Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change: a study of long-term daily temperature in Australia

TL;DR

This study introduces a joint quantile regression model to analyze long-term daily temperature data in Australia, revealing nuanced, location- and season-dependent patterns of climate change beyond average temperature trends.

Contribution

It proposes a novel joint model of quantile regression and variability to better understand heterogeneity in temperature data, addressing a gap in climate change analysis.

Findings

Daily maximum temperature increases by 0.21°C per decade.

Daily minimum temperature increases by 0.13°C per decade.

Climate change patterns vary by location, season, and temperature percentiles.

Abstract

Climate change is commonly associated with an overall increase in mean temperature in a defined past time period. Many studies consider temperature trends at the global scale, but the literature is lacking in in-depth analysis of the temperature trends across Australia in recent decades. In addition to heterogeneity in mean and median values, daily Australia temperature data suffers from quasi-periodic heterogeneity in variance. However, this issue has barely been overlooked in climate research. A contribution of this article is that we propose a joint model of quantile regression and variability. By accounting appropriately for the heterogeneity in these types of data, our analysis reveals that daily maximum temperature is warming by 0.21 Celsius per decade and daily minimum temperature by 0.13 Celsius per decade. However, our modeling also shows nuanced patterns of climate change depends on location, season, and the percentiles of the temperature series over Australia.