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.

Researchers found that specific landscape metrics consistently outperform traditional population downscaling methods.

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.

Haewon McJeon will co-edit a special issue of Energy and Climate Change focused on national decarbonization strategies across the globe.

Extreme winter storms are growing wetter and changing shape in the Western United States—such changes could compromise infrastructure designed to withstand only so much water.

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.

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

PNNL’s contributions over the last year span scientific discovery, sustainable energy and national security.

Clarivate recently released the list of Highly Cited Researchers for 2022.

Abigail Snyder named to international team focused on designing and scoping models for use in large intercomparison project.

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

The American Geophysical Union will honor Andrew Gettelman at their Fall Meeting as a new Fellow.

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

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

Steven Smith joins an international team that will evaluate current data and identify what should be included for future climate modeling.

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.