Anticyclonic Rossby wave breaking (RWB) is characterized by the rapid and irreversible deformation of potential vorticity (PV) contours on isentropic surfaces manifesting as a pair of meridionally elongated high- and low-PV tongues that transport extratropical stratospheric air equatorward and tropical tropospheric air poleward, respectively. RWB occurs most commonly during the summer months and has been shown to have far-reaching impacts on tropical convection and tropical cyclone genesis. These important implications and the lack of literature surrounding summertime RWB motivated a comprehensive investigation into what drove the frequency, genesis, and variability of these phenomena through both observational and modeling studies.
Results from the observational study exhibited a potentially meaningful correlation between the intrabasin distribution of anticyclonic RWB events and the June–September-averaged Pacific decadal oscillation (PDO) index, such that when the PDO is positive, there is a favorability for RWB events over the eastern half of the North Atlantic, while the opposite holds true when the averaged PDO index is negative. To test this hypothesized PDO-RWB relationship an idealized modeling experiment was performed, with results supporting those in the observational study. Analysis of the large-scale circulation and synoptic environment changes imposed by the sea surface temperature anomalies of each simulation reveals different pathways for precursor Rossby wave train (RWT) development that, in turn, affect North Atlantic RWB statistics. When the PDO signals are divided into different components, the largest changes in RWB statistics are shown to occur whenever positive sea surface temperature anomalies are present in the North Pacific, as these serve as fuel for higher frequency RWT development and therefore more dramatic changes to North Atlantic RWB statistics.
Atmospheric rivers (ARs) are narrow filaments of high water vapor content that extend thousands of kilometers, carry more water than 27 Mississippi Rivers combined on average, and play integral roles in the global water cycle. For this project a large-scale analysis of the dynamic and thermodynamic properties of landfalling ARs over western Europe is perfomed. A climatology of landfalling ARs is established from 1980 to 2017, in which 578 ARs are identified. Examination of the upper-level PV fields shows that 73% of these AR events are related to anticyclonic RWB, a dynamic feature which has been shown to play a role in AR strength and structure. AR variability is also found to be closely tied to jet-stream latitude modulation by the North Atlantic Oscillation (NAO), such that during a positive NAO the North Atlantic jet is shifted north, creating an environment that is more favorable for anticyclonic RWB and AR landfalls over northern Europe, and during a negative NAO it is shifted south, creating such an environment over southern Europe. Through the use of linear regression analysis, AR strength is shown to be dependent on atmospheric moisture availability, which is found to increase as sea surface temperatures increase. Therefore, in a warming climate warmer sea surface temperatures leading to higher atmospheric moisture availability will result in an increase in the average strength and intensity of ARs over western Europe—a trend that has already been observed.
The Madden-Julian Oscillation (MJO), a large-scale system that moves west to east from the Indian Ocean to the Pacific Ocean over the course of 2 to 3 months, exerts significant influence on atmospheric circulation worldwide. Sitting at the crossroads of weather and climate, accurate MJO predictions can be used to extend forecast skill into the subseasonal, or 3 to 4 week, timescale. Despite its importance as a primary source of subseasonal predictability, numerical models struggle with MJO prediction as its convection moves through the complex Maritime Continent (MC) environment. Motivated by the ongoing effort to improve MJO prediction, the System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model is utilized to run two sets of forecasts, one with and one without a nested grid over the MC. By efficiently leveraging high-resolution grid spacing, the nested grid reduces amplitude and phase errors and extends the model's predictive skill by about 10 days. These enhancements are tied to improvements in predicted zonal wind from the Indian Ocean to the Pacific, facilitated by westerly wind bias reduction in the nested grid. These results suggest that minimizing circulation biases over the MC can lead to substantial advancements in skillful MJO prediction.