@article{e9519b5dbbf3446bb022b1cab797d11f,
title = "Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields",
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.",
author = "Lafferty, {David C.} and Sriver, {Ryan L.} and Iman Haqiqi and Hertel, {Thomas W.} and Klaus Keller and Nicholas, {Robert E.}",
note = "Funding Information: We thank the editor and three anonymous reviewers whose constructive comments greatly improved the manuscript. This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics, Contract No. DE-SC0016162 as well as the Penn State Center for Climate Risk Management. Computations for this research were performed on the Pennsylvania State University{\textquoteright}s Institute for Computational and Data Sciences Advanced CyberInfrastructure (ICDS-ACI). Climate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). We thank Murali Haran for insightful discussions. All errors and opinions are those of the authors and not of the funding entities. Funding Information: We thank the editor and three anonymous reviewers whose constructive comments greatly improved the manuscript. This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics, Contract No. DE-SC0016162 as well as the Penn State Center for Climate Risk Management. Computations for this research were performed on the Pennsylvania State University{\textquoteright}s Institute for Computational and Data Sciences Advanced CyberInfrastructure (ICDS-ACI). Climate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). We thank Murali Haran for insightful discussions. All errors and opinions are those of the authors and not of the funding entities. Publisher Copyright: {\textcopyright} 2021, The Author(s).",
year = "2021",
month = dec,
doi = "10.1038/s43247-021-00266-9",
language = "English (US)",
volume = "2",
journal = "Communications Earth and Environment",
issn = "2662-4435",
publisher = "Springer Nature",
number = "1",
}