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

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.

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.

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.

Investigating the impact of global warming on mesoscale convective systems initiation and growth with a theoretical model.

Understanding the contrasting response in relative humidity at ocean and land surfaces under global warming.

The cross-equatorial surge enhances the southward development of the Madden–Julian oscillation because of increased moisture convergence.

The roles of the various environmental variables in the transition from suppressed to active tropical precipitation regimes are characterized using statistical analysis and machine learning.

A new moist potential vorticity model helps researchers understand atmosphere–land moisture exchanges over the United States.

A modeling study finds that multiple factors almost perfectly balance under anthropogenic greenhouse gas forcing, leaving no footprint on the dynamically induced ocean heat storage in the Southern Ocean.

Climate change and socioeconomic pressures are transforming passenger and freight transportation in the Arctic, producing effects that have yet to be fully understood.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.

Testing the assumption that different future socio-economic development patterns, which result in different land-use changes, can be paired with different future climate outcomes for risk assessments in a multi-model framework.

PNNL is honoring its postdoctoral researchers as part of the fourteenth annual National Postdoc Appreciation Week with seven profiles of postdocs from around the Laboratory.

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.