As in operational numerical weather prediction, several characteristics of operational climate prediction make it distinct from climate model research and development. First, the goals of operations are driven by a user community rather than scientific advancement. There is no value judgment implied by this, but the implication is that the needs of the user community have to be assessed regularly, operational products must respond to users’ needs, and there is an expectation for improvement over time in various aspects of users’ experience. Second, operations must conform to a specified schedule of generation and delivery of products. Users expect products to be available in time, on time, every time, which requires a mindset and a working protocol that is not necessarily appropriate in a research and development setting. Third, operational prediction requires dedicated resources and contingency (failsafe) planning. Model developers often work with resources that have been obtained through a competitive process, on an ad hoc basis, or through windfall opportunities, but those modes are far too undependable for operational requirements. Operations must have a platform for product generation that is fully functional when needed and a plan in place for utilizing backup resources when that platform is out of order. Finally, operational computer code should conform to rigorous standards of software engineering that may or may not apply to research codes. While many climate prediction research groups are shifting to a more formal software engineering approach (Chapter 10), primarily motivated by the need for including the input from a wide community of researchers and model developers, there remains a more informal methodology in most model development groups that enables and even encourages risk taking, as is appropriate in a research and development enterprise.


(System41), and other nations have similarly developed seasonal climate prediction systems that include a climate model developed specifically for this purpose.

There is a desire within the research community to migrate experimental climate prediction models into operational use (e.g., the National Oceanic and Atmospheric Administration [NOAA] Climate Test Bed effort to build a multimodel ensemble [NOAA, 2011]) and to improve on operational models by transitioning model components and/or parameterization schemes from experimental models developed in the broader community, motivated by the growing expectation for governments to provide climate services (e.g., Dr. Jane Lubchenco’s testimony before Congress during the hearings to confirm her as Undersecretary of Commerce for Oceans and Atmosphere and Administrator of the National Oceanic and Atmospheric Administration


1 (accessed October 11, 2012).

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