Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other…
What Sets the Tropical Cold Point in GSRMs During Boreal Winter? Overshooting Convection Versus Cirrus Lofting
The cold point tropopause, the minimum temperature within the tropical upper troposphere‐lower stratosphere region (UTLS), significantly impacts Earth's climate by influencing the amount of water…
ACE2: accurately learning subseasonal to decadal atmospheric variability and forced responses
Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and…
Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model
Green's functions are a useful technique for interpreting atmospheric state responses to changes in the spatial pattern of sea surface temperature (SST). Here we train version 2 of the Ai2 Climate…
Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data
Machine learning weather models trained on observed atmospheric conditions can outperform conventional physics-based models at short- to medium-range (1-14 day) forecast timescales. Here we take the…
Applying Corrective Machine Learning in the E3SM Atmosphere Model in C++ (EAMxx)
. The Simplified Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of earth system models capable of explicitly resolving convective systems. SCREAM is a…
ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO$_2$
Although autoregressive machine learning‐based emulators have been trained to produce stable and accurate rollouts in the climate of the present‐day and recent past, none so far have been trained to…
Application of the AI2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity
Can the current successes of global machine learning-based weather simulators be generalized beyond 2-week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate…
Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond…
Weather and climate predicted accurately — without using a supercomputer
A cutting-edge global model of the atmosphere combines machine learning with a numerical model based on the laws of physics. This ‘hybrid’ system accurately predicts the weather — and even shows…