Environmental Superstatistics

TL;DR

This paper explores how long-term temperature variations and global warming influence local ecosystems and thermodynamic devices, using superstatistics to model inverse temperature distributions across different locations.

Contribution

It introduces a superstatistical framework for analyzing long-term temperature data, revealing location-dependent distributions and effects of global warming on system responses.

Findings

f(beta) varies significantly across locations

f(beta) often shows a double-peak structure

Global warming causes systematic drifts in temperature distributions

Abstract

A thermodynamic device placed outdoors, or a local ecosystem, is subject to a variety of different temperatures given by short-tem (daily) and long-term (seasonal) variations. In the long term a superstatistical description makes sense, with a suitable distribution function f(beta) of inverse temperature beta over which ordinary statistical mechanics is averaged. We show that f(beta) is very different at different geographic locations, and typically exhibits a double-peak structure for long-term data. For some of our data sets we also find a systematic drift due to global warming. For a simple superstatistical model system we show that the response to global warming is stronger if temperature fluctuations are taken into account.