Date of Award
8-1-2023
Language
English
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College/School/Department
Department of Atmospheric and Environmental Sciences
Dissertation/Thesis Chair
Andrea Lang
Keywords
cold air outbreaks, stratospheric variability, Subseasonal-to-Seasonal Prediction
Subject Categories
Atmospheric Sciences
Abstract
The February 2021 cold air outbreak (CAO) brought long-lasting, freezing temperatures and extreme winter weather to the United States and Canada. The event resulted in major societal and economic impacts, including millions of power outages, hundreds of fatalities, and approximately $25 billion in losses. This thesis first analyzes the synoptic evolution of this CAO in order to explain how Arctic air arrived and stalled over central North America. Given the synoptic context, the analysis next considers the subseasonal-to-seasonal (S2S) predictability of this CAO by examining S2S ensemble model predictions from both high-top and low-top models. The S2S predictability analysis is motivated by the idea that processes and variability in the stratosphere, such as sudden stratospheric warmings (SSWs) and stratospheric wave reflection events, can play a significant role in the large-scale evolution and development of CAOs. Given the potential influence of the stratosphere, high-top and low-top forecast data are compared to determine if models that better resolve the stratosphere were able to predict the high impact nature of the CAO with longer lead times. The results suggest that the high-top models did better at predicting both the onset date and the duration of the CAO at S2S lead-times (i.e., week 3 and 4) but did not outperform the low-top models at synoptic time scales (i.e., week 2). The results highlight the importance of resolving the stratosphere to best realize the S2S prediction windows-of-opportunity that result from stratospheric variability.
Recommended Citation
Dines, Grace, "An Analysis Of The Predictability Of The February 2021 Cold Air Outbreak At S2S Timescales" (2023). Legacy Theses & Dissertations (2009 - 2024). 3114.
https://scholarsarchive.library.albany.edu/legacy-etd/3114
