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Climate Process and Modeling Teams (CPT)

Program Title Climate Process and Modeling Teams (CPT)
Funding Agency NSF
Website http://www.nsf.gov/pubs/2009/nsf09568/nsf09568.htm?govDel=USNSF_25
Due Date Sep 24, 2009 12:00 AM


 
Due September 24

    The aim of the Climate Process Modeling Teams (CPTs) is to speed development of global coupled climate models by bringing together theoreticians, observationalists, process modelers and the large modeling centers to concentrate on the leading problems facing models. Demand is growing for climate models to provide more accurate simulations of the present and past climates and more credible and reliable predictions and projections of future climates. Meeting this demand requires that progress in model development accelerate, a goal that will be met most effectively by bringing field experimentalists and remote sensing experts, process modelers and global-scale modelers together to tackle the most persistent and vexing problems in how global models represent key processes.
    Each CPT will comprise a number of PIs and institutions proposing as a collaborative group (see Section II.D).  Each team must include at least one, and preferably more, of the modeling centers identified in Section II.C, as collaborating institutions.
    It is the objective of the CPTs to bridge the gaps among the field and remote sensing observation programs, process models, and global modelers by building new communities, in which those with observational expertise and data, those with highly detailed process models, and those building global models work together to address systematically the critical issues that limit progress in improving global climate models. The CPT is envisioned to support collaborations that will accelerate progress in climate model development. Such support should include visiting scientist programs, post-doctoral programs that give incentives for modelers and field scientists to interact, workshops for the teams to interact regularly, and computational resources to test and assess new parameterizations.

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