Atmospheric pattern-based predictions of S2S sea-level anomalies for two selected US locations

Abstract

With climate change causing rising sea-levels around the globe, multiple recent efforts in the United States have focused on the prediction of various meteorological factors that can lead to periods of anomalously high-tides despite seemingly benign atmospheric conditions. As part of these efforts, this research explores monthly-scale relationships between sea-level variability and atmospheric circulation patterns, and demonstrates two options for sub-seasonal to seasonal (S2S) predictions of anomalous sea-levels using these patterns as inputs to artificial neural network (ANN) models. Results on the monthly scale are similar to previous research on the daily scale, with above-average sea-levels and an increased risk of high-water events on days with anomalously low atmospheric pressure patterns and wind patterns leading to on-shore or downwelling-producing wind stress. Some wind patterns show risks of high-water events to be over 6-times higher than baseline risk, and exhibit an average water level anomaly of +94mm above normal. In terms of forecasting, nonlinear autoregressive ANN models with exogenous input (NARX models) and pattern-based lagged ANN (PLANN) models show skill over post-processed numerical forecast model output, and simple climatology. Damped-persistence forecasts and PLANN models show nearly the same skill in terms of predicting anomalous sea-levels out to 9 months of lead time, with a slight edge to PLANN models, especially with regard to error statistics. This perspective on forecasting – using predefined circulation patterns along with ANN models – should aid in the real-time prediction of coastal flooding events, among other applications.