Forecasting the North Atlantic Oscillation index using altimetric sea level anomalies

Abstract The objective of this paper is to present a new approach for forecasting NAO index (NAOi) based on predictions of sea level anomalies (SLAs). We utilize significant correlations (Pearson’s r up to 0.69) between sea surface height (SSH) calculated for the North Atlantic (15–65°N, basin-wide)...

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Bibliographic Details
Published in:Acta Geodaetica et Geophysica
Main Authors: Świerczyńska-Chlaściak, Małgorzata, Niedzielski, Tomasz
Other Authors: Narodowe Centrum Nauki, European Regional Development Fund
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2020
Subjects:
Geology
Geophysics
Building and Construction
North Atlantic
North Atlantic oscillation
Online Access:http://dx.doi.org/10.1007/s40328-020-00313-5
https://link.springer.com/content/pdf/10.1007/s40328-020-00313-5.pdf
https://link.springer.com/article/10.1007/s40328-020-00313-5/fulltext.html
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Summary:Abstract The objective of this paper is to present a new approach for forecasting NAO index (NAOi) based on predictions of sea level anomalies (SLAs). We utilize significant correlations (Pearson’s r up to 0.69) between sea surface height (SSH) calculated for the North Atlantic (15–65°N, basin-wide) and winter Hurrell NAOi, as shown by Esselborn and Eden (Geophys Res Lett 28:3473–3476, 2001). We consider the seasonal and monthly data of Hurrell NAOi, ranging from 1993 to 2017. Weekly prognoses of SLA are provided by the Prognocean Plus system which uses several data-based models to predict sea level variation. Our experiment consists of three steps: (1) we calculate correlation between the first principal component (PC1) of SSH/SLA data and NAOi, (2) we determine coefficients of a linear regression model which describes the relationship between winter NAOi and PC1 of SLA data (1993–2013), (3) we build two regression models in order to predict winter NAOi (by attaching SLA forecasts and applying coefficients of the fitted regression models). The resulting 3-month prognoses of winter NAOi are found to reveal mean absolute errors of 1.5 or less. The choice of method for preparing SLA data for principal component analysis is shown to have a stronger impact on the prediction performance than the selection of SLA prediction method itself.

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