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.

PNNL researchers teamed up with marine energy developers to test a new wave energy conversion device through the TEAMER program.

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.

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.

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

Information encoded in streamflow reveals how the water-storage capacity of thawed ground changes as permafrost thaws.

A new model improves the representations of river networks in climate models across scales.

Variations in burn severity are a key control on the chemical constituents of dissolved organic matter delivered to streams within a single burn perimeter.

Mesoscale convective systems that are clustered in time and space have broader impacts on flooding than individual systems.

Atmospheric rivers are increasingly reaching the Arctic in winter, slowing sea ice recovery and accounting for a third of winter sea ice decline from 1979-2021.

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

A newly developed basin-scale river corridor model can quantify how riverbed microbes drive respiration and the generation of carbon dioxide in the Columbia River Basin.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

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

A comparison between model simulations and high-resolution satellite observations shows discrepancies in projections of convective storms.