Researchers at PNNL are developing a better model of the soil, better representing the atmosphere, and identifying sources of record high rainfall within a model of the Earth system.

An improved model captures the interactions between the U.S. economy, energy, water, and land systems with increased spatial and temporal detail.

Recalibrating both cloud and subgrid representations improves model projections of the base climate, aerosol-cloud interactions, and cloud feedbacks.

A climate change study co-authored by PNNL scientist Hailong Wang inspired an artist to create an exhibit based on the research.

New report takes a comprehensive look at offshore aquaculture as a market for marine energy.

A newly developed tool helps researchers analyze process-level relationships within atmospheric models.

A novel database of tracked deep convective storms reveals how environmental factors affect storm formation and growth in South America.

Soil transplant experiment gives new insights into coastal forest resilience, rising seas, and increasing storms.

Investigating cloud condensation nuclei activities in various airmasses enabled linking activity variations with organic oxidation levels and volatility

New study investigates how to use a non-toxic condensation particle counter for airborne atmospheric research

ICON science is a Department of Energy-developed framework to enhance scientific outcomes via more intentional design of research efforts across all domains of science.

A global warming-induced poleward shift of the westerly jet affects the hydroclimate of the U.S. Midwest differently in the spring and summer.

A new capability facilitates source apportionment of organic aerosols in real time.

The COVID-19 pandemic massively disrupted human activities, which caused additional turmoil through regional record rainfall and floods.

Measuring the ice nucleation activity of particles in the ambient atmosphere can help develop better representations for use in climate models.

PNNL contributes to 30 years of data on clouds, radiation, and other climate-making factors as part of field campaigns and analysis conducted by DOE's Atmospheric Radiation Measurement user facility.

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.

A novel ecological measurement uncovered interactions between river corridor organic matter assemblages and microbial communities, highlighting potentially important microbial taxa and molecular formula types.

New data analysis approach gives researchers deeper understanding of microbial ecology and evolution.

The climate models best at simulating current precipitation and its drivers show more consensus in projecting future precipitation.