A Physics-Oriented Approach to the Classification of Extreme Weather Events

Abstract

Common approaches to the classification of extreme weather events often only consider the intensity and/or rarity of the event without considering the physical processes driving these events. This approach could negatively impact the quality of data used for the study of these events by falsely including data from events driven by different physical processes. Alternatively, these approaches could also artificially limit the quantity of available data to a subset of a larger group of similar events. To address this issue, I suggest an alternative approach that groups events based on similarities in the physical processes driving them. More specifically, throughout this thesis, I outline a method of defining thresholds for the classification of extreme events based on qualitative shifts in the relative contribution of these processes. This method is demonstrated on a simple synthetic example in which it is able to pick up on changes to the standard deviation from which values are being sampled. After this, the method is applied to temperature data from two model runs (historical and warming scenarios) using the Geophysical Fluid Dynamics Laboratory's AM4 and CM4 models. This data is used to demonstrate the physical differences on either side of the threshold selections and the variability in how these events may be affected by anthropogenic climate change.