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Parameterization: Parameterization involves substituting specific parameters or values into a model or system to simplify or analyze complex processes. It allows for customization and adjustment of variables, enabling simulations or calculations to represent different scenarios, enhancing understanding, and facilitating predictions within various fields such as science, engineering, and computer science. See also Simulation, Models, Model theory.
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Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments.

 
Author Concept Summary/Quotes Sources

Meteorology on Parameterization - Dictionary of Arguments

Edwards I 393
Parameterization/meteorology/climatology/Edwards: far from expressing pure theory, analysis models are data-laden.(1) And the same can also be said of all forecast models and general circulation models.
Stephen Schneider writes: . . . even our most sophisticated ‘first principles’ models contain ‘empirical statistical’ elements within the model structure. . . .We can describe the known physical laws mathematically, at least in principle. In practice, however, solving these equations in full, explicit detail is impossible. First, the possible scales of motion in the atmospheric and oceanic components range from the submolecular to the global. Second are the interactions of energy transfers among the different scales of motion. Finally, many scales of disturbance are inherently unstable; small disturbances, for example, grow rapidly in size if conditions are favorable.(2)
Edwards: Hence the necessity of parameterization, much of which can be described as the integration of observationally derived approximations into the “model physics.” Schneider and others sometimes refer to parameters as “semi-empirical,” an apt description that highlights their fuzzy relationship with observational data. For the foreseeable future, all analysis models, forecast models, and climate models will contain many “semi-empirical” elements. >Wheather forecasting/Edwards
, >Models/meteorology, cf. >Homogenization/climatology, >Reanalysis/climatology.
Edwards I 465
Parameter: (…) the term is often used to distinguish, from dependent variables, quantities that may be more or less arbitrarily assigned values for purposes of the problem at hand” (emphasis added). So a parameter is a kind of proxy - a stand-in for something that cannot be modeled directly but can still be estimated, or at least guessed.
Parameterization illustrates the interaction of computational friction with the limits of human knowledge. In an ideal climate model, the only fixed conditions would be the distribution and the altitude of continental surfaces. Virtually all other variables - sea-surface temperature, land-surface albedo (reflectance), cloud formation, etc. - would be generated internally by the model itself from the lower-level physical properties of air, water, and other basic elements of the climate system. Instead, most physical processes operating in the atmosphere require some degree of parameterization; these parameterized processes are known as the “model physics.” >Models/climatology.
Parameter: (…) parameters represent a variable physical process rather than a fixed quantity.
Edwards I 466
Parameterization/Example: A major parameterization in all climate models is radiative transfer. The atmosphere contains both gases (CO2, methane, nitrogen, ozone, oxygen, water vapor, etc.) and solids (particulate aerosols, ice clouds, etc.). Each one of these materials absorbs solar energy at particular frequencies. Each also emits radiation at other frequencies. Those emissions are then absorbed and re-radiated by other gases and solids. These radiative transfers play a huge role in governing the atmosphere’s temperature. Thus, models must somehow estimate how much radiation the atmosphere in a given grid box absorbs, reflects, and transmits, at every level and horizontal location. “Line-by-line models,” which combine databases of spectrographic measurements for the various gases with physical models, can carry out this summing.(3)
Edwards I 469
Ad hoc parameter/example: An example of an ad hoc parameter is “flux adjustment” in coupled atmosphere-ocean circulation models (AOGCMs). The interface between the atmospheric model and the ocean model must represent exchanges of heat, momentum (wind and surface resistance), and water (precipitation, evaporation) between the atmosphere and the ocean. These fluxes - flows of energy and matter between atmosphere and ocean—are very difficult to measure empirically. Yet they profoundly affect model behavior. Modelers spoke of flux adjustments as “non-physical” parameterizations - i.e., ones not based on physical theory—but also sometimes characterized them as “empirically determined.”(4)
Any given GCM’s model physics contains hundreds or even thousands of parameterizations.
Edwards I 470
An entire subfield—climate model diagnosis - works out ways to isolate the origin of particular problems to specific parameterizations and their interactions.
Tuning: “Tuning” means adjusting the values of coefficients and even, sometimes, reconstructing equations in order to produce a better overall model result. “Better” may mean that the result agrees more closely with observations, or that it corresponds more closely to the modeler’s expert judgment about what one modeler I interviewed called the “physical plausibility” of the change. >Models/climatology.

1. P. N. Edwards, “Global Climate Science, Uncertainty and Politics: Data-Laden Models, Model-Filtered Data,” Science as Culture 8, no. 4 (1999): 437–.
2. S. H. Schneider, “Introduction to Climate Modeling,” in Climate System Modeling, ed. K. E. Trenberth (Cambridge University Press, 1992).
3. J. T. Kiehl, “Atmospheric General Circulation Modeling,” in Climate System Modeling, ed. K. E. Trenberth (Cambridge University Press, 1992), 338.
4. 8. J. T. Houghton et al., Climate Change 1995: The Science of Climate Change (Cambridge University Press, 1996).

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Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments
The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.
Meteorology
Edwards I
Paul N. Edwards
A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Cambridge 2013


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