July 31, 2009
Models Blur Science and Advocacy: A Note from Ian Read
MANY mainstream media science, economic and environmental journalists are not sufficiently trained to be aware of the limitations of models when they present climate-modelled output computated projections not only as data but also advocate this output as supposed proof of the threat posed by anthropogenic global warming, particularly with regard to runaway or catastrophic climate change. This disjunct between the scientific and media presentation when contained within the paradigm of advocacy represents a threat to the integrity and falsifiability of science.
Science seeks the truth in knowledge; (some) media advocacy seeks to propagandise this knowledge. The impact is reinforced if a climate scientist/modeller is directly quoted as an expert, further blurring the line between science and advocacy. This has societal repercussions as the science of anthropogenic global warming (AGW) and the perceived impact of runaway or catastrophic climate change is so model-dependent that the citizenry is not always able to differentiate between the science and advocacy – the implications of which, as regards policy development in term of climate change mitigation, are likely to have a profound effect on society.
Climate models are used, in part, to determine future climate change scenarios related to anthropogenic global warming (AGW) and are described by the Intergovernmental Panel on Climate Change (IPCC) as “mathematical representations of the climate system, expressed as computer codes and run on powerful computers.”
Furthermore, the IPCC states that climate models:
“Are derived from fundamental physical laws (such as Newton’s laws of motion), which are then subjected to physical approximations appropriate for the large-scale climate system, and then further approximated through mathematical discretization. Computational constraints restrict the resolution that is possible in the discretized equations, and some representation of the large-scale impacts of unresolved processes is required (the parametrization problem).”
In other words a climate model is a numerical model or simplified mathematical representation of the Earth’s climate system, or parts thereof. It includes data from real world observations and creates parameters or variables for the unresolved or unknown processes.
The ability of a model to simulate interactions within the climate system depends on not only the level of understanding of the physical, geophysical, chemical and biological processes that govern the climate system but on how accurately these processes are expressed as algorithms within the model, and how closely they represent real-world data. These models do contain some well-established science but they also contain implicit and explicit assumptions, guesses, and gross approximations, referred to as parameters (the parametrization problem mentioned above), mistakes in any of which can invalidate the model outputs when compared to real world observations. In other words computer models are just concatenations of theoretical calculations; as such they do not constitute evidence.
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