A set of papers combines surface science measurements on tiny particles with uncrewed aerial systems to better understand and model aerosols.

Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.

Models have identified a new source of anthropogenic emissions driving the observed weakening of the Atlantic Meridional Overturning Circulation.

Atmospheric rivers, filaments of intense moisture transport in the atmosphere, can now be automatically detected in satellite observations.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

This study revealed that fresh organic vapors are soluble in particulate organics that are actively growing in size. However, if the particulate matter ages, fresh organic vapors can no longer mix with the organic matter.

Partitioning measured ice nucleating particle concentrations into individual particle types leads to a better understanding of the sources and model representations of these particles.

The results of this study are consistent with the idea that the stress of chronic salinity exposure changes tree leaf shape and function, weakening their physiology and setting in motion processes that lead to death.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

The complex interactions between the land and atmosphere can affect cloud growth.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.

Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.

How a model treats gas transfer behavior onto dust and sea salt particles significantly influences nitrate aerosols and their effects on climate.

Scientists used a new analysis approach—coupled laser ablation sampling and capillary absorption spectroscopy—to gather more information on how the rhizosphere processes carbon.