cade ago allowing them to run either in-house (regional and global) component models, component models from the larger centers, or a combination of the two. Smaller centers can even run coupled climate models but at coarse resolution (e.g., 800 km) or a higher resolution (300 km) for shorter time periods. Responses to the question about improvements that are being planned for models at all centers were varied, but most involved a mixture of increased model physics, dynamics, numerics, efficiency, and applicability. Many respondents also noted the desire to better incorporate new types of satellite and radar data.

In general, most respondents stated that their code was portable on platforms other than those on which they normally operated although some models required a moderate amount of optimization to ensure that they ran with minimal performance loss. Most centers release their modeling results to the wider scientific community without restrictions. A few centers freely release their data but stipulate that the results be used only for research purposes; others limit the release of modeling results to collaborators.

Large and intermediate-size modeling centers were asked whether there were plans to convert model code to run on massively parallel (MPP) architectures. Most institutions responded that this conversion had already taken place although those that have converted or are in the process of converting noted the difficulty in transferring certain models to an MPP architecture. Many respondents also noted that this conversion required significant programmer time and drained resources that could have been devoted to other activities. When asked for comment on the relative merits and hindrances of MPP versus VPP architectures, the majority of respondents preferred VPP architecture for the following reasons:

  1. MPP systems are generally more difficult to program and require increased computer expertise. There are therefore significant training issues involved in the use of these systems. These difficulties are particularly significant for university centers as they often rely on graduate student labor that is characterized by high turnover.

  2. Data assimilation and processing are more difficult on MPP systems.

  3. VPP systems are more stable and reliable.

  4. There are significant scalability problems on MPP systems.

  5. There is a lack of compilers on current MPP systems that make these systems difficult to use.

Despite the difficulties with MPP systems some respondents felt that these systems had significant benefits over VPP systems (e.g., lower memory cost and increased aggregate CPU power).



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