One of the greatest challenges facing climate modelers is incorporating cloud-climate interactions accurately. Although cloud systems have been included in climate models in the past, they lack the details that could improve the accuracy of climate predictions. In a paper published in the May issue of the International Journal of High Performance Computing Applications, Office of Science (SC) funded researcher Michael Wehner and colleagues at the Lawrence Berkeley National Laboratory (LBNL) lay out the benefit of a new class of supercomputers for modeling climate conditions and understanding climate change. They are working with SC-funded scientist Dave Randall from Colorado State University to build a prototype system in order to run the new global cloud resolving model being developed at Colorado State University.
Wehner and colleagues set out to establish a practical estimate for building a supercomputer capable of creating climate models at 1-kilometer (km) scale. A cloud system model at the 1-km scale would provide rich details that are not available from existing models. Using the embedded microprocessor technology used in cell phones, iPods, toaster ovens and most other modern day electronic conveniences, the authors propose designing a cost-effective machine for running these models and improving climate predictions. This is a radical alternative that would cost substantially less to build and require less electricity to operate. LBNL has signed a collaboration agreement with Tensilica®, Inc. to explore such new design concepts for energy-efficient high-performance scientific computer systems.
Reference: Towards Ultra-High Resolution Models of Climate and Weather, Wehner et al. 2008: International Journal of High Performance Computing Applications 2008; 22: 149-165.
Contact: Anjuli Bamzai, SC-23.3, (301) 903-0294
SC-23.1 Climate and Environmental Sciences Division, BER
(formerly SC-23.3 Climate Change Research Division, OBER)
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