From Supercomputers to Regional Climate Data: Exploring Best Practices for “Variable-Resolution” Modeling
A holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.
Delivering Mighty Impact for DOE User Facility
ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.
Finding A Natural Cycle in Antarctic Oscillation
Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.
Resolution Matters When Choosing the Meteorological Forcing for Watershed Simulations
Watershed hydrological responses are sensitive to the spatial and temporal resolution of the gridded meteorological forcing used in simulations.
PNNL Scientist Inspires Next Generation at the National Science Bowl
Physicist Emily Mace will share her science journey and an interactive presentation about her current research with middle school and high school students from across the country at the National Science Bowl.
Embedding a Physics Informed Deep Learning Model in a Chemical Transport Model
A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.
Heightened U.S. Coastal Hurricane Risk Under Global Warming
The frequency and intensity of storms affecting U.S. coastal areas will likely increase in a warming world.
Modeling Agriculture Matters for Carbon Cycling
Researchers investigated the impact of using constant versus spatially varying crop parameters on carbon and energy fluxes in a realistic crop rotation scenario.
Generating Hourly Electricity Load Profiles for Resilient Grid Planning
A new model uses machine learning to project electricity loads across the United States.