New research shows how cloud shapes affect the process of cloud evolution, resulting in better understanding of how clouds behave, improving weather forecasts, and enhancing comprehension of climate systems.
The roles of the various environmental variables in the transition from suppressed to active tropical precipitation regimes are characterized using statistical analysis and machine learning.
A modeling study finds that multiple factors almost perfectly balance under anthropogenic greenhouse gas forcing, leaving no footprint on the dynamically induced ocean heat storage in the Southern Ocean.
Climate change and socioeconomic pressures are transforming passenger and freight transportation in the Arctic, producing effects that have yet to be fully understood.
Testing the assumption that different future socio-economic development patterns, which result in different land-use changes, can be paired with different future climate outcomes for risk assessments in a multi-model framework.
PNNL is honoring its postdoctoral researchers as part of the fourteenth annual National Postdoc Appreciation Week with seven profiles of postdocs from around the Laboratory.
The Distributed Wind Market Report provides market statistics and analysis, along with insights into market trends and characteristics of wind technologies used as distributed energy resources.
A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.
