Date of Award

1-1-2019

Language

English

Document Type

Master's Thesis

Degree Name

Master of Science (MS)

College/School/Department

Department of Atmospheric and Environmental Sciences

Content Description

1 online resource (iv, 51 pages) : color illustrations, color maps.

Dissertation/Thesis Chair

Jeffrey Michael Freedman

Committee Members

Jiping Liu

Keywords

CMIP5, EOF, External forcing, Internal Variability, Wavelet analysis, Wind energy, Wind power, Atmospheric circulation, Wind energy conversion systems, Climatic changes, Winds

Subject Categories

Atmospheric Sciences

Abstract

Wind power is playing a greater role as an alternative energy resource to fossil fuels. The prediction skills for hourly and daily forecasts of hub-height (80 – 100 m) wind power are increasingly reliable. However, regarding historical trends in wind energy availability (an important variable for determining wind farm capacity factors), little is known about the interannual and decadal variability of the hub-height wind speed. With climate change uncertainty now incorporated into wind energy resource assessment, insight into the relative contribution from the internal variability of the climate system versus the external forcing is presently lacking. Here, Empirical Orthogonal Function (EOF) analyses are applied to Coupled ECMWF Reanalysis of the Twentieth Century (CERA-20C) from 1901 to 2010 to investigate the characteristic of the spatial and temporal variability of the hub-height wind speed (100m) in June, July and August (JJA) and December, January, February and March (DJFM). Correlation and regression analyses and Morlet wavelet analyses are used to examine the relationship between wind speed variability and atmospheric circulation effects. In DJFM, the first leading EOF mode for the Contiguous (or coterminous) United States (CONUS; the 48 states excluding Alaska and Hawaii) explains 34.9% of the total variance of wind speed, corresponding to the Pacific Decadal Oscillation (PDO) and the Pacific-North American teleconnection pattern (PNA). In JJA, the first EOF mode explains 19.4% of the total variance and strongly correlates to the El Niño-Southern Oscillation (ENSO) on the decadal time scale. In the North Atlantic Ocean and European sector, the first leading EOF mode in the DJFM is closely related to the NAO. In JJA, the first leading EOF mode corresponds to the East Atlantic pattern (EA) and the second EOF mode is related to the interannual variations in the NAO. Analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations shows that the external forcing signal is less important than internal variability to trends of hub-height wind speed over the CONUS.

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