LowFrequency Component Analysis (LFCA)
Lowfrequency component analysis (LFCA) is a method that transforms the leading empirical orthogonal functions (EOFs) of a data set in order to identify a pattern with the maximum ratio of lowfrequency to total variance (based on application of a lowpass filter). The resulting lowfrequency patterns (LFPs) and lowfrequency components (LFCs) isolate lowfrequency climate variability and are useful in diagnosing the corresponding mechanisms. This method is presented in Wills et al. (2018, GRL). Matlab and Python code for LFCA is available on GitHub: https://github.com/rcjwills/lfca.
Reference:
Wills, R.C., T. Schneider, J.M. Wallace, D.S. Battisti, and D.L. Hartmann, 2018: Disentangling global warming, multidecadal variability, and El Niño in Pacific temperatures. Geophysical Research Letters, 45, doi:10.1002/2017GL076327. [PDF] [SI] [Official version]
