Using a refined Earth system model, researchers found that wetlands over North America will be significantly affected by climate change under future scenarios
Researchers use dataset combining observational data with advanced numerical simulations to investigate the characteristics, drivers, and trends of extreme heat events in the High Arctic over past four decades
Researchers develop framework that tracks the aerosol–cloud interactions along the trajectories of air parcels and embed framework into Weather Research Forecast model.
Study develops high-resolution land surface data for 2001 to 2020, including parameters of land use, vegetation, soil, and topography and demonstrated its use in k-scale simulation using the Energy Exascale Earth System Model.
A team of researchers from Pacific Northwest National Laboratory and the Environmental Molecular Sciences Laboratory developed a new and flexible software tool called “Advanced Spectra PCA Toolbox.”
Researchers show how satellite observations from the MODerate Resolution Imaging Spectroradiometer and CloudSat radar can be used to constrain the ACI radiative forcing that is linked to droplet collection in marine liquid clouds.
Researchers provide clear evidence to show that the fourfold Arctic Amplification over recent decades is an anomaly caused by dominant modes of natural variability.
The Lab’s newly formed Center for AI, in partnership with NVIDIA, recently hosted a joint “LLM Day.” During the day, NVIDIA AI experts engaged with PNNL scientists on opportunities to make generative AI a powerful tool for science.
Researchers synthesize molecular-level laboratory experiments to develop comprehensive model representations of new particle formation and the chemical transformation of precursor gases.
Researchers show application of a causal model better identifies direct and indirect causal relations compared to correlation and random forest analyses performed over the same dataset.
PNNL played host in mid-May to the Artificial Intelligence for Robust Engineering & Science workshop, an annual event that explores advances in artificial intelligence
Scientists at PNNL harnessing advances in deep learning, deep reinforcement learning and generative AI to change how science is conducted and achieve original scientific results and breakthroughs.
This study demonstrates a new model that integrates complex organic matter (OM) chemistry and multiple electron acceptors to predict kinetic rates of OM oxidation.
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
