Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics
Lumping, testing, tuning: The invention of an artificial chemistry in atmospheric transport modeling
Introduction
Computer models and simulation in the atmospheric sciences involve peculiar features, which have been described as “epistemic opacity”, “semi-autonomy” and “simplification”. These features have been considered as problematic, because they cause problems of model validation and assessment and involve new forms and not quantifiable ranges of uncertainty of simulation results (e.g. Petersen, 2006). The case of atmospheric chemistry modeling shows that epistemic opacity, semi-autonomy and simplification also provided gains. These characteristics—in spite of being problematic—opened windows of opportunity and created spaces for the realisation of computer models and simulations, which otherwise would have been much more restricted. The construction of the so-called Urban Airshed Model (UAM) to simulate photochemical air pollution in the Los Angeles area provides an interesting example. This model was developed in the early 1970s by a group of researchers around the atmospheric scientist John Seinfeld at the California Institute of Technology.1 It represented a scientific response to a new type of complex air pollution problem, which was first discovered in the Los Angeles area in the early 1940s.
In this paper I will analyse the construction of the UAM and explore two main issues: first, the practices that have been developed and employed in the construction and use of the Urban Airshed Model, and second, the creation of confidence and trust which the scientists eventually put into the model. The introductory section consists, first, of an outline of epistemic opacity, semi-autonomy and simplification as significant features of computer models and, second, of an introduction into the problem of photochemical air pollution. The core section is dedicated to the analysis of scientific practices developed by Seinfeld and his research group. In the final section I will discuss the characteristics of simulation practices in atmospheric chemistry modeling and the requirements and conditions for the creation of confidence in these practices.
Section snippets
Opacity, semi-autonomy and simplification
Computer models and simulation results need to be tested by comparison with measured data like any physical theory. These model tests are usually called “model validation” and—in spite of its limitations—considered crucial for creating trust into the model (Oreskes et al., 1994, Oreskes, 1997). But the interpretation of a comparison of simulated and measured data is usually not straightforward and often ambiguous. Testable results of physical theories can be established and scrutinized step by
The problem: A new type of smog in the Los Angeles area
The majority of air pollution problems in the nineteenth and large parts of the twentieth century were related to smoke, dust and sulphur emissions from coal burning. A completely new type of air pollution was discovered in the early 1940s in the Los Angeles area. It became apparent by excessive eye irritation, reduced visibility, crop damages and rubber cracking and occurred in warm, high pressure periods (Bartel and Temple, 1952, Haagen-Smit, 1952).5
The case: Constructing the Urban Airshed Model
Seinfeld was fully aware of the difficulties he faced in his attempt to construct a computer-based simulation model for urban photochemical pollution. The involvement of hundreds of chemical substances, among them many complex organic compounds, and an even larger number of reaction pathways, the lack of empirically established rate constants for many of them, the combination of gas-phase, liquid phase and aerosol chemistry, the non-linearity of chemical processes and the additional role of
Fitting practices and the creation of confidence
The development of the Urban Airshed Model (UAM) was an influential pioneering effort. For the first time it linked a Eulerian atmospheric transport model with a chemistry model describing atmospheric photochemistry and ozone formation. With the invention of an artificial chemistry based on lumping chemical species the model builders consciously and explicitly left established chemical knowledge. This approach proved successful, both in terms of model performance and in terms of scientific
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