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.
Characterizing Aerosol Effects on Modeled Cloud Lifetimes Using a Novel Framework
Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.
Artificial Intelligence-Enhanced Tropical Forest Coexistence Modeling
Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.
New Deep Neural Network Could Speed Calculations in Climate Models
Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.
Increasing Catchment Size Enhances the Variety of Functions Provided by Organic Matter in Rivers
The diversity and function of organic matter in rivers at a large scale are influenced by factors, such as the types of vegetation covering the land, the energy characteristics, and the breakdown potential of the molecules.
Simulating Large-Scale Hydropower under Climate Change: A State-of-the-Art Review
A review describes the latest techniques for simulating hydropower at large scales to inform power grid planning and operations.