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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 the 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.
", "funding": [ { "award": { "id": "05mmh0f86::SR140300001", "identifiers": [ { "identifier": "http://purl.org/au-research/grants/arc/SR140300001", "scheme": "url" } ], "number": "SR140300001", "program": "Special Research Initiative (Antarctic)", "title": { "en": "Special Research Initiatives - Grant ID: SR140300001" } }, "funder": { "id": "05mmh0f86", "name": "Australian Research Council" } } ], "publication_date": "2020-11-16", "publisher": "Zenodo", "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "Geoscientific model" }, { "subject": "Parameterisation" }, { "subject": "Parameter uncertainty" }, { "subject": "Parameter optimisation" }, { "subject": "Parallel Ice Sheet Model (PISM)" }, { "subject": "Antarctic Ice Sheet" }, { "subject": "Large ensemble modelling" } ], "title": "An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3" }, "parent": { "access": { "owned_by": { "user": 101640 } }, "communities": {}, "id": "4275052", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.4275052", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.4275053", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:4275053", "provider": "oai" } }, "revision_id": 2, "stats": { "all_versions": { "data_volume": 5715348564.0, "downloads": 91, "unique_downloads": 79, "unique_views": 212, "views": 224 }, "this_version": { "data_volume": 5715348564.0, "downloads": 91, "unique_downloads": 79, "unique_views": 212, "views": 224 } }, "status": "published", "updated": "2020-11-20T00:27:09.038727+00:00", "versions": { "index": 1, "is_latest": true } }