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ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulations
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in …
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Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations
In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice-ocean model. For …
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A new conceptual model of global ocean heat uptake
We formulate a new conceptual model, named “MT2”, to describe global ocean heat uptake, as simulated by atmosphere–ocean general …
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A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer
Parameterizations of O(1-10)km submesoscale flows in General Circulation Models (GCMs) represent the effects of unresolved vertical …
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Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur …
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Background Pycnocline depth constrains Future Ocean Heat Uptake Efficiency
The Ocean Heat Uptake Efficiency (OHUE) quantifies the ocean’s ability to mitigate surface warming through deep heat …
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Remote Versus Local Impacts of Energy Backscatter on the North Atlantic SST Biases in a Global Ocean Model
The use of coarse resolution and strong grid-scale dissipation has prevented global ocean models from simulating the correct kinetic …
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Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks
Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. …
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Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be …
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Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its …
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