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Keywords:

  • Bayesian model averaging;
  • agriculture;
  • climate change;
  • downscaling;
  • uncertainty

[1] We develop probabilistic projections for three agro-climate indices (frost days, thermal time, and a heat stress index) for North America. The selected indices are important for understanding the potential impacts of future anthropogenic climate change on agricultural production. We use Bayesian Model Averaging (BMA) and bootstrapping to quantify the structural uncertainty in an ensemble of downscaled General Circulation Models (GCMs). The prior information contained in the observations and model hindcasts is used to construct physically meaningful temporal comparisons for the period 1961–2010. The comparisons are used to derive model-specific posterior weights to construct probabilistic projections of agro-climate change in the 21st century. A cross validation test covering the most recent 25 years of the observation period indicates considerable overconfidence in the projections when using the calibrated BMA approach. In contrast the probabilistic projections using equally weighted climate models are not overconfident. The strong consensus among the probabilistic projections that shows warming effects for all three agro-climate indices is tempered by the short 50-year calibration period and the small ensemble size. The short calibration period provides a relatively poor observational constraint on estimates of model weights and predictive variance, while the small ensemble size limits the climate sample space. However, the consensus that emerges in spite of the large uncertainties suggests large potential changes in the conditions that farmers will experience over the remainder of the 21st century. Of particular concern is the projected increase in the heat stress index which could lead to large crop damages and associated yield declines.