  With the success of machine learning (ML) applied to climate reaching further
every day, emulators have begun to show promise not only for weather but for
multi-year time scales in the atmosphere. Similar work for the ocean remains
nascent, with state-of-the-art limited to models running for shorter time
scales or only for regions of the globe. In this work, we demonstrate
high-skill global emulation for surface ocean fields over 5-8 years of model
rollout, accurately representing modes of variability for two different ML
architectures (ConvNext and Transformers). In addition, we address the
outstanding question of generalization, an essential consideration if the
end-use of emulation is to model warming scenarios outside of the model
training data. We show that 1) generalization is not an intrinsic feature of a
data-driven emulator, 2) fine-tuning the emulator on only small amounts of
additional data from a distribution similar to the test set can enable the
emulator to perform well in a warmed climate, and 3) the forced emulators are
robust to noise in the forcing.
