Modeling spatial asymmetries in teleconnected extreme temperatures

TL;DR

This paper explores the use of deep learning combined with extreme value theory to model complex spatial asymmetries in teleconnected extreme temperatures, especially during blocking events, outperforming traditional copula models.

Contribution

It introduces probabilistic generative models that effectively capture spatial asymmetries and long-range anticorrelations in temperature fields, advancing climate extreme modeling.

Findings

Deep learning models better reproduce temperature asymmetries than vine copulas.

Models capture spatial extent of extreme events more accurately.

Proposed metrics quantify spatial asymmetries effectively.

Abstract

Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.