Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change: A study of long-term daily temperature in Australia.
PLoS One
; 17(8): e0271457, 2022.
Article
in En
| MEDLINE
| ID: mdl-36001585
ABSTRACT
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 â¼0.21°C per decade and daily minimum temperature by â¼0.13°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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Climate Change
Type of study:
Diagnostic_studies
Country/Region as subject:
Oceania
Language:
En
Journal:
PLoS One
Journal subject:
CIENCIA
/
MEDICINA
Year:
2022
Document type:
Article
Affiliation country:
Australia
Country of publication:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA