Two renewable energy approaches—enhanced geothermal systems and floating offshore wind energy—get new focus as Energy Earthshot™ Research Centers at PNNL.

Earth Scientist Mingxuan Wu was recognized with an Outstanding Contribution Award for his work on nitrate aerosol modeling in the Energy Exascale Earth System Model.

Testing the assumption that different future socio-economic development patterns, which result in different land-use changes, can be paired with different future climate outcomes for risk assessments in a multi-model framework.

PNNL convenes ocean leaders to tackle climate challenges and support the Ocean Climate Action Plan.

Incorporating spatially explicit land characteristics in a global model illustrates the complex effects of applying uniform regional protection assumptions in a global analysis.

PNNL is honoring its postdoctoral researchers as part of the fourteenth annual National Postdoc Appreciation Week with seven profiles of postdocs from around the Laboratory.

PNNL will lead support to one of six community teams chosen today by the U.S. Department of Energy to receive a total of up to $25 million.

PNNL chemists investigate the performance of ion exchange resins for subsurface remediation of uranium, technetium, and chromium.

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

A panel at the National Homeland Security Conference highlighted latest modeling and operational tools aiding response.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

David Barajas-Solano and Aaron Luttman worked with students to create models to evaluate remediation strategies at the Hanford Site.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

At the Nonproliferation, Counterproliferation, and Disarmament Science Gordon Research Conference, researchers from PNNL shared research and scientific approaches for countering diverse threats.

Regional wind data helps utilities save money, plan for windy days.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.

A holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.

Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.

Through collaboration with the Department of Homeland Security Soft Target Engineering to Neutralize the Threat Reality Center of Excellence, PNNL is advancing research and development of tools and methodologies to protect crowded places.