Climate Causality: Connecting data and theory to understand the drivers of regional weather extremes
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In the realm of predicting extreme regional weather and climate events, substantial uncertainties prevail. These uncertainties are compounded by a limited causal understanding of the physical drivers behind extremes, such as their connections to large-scale phenomena like the North Atlantic Oscillation or Madden-Julian Oscillation, or sea ice loss. This inherent complexity challenges our interpretation of climate model forecasts and underscores the need for robust explanations. To make informed decisions in the face of uncertainty, explanatory insights become imperative as they provide decision-makers with a level of plausibility that is essential for proactive measures. Consequently, progress in weather and climate forecasting hinges on advancing our causal understanding of the climate system.
In this presentation, I will demonstrate how recent advancements in causality research can help in bridging these critical gaps. The causal approach integrates expert knowledge directly into statistical analyses, enabling quantitative conclusions. In practice, this usually involves the analysis of vast datasets from observations and climate models, requiring a synthesis of both physical knowledge and different statistical techniques. Here, the key concepts underpinning this approach will be discussed and concrete examples will be presented.
This live event includes a 15-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.