Application of detrended fluctuation analysis to monthly average of the maximum daily temperatures to resolve different climates

TL;DR

This study applies detrended fluctuation analysis to monthly maximum daily temperatures across US locations, revealing that climate types can be distinguished based on long-term temperature fluctuation trends.

Contribution

It introduces a novel application of detrended fluctuation analysis to climate data, enabling climate classification through temperature fluctuation patterns.

Findings

Clustering of data points by climate type based on scaling exponents

Long-term temperature trends can differentiate climates

Method shows potential for climate classification using temperature fluctuations

Abstract

Detrended fluctuation analysis is used to investigate correlations between the monthly average of the maximum daily temperatures for different locations in the continental US and the different climates these locations have. When we plot the scaling exponents obtained from the detrended fluctuation analysis versus the standard deviation of the temperature fluctuations we observe crowding of data points belonging to the same climates. Thus, we conclude that by observing the long-time trends in the fluctuations of temperature it would be possible to distinguish between different climates.