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

Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.

Harnessing the sea for decarbonized energy is the focus of a new collaboration between PNNL and the University of Guam.

A systematic, multiple scenario approach was used to analyze the compounding impacts of demands for land for biofuels with increased land scarcity under a diverse set of uncertainties.

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

Scientists used a new analysis approach—coupled laser ablation sampling and capillary absorption spectroscopy—to gather more information on how the rhizosphere processes carbon.

STEM Education programs at DOE and national labs offer environmental management internships; applications are due soon for summer 2023.

A collaborative effort led by PNNL researchers analyzed decarbonization pathways for Argentina, Chile, Egypt, Indonesia, and Nigeria.

rmap enables users with limited to no geographic information system knowledge use R to visualize simple tabular data.

A review article led by researcher Jade Holliman explores the different classes of metamaterials, from the underlying fundamental science to potential applications.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

The methods researchers use to estimate crop yield gaps determines the location of projected future agricultural “breadbasket” regions.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.

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

A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

Multidisciplinary teams from research and industry are brought together using management systems developed by a team of software engineers at PNNL.

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.