High performance computing researchers at
Accelerated Climate Modeling for Energy, or ACME, is designed to accelerate the development and application of fully coupled, state-of-the-science Earth system models for scientific and energy applications.
Fourteen institutions will work together to develop the most accurate climate change predictions yet, and investigate key fundamental science questions, such as the interaction of clouds and climate and the role of secondary organic aerosols. The partners include eight national laboratories -- Sandia, Argonne, Brookhaven,
Over the next decade, the project will conduct simulations and develop models on the most sophisticated high-performance computers as they become available, including 100-plus petaflop machines and, eventually, exascale supercomputers.
The project initially will examine and try to answer "big science" questions in three areas that drive climate change: the water cycle, biogeochemistry and the cryosphere (areas of the earth where water exists as ice or snow).
* (Water Cycle) How do the hydrological cycle and water resources interact with the climate system on local to global scales? How will more realistic portrayals of features important to the water cycle (resolution, clouds, aerosols, snowpack, river routing, land use) affect river flow and associated freshwater supplies at the watershed scale?
* (Biogeochemistry) How do biogeochemical cycles interact with global climate change? How do carbon, nitrogen and phosphorus cycles regulate climate system feedbacks, and how sensitive are these feedbacks to model structural uncertainty?
* (Cryosphere systems) How do rapid changes in cryospheric systems, or areas of the earth where water exists as ice or snow, interact with the climate system? Could a dynamical instability in the Antarctic Ice Sheet be triggered within the next 40 years?
"Our work ensures ACME's scientific program can be achieved with DOE's Leadership Computing Facilities. We will be applying a wide range of high-performance computing approaches including developing new algorithms specially adapted to these architectures. Only through efficient use of these computers will we be able to model the many physical processes at the fidelity necessary to answer ACME's science questions," Taylor said.
Taylor added that Sandia is in an ideal position for its share of the work on ACME because of its extensive experience in developing high-performance-computing environments, in software engineering and in the quantification of computational uncertainties.
The computational team initially will customize the model for use on machines at two
"We'll be setting up the ACME project with modern software engineering tools and processes, improving productivity for the whole project, helping people to develop new features in the code more quickly and making it easier for new people to join the project. We'll also be putting more software testing into place so that performance experts can make code modifications and be confident that they aren't changing the science in the process of optimizing the code," Salinger said.
Salinger said he'll incorporate computational mathematics libraries developed by DOE into ACME, such as those from the Trilinos project for solving non-linear problems and performing load-balancing. Leveraging DOE's investment in these areas is one of ACME's strategies for obtaining the best possible performance on evolving computing systems.
To explain how uncertainties come into play, Khachik provided a simple example. "A single simulation of the land model may show that in 100 years, a certain type of vegetation will vanish. With UQ, we can actually quantify how likely it is."
The UQ methods Khachik and his colleagues develop are nonintrusive, that is, they do not require programming changes to the original model (in this case, the climate model) to which UQ is applied. This important feature allows researchers to apply UQ methods broadly, to fields ranging from chemistry to materials science to nuclear engineering. It also means that older modeling efforts and data gathered in different disciplines can expedite climate modeling UQ.
Initial funding for the effort has been provided by
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