PCMDI works to develop improved methods and tools for the diagnosis and intercomparison of general circulation models that simulate the global climate.
Current projects focus on: supporting the intercomparison of models results from every major international climate modeling center; developing a model parameterization testbed; identification of robust cloud feedbacks in observations and models; and on devising robust statistical methods for climate-change detection/attribution.
Working across U.S. federal agencies, international agencies, and multiple worldwide data centers—and spanning seven international network organizations—the ESGF allows users to access, analyze, and visualize climate model output and observational data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces.
The Accelerated Climate Modeling for Energy Project (ACME) is an ongoing, multi-institution effort to develop a state-of-the-science Earth system modeling, simulation, and prediction model that optimizes the use of DOE laboratory resources. The ACME model simulates the fully coupled Earth system at high-resolution, and is incorporating coupling among energy, water, land-use and related energy-relevant activities with a focus on near-term hind-casts for model validation and a near-term projection for energy sector planning. A major motivation for the ACME project is the paradigm shift in computing architectures and their related programming models as computational capabilities move towards the exascale era. ACME is optimizing code performance for current and next-generation DOE computer facilities.
The Cloud-Associated Parameterizations Testbed (CAPT) aims to diagnose and improve the representation of cloud-associated physical processes in climate models. In the CAPT, weather forecast techniques are applied to climate models, with an emphasis on the simulations of the Community Atmosphere Model. The three main elements of this effort are:
- Comparing model simulations to detailed process observations available from DOE Atmospheric Radiation Measurement (ARM) data
- Diagnosing the origin of errors in model simulations of climate
- Testing new model parameterizations to identify their strengths/weaknesses in simulating cloud-associated processes
To reduce uncertainties associated with climate-change, a team of LLNL and University of California at Los Angeles researchers are using observations to determine which of the climate-model predicted responses of clouds to climate change are realistic.
A comprehensive understanding of the interactions within complex microbial communities is needed to advance the use of microbial systems for the practical production of biofuels or other valuable chemical products. Biofuels seeks to understand and predict the biophysical and biochemical dynamics of multi-taxa communities to determine the functional roles of individual organisms or groups of organisms within the community.
Subsurface Biogeochemistry of Actinides aims to reliably predict and control actinide cycling and mobility in the subsurface environment. Our main focus is identifying the dominant biogeochemical processes and underlying mechanisms that control actinide behavior in the subsurface.