Exploratory analysis of climate data using source separation methods

We present an example of exploratory data analysis of climate measurements using a recently developed denoising source separation (DSS) framework. We analyzed a combined dataset containing daily measurements of three variables: surface temperature, sea level pressure and precipitation around the globe, for a period of 56 years. Components exhibiting slow temporal behavior were extracted using DSS with linear denoising. The first component, most prominent in the interannual time scale, captured the well-known El Niño-Southern Oscillation (ENSO) phenomenon and the second component was close to the derivative of the first one. The slow components extracted in a wider frequency range were further rotated using a frequency-based separation criterion implemented by DSS with nonlinear denoising. The rotated sources give a meaningful representation of the slow climate variability as a combination of trends, interannual oscillations, the annual cycle and slowly changing seasonal variations. Again, components related to the ENSO phenomenon emerge very clearly among the found sources.