climate modelling; philosphy of science; complex systems
Climate models and climate modelling are a central part of climate science with particular importance for long term prognoses of future climate development. In the context of global climate change, which is a fact undoubted by climate scientists but sceptically discussed in the public, their importance not only for climate sciences is increasing.
However, most findings of climate modelling approaches are highly uncertain and span
a very broad range of values for climate variables and impacts of climate change.
Climate models are different from experiments in physics, thus their results must be
valued accordingly. Furthermore climate models are epistemically very different from
physics theories, which are normally topic of the debate in the philosophy of science. In
this thesis climate modelling is analysed according to ascertain the epstemic status of
climate models and to discuss its consequences.
Climate modelling is not based on a comprehensive physics theory and is not analogous
to experimenting. Moreover, climate models play a double role as an outsourced human
brain and a copy of the earth and are thus something in between an experiment and a
theory in progress.
Due to this fact several problems of climate modelling result, two of which are fundamental
and others are principally to overcome but practically pressing. The fundamental
problems of understanding the climate system are the nonlinearity of the system and
lack of observational data. The main practical problem of climate modelling is the problem
of parameterisation, which is the need to represent processes of the climate system
in the modelling approach that are insufficiently understood or on a smaller scale than
the resolution of the model.
Parameterisations in nonlinear models make it nearly impossible to detect chains of
causes and effects in a climate model. Therefore an intransparent method of fitting
the model to data, which is called tuning, results in manipulated physics of the climate
model and prevents a meaningful analysis of the modelling results.
As a conclusion of this thesis certain rules are provided that could avoid abuse of climate
model tuning. Furthermore basic guidelines are provided to make the climate modelling
process more transparent in general and thus to refer to the main uncertainties integral
to climate modelling appropriately.
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