During the boreal cool season (December – February) extreme temperature regimes (ETRs), including cold air outbreaks (CAOs) and warm waves (WWs), affect regional economies and human safety via their significant impacts on energy consumption, local agriculture and human health. This work aims to improve our understanding of the trends and variability in ETRs, their physical connections to low frequency modes, and the dynamical mechanisms leading to ETR onset. Earlier studies on ETR trends and variability do not consider the last decade. Further, little is known about the physical and dynamical nature of ETR onset. These unknowns motivate this dissertation and are particularly important for WWs, which have rarely been studied. This study begins with an updated analysis of the long-term trends and interannual variability in ETRs. Even with the inclusion of the last decade, no significant trends in either WW or CAO occurrence are identified over the continental United States between 1949-2011. The accompanying correlation analysis reveals that on interannual time scales, ETRs in specific regions of the U.S. tend to be modulated by certain low frequency modes. This analysis highlights an important regional asymmetry in the low frequency mode modulation of ETRs, and also indicates that the influence of ENSO upon ATRs is mainly limited to a modest modulation of WWs over the southeast US. Further, a multiple linear regression analysis reveals that the regional collective influence of low-frequency modes accounts for as much as 50% of interannual ETR variability. A synoptic-dynamic characterization of ETR onset over the southeast US is then performed using composite time-evolution analyses of events occurring between 1949-2011 to provide a qualitative indication of the role of low frequency modes. During CAO (WW) onset, negative (positive) geopotential height anomalies are observed in the upper troposphere over the Southeast with oppositely-signed anomalies in the lower troposphere over the central US. In most cases, there is a surface east-west height anomaly dipole, with anomalous northerly (CAO) or southerly (WW) flow into the Southeast leading to cold or warm surface air temperature anomalies, respectively. Companion potential vorticity anomaly analyses reveal prominent features in the mid- to upper-troposphere consistent with the geopotential height anomaly patterns. The composite analyses reveal significant roles for both synoptic and large-scale disturbances in ETR development. Synoptic-scale disturbances serve as dynamic triggers for ETR events, while low-frequency modes can provide a favorable environment for ETR onset. A suite of diagnostic analyses is conducted next and aims to identify the primary thermodynamic processes and dynamical mechanisms responsible for ETR development over the Southeast US. Heat budget analyses implicate linear temperature advection as the primary contributor to ETR development, while nonlinear advection plays a smaller role. Both the linear and the nonlinear terms contribute positively to the temperature tendencies of interest, while the adiabatic and diabatic terms offset some of the advection contributions. Piecewise PV inversion analyses are then conducted to assess which dynamical features directly contribute to the local temperature changes that occur in association with ETRs. A novel result is the discovery of the potential pathway through which the low frequency mode modulation of ETRs takes place. An upper-tropospheric PV feature first induces near-surface temperature advection, which then creates a near-surface temperature anomaly and a corresponding circulation that further enhances the initial temperature advection and ultimately leads to the ETR event.

During the boreal cool season, regional climate in the United States is strongly impacted by extreme temperature regimes (ETRs), including both cold air outbreaks (CAOs) and warm waves (WWs), which have significant impacts on energy consumption, agriculture, as well as the human population. Using NCEP/NCAR and MERRA reanalysis data, the statistical characteristics of ETRs over three distinct geographical regions are studied: the Midwest (MW), Northeast Megalopolis (NE), and Deep South (SE). The regional long-term variability in the frequency and amplitude of ETRs is examined, and the modulation of these ETRs by low frequency modes is quantified. ETR behavior is characterized using three different metrics applied to both T and Twc: 1) the number of extreme cold/warm days, 2) a seasonal cumulative "impact factor", and 3) a peak normalized anomaly value. A trend analysis reveals a significant downward trend in SE WW events from 1949-2011. Otherwise, no significant trends are found for ETRs in any of the other regions. Thus, these results indicate that there has not been any significant reduction in either the amplitude or frequency of CAOs over the United States during the period of analysis. In fact, for the SE region, the recent winters of 2009/2010 and 2010/2011 both rank among the top 5 in terms of CAO metrics. In addition, strong interannual variability in ETRs is evident from 1949-2011 in each region. Linear regression analysis is then used to determine the associations between ETR metrics and the seasonal mean state of several low frequency modes, and it is found that ETRs tend to be modulated by certain low frequency modes. For instance, in the SE region, there is a significant association between ETRs and the phase of the North Atlantic (or Arctic) Oscillation (NAO/AO), the Pacific North American (PNA) pattern (for WWs only), the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (for WWs only). Over the MW region, WWs are modulated by the NAO/AO and PNA patterns, while in the NE region, the AO, NAO (for WWs only) and PDO (for WWs only) are implicated. In addition, it is found that there is an asymmetry between the low frequency mode modulation of CAOs and WWs. Multiple linear regression analysis is then used to quantify the relative roles of the various low frequency modes in explaining interannual variability in ETR metrics, and reveals that various combinations of low frequency modes can explain anywhere between 10% and 50% of the variance in the ETR metrics.