Statistically Bias-Corrected and Downscaled Climate Models Underestimate the Adverse Effects of Extreme Heat on U.S. Maize Yields
Many studies rely on bias-corrected and downscaled climate information for coupled human-environment analysis. We quantify potential biases of this approach in the agricultural sector by using an ensemble of statistically bias-corrected and downscaled climate models (NEX-GDDP), as well as the corresponding parent models (CMIP5), to drive a statistical panel model of U.S. maize yields and analyze uncertainty in hindcasts and projections.
Most CMIP5 models considerably overestimate historical yield variability while the NEX-GDDP models underestimate the magnitude of the largest yield shocks, which we attribute to the effects of downscaling and bias correction on temperature extremes. We also find large differences between the ensembles in projections, which amplify uncertainties surrounding future modeled agricultural losses.
Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.