Decreased snow cover observed over the past few decades and projected for the future suggest increasing snow droughts that threaten water security and management.

The Emissions Model Intercomparison Project examined how selected emissions-related properties affected results in 11 global chemistry and Earth-system models.

Using regional meteorological data from an atmosphere reanalysis product, scientists identified 12 unique winter weather systems in the Puget Sound area, featuring differing precipitation and temperature responses to climate variabilities.

ChatGrid™ is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.

The physics-informed machine learning optimization framework is now PNNL’s most popular open-source repository on GitHub.

PNNL researchers use chemometric analysis to model chemistry in molten salt.

With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.

Ang participated in a White House-hosted CHIPS R&D event and roundtable discussion with senior leaders from industry, academia and key government agencies.

Clustering wildfires into four types based on their land-atmosphere drivers can potentially improve regional wildfire predictions.

PNNL helps deliver efficiency-related rules and requirements that steadily improve performance of America’s buildings, saving energy and costs and reducing carbon emissions.

Results of the PR100 study outline near- and short-term strategies for Puerto Rico to achieve a renewable power grid by 2050.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

Three scientists at PNNL are lending more than 50 years of expertise to lead efforts to advance the nation’s Blue Economy.

PNNL scientists developed a new method to map exactly how a fungus works with leafcutter ants in a complex microbial community to degrade plant material at the molecular level. The team’s insights are important for biofuels development.

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

Atmospheric rivers, filaments of intense moisture transport in the atmosphere, can now be automatically detected in satellite observations.

Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.