Abstract
Abstract. Physical processes within geoscientific models are sometimes described by simplified schemes known as parameterisations. The values of the parameters within these schemes can be poorly constrained by theory or observation. Uncertainty in the parameter values translates into uncertainty in the outputs of the models. Proper quantification of the uncertainty in model predictions therefore requires a systematic approach for sampling parameter space. In this study, we develop a simple and efficient approach to identify regions of multi-dimensional parameter space that are consistent with observations. Using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet, we find that co-dependencies between parameters preclude any simple identification of a single optimal set of parameter values. Approaches such as large ensemble modelling are therefore required in order to generate model predictions that incorporate proper quantification of the uncertainty arising from the parameterisation of physical processes.
Highlights
The aim of any geoscientific model is typically to replicate the state and behaviour of real-world systems as accurately as possible, or at least with sufficient accuracy to generate useful insights into the problem being studied
This study focuses upon the first of these three potential sources of error, aiming to explore the contribution to model prediction error that arises from the simplifications made in the representation of physical processes within geoscientific models
We demonstrate the application of this technique by using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet
Summary
The aim of any geoscientific model is typically to replicate the state and behaviour of real-world systems as accurately as possible, or at least with sufficient accuracy to generate useful insights into the problem being studied. This requires the model to incorporate a sufficiently accurate description of the real world, as well as sufficiently accurate data to provide boundary conditions and an initial state. This study focuses upon the first of these three potential sources of error, aiming to explore the contribution to model prediction error that arises from the simplifications made in the representation of physical processes within geoscientific models
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