This study shows that dry dynamics alone is not enough to understand jet stream persistence. Instead, clouds and precipitation are more important contributors than internal “dry” mechanisms to this memory of the Southern Hemisphere jet.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
This study provides a comprehensive analysis of isolated deep convection & mesoscale convective systems using self-organizing maps to categorize large-scale meteorological patterns and a tracking algorithm to monitor their life cycle.
This study explored the future effects of climate change and low-carbon energy transition (i.e., emission reduction) on Arctic offshore oil and gas production.
Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.
This work shows that linear pattern scaling is an effective means of obtaining global-to-local relationships for CMIP6 models, as it has been in past model eras.
This study examined the role of river sinuosity using computer models to understand what drives hyporheic exchange, a process that significantly affects water quality and ecosystem health.
PNNL postdoc Pengfei Shi won first place in the Early Career Researcher Poster Competition at the recently concluded NOAA Subseasonal and Seasonal Applications Workshop.
Data-gathering instruments will be positioned on commercial, ocean-going ships in a Department of Energy-funded project that is expected to improve understanding of marine atmosphere and aerosol–cloud interactions.
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.
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.
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 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
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.
