  This work builds an automated anomaly detection method for chaotic time
series, and more concretely for turbulent, high-dimensional, ocean simulations.
We solve this task by extending the Echo State Network by spatially aware input
maps, such as convolutions, gradients, cosine transforms, et cetera, as well as
a spatially aware loss function. The spatial ESN is used to create predictions
which reduce the detection problem to thresholding of the prediction error. We
benchmark our detection framework on different tasks of increasing difficulty
to show the generality of the framework before applying it to raw climate model
output in the region of the Japanese ocean current Kuroshio, which exhibits a
bimodality that is not easily detected by the naked eye. The code is available
as an open source Python package, Torsk, available at
https://github.com/nmheim/torsk, where we also provide supplementary material
and programs that reproduce the results shown in this paper.
