Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution. (arXiv:2304.07029v1 [physics.flu-dyn])

Long-term stability is a critical property for deep learning-based
data-driven digital twins of the Earth system. Such data-driven digital twins
enable sub-seasonal and seasonal predictions of extreme environmental events,
probabilistic forecasts, that require a large number of ensemble members, and
computationally tractable high-resolution Earth system models where expensive
components of the models can be replaced with cheaper data-driven surrogates.
Owing to computational cost, physics-based digital twins, though long-term
stable, are intractable for real-time decision-making. Data-driven digital
twins offer a cheaper …

arxiv climate components computational cost data data-driven decision deep learning digital digital twins earth ensemble environmental events long-term making physics predictions property real-time solution twins