TY - JOUR
T1 - Climate extremes and their impacts on agriculture across the Eastern Corn Belt Region of the U.S.
AU - Wilson, Aaron B.
AU - Avila-Diaz, Alvaro
AU - Oliveira, Lais F.
AU - Zuluaga, Cristian F.
AU - Mark, Bryan
N1 - Funding Information:
This work was sponsored by a National Institute of Food and Agriculture Grant GRT-00048345 . This is Contribution C1618 of the Byrd Polar and Climate Research Center at The Ohio State University. We also acknowledge the Universidad de Ciencias Aplicadas y Ambientales and Universidade Federal de Viçosa.
Funding Information:
Observed ETCCDI indices are calculated using a National Aeronautics and Space Administration (NASA)-supported gridded observational dataset, DAYMET Version 3 (Thornton et al., 1997, 2016). DAYMET provides daily weather parameters for North America, including Canada, the United States, Mexico, Puerto Rico, and Bermuda, during the 1980–2018 period at a 1-km spatial resolution. DAYMET inputs include a digital elevation model and in-situ weather observations of daily maximum temperature, minimum temperature, and precipitation from the Global Historical Climatology Network (GHCN) (Thornton et al., 2016).This work was sponsored by a National Institute of Food and Agriculture Grant GRT-00048345. This is Contribution C1618 of the Byrd Polar and Climate Research Center at The Ohio State University. We also acknowledge the Universidad de Ciencias Aplicadas y Ambientales and Universidade Federal de Viçosa.
Publisher Copyright:
© 2022 The Authors
PY - 2022/9
Y1 - 2022/9
N2 - The Eastern Corn Belt Region (ECBR) is an important agricultural sector for the U.S. This study analyzes the climate extremes over the contemporary (1980–2018) and future (2036–2099) periods over the ECBR. We evaluated the performance of 32 downscaled models from the U.S. Global Change Research Program's Localized Constructed Analogs (LOCA) of the Coupled Model Intercomparison Project (CMIP5) to simulate extreme temperature and precipitation indices. The LOCA downscaled models were evaluated for the recent past against the National Aeronautics and Space Administration (NASA)-supported gridded observational dataset DAYMET. Results reveal key trends throughout the region that are consistent with previous studies, including significant increases in extreme minimum temperatures, reduction of cold nights, increase of warm nights, and decreases in diurnal temperature ranges. Much of the region demonstrates extreme warming trends in the coldest night of the year (more than 5 °C) and an increase in the heaviest precipitation events over 1980–2018. An optimal model ensemble (OME) was constructed using a Kling-Gupta Efficiency and Bhattacharyya coefficient evaluation to construct a comprehensive ranking procedure. Having outperformed a standard multi-model ensemble approach, the OME was used to evaluate the future changes of extreme climate indices under RCP4.5 and RCP8.5 scenarios. Though the OME showed consistently strong warming throughout the ECBR, variability among the optimal models and across watersheds is quite significant, especially for precipitation indices. Thus, constraining the uncertainty in future climate models, specifically as it relates to agriculture decisions that support climate-resilience, remains a challenge.
AB - The Eastern Corn Belt Region (ECBR) is an important agricultural sector for the U.S. This study analyzes the climate extremes over the contemporary (1980–2018) and future (2036–2099) periods over the ECBR. We evaluated the performance of 32 downscaled models from the U.S. Global Change Research Program's Localized Constructed Analogs (LOCA) of the Coupled Model Intercomparison Project (CMIP5) to simulate extreme temperature and precipitation indices. The LOCA downscaled models were evaluated for the recent past against the National Aeronautics and Space Administration (NASA)-supported gridded observational dataset DAYMET. Results reveal key trends throughout the region that are consistent with previous studies, including significant increases in extreme minimum temperatures, reduction of cold nights, increase of warm nights, and decreases in diurnal temperature ranges. Much of the region demonstrates extreme warming trends in the coldest night of the year (more than 5 °C) and an increase in the heaviest precipitation events over 1980–2018. An optimal model ensemble (OME) was constructed using a Kling-Gupta Efficiency and Bhattacharyya coefficient evaluation to construct a comprehensive ranking procedure. Having outperformed a standard multi-model ensemble approach, the OME was used to evaluate the future changes of extreme climate indices under RCP4.5 and RCP8.5 scenarios. Though the OME showed consistently strong warming throughout the ECBR, variability among the optimal models and across watersheds is quite significant, especially for precipitation indices. Thus, constraining the uncertainty in future climate models, specifically as it relates to agriculture decisions that support climate-resilience, remains a challenge.
UR - http://www.scopus.com/inward/record.url?scp=85132386209&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132386209&partnerID=8YFLogxK
U2 - 10.1016/j.wace.2022.100467
DO - 10.1016/j.wace.2022.100467
M3 - Research Article
AN - SCOPUS:85132386209
SN - 2212-0947
VL - 37
JO - Weather and Climate Extremes
JF - Weather and Climate Extremes
M1 - 100467
ER -
