Assessing the variability and forecastability of time-trends for air temperatures in Greece: a Bayesian approach

This study investigates the variability and forecasting ability of time-trend in mean annual surface air temperatures in Greece. Using Gaussian time-trend models, we first investigate some basic statistical characteristics associated with time-trends, such the mean and variance. This can reveal whet...

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Bibliographic Details
Published in:Theoretical and applied climatology 2024-08, Vol.155 (8), p.8089-8118
Main Authors: Tsiotas, Georgios, Argiriou, Athanassios, Mamara, Anna
Format: Article
Language:English
Subjects:
Air temperature
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Bayesian analysis
Climatology
Earth and Environmental Science
Earth Sciences
Mathematical models
Mean
Meteorological services
Probability theory
Statistical models
Surface temperature
Surface-air temperature relationships
Temperature
Temperature trends
Time measurement
Time series
Trends
Variability
Waste Water Technology
Water Management
Water Pollution Control
Weather forecasting
Weather stations
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Summary:This study investigates the variability and forecasting ability of time-trend in mean annual surface air temperatures in Greece. Using Gaussian time-trend models, we first investigate some basic statistical characteristics associated with time-trends, such the mean and variance. This can reveal whether temperatures’ mean and volatility changes are associated with time. To do so, we have used mean measures of the minimum and maximum air temperatures observed at several meteorological stations of the Hellenic Meteorological Service located in Greece for the 1960-2010 period. As a second experiment, we investigate whether temperature trends are forecastable or not using various Gaussian time-trend and no-time-trend models. The results are highly significant since they reveal the seasons, the periods and the type of models for which the inter-annual trends out-perform the no-trend ones. Moreover, they also show the statistical characteristics, such as the mean and variability of the time-trend under various seasons and sub-periods.
ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-024-05118-y