Volatility in atmospheric temperature variability

TL;DR

This study analyzes the long-range correlations in daily temperature volatility across ten global sites using DFA, revealing consistent power-law behavior that current climate models fail to replicate.

Contribution

It demonstrates the presence of universal long-range correlations in temperature volatility and evaluates climate models' ability to reproduce this scaling.

Findings

Volatility exhibits long-range power-law correlations with an exponent near 0.8.

Climate models do not accurately reproduce the observed scaling behavior.

The study highlights a gap in climate model performance regarding temperature variability.

Abstract

Using detrended fluctuation analysis (DFA), we study the scaling properties of the volatility time series $V_i=| T_{i+1}-T_i|$ of daily temperatures $T_i$ for ten chosen sites around the globe. We find that the volatility is long range power-law correlated with an e xponent $\gamma$ close to 0.8 for all sites considered here. We use this result to test the scaling performance of several state-of-the art global climate models and find that the models do not reproduce the observed scaling behavior.