Long-term temporal evolution of extreme temperature in a warming Earth

TL;DR

This paper introduces a novel modeling approach using non-stationary generalized extreme value distributions with logistic functions to project long-term changes in extreme temperatures globally, accounting for geographic and temporal variations.

Contribution

It develops a new method combining non-stationary GEV models with logistic functions to analyze the evolution of temperature extremes over time and space.

Findings

Extremes change more rapidly over land than oceans.

Mean and variance changes often occur simultaneously, shape remains stable.

Arctic shows abrupt variability increase around 2050 due to ice melt.

Abstract

We present a new approach to modeling the future development of extreme temperatures globally and on a long time-scale by using non-stationary generalized extreme value distributions in combination with logistic functions. This approach is applied to data from the fully coupled climate model AWI-ESM. It enables us to investigate how extremes will change depending on the geographic location not only in terms of the magnitude, but also in terms of the timing of the changes. We observe that in general, changes in extremes are stronger and more rapid over land masses than over oceans. In addition, our models differentiate between changes in mean, in variability and in distributional shape, allowing for developments in these statistics to take place independently and at different times. Different models are presented and the Bayesian Information Criterion is used for model selection. It turns out that in most regions, changes in mean and variance take place simultaneously while the shape parameter of the distribution is predicted to stay constant. In the Arctic region, however, a different picture emerges: There, climate variability drastically and abruptly increases around 2050 due to the melting of ice, whereas changes in the mean values take longer and come into effect later.