APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022;
Chicago
Session A10: Rare Events, Tipping Points, and Abrupt Changes in the Climate System
8:00 AM–10:48 AM,
Monday, March 14, 2022
Room: McCormick Place W-181A
Sponsoring
Unit:
GPC
Chair: Hussein Aluie, University of Rochester
Abstract: A10.00004 : Atmospheric dynamics across scales: Jet streams and gravity waves
9:00 AM–9:36 AM
Abstract
Presenter:
Aditi Sheshadri
(Stanford)
Author:
Aditi Sheshadri
(Stanford)
On planetary scales, the midlatitude jet streams and storm tracks set the weather patterns experienced by a large fraction of humanity. Understanding and preparing for their variability on daily to decadal timescales is a critical challenge. Events that bring about abrupt transitions in their behavior, such as the breakdown of the polar vortex, have the potential to suddenly alter weather patterns and have been associated with extremes such as cold snaps and floods. I will present work investigating these transitions with application to the behavior of the jet stream in the Atlantic basin, which exhibits three preferred positions, and show results suggesting that the northernmost of these is a consequence of the presence of Greenland. Additionally, our recent work demonstrates that jet responses in the Atlantic basin may be described as regime transitions, resulting in a shifting of the probability distribution under forcing by polar vortex events, rather than a straightforward shift of the position of the jet stream. On smaller scales, atmospheric gravity waves (GWs) are ubiquitously excited on the Earth and are critical drivers of the atmospheric circulation, however, they present a challenge to climate prediction. I will describe collaborative efforts aimed at developing an observationally constrained, physically meaningful representation of the effects of GWs on the resolved flow for use in global climate models. We have leveraged high-resolution data from tens of thousands of balloon flights with high-resolution measurements of position, pressure, and temperature from which we have inferred statistics of gravity wave motions in the lower stratosphere. We have also developed a machine learning GW parameterization, coupled it to a global climate model, and showed that it is stable and accurate when run online, and that it reproduces features of the climate that depend critically on GWs.