Editorial Type:
Article
Article Type:
Research Article

Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis

Lejiang Yu Applied Hydrometeorological Research Institute, Nanjing University of Information Science and Technology, Nanjing, China, and Department of Geography, and Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan

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Shiyuan Zhong Department of Geography, and Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan

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Xindi Bian Northern Research Station, USDA Forest Service, Lansing, Michigan

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Warren E. Heilman Northern Research Station, USDA Forest Service, Lansing, Michigan

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Print Publication:
01 Feb 2015
DOI:
https://doi.org/10.1175/JCLI-D-14-00322.1
Page(s):
1166–1183
Restricted access

Abstract

This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

Corresponding author address: Dr. Shiyuan “Sharon” Zhong, Department of Geography, Michigan State University, 673 Auditorium Rd., East Lansing, MI 48824. E-mail: zhongs@msu.edu

Abstract

This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

Corresponding author address: Dr. Shiyuan “Sharon” Zhong, Department of Geography, Michigan State University, 673 Auditorium Rd., East Lansing, MI 48824. E-mail: zhongs@msu.edu
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