  Thermal history models, that have been used to understand the geological
history of Earth, are now being coupled to climate models to map conditions
that allow planets to maintain surface water over geologic time - a criteria
considered crucial for life. However, the lack of intrinsic uncertainty
assessment has blurred guidelines for how thermal history models can be used
toward this end. A model, as a representation of something real, is not
expected to be complete. Unmodeled effects are assumed to be small enough that
the model maintains utility for the issue(s) it was designed to address. The
degree to which this holds depends on how unmodeled factors affect the
certainty of model predictions. We quantify this intrinsic uncertainty for
several parameterized thermal history models (a widely used subclass of
planetary models). Single perturbation analysis is used to determine the
reactance time of different models. This provides a metric for how long it
takes low amplitude, unmodeled effects to decay or grow. Reactance time is
shown to scale inversely with the strength of the dominant feedback (negative
or positive) within a model. A perturbed physics analysis is then used to
determine uncertainty shadows for model outputs. This provides probability
distributions for model predictions and tests the structural stability of a
model. That is, do model predictions remain qualitatively similar, and within
assumed model limits, in the face of intrinsic uncertainty. Once intrinsic
uncertainty is accounted for, model outputs/predictions and comparisons to
observational data should be treated in a probabilistic way.
