Initial results will be presented from the Coordinated Climate-Crop Modeling Project (C3MP), an initiative underway that mobilizes the international community of crop modelers in a coordinated climate impacts assessment via the Agricultural Model Intercomparison and Improvement Project (AgMIP).  Crop modelers were invited to run a set of common climate sensitivity experiments at sites where their models are already calibrated and then submit their results to enable coordinated analyses for high-impact publications and data products.  Of particular interest is the sensitivity of regional agricultural production to changes in precipitation, temperature, and carbon dioxide concentrations, which in many cases is more robust across crop models and locations than are the absolute yields.  By coordinating an investigation into these fundamental sensitivities, C3MP enables an investigation of projected climate impacts across a range of global climate models, regional downscaling approaches, and crop model configurations.  As more crop modelers conduct these experiments, coverage will increase in crops, models, farming systems, and locations to enable additional analyses of uncertainty in the agricultural impacts of climate change.  By analyzing carbon, temperature, and water sensitivities with today’s climate as the origin, C3MP results will also facilitate the identification of key vulnerabilities and urgent interventions.

This presentation will describe the C3MP process and show preliminary results, contributed for crops and sites around the globe, which demonstrate the expected utility of this international, community effort.  In particular, results from a C3MP prototype study will be presented that was conducted for a peanut-growing location in the US Southeast. Analysis reveals thresholds for major yield changes as well as the probability that these thresholds will be exceeded in several ensembles of global and regional model projections.  This study also shows the downstream effects of biases in the baseline climate time series and the robustness of changes across different soil types in the region.