Data assimilation has emerged as an integral part of numerical weather prediction (NWP). More recently, atmospheric aerosol processes have been incorporated into NWP models to provide forecasts and guidance on air quality. There is, however, a unique opportunity within this coupled system to investigate the additional benefit of constraining model dynamics and physics due to aerosols. Several studies have reported the strong interaction between aerosols and meteorology through radiation, transport, emission, and cloud processes. To examine its importance to NWP, we conduct an ensemble-based sensitivity analysis of meteorological fields to the aerosol fields within the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART) framework. In particular, we examine the sensitivity of the forecasts of surface temperature and related dynamical fields to the initial conditions of dust and other aerosol concentrations in the model over the continental United States within the summer 2008 time period. We use an ensemble of meteorological and aerosol predictions within WRF-Chem/DART to calculate the sensitivities. This approach is similar to recent ensemble-based sensitivity studies in NWP. The use of an ensemble prediction is appealing because the analysis does not require the adjoint of the model, which to a certain extent becomes a limitation due to the rapidly evolving models and the increasing number of different observations. Here, we introduce this approach as applied to atmospheric aerosols. We also show our initial results of the calculated sensitivities from joint assimilation experiments of conventional meteorological observations from the National Centers for Environmental Prediction (NCEP) and retrievals of aerosol optical depth from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The results are then compared to coincidental measurements of these variables from surface network sites (e.g. Aerosol Robotic Network).