How a model treats gas transfer behavior onto dust and sea salt particles significantly influences nitrate aerosols and their effects on climate.

A comparison between model simulations and high-resolution satellite observations shows discrepancies in projections of convective storms.

With future warming, storms in the Western U.S. will be larger and produce more intense precipitation, particularly near the storm center, and increase flood risks.

A PNNL innovation uses steam to recover heat from the high-temperature reactor effluent in the HTL process, substantially reducing the propensity for fouling and potentially reducing costs.

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 discovered a faster, more economical method to verify the amount of biofuel in mixtures of bio and fossil fuel.

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.

A research buoy managed by PNNL has been deployed in Hawai’ian waters, collecting oceanographic and meteorological measurements off the coast of O’ahu.

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

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.

The Joint Appointment program at PNNL is one of the most diverse among other U.S. national laboratories, involving nearly 60 universities and research institutions in the United States and abroad.

A multi-institutional team of wind energy experts led by PNNL assessed the scientific grand challenges for offshore wind and provided recommendations for closing gaps in models.

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

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.