  Operational forecasting centers are investing in decadal (1-10 year) forecast
systems to support long-term decision making for a more climate-resilient
society. One method that has previously been employed is the Dynamic Mode
Decomposition (DMD) algorithm - also known as the Linear Inverse Model - which
fits linear dynamical models to data. While the DMD usually approximates
non-linear terms in the true dynamics as a linear system with random noise, we
investigate an extension to the DMD that explicitly represents the non-linear
terms as a neural network. Our weight initialization allows the network to
produce sensible results before training and then improve the prediction after
training as data becomes available. In this short paper, we evaluate the
proposed architecture for simulating global sea surface temperatures and
compare the results with the standard DMD and seasonal forecasts produced by
the state-of-the-art dynamical model, CFSv2.

Machine Learning;Artificial Intelligence