This study investigates cropland-atmosphere feedbacks in the Midwestern United States. Growing crops impact local climate during the growing season by influencing heat, moisture and momentum exchange between the land and the atmosphere. These changes in turn affect the crop growth, thus completing a feedback loop. A computationally efficient modeling tool has been specifically developed to study these feedbacks. A vegetation module derived from a crop growth model SUCROS has been incorporated in the Weather Research Forecasting (WRF) model. This coupled model has the capability to explore cropland-atmosphere feedbacks at a high spatial resolution at mesoscale. Results from soybean fields in Nebraska and Illinois show that the crop growth depends directly on temperature, incoming shortwave radiation and precipitation. As the crops grow, they affect energy partitioning between sensible and latent heat leading to a change in the cloud cover and consequently changing incoming shortwave radiation, air temperature and precipitation. An increase in cloud cover reduces incoming shortwave radiation and hence photosynthesis, exerting a negative feedback. However, an increase in precipitation reduces water stress and promotes growth, resulting in a positive feedback. The net impact on crop growth is a nonlinear combination of these feedbacks
