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DFN is a deep neural network that predicts ocean surface flow fields from operational satellite observations that reduces the mean positioning error after a 7-day forecast compared with the operational model and demonstrates the potential of deep learning for ocean surface flow prediction.
BG4Sea is introduced, which to this knowledge is the first global, data-driven system to produce multivariate seasonal forecasts of the marine biogeochemical state, and predictability attribution to each component is discussed, alongside the model's structural limitations.
This research demonstrates that the model, ArchesClimate -- SSP, does not simply imitate scenarios seen during training, but is actually capable of modeling the response of a climate state to diverse forcings, an important step towards reliable and rapid climate model scenario generation.
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