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

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.

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.

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.

Researchers measured ice nucleating particles composition at the Southern Great Plains atmospheric observatory, enabling them to identify sources of particles that make them effective ice nucleators.

A recent study suggests a higher global mean precipitation increase and narrows the uncertainty range.

Researchers find ultrafine particles abundant in smoke from Amazon forests fire may intensify storm clouds and heavy rain.

Using a refined Earth system model, researchers found that wetlands over North America will be significantly affected by climate change under future scenarios

Researchers use dataset combining observational data with advanced numerical simulations to investigate the characteristics, drivers, and trends of extreme heat events in the High Arctic over past four decades

Researchers develop framework that tracks the aerosol–cloud interactions along the trajectories of air parcels and embed framework into Weather Research Forecast model.

Study develops high-resolution land surface data for 2001 to 2020, including parameters of land use, vegetation, soil, and topography and demonstrated its use in k-scale simulation using the Energy Exascale Earth System Model.

Researchers show how satellite observations from the MODerate Resolution Imaging Spectroradiometer and CloudSat radar can be used to constrain the ACI radiative forcing that is linked to droplet collection in marine liquid clouds.

Researchers provide clear evidence to show that the fourfold Arctic Amplification over recent decades is an anomaly caused by dominant modes of natural variability.

Researchers synthesize molecular-level laboratory experiments to develop comprehensive model representations of new particle formation and the chemical transformation of precursor gases.

Researchers introduce revised emission treatment that conserves mass and preserves the original heterogeneity.

Researchers show application of a causal model better identifies direct and indirect causal relations compared to correlation and random forest analyses performed over the same dataset.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.