Characterizing change in the variability of surface air temperature records: a comparative approach

Abstract

An accurate and comprehensive characterization of surface air temperature (SAT) variability is important for numerous purposes: studies on climate and climate change, the evaluation of climate model outputs, research on the impacts of changes in variability, etc. In this paper, SAT variability is considered from two different points of view: a measure of dispersion referring to the values in the time series, but ignoring their temporal sequence (the standard deviation); and a measure of persistence, for which the succession of the values in the time series is important (the exponent H established with Haar wavelet analysis). This paper uses daily minimum and maximum temperature records from Canadian stations in the Atlantic region and finds that: (i) SAT pattern variability can be assessed with the help of distinct methods applied together, in ways that might not be possible with any of the applied methods used separately; (ii) SAT pattern variability changes significantly over time; (iii) oscillations on scales from years to decades in both standard deviation S and the H exponent take place; (iv) the temporal change in SAT variability is reflected differently by the two applied methods: general statements concerning increases or decreases in variability should not be made without specifying the applied measure of variability.

Forecasting Temperature Records in PEI, Canada

“…Chattapadhyay et al (2011) developed alternative autoregressive neural network models to study maximum temperature variation in northeast India. Suteaunu (2013) used daily minimum and maximum temperature records from Canadian stations in the Atlantic region and suggested a new approach to study surface air temperature pattern variability.…”