The Variability of Seasonality

Abstract

Seasons are the complex nonlinear response of the physical climate system to regular annual solar forcing. There is no a priori reason why they should remain fixed/invariant from year to year, as is often assumed in climate studies when extracting the seasonal component. The widely used econometric variant of Census Method II Seasonal Adjustment Program (X-11), which allows for year-to-year variations in seasonal shape, is shown here to have some advantages for diagnosing climate variability. The X-11 procedure is applied to the monthly mean Niño-3.4 sea surface temperature (SST) index and global gridded NCEP-NCAR reanalyses of 2-m surface air temperature. The resulting seasonal component shows statistically significant interannual variations over many parts of the globe. By taking these variations in seasonality into account, it is shown that one can define less ambiguous ENSO indices. Furthermore, using the X-11 seasonal adjustment approach, it is shown that the three cold ENSO episodes after 1998 are due to an increase in amplitude of seasonality rather than being three distinct La Niña events. Globally, variations in the seasonal component represent a substantial fraction of the year-to-year variability in monthly mean temperatures. In addition, strong teleconnections can be discerned between the magnitude of seasonal variations across the globe. It might be possible to exploit such relationships to improve the skill of seasonal climate forecasts.

Seasonal and Inter-Annual Analysis of Chlorophyll-a and Inherent Optical Properties from Satellite Observations in the Inner and Mid-Shelves of the South of Buenos Aires Province (Argentina)

“…The monthly time series were decomposed using the Census X-11 method [61], whose application to time-series analysis of satellite ocean color data has been extensively documented [62][63][64]. The statistical analysis is conducted at the level of each grid point, considering the spatial variations in the temporal coverage of the data record [64].…”

Mean Sea Level Variability and Influence of the North Atlantic Oscillation on Long-Term Trends in the German Bight

“…There are different ways of how to compute the seasonal MSL cycle from tidal observations. An empirical method is described by Pezzulli et al [28], in which the seasonal cycle is represented by averaging each calendar month over a defined time span. Another option is to use harmonic analysis, in which the annual cycle can be described by its amplitude and phase [29].…”

On the changing seasonal cycles and trends of ozone at Mace Head, Ireland

“…Latterly, the method has been developed to include Autoregressive Integrated Moving Average (ARIMA) modelling to improve "end-effect" problems and extensive statistical diagnostic tools (Dagum, 1980;Findlay et al, 1998). Recently, the X-11 approach has been used to highlight the seasonal variability sea surface temperatures (Pezzulli et al, 2005). Model-based approaches have also been used to examine changes in ozone seasonality at Jungfraujoch in Switzerland, where a state-space method was applied (Schuepbach et al, 2001).…”