An energy expert and economist who has played a leading role in formulating and coordinating U.S. climate policy is the new director of the Joint Global Change Research Institute in College Park, Maryland.
A 19-person, multi-institutional national laboratory team received the inaugural Gordon Bell Prize for Climate Modeling from the Association for Computing Machinery for their work on more accurately modeling deep convective clouds.
The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.
PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.
Researchers use models to represent relationships between climate and socio-economic processes, helping inform decisions for slowing climate change and enhancing resilience.
Ripples demonstration will take place at the DOE booth at the International Conference for High Performance Computing, Networking, Storage, and Analysis.
Claudia Tebaldi, a PNNL Earth scientist, has been named a Fellow of the American Geophysical Union. Tebaldi and others will be recognized at AGU23 in December.
Variations in the level of market globalization can greatly affect the amount of water required to meet future global demand for agricultural commodities.
Climate change and socioeconomic pressures are transforming passenger and freight transportation in the Arctic, producing effects that have yet to be fully understood.
In a new paper, researchers point to three major efforts where the biggest climate mitigation gains stand to be realized: ramping up carbon dioxide removal, reigning in non-carbon dioxide emissions and halting deforestation.
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.
Incorporating spatially explicit land characteristics in a global model illustrates the complex effects of applying uniform regional protection assumptions in a global analysis.