Study reveals importance of accounting for micrometer scale distribution of substrates to predict carbon emissions from soil.

PNNL scientist Brian O’Neill led the compilation of an IPCC chapter about climate-related risks.

Developing a model for global soil erosion and sediment flux to advance understanding of land, river, and ocean geomorphology and biogeochemistry.

Researchers are developing an understanding of how either wet or dry soil patches change the sizes of atmospheric eddies and cloud clusters.

PNNL scientists reveal that climate change will increase lake evaporation most dramatically in the Mediterranean, Southeast China, and Tropical America.

The overlooked impact of regional hydroclimate changes on lake evaporation implies more robust regional lake volume changes around the globe.

Changes in the downward flow of infrared energy in clear skies are a key factor influencing recent rapid wintertime Arctic warming.

Ensembles of 20–25 members, notably smaller than traditional large ensembles, can accurately represent changes in extremes of temperature and precipitation.

Researchers developed a new model to understand the initiation of summer-time mesoscale convective systems over the central United States.

Implementing and evaluating a new radiative transfer scheme that accounts for various topographic effects on solar radiation in an Earth system model.

PNNL researchers uncover the mechanics behind dwindling Arctic sea ice and its influence on wildfire weather in the western United States.

Better representing cloud microphysics can Reduce model bias in simulating mesoscale convective system precipitation.

What’s at the heart of California’s uncertain future precipitation? New study finds natural cycles are likely cause.

New analysis demonstrates that the Madden-Julian Oscillation can influence tropical cyclones in the Pacific through both the atmosphere and the ocean.

PNNL researchers contributed to the International Conference on Data Science and Advanced Analytics 2021 Best Paper Award-winning publication.

DOE’s continued support will allow CENATE researchers to advance computing through the evaluation of novel technologies.

PNNL computer scientist Ang Li serves as one of the principal investigators for a project selected to receive DOE funding.

Researchers to design their own computer accelerators for faster analysis of large datasets.

PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.

Sriram Krishnamoorthy, a computer scientist at PNNL, collaborated with a University of Utah team on a student computing research project that won Best Student Paper at SC20.