  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.
