Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
ClimateBench v1.0: A bench- mark for data-driven climate projections
9 Pith papers cite this work, alongside 29 external citations. Polarity classification is still indexing.
9
Pith papers citing it
29
external citations · external index
representative citing papers
Training climate emulators on random-CO2 runs that break SST–CO2 correlation, plus an energy constraint, yields a data-efficient model that works on AMIP+4K and abrupt 4xCO2 cases prior ACE models mishandled.
Adversarial optimal transport objectives jointly learn summary statistics and a chaotic-system emulator from a single noisy trajectory, improving long-term statistical fidelity over handcrafted-feature baselines.
A GraphCast-based ocean emulator achieves skillful 10-15 day forecasts, with a Mahalanobis loss that accounts for variable correlations improving performance over MSE and acting as a statistical-dynamical regularizer.
Standard PMP diagnostics show DL-ESMs match CMIP-class skill on many large-scale fields but still fail key precipitation, monsoon, and long-run stability tests.
A discrete-time energy-conserving neural map with FDT-derived causal regularization reproduces stationary statistics and forced responses of CdV and Lorenz-96 turbulence from unperturbed data alone.
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.
Image-to-image networks estimate parameters of non-stationary SAR models faster and more accurately than traditional methods by framing fields and parameters as images.
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.
citing papers explorer
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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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Disentangling the effects of sea surface temperature and CO$_2$ in global machine learned weather-climate emulators
Training climate emulators on random-CO2 runs that break SST–CO2 correlation, plus an energy constraint, yields a data-efficient model that works on AMIP+4K and abrupt 4xCO2 cases prior ACE models mishandled.
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Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
Adversarial optimal transport objectives jointly learn summary statistics and a chaotic-system emulator from a single noisy trajectory, improving long-term statistical fidelity over handcrafted-feature baselines.
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Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss
A GraphCast-based ocean emulator achieves skillful 10-15 day forecasts, with a Mahalanobis loss that accounts for variable correlations improving performance over MSE and acting as a statistical-dynamical regularizer.
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A PMP-inspired Evaluation Framework for Assessing Deep-Learning Earth System Models
Standard PMP diagnostics show DL-ESMs match CMIP-class skill on many large-scale fields but still fail key precipitation, monsoon, and long-run stability tests.
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Physics constraints and response validation in discrete-time reduced-order modeling: from idealized turbulent systems to climate dynamics
A discrete-time energy-conserving neural map with FDT-derived causal regularization reproduces stationary statistics and forced responses of CdV and Lorenz-96 turbulence from unperturbed data alone.
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HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts
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.
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LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
Image-to-image networks estimate parameters of non-stationary SAR models faster and more accurately than traditional methods by framing fields and parameters as images.
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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.