A recurrence-based technique for detecting genuine extremes in instrumental temperature records

TL;DR

This paper introduces a recurrence-based method to distinguish genuine extreme temperature events from normal variability in instrumental records, leveraging techniques from dynamical systems analysis.

Contribution

It develops a novel criterion for identifying true temperature extremes using recurrence time statistics, applicable to various instrumental temperature records.

Findings

Recurrence time statistics match those of perturbed chaotic systems.

The method effectively discriminates genuine extremes from normal variability.

Provides a self-consistent estimate of convergence to extreme value laws.

Abstract

In this paper, we analyze several instrumental records of temperatures at different locations by using new techniques originally developed for the analysis of extreme values of dynamical systems. We show that they have the same recurrence time statistics as a chaotic dynamical system perturbed with dynamical noise and by instrument errors. The technique provides a criterion to discriminate whether the recurrence of a certain temperature belongs to the normal variability or can be considered as a genuine extreme event with respect to a specific timescale fixed as parameter. The method gives a self-consistent estimation of the convergence of the statistics of recurrences toward the theoretical extreme value laws.