Accurately predicting total sea-level including tides and storm surges is key to protecting and managing our coastal environment. However, dynamically forecasting sea level extremes is ...computationally expensive. Here a novel alternative based on ensembles of artificial neural networks independently trained at over 600 tide gauges around the world, is used to predict the total sea-level based on tidal harmonics and atmospheric conditions at each site. The results show globally-consistent high skill of the neural networks (NNs) to capture the sea variability at gauges around the globe. While the main atmosphere-driven dynamics can be captured with multivariate linear regressions, atmospheric-driven intensification, tide-surge and tide-tide non-linearities in complex coastal environments are only predicted with the NNs. In addition, the non-linear NN approach provides a simple and consistent framework to assess the uncertainty through a probabilistic forecast. These new and cheap methods are relatively easy to setup and could be a valuable tool combined with more expensive dynamical model in order to improve local resilience.
The TICON (TIdal CONstants) dataset contains harmonic constants of 40 tidal constituents computed for 1,145 tide gauges distributed globally. The tidal estimations are based on publicly available sea ...level records of the second version of the Global Extreme Sea Level Analysis (GESLA) project and were derived through a least squares‐based harmonic analysis on the single time series. A preliminary screening was performed on all records to exclude doubtful observations. Only the records containing more than 70% of valid measurements were processed, that correspond to 89.7% of the total 1,276 original public GESLA records. The results are stored in a text file, and include additional information on the position of the stations, the starting and ending years of the analysed record, the estimated error of the fit, a code that corresponds to the source of the record and additional information on the single time series. In ocean tide models, data from in situ stations are used for validation purposes, and TICON is a useful and easy‐to‐handle data set that allows the users to select the records according to different criteria most suitable for their purposes. The data are provided with DOI identification in the PANGAEA repository.
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