PNNL experts perform fundamental climate science work at multiple scales to understand impacts on energy, water, food, and infrastructure systems.

Examining the vast scientific literature about the urban impact on weather and climate enabled researchers to synthesize current understanding and to inform future work.

State-of-the-art numerical models can better simulate the convective clouds that produce most of the Amazon’s wet season rainfall.

Researchers used ground-based and remote-sensed datasets to comprehensively validate a tool for simulating land surface processes.

A new hexagonal framework helps more accurately model watersheds at a range of scales.

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

Anika Halappanavar’s research into COVID-19 misinformation earned her recognition by the Washington State Academy of Sciences as one of the state’s top high school researchers.

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.

Researchers have been investigating if machine learning techniques could be used to help create novel beers.

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.