<div>Machine learning (ML) has proven to be a very powerful body of techniques for identifying rare but highly impactful weather events in huge volumes of climate model output and satellite data.&#160; When these events and the changes in them are studied in the context of global warming, these phenomena are known as climate extremes.&#160; This talk concerns the challenges in applying ML to identify climate extremes, which often center on how to provide suitable <span>training</span>&#160;data to these algorithms.&#160; The challenges are:</div><ol><li>In many cases, the official definitions for the weather events in the current climate are either ad hoc and/or subjective, leading to considerable variance in the statistics of these events even in literature concerning the historical record;&#160;</li>
<li>Operational methods for identifying these events are also typically quite ad hoc with very limited quantification of their structural and parametric uncertainties; and</li>
<li>Both the generative mechanisms and physical properties of these events are both predicted to evolve due to well-understood physics, and hence the <span>training</span> data set&#160; should but typically does not reflect these secular trends in the formation and statistical properties of climate extremes.&#160;&#160;</li>
</ol><div>We describe several approaches to addressing these issues, including:</div><ol><li>The recent creation of the first labeled data set specifically designed for algorithm training on atmospheric extremes, known as ClimateNet;</li>
<li>Probabilistic ML algorithms that identify events based on the level of agreement across an ensemble of operational methods;</li>
<li>Bayesian methods for that identify events based on the level of agreement across an ensemble of human expert-generated labels; and&#160;</li>
<li>The prospects for physics-based detection using fundamental properties of the fluid dynamics (i.e., conserved variables and Lyapunov exponents) and/or information-theoretic concepts.</li>
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Development of operational methods to predict soil classes and properties in Canada using machine learning