Skillful predictions of tropical cyclone activity on subseasonal time scales may help mitigate their destructive impacts. This study investigates the combined impacts of atmospheric phenomena to better understand cyclone activity.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

To gain a mechanistic understanding of the physical processes responsible for the enhanced hurricane cold wakes near the Southeast United States, investigators used ocean reanalysis datasets.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

Cloud and its radiative effect are among the determining processes for the energy balance of the global climate; they are also the most challenging processes for the climate models to simulate.

Researchers develop a diagnostic framework to identify the sources of global monsoon simulation biases from an energy transport perspective.

Bylaska and team will aim to make computational design of functional materials more targeted, impactful, and user friendly through this award.

Researchers found that in a future where the Great Plains are 4 to 6 degrees Celsius (°C) warmer as projected in a high-emission scenario, these storms could bring three times more intense rainfall.

Researchers develop comprehensive framework for the Energy Exascale Earth System Model, incorporating advanced river and ocean models that improve how such interactions are simulated

New findings document fewer ship tracks, reduced cloud cover, and boosted warming after ship emissions regulations took effect in 2020.

Researchers use observations and numerical models to examine changes in tropical cyclone intensification rates and their environment in global coastal regions

As launch time draws near, PNNL scientists are eager to explore how plants might grow in space, where the effect of gravity is substantially weaker.

Scientists develop a water tracer tool in an advanced hydrologic model to understand the importance of modeling lateral flow in hydrologic simulations.

Researchers develop new, weakly coupled land data assimilation system to make better climate predictions and projections.

Scientists use water vapor tracers incorporated in a climate model to tag moisture from local evapotranspiration and trace their evolution through different processes.

Aerosol particles imbue climate models with uncertainty. New work by PNNL researchers reveals where in the world and under what conditions new particles are born.

Researchers evaluate a new slab ocean capability in the Energy Exascale Earth System Model (E3SM) version 2 by comparing its climate simulation to that of the full version of E3SM that uses a dynamic ocean model.

Accessing groundwater may become more difficult—and more expensive—as groundwater supplies become increasingly scarce and underground aquifer levels fall.

Researchers developed a framework for representing aerosol particles in climate models.

Researchers demonstrate an AI that can be taught to recognize cloud types by looking at millions of satellite images of clouds without requiring human input.