We have distinguished modeling for societal benefit and modeling for understanding. Modeling for societal benefit is product oriented, requires regular and systematic runs of climate models, and is user driven and user evaluated. Modeling for understanding is freer, competitive, driven and evaluated by scientific priorities, evaluated by peers, and generally less capable of being organized or constrained. We recognize that these distinctions are not universal and are therefore imperfect, but we have found it useful to make these distinctions in order to consider the needed organizational aspects for each type of modeling.

It is the nature of all climate research that a proper balance between process studies, background observations, and modeling best advances the understanding of the entire system. It is the difficulty of climate research that, while it is easy to put together field programs of limited duration to measure poorly understood processes, it is almost impossible to sustain measurements on climatic time scales. The climate research community neither has the infrastructure for doing so nor are sustained observations amenable to the usual peer review process, because sustaining observations is not itself research.

Climate operations has an analogous structure. It needs an observing system and it produces model products using high-end modeling, some for analyzing and improving the observing system and some for diagnostic and predictive information. A prime function of climate operations is the design and delivery of climate information products that benefit society and put demands on the observational system and on modeling. These societal functions are qualitatively different from research functions. They tend to have time constraints determined by the nature of the decision to be made, they require specific products to be delivered in forms most useful to decision makers, and they are judged by a different standard from curiosity-driven research. In practice the difference between this type of product-driven research and curiosity-driven research shows up as differences in resources and organizations required, which, in turn, implies different modes of management and funding. Product-driven research tends to be large scale (beyond the scale of a single principal investigator), more expensive, and more highly centralized. The additional resources required are justified in terms of benefits to users with the ultimate evaluation done not by modelers but by the users themselves. In particular, operations can (and must) sustain infrastructure and can (and must) sustain an observing system.


Operations provide enormous benefits to research and are most likely to be successful when interacting strongly with research. For example, the

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement