Bayesian Sensitivity Analysis to Quantifying Uncertainty in a Dendroclimatology Model

Abstract

A nonlinear forward model named VSLite is used to simulate tree ring-width growth from climate data. There is always uncertainty in such data inputs, which might influence the uncertainty of the model outputs. The present work performs a Bayesian sensitivity analysis (BSA) to the VSLite model using a Gaussian process emulator. BSA aims to understand and quantify the uncertainty of the model’s outputs due to a change in its inputs. The model was successfully implemented at different geographical locations around the world. To examine the accuracy of the model, we first compared real tree-ring data at different locations with those simulated from VSLite. The variability in the model output was then explored and quantified via BSA. Results show that BSA has successfully classified model parameters in terms of their influences on the model output variation.