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Probabilistic nonlinear lagged teleconnections of the sea surface temperature field

Participants in the publication
Carlos Pires (Author)
Dep. Engenharia Geográfica, Geofísica e Energia
Abdel Hannachi (Author)

The monthly anomaly sea surface temperature field over the global ocean exhibit probabilistic dependencies between remote points and lagged times, which are explained eventually by some oceanic or atmospheric bridge of information transfer. Despite much of the bivariate SST dependencies appear to be linear, others are characterized by robust and statistically significant nonlinear correlations. In order to enhance that, we present a general method of extracting bivariate (X,Y) dependencies, seeking for pairs of polynomials P(X) and Q(Y) which are maximally correlated. The method relies on a Canonical correlation Analysis (CCA) between sets of standardized monomials of X and Y, up to a certain (low) degree (e.g. 4). Polynomial coefficients are obtained from the leading CCA eigenvector. Polynomials are calibrated and validated over independent periods, being afterwards subjected to marginal Gaussian anamorphoses. The bivariate non-Gaussianity in the space of marginally Gaussianized polynomials remains residual because of the correlation concentration and maximization. Consequently, the bivariate Gaussian pdf or in alternative, a copula pdf in the space of maximally correlated polynomials can accurately approximate the bivariate dependency. That probabilistic model is then used to determine conditional pdfs, cdfs and probabilities of extremes.\n\nThe method is applied to various (X,Y) pairs. In the first example, X is an optimized polynomial of the El-Niño 3.4 index while Y is that index lagged to the future. For lags between 6 and 18 months, the nonlinear El-Niño forecast clearly surpasses the linear one, contributing to lower the El-Niño seasonal predictability barrier. In the second example, we relate El-Niño (X) with the lagged Atlantic multidecadal oscillation index (Y). Nonlinear, robust correlations appear, both for positive and negative lags up to 5 years putting in evidence Pacific-Atlantic basin oceanic teleconnections.\n\nThe above probabilistic (polynomial based) model appears to be a good candidate tool for the statistical (seasonal up to decadal) forecast of regime probabilities (e.g. dry/wet) and extremes, given certain antecedent precursors.

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Copernicus GmbH

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Carlos Pires, Abdel Hannachi, (2023). Probabilistic nonlinear lagged teleconnections of the sea surface temperature field. egusphere-egu23, -

Carlos Pires, Abdel Hannachi, "Probabilistic nonlinear lagged teleconnections of the sea surface temperature field" in egusphere-egu23, , 2023, pp. -, doi: 10.5194/egusphere-egu23-9986

@InProceedings{60494, author = {Carlos Pires and Abdel Hannachi}, title = {Probabilistic nonlinear lagged teleconnections of the sea surface temperature field}, booktitle = {egusphere-egu23}, year = 2023, pages = {-}, address = {}, publisher = {Copernicus GmbH} }