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Abstract. Physical processes within geoscientific models are sometimes described bysimplified schemes known as parameterisations. The values of the parameterswithin these schemes can be poorly constrained by theory orobservation. Uncertainty in the parameter values translates into uncertaintyin the outputs of the models. Proper quantification of the uncertainty inmodel predictions therefore requires a systematic approach for samplingparameter space. In this study, we develop a simple and efficient approach toidentify regions of multi-dimensional parameter space that are consistent withobservations. Using the Parallel Ice Sheet Model to simulate the present-daystate of the Antarctic Ice Sheet, we find that co-dependencies betweenparameters preclude any simple identification of a single optimal set ofparameter values. Approaches such as large ensemble modelling are thereforerequired in order to generate model predictions that incorporate properquantification of the uncertainty arising from the parameterisation ofphysical processes.

geoscientific model development

An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3