1. Principal Component Analysis (PCA) or Empirical Orthogonal Functions (EOFs) Arnaud Czaja (SPAT Data analysis lecture Nov. 2011)

2. Outline Motivation Mathematical formulation (on the board) Illustration: analysis of ~100yr of sea surface temperature fluctuations in the North Atlantic How to compute EOFs Some issues regarding EOF analysis

3. Motivation Data compression...to “carry less luggage” Original pictures 6 EOFs 12 EOFs 24 EOFs

4. Motivation Data compression... to simplify with the hope of better understanding and forecasting Selten (1995) Mean Z300 (CI=100m) r.m.s Z300 (CI=10m) 20-EOF modelQG model (231 var.)

5. Motivation Identify “modes” empirically from data “Annular modes” in pressure data Thompson and Wallace (2000)

6. Some examples of calculations

7. Pictures Mean “picture” EOF1 EOF2EOF3

8. North Atlantic sea surface temperature variability (Deser and Blackmon 1993) PC2PC1 EOF2 12% EOF1 45%

9. How to compute EOFs Compute the covariance matrix Σ of the observation matrix X Compute its eigenvalues (variance explained) and eigenvectors (=eof) The principal component is then obtained by “projection”: pc(t) = X * eof Another (more efficient) method: singular value decomposition of X (come and see me if you are interested)

10. Main issues with EOF analysis Sensitivity to size of dataset (“sampling” issues) See North et al. (1982)

11. Main issues with EOF analysis Sensitivity to size of dataset (“sampling” issues)

12. Main issues with EOF analysis Sensitivity to size of dataset (“sampling” issues)

13. Main issues with EOF analysis Orthogonality constraint is not physical. Methods have been developed to deal with this (“rotated EOFs”) The link between EOFs and physical modes of a system is not clear

14. Main issues with EOF analysis Orthogonality constraint is not physical. Methods have been developed to deal with this (“rotated EOFs”) The link between EOFs and physical modes of a system is not clear Good luck if you try EOFs... Do not hesitate to come and see me!