Sea Level Anomaly Prediction using Recurrent Neural Networks

TL;DR

This paper introduces a neural network approach combining CNN and RNN to predict interannual sea level anomalies, demonstrating improved accuracy and stability over traditional methods in the Pacific Ocean.

Contribution

The study develops a novel CNN-RNN model for sea level anomaly prediction, showing significant performance improvements over simple regression methods.

Findings

CNN improves prediction skill significantly.

Predictions remain stable over multiple years.

Outperforms traditional regression methods.

Abstract

Sea level change, one of the most dire impacts of anthropogenic global warming, will affect a large amount of the world's population. However, sea level change is not uniform in time and space, and the skill of conventional prediction methods is limited due to the ocean's internal variabi-lity on timescales from weeks to decades. Here we study the potential of neural network methods which have been used successfully in other applications, but rarely been applied for this task. We develop a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) to ana-lyse both the spatial and the temporal evolution of sea level and to suggest an independent, accurate method to predict interannual sea level anomalies (SLA). We test our method for the northern and equatorial Pacific Ocean, using gridded altimeter-derived SLA data. We show that the used network designs outperform a simple regression and that adding a CNN improves the skill significantly. The predictions are stable over several years.