Technical Note: Remarks on Assessing Complexity in Thermal History Models

Abstract. Modelling low-temperature thermochronology data to understand geological history relating to near-surface thermal perturbations caused by processes like faulting, erosion, intrusion, or hydrothermal circulation, has become relatively routine. However, it is clear that not all modelling efforts include rigorous testing of various modelling decisions. This happens in part because of a lack of understanding about each of the different model parameters and how modifications to those parameters may control different model outputs or predictions. In an effort to reduce ambiguity around how model complexity is dealt with in the modelling program QTQt, we delve into the details behind the algorithm that accepts and/or rejects models with greater complexity (i.e., many time-temperature points within a thermal history), and explore example thermal histories to show the effect of choosing the accept or reject more complex models that do not improve the data fit. Generally, where the data control the model outputs and the data fit is good the model outputs and age predictions are indistinguishable. When the choice is made to accept more complex models, users must be aware that this choice adds more complexity in the areas of the model space that are not controlled by the data and effectively smooths the expected thermal history. Because of this effect, caution should be used when interpreting the expected thermal history from a run that accepts more complex models. To verify if the choice to accept or reject more complex models plays an important part in model interpretation, we suggest this decision is always tested by running the same model rejecting the complex models and comparing the model outputs.