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.
Highlights
Periodic, short- and medium-term, as well as quickly noticeable changes in ocean level are associated with a number of processes and phenomena occurring in the atmosphere and hydrosphere
We carried out two analyses: (1) we performed the calculations along the lines of the paper by Esselborn and Eden (2001) on a basis of sea surface height (SSH) and sea level anomaly (SLA) datasets, which allowed us to confirm the strong relationship between North Atlantic Oscillation (NAO) index (NAOi) and SSH/SLA for the 20-year time span and to quantify the relation in question; (2) based on the dependency we used the associated regression models along with the real-time SLA predictions computed by the Prognocean Plus system
We developed a new method for forecasting winter Hurrell NAOi based solely on SLA predictions calculated by the Prognocean Plus system
Summary
Short- and medium-term, as well as quickly noticeable changes in ocean level are associated with a number of processes and phenomena occurring in the atmosphere and hydrosphere. Meteorological conditions such as pressure, wind strength and direction, precipitation and evaporation cause irregular and short fluctuations in sea and ocean levels. Because the oceans have an extremely high thermal capacity, when compared to the atmosphere, the ocean temperatures fluctuate seasonally much less than the atmospheric. The pattern of atmospheric circulation largely determines the pattern of oceanic surface circulation, which in turn determines the location and amount of heat that is released to the atmosphere. The pattern of atmospheric circulation partly determines the location of clouds, which influences the locations of heating of the ocean surface
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