Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields
Abstract
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.
- Publication Date:
- Research Org.:
- Univ. of Illinois at Urbana-Champaign, IL (United States); Purdue Univ., West Lafayette, IN (United States); Pennsylvania State Univ., University Park, PA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1821031
- Alternate Identifier(s):
- OSTI ID: 1820980
- Grant/Contract Number:
- SC0016162
- Resource Type:
- Published Article
- Journal Name:
- Communications Earth & Environment
- Additional Journal Information:
- Journal Name: Communications Earth & Environment Journal Volume: 2 Journal Issue: 1; Journal ID: ISSN 2662-4435
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Agriculture; Climate and Earth system modelling
Citation Formats
Lafferty, David C., Sriver, Ryan L., Haqiqi, Iman, Hertel, Thomas W., Keller, Klaus, and Nicholas, Robert E. Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields. United Kingdom: N. p., 2021.
Web. doi:10.1038/s43247-021-00266-9.
Lafferty, David C., Sriver, Ryan L., Haqiqi, Iman, Hertel, Thomas W., Keller, Klaus, & Nicholas, Robert E. Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields. United Kingdom. https://doi.org/10.1038/s43247-021-00266-9
Lafferty, David C., Sriver, Ryan L., Haqiqi, Iman, Hertel, Thomas W., Keller, Klaus, and Nicholas, Robert E. Mon .
"Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields". United Kingdom. https://doi.org/10.1038/s43247-021-00266-9.
@article{osti_1821031,
title = {Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields},
author = {Lafferty, David C. and Sriver, Ryan L. and Haqiqi, Iman and Hertel, Thomas W. and Keller, Klaus and Nicholas, Robert E.},
abstractNote = {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.},
doi = {10.1038/s43247-021-00266-9},
journal = {Communications Earth & Environment},
number = 1,
volume = 2,
place = {United Kingdom},
year = {Mon Sep 20 00:00:00 EDT 2021},
month = {Mon Sep 20 00:00:00 EDT 2021}
}
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