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Models
Combining existing data sets with scientific and social theory and modeling will enhance
understanding and address user needs by providing a window on past conditions, permitting
an assessment of present conditions, and enabling predictions of future conditions.
Models may be developed for several reasons, including exploring the relationships
among physical, chemical, biological, and socioeconomic variables; hindcasting
previous conditions in an area; and forecasting the impacts of environmental change or
management practices.
Models can comprise a certain configuration, including parameters, domain, and
boundary conditions. The term model can also be used to describe a modeling system,
which incorporates an array of algorithms (a set of sequenced, computational instructions)
from which the most appropriate combination can be selected for a particular application.
The discussion of models throughout the societal themes incorporates both model
configurations as well as modeling systems.
Key elements to enable modeling efforts nationwide:
• Expand translation of data and insight derived from ocean research and observations
to improve representation of this information in models.
• Develop new application-specific model configurations where necessary.
• Consolidate existing modeling capabilities into a flexible and comprehensive unified
ocean-modeling software environment, building on the diverse existing community
and developmental ocean models.
• Improve model analysis and capabilities through model diagnostics and intercomparison
activities.
• Conduct systematic best-practice studies to develop guidance in the selection of
optimal algorithms and techniques for particular modeling applications.
• Expand development of high-resolution, global ocean model configurations for
use in real-time, short-term forecasts out to days or months, reanalysis of historical
ocean state, and multi-century climate forecasts.
• Expand current computational capacity to assimilate increasing amounts of data
provided by ocean-observing assets and to support and process model advancements
incorporating increased process and mechanistic understanding.
To ensure the effective use of model output, developers and users must address several
issues, including how the temporal and spatial scales of the input data compare to the
outputs and products, how uncertainty in data sets and model calculations is propagated,
and how the models are intended to be and are actually used.
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