Oluwayemi Garuba brings Arctic expertise to panel on climate modeling.

By mimicking nature, scientists make a breakthrough in breaking down wood.

The popular approach of organizing soil bacteria into fast- or slow-growing groups is problematic because most bacteria grow at comparable rates in soil.

Yun Qian has been selected as an editor of the Journal of Geophysical Research: Atmospheres, a prominent atmospheric science journal.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

The ChemSpace Tool, when fully developed, is intended to divide chemical space into three subsets: the detectable space, the identifiable space, and the region that includes compounds that are not detectable or identifiable.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

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 review describes the latest techniques for simulating hydropower at large scales to inform power grid planning and operations.

Hydropower generation estimated using water proxy data leads to a revision of the nation’s historical seasonal hydropower output.

COVID-19 infections at PNNL early in the pandemic were caused by a wide variety of viral sequences, according to a new analysis by Laboratory researchers.

A new model uses machine learning to project electricity loads across the United States.

Watershed hydrological responses are sensitive to the spatial and temporal resolution of the gridded meteorological forcing used in simulations.

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

The frequency and intensity of storms affecting U.S. coastal areas will likely increase in a warming world.

Researchers investigated the impact of using constant versus spatially varying crop parameters on carbon and energy fluxes in a realistic crop rotation scenario.

SAGE is a high-efficiency genome integration strategy for bacteria that makes the stable introduction of new traits simple for newly discovered microbes.

Future global warming-induced changes to watershed and coastal processes can significantly affect the magnitude of fluvial-coastal compound flooding.