Trains ACE emulator on independent SST-CO2 variations plus energy constraint to improve accuracy in decoupled climate forcing scenarios.
SamudrACE: Fast and accurate coupled climate modeling with 3D ocean and atmosphere emulators
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OceanCBM is the first concept bottleneck model for spatiotemporal ocean prediction that uses mixed supervision on physical concepts and a free concept to deliver consistent mechanistic representations for mixed layer heat content forecasts.
Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
HealDA supplies ML-based initial conditions for AI weather models that produce forecasts trailing ERA5-initialized runs by less than one day of effective lead time, with the skill gap arising mainly from initial error size.
LSTM and 1D CNN emulators replicate a 1D marine biogeochemistry model at daily resolution, remain stable over decades, reproduce spring bloom timing years ahead, and outperform the parent model on reanalysis-driven forecasts for key variables.
RecFM uses recursive self-consistency in flow matching to enable high-fidelity one- and few-step (2-4 step) generation of scientific dynamics, claiming 20x speedup over diffusion emulators and 15% lower MSE than vanilla flow matching.
With proper scaling and training convergence, a moderate-sized feedforward neural network can reproduce key aerosol concentration changes from the MAM4 microphysics module in E3SMv2.
AI methods can strengthen cross-domain interactions and support more coherent multi-component representations in Earth system models.
citing papers explorer
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Disentangling the effects of sea surface temperature and CO$_2$ in global machine learned weather-climate emulators
Trains ACE emulator on independent SST-CO2 variations plus energy constraint to improve accuracy in decoupled climate forcing scenarios.
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OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting
OceanCBM is the first concept bottleneck model for spatiotemporal ocean prediction that uses mixed supervision on physical concepts and a free concept to deliver consistent mechanistic representations for mixed layer heat content forecasts.
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Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
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Deep learning model emulators for marine biogeochemistry forecasting from days to decades
LSTM and 1D CNN emulators replicate a 1D marine biogeochemistry model at daily resolution, remain stable over decades, reproduce spring bloom timing years ahead, and outperform the parent model on reanalysis-driven forecasts for key variables.
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Recursive Flow Matching
RecFM uses recursive self-consistency in flow matching to enable high-fidelity one- and few-step (2-4 step) generation of scientific dynamics, claiming 20x speedup over diffusion emulators and 15% lower MSE than vanilla flow matching.
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Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2
With proper scaling and training convergence, a moderate-sized feedforward neural network can reproduce key aerosol concentration changes from the MAM4 microphysics module in E3SMv2.
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Toward Artificial Intelligence Enabled Earth System Coupling
AI methods can strengthen cross-domain interactions and support more coherent multi-component representations in Earth system models.