The US Department of Energy (DOE) through its CHAMMP initiative, hopes to develop the capability to make meaningful regional climate forecasts on time scales exceeding a decade, such capability to be based on numerical prediction type models. We propose research to consider theoretical limits to prediction of climate and climate change on appropriate time scales, to develop new mathematical techniques to utilize massively parallel processors (MPP), to actually utilize MPP's as a research tool, and to develop improved representations of some processes essential to climate prediction. In particular, our goals are to: Reconfigure the prediction equations such that the time …
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The US Department of Energy (DOE) through its CHAMMP initiative, hopes to develop the capability to make meaningful regional climate forecasts on time scales exceeding a decade, such capability to be based on numerical prediction type models. We propose research to consider theoretical limits to prediction of climate and climate change on appropriate time scales, to develop new mathematical techniques to utilize massively parallel processors (MPP), to actually utilize MPP's as a research tool, and to develop improved representations of some processes essential to climate prediction. In particular, our goals are to: Reconfigure the prediction equations such that the time iteration process can be compressed by use of MMP architecture, and to develop appropriate algorithms; develop local subgrid scale models which can provide time and space dependent parameterization for a state-of-the-art climate model to minimize the scale resolution necessary for a climate model, and to utilize MPP capability to simultaneously integrate those subgrid models and their statistics; and capitalize on the MPP architecture to study the inherent ensemble nature of the climate problem.
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Baer, F.; Tribbia, J.J. & Williamson, D.L.Exploitation of parallelism in climate models,
report,
January 1, 1993;
United States.
(https://digital.library.unt.edu/ark:/67531/metadc1192023/:
accessed June 5, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT Libraries Government Documents Department.