The research presented in this article significantly enhances the ability to predict and prepare for compound flood events along the U.S. coastline.

This study evaluated the sensitivity of multiple geophysical methods to measure and evaluate the spatiotemporal variability of select soil properties across terrestrial–aquatic interfaces.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

A team of researchers at PNNL is developing a new approach to explore the higher-dimensional shape of cyber systems to identify signatures of adversarial attacks.

Researchers integrated field measurements, lab experiments, and model simulations to study oxygen consumption dynamics in soils along a coastal gradient.

Pyrocumulonimbus clouds are increasing in frequency as large wildfires become more prevalent in a warming climate. These clouds can inject smoke particles into the atmosphere, where they can remain suspended for several months.

Samrat (Sam) Chatterjee, a PNNL chief data scientist and team leader with the Data Sciences and Machine Intelligence group, was co-author of a CSET workshop report on agentic artificial intellilligence

This study provides a comprehensive analysis of isolated deep convection & mesoscale convective systems using self-organizing maps to categorize large-scale meteorological patterns and a tracking algorithm to monitor their life cycle.

This research explores how changes in groundwater levels affect the chemistry of underground water, especially in areas where land meets water, like wetlands.

The speed and agility of cloud computing opens doors to completing advanced computational chemistry workflows in days instead of months.

This study explored the future effects of climate change and low-carbon energy transition (i.e., emission reduction) on Arctic offshore oil and gas production.

Hydropower could expand substantially during the 21st century in many regions of the world to meet rising or changing energy demands. However, this expansion might harm river ecosystems.

PNNL computational and data scientist Mark Maupin was the team lead for a paper published in the journal Molecules.

Mengqi Zhao and Chris Vernon were selected to serve in editorial roles at JOSS for Earth science and data science, respectively.

PNNL chemist John Shilling builds custom chamber helping uncover how aerosol particles and clouds contribute to Earth’s changing climate.

Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.

This work shows that linear pattern scaling is an effective means of obtaining global-to-local relationships for CMIP6 models, as it has been in past model eras.

Researchers discussed this topic at the 2024 Analog Computing for Science Workshop co-led by Antonino Tumeo.

ARQUIN computational pipeline provides a holistic framework for simulating a system with distributed quantum computing ingredients.

This study examined the role of river sinuosity using computer models to understand what drives hyporheic exchange, a process that significantly affects water quality and ecosystem health.