AI summaryDiscusses numerical stability, workflow transparency and differences in computing resources, and proposes a framework for assessing and supporting reproducibility in hybrid Earth system models.
AI summaryExamines connections between weather forecasting and climate modelling, including unresolved issues in physical consistency, generalisation, transparency and computational cost.
Weather and climateCross-scale modellingPhysical consistency
AI summaryProposes 11 features of an ideal Earth foundation model, including multisensor integration, awareness of location and scale, and physical consistency, and discusses evaluation and research directions.
AI summaryLearns from ocean-model data at multiple depths to emulate temperature, salinity, currents and sea-surface height. Long integrations are stable in the study, while responses to trends in external forcing remain limited in amplitude.
AI summaryPretrains on diverse geophysical data and adapts to forecasting weather, ocean waves, air quality and tropical cyclone tracks, exploring a foundation model for multiple Earth-system tasks.
AI summaryCombines a differentiable dynamical core with neural-network parameterisations to study weather forecasting and climate simulation, including physical consistency over long integrations.
AI summaryReviews how differentiable computation can jointly train physical components and neural networks to support process understanding, parameter estimation and geoscientific prediction.