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Deep learning of systematic sea ice model errors from data assimilation increments
Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When …
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Data-driven multiscale modeling of subgrid parameterizations in climate models
We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information …
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Exploring the non-stationarity of coastal sea level probability distributions
Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st century acceleration in the satellite …
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Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated …
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Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
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Parametrization in Weather and Climate Models
Recently, a growing number of studies have used machine learning (ML) models to parameterize computationally intensive subgrid-scale …
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Influences of Mesoscale Ocean Eddies on Flow Vertical Structure in a Resolution-Based Model Hierarchy
The understanding and representation of energetic transfers associated with ocean mesoscale eddies is fundamental to the development of …
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NeverWorld2: An idealized model hierarchy to investigate ocean mesoscale eddies across resolutions
We describe an idealized primitive-equation model for studying mesoscale turbulence and leverage a hierarchy of grid resolutions to …
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NeverWorld2: An idealized model hierarchy to investigate ocean mesoscale eddies across resolutions
Past and future ocean warming
Changes in ocean heat content (OHC) provide a measure of ocean warming, with impacts on the Earth system. This Review synthesizes …
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Greenhouse-gas forced changes in the Atlantic meridional overturning circulation and related worldwide sea-level change
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