UC Davis

The northeastern United States (hereinafter, the Northeast) is home to a dense human population and encompasses a variety of agricultural and economic interests that are reliant on the available water resources and the replenishment of those resources via precipitation. Due to ongoing climate change, water availability is expected to be altered in this region. This expected change is particularly important at the ends of the precipitation spectrum (i.e., extreme precipitation and droughts), as these events can lead to devastating economic damages to infrastructure, property, and agriculture. Given the many problems that can be associated with an increased frequency of both wet and dry extremes, it has become increasingly important to gain a better understanding of the large-scale meteorology related to precipitation variability in the Northeast. Such insight provides meaningful insight for stakeholders and policymakers with interests pertaining to future resource allocations and water management practices in the region.This PhD work seeks to build upon the existing literature related to understanding the large-scale processes that are important in producing conditions favorable to precipitation in the Northeast, and uses that understanding to examine the meteorological conditions that accompany short-duration dry spells (droughts) that occur over the region. My dissertation establishes a unique framework by which to explore scientific questions related to Northeast precipitation variability by utilizing a novel linear orthogonal decomposition technique, large-scale meteorological pattern analysis, analysis of relevant dynamical and thermodynamical fields, and reanalysis and climate model datasets. Such a framework provides a comprehensive assessment of the meteorological conditions associated with different precipitation regimes over the Northeast. This research topic sits at the intersection of atmospheric science, regional climate, machine learning, and climate model validation, with the overall goal of improving our capabilities in regional climate analysis. Chapter 1 provides an introduction and background information related to precipitation in the Northeast. Chapter 2 focuses on identifying the large-scale drivers of precipitation over the Northeast using a novel linear orthogonal decomposition (LOD) approach. Chapter 3 examines the absence (or reduction) of those drivers during short-duration dry spells (droughts) over the region primarily using large-scale meteorological pattern (LSMP) analysis. Chapter 4 assesses the fidelity of current-generation general circulation models (GCMs) in representing different characteristics of the dry spells in comparison to those obtained from observations. Lastly, Chapter 5 summarizes the work discussed in this dissertation and provides insight into potential avenues for future work.