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.

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

In an invited review article, PNNL researchers examined the literature surrounding modeling and measuring the ice-nucleating particles that help form clouds.

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

The size of the alkali metal within the material influences the crystallization pathway.

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

The collaboration will further accelerate the science behind radioactively contaminated land management and radioactive waste disposal.

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.

Sue Southard's one thousand dives as a PNNL staff member leave a ripple effect on efforts to keep our ocean healthy, our economy thriving, and our waters safe.

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

This PNNL-developed separation system quickly and successfully separates larger particles from smaller ones at various scales, in different solid-liquid mixtures and at different flow rates.

Brian O’Neill joins a Roundtable on climate security organized by the National Academies of Sciences, Engineering, and Medicine.

Performing a network analysis of forces between particles reveals new insight into shear induced thinning and thickening in non-Newtonian fluids.