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Abstract North American cold air outbreaks (CAOs) are large-scale temperature extremes that typically originate in the high latitudes and impact the midlatitudes in winter. As they transit southward, they can have significant socioeconomic consequences. CAOs from winter (DJF) 1979 to 2020 were identified in the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) using an automated feature tracking approach (TempestExtremesV2.1). This allowed for the systematic identification of a large number of cases without using predetermined, Eulerian regions. Another important advantage of this approach was the ability to compute a feature tracked thermodynamic energy budget in a nonfixed domain for every identified CAO event. As an example, the thermodynamic energy budget analysis was used to quantify important processes for the 18–23 January 1985 CAO. The dominant mechanisms of cooling and warming as well as lysis locations (i.e., eastern or western) were then used to generalize detected CAO events into subcategories. The associated statistics, spatial footprints, and composites of 500-hPa height, sea level pressure, and temperature and winds at 850 hPa were analyzed for three subcategories that contained the majority of events. This analysis revealed that CAO events that form and dissipate through different mechanisms occur in different regions, have different intensities, and are associated with different large-scale circulation patterns. Finally, the analysis of associated North Atlantic Oscillation (NAO) and Pacific–North American (PNA) teleconnection pattern revealed that the PNA is typically in a positive phase for eastern CAO events and in a negative phase for western events resulting primarily from horizontal advection, whereas the NAO did not have any significant relationship. 

Abstract Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. Traditionally, the type of breaking is categorized as anticyclonic (AWB) or cyclonic (CWB) and can be identified using the orientation of streamers of high potential temperature (θ) and lowθair on a PV surface. However, an examination of the differences in RWB structure and their associated tropospheric impacts within these types remains unexplored. In this study, AWB and CWB are identified from overturning isentropes on the dynamic tropopause (DT), defined as the 2 potential vorticity unit (PVU; 1 PVU = 10⁻⁶K kg⁻¹m²s⁻¹) surface, in the ERA5 dataset during December, January, and February 1979–2019. Self-organizing maps (SOM), a machine learning method, is used to cluster the identified RWB events into archetypal patterns, or “flavors,” for each type. AWB and CWB flavors capture variations in theθminima/maxima of each streamer and the localized meridionalθgradient (∇θ) flanking the streamers. Variations in the magnitude and position of ∇θbetween flavors correspond to a diversity of jet structures leading to differences in vertical motion patterns and troposphere-deep circulations. A subset of flavors of AWB (CWB) events are associated with the development of strong surface high (low) pressure systems and the generation of extreme poleward moisture transport. For CWB, many events occurred in similar geographical regions, but the precipitation and moisture patterns were vastly different between flavors. Our findings suggest that the location, type, and severity of the tropospheric impacts from RWB are strongly dictated by RWB flavor. Significance StatementLarge-scale atmospheric waves ∼15 km above Earth’s surface are responsible for the daily weather patterns that we experience. These waves can undergo wave breaking, a process that is analogous to ocean waves breaking along the seashore. Wave breaking events have been linked to extreme weather impacts at the surface including cold and heat waves, strong low pressure systems, and extreme precipitation events. Machine learning is used to identify and analyze different flavors, or patterns, of wave breaking events that result in differing surface weather impacts. Some flavors are able to generate notable channels of moisture that result in extreme high precipitation events. This is a crucial insight as forecasting of extreme weather events could be improved from this work.