Decadal Forecasts with ResDMD: a residual DMD neural network (Papers Track)

EDUARDO ROCHA RODRIGUES (IBM Research); Campbell Watson (IBM Reserch); Bianca Zadrozny (IBM Research); David Gold (IBM Global Business Services)

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Significant investment is being made by operational forecasting centers to produce decadal (1-10 year) forecasts that can support long-term decision making for a more climate-resilient society. One method that has been employed for this task is the Dynamic Mode Decomposition (DMD) algorithm – also known as the Linear Inverse Model– which is used to fit 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 to explicitly represent 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.

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