ENSO as the component with the most prominent interannual variability

The goal of this experiment is to find climate phenomena which would have prominent variability in the interannual timescale. In other words, we would like to find climatic events which last longer than one year and which might have quasi-periodic behavior. A climate phenomenon has prominent variability in a given timescale if its corresponding time signal contains a large relative amount of relevant frequencies. For interannual variability, the period of the relevant spectral components would be longer than one year. Thus, the interannual signal structure can be emphasized by using, for example, the band-pass filter whose frequency response is shown on the right. The abscissa of the figure is labeled in terms of periods in years (y) and months (m). This is a linear temporal filter and therefore the three-step DSS procedure (whitening-filtering-PCA) can be used to find the most prominent interannual phenomena.

Combined data

The following table contains the results of the analysis when applied to the dataset combining three variables: surface temperature, sea level pressure and precipitation. The first component is easily identified the index of the well known El Niño--Southern Oscillation (ENSO) phenomenon. The second component contains some features resembling the derivative of the El Niño index.
You can click on the maps to see larger images.

Time course

Surface temperature, °C

Sea level pressure, Pa

Precipitation, kg/m²

Separate datasets

The figures below present the same results obtained by analyzing the three atmospheric variables separately. ENSO index emerges as the most prominent component in all three datasets. Some of the other phenomena shown below can also be seen in the results of the same experiment for the combined data and for the frequency based representation of the slowest climate variability described here.

Surface temperature, °C

Sea level pressure, Pa

Precipitation, kg/m²

References

• A. Ilin, H. Valpola and E. Oja. (2006) Exploratory Analysis of Climate Data Using Source Separation Methods.
  Neural Networks, Vol. 19, No. 2, pp. 155-167, March 2006.
  
  
• A. Ilin, H. Valpola and E. Oja. (2005) Semiblind Source Separation of Climate Data Detects El Niño as the Component with the Highest Interannual Variability.
  In Proc. of the Int. Joint Conf. on Neural Networks, IJCNN 2005, pp. 1722-1727, Montréal, Québec, Canada, August 2005.