The Community Earth atmospheric Data System (CEDS) provides historical surface flux data that are used for general analysis and also for model validation through comparisons with observations.
PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.
The Joint Global Change Research Institute conducts research to advance fundamental understanding of human and Earth systems and provide decision-relevant information for management of emerging global risks and opportunities.
PNNL is a testbed for the latest research and technologies in marine carbon dioxide removal (mCDR)—leveraging the ocean’s strength as a natural carbon sink to address pressing climate concerns.
The Molecular Observation Network is a national open science network designed to produce a comprehensive database of molecular and microstructural information on soil, water, microbial communities, and biogenic emissions.
PNNL's Ocean Dynamics Modeling group studies coastal processes such as marine-hydrokinetic energy, coastal circulations, storm surge and extreme waves, tsunamis, sediment transport and nutrient-macroalgal dynamics.
Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.
PNNL is working on behalf of the U.S. Department of Energy to create a prototype system that enables homes to help provide services to the power grid while delivering economic benefits to residents.
The Salish Sea Model (SSM) is a predictive coastal ocean model for estuarine research, restoration planning, water-quality management, and climate change response assessment.
The Water cycle: Modeling of Circulation, Convection, and Earth system Mechanisms (WACCEM) Scientific Focus Area advances predictive understanding of water cycle variability and change.
