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-Angle Imaging SpectroRadiometer (MISR). Uncertainty in observational data used in the assimilation is computed by developing different error bands around regional AOD observations, based on their quality assurance flags. The assimilation, evaluated on monthly and daily scales, compares well with Aerosol Robotic Network (AERONET) observations as determined by goodness of fit statistics. Assimilation increased both model predicted atmospheric absorption and clear sky radiative forcing by factors consistent with recent estimates in literature. Thus, the constrained assimilation algorithm helps in systematically reducing uncertainties in aerosol simulations.