A Quantile Generalised Additive Approach for Compound Climate Extremes: Pan-Atlantic Extremes as a Case Study

Abstract

We present an application of quantile generalised additive models (QGAMs) to study the rela-tionship between spatially compounding climate extremes - namely extremes that occur (near-)simultaneously in geographically remote regions. We take as example wintertime cold spellsin North America and co-occurring wet or windy surface weather extremes in Western Europe,which we collectively term Pan-Atlantic compound extremes. QGAMS are largely novel in cli-mate science applications and present three key advantages over conventional statistical modelsof weather extremes:1. they do not require a direct identification and parametrisation of the extremes themselves,since they model all quantiles of the distributions of interest;2. they do not require any a priori knowledge of the functional relationship between the predic-tors and the dependent variable;3. they make use of all information available, and not only of a small number of extreme values.Here, we use QGAMs to both characterise the co-occurrence statistics and investigate possibledynamical drivers of the Pan-Atlantic compound extremes. We find that recent cold spells inNorth America are a useful predictor of upcoming near-surface extremes in Western Europe,and that QGAMs can predict those extremes more accurately than conventional peak-over-threshold models.