How to Recognize a True Mode of Atmospheric Circulation Variability

0541896 - ÚFA 2022 RIV US eng J - Journal Article
Huth, Radan - Beranová, Romana
How to Recognize a True Mode of Atmospheric Circulation Variability.
Earth and Space Science. Roč. 8, č. 3 (2021), č. článku e2020EA001275. E-ISSN 2333-5084
R&D Projects: GA ČR(CZ) GA17-07043S
Institutional support: RVO:68378289
Keywords : Arctic oscillation * Barents oscillation * North Atlantic Oscillation * modes of low-frequency variability * principal component analysis * teleconnections
OECD category: Meteorology and atmospheric sciences
Impact factor: 3.680, year: 2021
Method of publishing: Open access
https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2020EA001275

It has been demonstrated several times that when principal component analysis (PCA) is used for detection of modes of atmospheric circulation variability (teleconnections), principal components must be rotated. Despite it, unrotated PCA is still often used. Here we demonstrate on the examples of North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Barents Oscillation (BO), and the summer East Atlantic (SEA) pattern that unrotated PCA results in patterns that are artifacts of the analysis method rather than true modes of variability. This claim is based on the comparison of the spatial patterns of the modes with spatial autocorrelations, on the sensitivity of the patterns to spatial and temporal subsampling, and, for the SEA pattern, on correlations with tropical sea surface temperature. Unlike NAO, which is defined by rotated PCA, the other modes, that is, AO, BO, and SEA pattern, defined by unrotated PCA, do not correspond well to underlying autocorrelation structures and are more sensitive to choices of spatial domain and time interval over which they are defined. We reiterate that a great care must be taken when interpreting outputs of PCA when applied to the detection of modes of circulation variability: a comparison with spatial autocorrelations and check for their spatial and temporal stability are necessary to distinguish true modes from statistical artifacts, which we call ´ghost patterns´.
Permanent Link: http://hdl.handle.net/11104/0319392