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Causal inference in spatiotemporal climate fields through linear response theory
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Data-driven multiscale modeling of subgrid parameterizations in climate models
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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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Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
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Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
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An observationally-based estimate of global ocean heat uptake closes the time-varying global energy budget since 1880
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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
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Influences of Mesoscale Ocean Eddies on Flow Vertical Structure in a Resolution-Based Model Hierarchy
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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