Low-frequency component analysis (LFCA)
Low-frequency 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 low-frequency to total variance (based on application of a lowpass filter). The resulting low-frequency patterns (LFPs) and low-frequency components (LFCs) isolate low-frequency climate variability and are useful in diagnosing the corresponding mechanisms. This method is presented in Wills et al. (2018, GRL).
LFCA Example (Zip file)
Here, I provide an example LFCA script in Matlab. The script run_lfca_example.m runs an example that creates Fig. 4 of the associated paper. By changing the value of truncation in the script to 3, the script will create Fig. 1 of the paper instead. The method is contained within lfca.m. Included data is from the NOAA Extended Reconstructed Sea-Surface Temperature data set (Smith et al. 2008).
Reference for Method:
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]
Reference for Data Used:
Smith, T.M., R.W. Reynolds, T.C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface
temperature analysis (1880–2006). Journal of Climate, 21 (10), 2283–2296. [Official version]