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Title: Exploitation of Parallelism in Climate Models

Abstract

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 contribute to each of the specific items enumerated in the CHAMMP announcement (Notice 91-3); i.e., 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 MPPs as a research tool, and to develop improved representations of some processes essential to climate prediction. In particular, our goals are to: (1) Reconfigure the prediction equations such that the time iteration process can be compressed by use of MMP architecture, and to develop appropriate algorithms. (2) 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. (3) Capitalize on the MPP architecture to study the inherent ensemble nature of the climate problem. By careful choice of initial states, many realizationsmore » of the climate system can be determined concurrently and more realistic assessments of the climate prediction can be made in a realistic time frame. To explore these initiatives, we will exploit all available computing technology, and in particular MPP machines. We anticipate that significant improvements in modeling of climate on the decadal and longer time scales for regional space scales will result from our efforts.« less

Authors:
; ;
Publication Date:
Research Org.:
University of Maryland, College Park, MD 20742 (US)
Sponsoring Org.:
USDOE Office of Energy Research (ER) (US)
OSTI Identifier:
6104
DOE Contract Number:
FG05-91ER61219
Resource Type:
Technical Report
Resource Relation:
Other Information: Supercedes report DE00006104; PBD: 1 Mar 1999; PBD: 1 Mar 1999
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ALGORITHMS; ARCHITECTURE; CLIMATE MODELS; CLIMATES; FORECASTING; PREDICTION EQUATIONS; RESOLUTION; SCALE MODELS; SIMULATION; STATISTICS

Citation Formats

Baer, F., Tribbia, J.J., and Williamson, D.L. Exploitation of Parallelism in Climate Models. United States: N. p., 1999. Web. doi:10.2172/6104.
Baer, F., Tribbia, J.J., & Williamson, D.L. Exploitation of Parallelism in Climate Models. United States. doi:10.2172/6104.
Baer, F., Tribbia, J.J., and Williamson, D.L. 1999. "Exploitation of Parallelism in Climate Models". United States. doi:10.2172/6104. https://www.osti.gov/servlets/purl/6104.
@article{osti_6104,
title = {Exploitation of Parallelism in Climate Models},
author = {Baer, F. and Tribbia, J.J. and Williamson, D.L.},
abstractNote = {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 contribute to each of the specific items enumerated in the CHAMMP announcement (Notice 91-3); i.e., 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 MPPs as a research tool, and to develop improved representations of some processes essential to climate prediction. In particular, our goals are to: (1) Reconfigure the prediction equations such that the time iteration process can be compressed by use of MMP architecture, and to develop appropriate algorithms. (2) 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. (3) Capitalize on the MPP architecture to study the inherent ensemble nature of the climate problem. By careful choice of initial states, many realizations of the climate system can be determined concurrently and more realistic assessments of the climate prediction can be made in a realistic time frame. To explore these initiatives, we will exploit all available computing technology, and in particular MPP machines. We anticipate that significant improvements in modeling of climate on the decadal and longer time scales for regional space scales will result from our efforts.},
doi = {10.2172/6104},
journal = {},
number = ,
volume = ,
place = {United States},
year = 1999,
month = 3
}

Technical Report:

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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 timemore » 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.« less
  • 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 contribute to each of the specific items enumerated in the CHAMMP announcement (Notice 9103); i.e., 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 tomore » climate prediction. To explore these initiatives, we will exploit all available computing technology, and in particular MPP machines. We anticipate that significant improvements in modeling of climate on the decadal and longer time scales for regional space scales will result from our efforts. This report summarizes the activities of our group during a part of the first year's effort to meet the objectives stated in our proposal. We will comment on three research foci, time compression studies, subgrid scale model studies, and distributed climate ensemble studies and additional significant technical matters.« less
  • 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 timemore » 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.« less
  • 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 contribute to each of the specific items enumerated in the CHAMMP announcement (Notice 9103); i.e., 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 tomore » climate prediction. To explore these initiatives, we will exploit all available computing technology, and in particular MPP machines. We anticipate that significant improvements in modeling of climate on the decadal and longer time scales for regional space scales will result from our efforts. This report summarizes the activities of our group during a part of the first year`s effort to meet the objectives stated in our proposal. We will comment on three research foci, time compression studies, subgrid scale model studies, and distributed climate ensemble studies and additional significant technical matters.« less
  • This final report includes details on the research accomplished by the grant entitled 'Exploitation of Parallelism in Climate Models' to the University of Maryland. The purpose of the grant was to shed light on (a) how to reconfigure the atmospheric prediction equations such that the time iteration process could be compressed by use of MPP architecture; (b) how to 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 theirmore » statistics; and (c) how to capitalize on the MPP architecture to study the inherent ensemble nature of the climate problem. In the process of addressing these issues, we created parallel algorithms with spectral accuracy; we developed a process for concurrent climate simulations; we established suitable model reconstructions to speed up computation; we identified and tested optimum realization statistics; we undertook a number of parameterization studies to better understand model physics; and we studied the impact of subgrid scale motions and their parameterization in atmospheric models.« less