Theses and Dissertations

Advisor

Mercer, Andrew E.

Committee Member

Fosu, Boniface

Committee Member

Rudzin, Johna E.

Committee Member

Fuhrmann, Chris M.

Committee Member

Wood, Kimberly M.

Date of Degree

12-12-2025

Original embargo terms

Visible MSU Only 1 year

Document Type

Dissertation - Campus Access Only

Major

Earth and Atmospheric Sciences

Degree Name

Doctor of Philosophy (Ph.D.)

College

College of Arts and Sciences

Department

Department of Geosciences

Abstract

The El Niño Southern Oscillation (ENSO) and off-equatorial climate modes significantly impact sea surface temperature (SST) patterns in the eastern tropical Pacific and the broader downstream climate. While much of the literature highlights ENSO’s role in influencing U.S. precipitation and tornado outbreak activity, focusing solely on ENSO has limitations. Many studies also rely on empirical orthogonal function (EOF) techniques to diagnose climate-scale SST structures, limiting the ability to link individual SST patterns with downstream phenomena. EOFs require statistical adjustments to generate representative composite maps; however, these maps often fail to accurately represent observed seasonal SST patterns because each composite reflects only a portion of the total SST variability. The goal of this dissertation is to enhance the understanding of how ENSO and other SST variability affect downstream climate by using machine learning techniques, specifically hierarchical and k-means clustering. Cluster analysis provides a more direct method to link specific SST patterns with corresponding downstream impacts. This approach identifies tropical eastern Pacific SST patterns, including both equatorial and off-equatorial regions, and assesses their relationships with U.S. precipitation and tornado outbreaks (TOs). Chapter 1 introduces ENSO and related SST modes while outlining the key objectives of the study. Chapter 2 applies hierarchical and k-means clustering to SST data from the ERSST dataset (1950–2021) to identify eastern tropical Pacific patterns, encompassing both ENSO-related and off-equatorial variability. Chapter 3 investigates how the four SSTA clusters identified in Chapter 2 influence U.S. precipitation, emphasizing the physical linkages between SSTAs, the thermal wind, and the 250-hPa jet stream. Chapter 4 explores the impact of the same SSTA clusters on U.S. TOs, highlighting changes in TO activity associated with spatial shifts in 500-hPa geopotential height and 250-hPa winds. Chapter 5 synthesizes key findings, discusses broader implications, and outlines opportunities for future research.

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