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.

本文的目的是提出一种基于海平面异常（SLA）预测的NAO指数（NAOi）预测新方法。我们利用显着的相关性（Pearson's rEsselborn和Eden指出，北大西洋（15-65°N，盆地范围）和冬季Hurrell NAOi的海面高度（SSH）之间的差异最大为0.69）（Geophys Res Lett 28：3473-3476，2001） 。我们考虑了Hurrell NAOi从1993年到2017年的季节性和月度数据。PrognoceanPlus系统提供了SLA的每周预测，该系统使用几种基于数据的模型来预测海平面变化。我们的实验包括三个步骤：（1）我们计算SSH / SLA数据的第一个主成分（PC1）与NAOi之间的相关性，（2）确定线性回归模型的系数，该系数描述了冬季NAOi与PC1的关系。 SLA数据（1993-2013年），（3）我们建立了两个回归模型以预测冬季NAOi（通过附加SLA预测并应用拟合的回归模型的系数）。结果表明，冬季NAOi的3个月预测显示平均绝对误差为1.5或更小。与选择SLA预测方法本身相比，选择用于主成分分析的SLA数据准备方法显示出对预测性能的影响更大。