•  model forecast skill, evaluated on both weather and intraseasonal to interannual time scales, and model fidelity for long-term climate simulations;

•  adequate model development manpower and expertise to simultaneously handle the challenges of both weather and climate applications; and

•  computational infrastructure allowing efficient execution and data management for simulations over the needed range of grid spacings and time scales.

Seamless prediction and unified modeling are strategies for better model engineering, aimed at constraining uncertain parameters and taking advantage of the considerable overlap between the internal structure of weather and climate simulation models. Indeed, to the extent such engineering produces more skillful climate models and climate-quality reanalyses, these strategies have clear value to the climate science and applications communities. They may also produce advances that can be transferred between models, including parameterization methodologies that work across a range of scales and new approaches to climate model testing and evaluation.

BENEFITS OF SEAMLESS PREDICTION

The observed climate system contains important features and processes that operate on a wide range of time and space scales, from cloud ice crystals to mesoscale weather systems to basin-scale ocean circulation processes to continental-scale ice sheets. All of these processes contribute to some degree to observed weather and climate phenomena across a range of time scales. Given the complexity of this overall system it has proven useful to construct models that focus on what are deemed the most essential processes for the particular application in mind. For example, weather prediction models used for daily to weekly forecasts focus on high spatial resolution in the atmosphere, and state-of-the-art atmospheric physics, but have less emphasis on a detailed representation of the ocean, because many aspects of ocean changes do not impact the weather on a time scale of a week or two. Similarly, climate models used for projections on decadal to centennial time scales have relatively coarse spatial resolutions for reasons of computational efficiency and thus do not accurately simulate phenomena that may be important on small space and time scales, such as mesoscale convective complexes or tropical cyclones. These choices reflect both limitations on resources, such as computer power, and an attempt to simplify and streamline the problem under consideration.

This approach has its drawbacks, however. Physical and chemical processes in the climate system can have an impact on many time and space scales. For example, small cumulus clouds driven by daytime surface heating can alter afternoon land-surface



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