English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The spatial coherence of a measured variable (e.g. temperature or pressure)is often studied to determine the regions where this variable varies the mostor to find teleconnections, i.e. correlations between specific regions. Whileusual methods to find spatial patterns, such as Principal Components Analysis(PCA), are constrained by linear symmetries, the dependence of variables suchas temperature or pressure at different locations is generally nonlinear. Inparticular, large deviations from the sample mean are expected to be stronglyaffected by such nonlinearities. Here we apply a newly developed nonlineartechnique (Maxima of Cumulant Function, MCF) for the detection of typicalspatial patterns that largely deviate from the mean. In order to test thetechnique and to introduce the methodology, we focus on the El Nino/SouthernOscillation and its spatial patterns. We find nonsymmetric temperature patternscorresponding to El Nino and La Nina, and we compare the results of MCF withother techniques, such as the symmetric solutions of PCA, and the nonsymmetricsolutions of Nonlinear PCA (NLPCA). We found that MCF solutions are morereliable than the NLPCA fits, and can capture mixtures of principal components.Finally, we apply Extreme Value Theory on the temporal variations extractedfrom our methodology. We find that the tails of the distribution of extremetemperatures during La Nina episodes is bounded, while the tail during El Ninosis less likely to be bounded. This implies that the mean spatial patterns ofthe two phases are asymmetric, as well as the behaviour of their extremes.

Detecting spatial patterns with the cumulant function. Part II: An application to El Nino