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

• Published in: PloS one Vol. 17; no. 8; p. e0271457
• Main Authors: Duan, Qibin, McGrory, Clare A, Brown, Glenn, Mengersen, Kerrie, Wang, You-Gan
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 24.08.2022; Public Library of Science (PLoS)
• Subjects: Analysis; Climate change; Climatic changes; Daily temperatures; Data analysis; Earth Sciences; Engineering and Technology; Evaluation; Heterogeneity; Maximum temperatures; Mean; Mean temperatures; Minimum temperatures; People and Places; Physical Sciences; Rain; Regression analysis; Regression models; Research and Analysis Methods; Sample variance; Temperature data; Temperature trends; Time series; Trends; Australia; Queensland Australia; New South Wales Australia; Tasmania Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0271457

Many studies have considered temperature trends at the global scale, but the literature is commonly associated with an overall increase in mean temperature in a defined past time period and hence lacking in in-depth analysis of the latent trends. For example, in addition to heterogeneity in mean and median values, daily temperature data often exhibit quasi-periodic heterogeneity in variance, which has largely been overlooked in climate research. To this end, we propose a joint model of quantile regression and variability. By accounting appropriately for the heterogeneity in these types of data, our analysis using Australian data reveals that daily maximum temperature is warming by ∼0.21°C per decade and daily minimum temperature by ∼0.13°C per decade. More interestingly, our modeling also shows nuanced patterns of change over space and time depending on location, season, and the percentiles of the temperature series.