Assessing a complex concert of the weather and terrain conditions that affect the growth of cumulus clouds into deep thunderstorms along mountain ranges.

Tropical cyclones induce sea surface cooling, but their associated heavy rainfall reduces the effect through increased ocean mixing.

Relaxing the simplifying assumptions in model energy budgets can affect their simulated regional water cycles.

New research quantifies the discrepancies between ground measurements of soil respiration and satellite estimates of photosynthesis.

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.

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.

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

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.

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

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