Development and Evaluation of a Simple Crop Model for WRF Seasonal Forecast Improvement

The landscape of the central United States is dominated by intense agriculture. Crop species in this region, such as corn and soybeans, are highly efficient transpiring vegetation. This transpiration provides an effective link to transfer soil moisture to the atmosphere in this region. This study focuses on the effect of including a simple crop model into the WRF atmospheric model via the Noah-MP land surface model. The crop model is a modified version of the existing Noah-MP dynamic vegetation that includes a grain carbon pool and dynamic roots. Though the model can predict crop yields, the primary focus of this study is to determine if the inclusion of the crop model can improve seasonal summer forecast skill for precipitation and near surface temperature and moisture. Initial results show that the crop model is highly sensitive to model produced precipitation. Three summer seasons (May – Oct) are simulated for 2012 (dry), 2013 (normal) and 2014 (wet). Overall, precipitation and temperature are improved in 2013 and 2014 by using the crop model, but the vegetation is not resilient to the dry year (2012) a high temperature bias and positive feedback to precipitation, making the simulations even drier.