A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.

Report for the Oregon Public Utility Commission highlights innovations and best practices for resilience and utility planning could be helpful to other states as well.

Improving a snow albedo model to account for non-spherical snow grain shapes and the mixing of dust inside snowflakes in an Earth system model.

Building new climate outcome scenarios by stitching together pieces of existing ones.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

The flux of carbon dioxide from forest soil may acclimate to some aspects of climate change.

Identifying links between large-scale and local-scale model biases in tropical precipitation.

Interactive freshwater flux works to prolong density anomalies over deep water formation areas in the North Atlantic.

Interactions between fronts and the land-sea breeze lead to strong vertical exchange in the northern Gulf of Mexico.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.

Evaluating the relative roles of soil water stress and vapor pressure deficit at limiting canopy conductance in coupled land-atmosphere simulations.

Understanding why the westerly jets shift poleward in certain seasons and regions but equatorward in others under global warming.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.

Launching a premier partnership, PNNL and the University of Texas El Paso (UTEP) signed a memorandum of agreement on UTEP’s campus on October 6.