Evaluating the utility of dynamical downscaling in agricultural impacts projections

Dssat probability distributions of 1980-1998 rain-fed maize yield driven by all climate products. a and b show yields driven by ccsm and cgcm output, respectively. observation-driven yield distributions are duplicated in both panels (black line) and mapped on right (time-averages, with outline demarking the corn belt, counties with ≥ 1/4 land cultivated with maize).  Aggregation is by county and not normalized for size or total yield.  Arrows and star in a and b show average county yiled.  yields driven by non-bias-corrected inputs (dashed) are generally skewed low, with the skew worse for dynamical downscaling than for simply interpolated gcm inputs. Bias-correcting climate inputs (solid) largely eliminates distributional discrepancies against the observation set.

Dssat probability distributions of 1980-1998 rain-fed maize yield driven by all climate products. a and b show yields driven by ccsm and cgcm output, respectively. observation-driven yield distributions are duplicated in both panels (black line) and mapped on right (time-averages, with outline demarking the corn belt, counties with ≥ 1/4 land cultivated with maize).  Aggregation is by county and not normalized for size or total yield.  Arrows and star in a and b show average county yiled.  yields driven by non-bias-corrected inputs (dashed) are generally skewed low, with the skew worse for dynamical downscaling than for simply interpolated gcm inputs. Bias-correcting climate inputs (solid) largely eliminates distributional discrepancies against the observation set.

Interest in estimating the potential socioeconomic costs of climate change has led to increasing use of dynamical downscaling: nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of food supply. Our results suggest that it does not. We simulate U.S. maize yields with the widely-used DSSAT crop model, driven by two GCMs, each in turn downscaled by two RCMs. While RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kms) GCM systematic errors that RCMs cannot compensate for. Once a simple statistical bias correction is applied, GCM- and RCM-driven U.S. maize yields are essentially indistinguishable. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands.

Comparison of the productivity effects of drought over time by evaluation of the 1988 and 2012 droughts and historical counterfactuals.

Comparison of the productivity effects of drought over time by evaluation of the 1988 and 2012 droughts and historical counterfactuals.

People:

Current: Joshua Elliott | Ian Foster | Michael Glotter | Elisabeth Moyer

Alumni: Neil Best

Recent Publications