Diagnosing flavors of tropospheric Rossby wave breaking and their associated dynamical and sensible weather features

Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. RWB events can lead to tropospheric impacts ranging from changes in intensity and position of the jet stream to extremes in precipitation resulting in significant societal impacts. Traditionally, RWB events are 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 potential vorticity surface. Self-organizing maps (SOM), a machine learning method, was used to cluster RWB events into archetypal patterns, or “flavors”, for each RWB event type (i.e., AWB and CWB). This allowed for an examination of differences in RWB event flavors, and their associated tropospheric impacts, using the European Centre for Medium Range Weather Forecasts Reanalysis v5 (ERA5) dataset. 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. 

Given these impacts and their importance for regional climates, it is important to also understand how RWB events, and their associated sensible weather features, are represented in climate models. Therefore, AWB and CWB events were identified from overturning isentropes on the dynamic tropopause (DT) in the Community Earth System Large Ensemble v2 (CESM-LENS2) climate model output during December, January, and February (DJF) 1980-2014 (i.e., historical period). RWB flavors are identified in the LENS2 for comparison to the ERA5 dataset for the same time period. Composites of tropospheric dynamic and thermodynamic fields were calculated for each RWB flavor in the LENS2 which allows for an evaluation of the impact of AWB and CWB structure on sensible weather extremes. First, the frequency of occurrence of each RWB flavor between datasets was found. Second, differences in the sensible weather features associated with each flavor were quantified. This process-orientated climate model evaluation of the LENS2 as compared to the ERA5 can provide insight into the source of model errors in the LENS2 climate model.