Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

Journal Article · 20 September 2021 · Communications Earth & Environment

DOI: https://doi.org/10.1038/s43247-021-00266-9 · OSTI ID:1821031

Lafferty, David C. ; Sriver, Ryan L. ; Haqiqi, Iman ; Hertel, Thomas W.; Keller, Klaus; Nicholas, Robert E.

Abstract 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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: NONE; SC0016162
• OSTI ID: 1821031
• Journal Information: Communications Earth & Environment, Journal Name: Communications Earth & Environment Journal Issue: 1 Vol. 2; ISSN 2662-4435
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English