Diagnosing Flavors of Tropospheric Rossby Wave Breaking and Their Associated Dynamical and Sensible Weather Features

Abstract

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 low θ air 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 = 10−6 K kg−1 m2 s−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 the θ minima/maxima of each streamer and the localized meridional θ gradient (∇θ) flanking the streamers. Variations in the magnitude and position of ∇θ between 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 Statement Large-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.