This study examines the roles of the semi-annual variation of solar radiation and soil moisture on the Madden-Julian Oscillation (MJO) propagation across the Maritime Continent islands.
University of Maryland, NASA Goddard Space Flight Center, and PNNL scientists explored how radiation-cloud-convection-circulation interactions (RC3I) affect the Intertropical Convergence Zone (ITCZ) and circulation at the global scale.
A study led by scientists at PNNL points to a new frontier for understanding the coupled climate system from the perspective of a nonlinear dynamical system.
By quantifying the contribution of snowpack to runoff and extreme flooding in mountainous regions in the western United States, PNNL researchers provided a unified view of the interactions between snowpack and precipitation.
DOE lab and university researchers used the Community Atmospheric Model 5.3 to investigate the power sea surface temperature has on the intensification or widening of the Hadley cell in the Northern and Southern hemispheres.
A team of researchers led by PNNL scientists have developed an open-source modeling platform, called Metis, that combines global human and Earth system dynamic tools with local datasets.
A study led by PNNL scientists reveals the influence of Arctic and midlatitude black carbon—or soot particles—on the frequency of extreme El Niño-Southern Oscillation (ENSO) events.
This study explores the relative role of temperature and humidity in extreme wet-bulb events and spurs further research into how these factors may change the frequency and intensity of life-threatening events in the future.
A team of researchers, including PNNL scientists, used 13 years of data to develop an automated algorithm that identifies seven different cloud types at the Atmospheric Radiation Measurement (ARM) site in the U.S. Southern Great Plains.
PNNL researchers used a new method for fingerprinting the sources of rainfall changes in tropical circulations. This new method was applied to the Asian Summer Monsoon (ASM) in DOE’s Energy Exascale Earth System Model.
By using the new reservoir storage-area depth dataset, PNNL researchers were able to improve surface temperature simulation for ~70% of validated reservoirs compared to using simplified reservoir geometry as in previously available models.
Researchers who explore the interactions between human and natural systems will now have the ability to generate thousands of scenarios that can include different kinds of extreme events to study.
The results of this study provide an analysis of the ice nucleation efficiency of bare and acid coated loess from the Columbia Plateau region of the northwestern United States.
Using machine learning, PNNL researchers identified four types of environments with favorable circulation patterns for spring mesoscale convective systems (MCSs) to form.
PNNL and University of Arizona researchers evaluated the performance of the Weather Research and Forecasting (WRF) model in simulating precipitation under different weather patterns.
A research team, led by scientists at PNNL, analyzed aerosols’ physical, chemical, and optical properties collected by a suite of airborne instruments during winter as part of a year-long measurement campaign in Cape Cod, Massachusetts.
Using two ice nucleation chambers, PNNL researchers found that ice particles, once nucleated, are more efficient at forming ice in the next ice nucleation event.
Researchers developed a high-resolution mesoscale convective systems database by synthesizing satellite and radar network observations available from 2004 to 2016.
