Streamflow variability plays a crucial role in shaping the dynamics and sustainability of Earth's ecosystems, which can be simulated and projected by ESMs. However, the simulation of streamflow is subject to considerable uncertainties.

A new study uses direct numerical simulations to develop a near-surface turbulence model for thermal convection using interpretable and physics-aware neural networks, broadening the applications of numerical simulations.

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

The Earth System Model Aerosol–Cloud Diagnostics package version 2 uses aircraft, ship, ground, and satellite measurements to evaluate detailed physical processes in aerosols, clouds, and aerosol–cloud interactions.

Decreased snow cover observed over the past few decades and projected for the future suggest increasing snow droughts that threaten water security and management.

The Emissions Model Intercomparison Project examined how selected emissions-related properties affected results in 11 global chemistry and Earth-system models.

Using regional meteorological data from an atmosphere reanalysis product, scientists identified 12 unique winter weather systems in the Puget Sound area, featuring differing precipitation and temperature responses to climate variabilities.

With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

Clustering wildfires into four types based on their land-atmosphere drivers can potentially improve regional wildfire predictions.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

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

New research shows how cloud shapes affect the process of cloud evolution, resulting in better understanding of how clouds behave, improving weather forecasts, and enhancing comprehension of climate systems.

PNNL researchers have found that as the shape of a cloud evolves, it demonstrates predictable patterns.

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

As electricity grids incorporate renewables, several factors will constrain future output, according to a new paper.

Researchers designed ultra-low radiation cables to reduce background noise for highly sensitive neutrino and dark matter detectors.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

PNNL’s DataLife tool analyzes and identifies bottlenecks in scientific workflows.