A Quantile Generalised Additive Approach for Compound Climate Extremes:
Pan-Atlantic Extremes as a Case Study
We present an application of quantile generalised additive models
(QGAMs) to study spatially compounding climate extremes, namely extremes
that occur (near-) simultaneously in geographically remote regions. We
take as an example wintertime cold spells in North America and
co-occurring wet or windy extremes in Western Europe, which we
collectively term Pan-Atlantic compound extremes.
QGAMS are largely novel in climate science applications and present a
number of key advantages over conventional statistical models of weather
extremes. Specifically, they remove the need for a direct identification
and parametrisation of the extremes themselves, since they model all
quantiles of the distributions of interest. They thus make use of all
information available, and not only of a small number of extreme values.
Moreover, they do not require any a priori knowledge of the functional
relationship between the predictors and the dependent variable.
Here, we use QGAMs to both characterise the co-occurrence statistics and
investigate the role of possible dynamical drivers of the Pan-Atlantic
compound extremes. We find that cold spells in North America are a
useful predictor of subsequent wet or windy extremes in Western Europe,
and that QGAMs can predict those extremes more accurately than
conventional peak-over-threshold models.