Study explores Exploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments, a consortium of scientists interested in the exchange between water and land in coastal systems.
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 recently partnered with Amazon Web Services for AWS GameDay, a gamified learning event that challenges participants to use AWS solutions to solve real-world technical problems in a team-based setting.
Study demonstrates that choosing more accurate numerical process coupling helps improve simulation of dust aerosol life cycle in a global climate model.
Researchers show that small-scale turbulent fluctuations lead to larger concentrations of cloud droplets than would be possible in conventional models of atmospheric clouds
Researchers seeking to enhance a climate model’s predictive capability identify parameters that cause the largest sensitivities for several important cloud-related fidelity metrics.
Researchers developed a groundbreaking database that includes 40,000 synthetic tropical cyclones, crafted using the Risk Analysis Framework for Tropical Cyclones and pioneering the application of advanced artificial intelligence.
Researchers developed a natural gas trade infrastructure capability within a computer planning model that includes representations of energy, agriculture and land use, economy, water, and climate systems in 32 regions of the world.
Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.
Streamflow variability plays a crucial role in shaping the dynamics and sustainability of Earth's ecosystems, which can be simulated and projected by ESMs. However, the simulation of streamflow is subject to considerable uncertainties.
A new study uses direct numerical simulations to develop a near-surface turbulence model for thermal convection using interpretable and physics-aware neural networks, broadening the applications of numerical simulations.