A time series analysis on the seasonality of air temperature anomalies

Abstract

AbstractSurface air temperature anomalies relative to seasonal variations are of great concern from a long‐term forecasting perspective. This article is concerned with marginally normalized time series in which the original data of the temperatures are standardized using the mean values and variances of the estimated deterministic seasonal cycles. A particular parametric form of a non‐stationary auto‐regressive (AR) model to quantify the anomalies is considered, by applying it to the normalized data. This model fits substantially better than an ordinary AR model for normalized datasets of the surface air temperature obtained from almost all of the stations in Japan, and exhibits a significant seasonal structure in their auto‐correlation. The model is applied to high‐pass filtered data to investigate the relation between the seasonal structure and a high‐frequency variability in anomalies, which helps in determining the climatic influence on anomalies of surface air temperature in Japan. Furthermore, an illustrative example in the pricing of weather derivatives to achieve better performance of the non‐stationary model than that of the ordinary AR model is presented. Copyright © 2007 Royal Meteorological Society