An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3
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 output...
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ftdatacite:10.5281/zenodo.4275052 2023-05-15T13:44:46+02:00 An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 Phipps, Steven J. Roberts, Jason L. King, Matt A. 2020 https://dx.doi.org/10.5281/zenodo.4275052 https://zenodo.org/record/4275052 unknown Zenodo https://dx.doi.org/10.5281/zenodo.4275053 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Geoscientific model Parameterisation Parameter uncertainty Parameter optimisation Parallel Ice Sheet Model PISM Antarctic Ice Sheet Large ensemble modelling dataset Dataset 2020 ftdatacite https://doi.org/10.5281/zenodo.4275052 https://doi.org/10.5281/zenodo.4275053 2021-11-05T12:55:41Z 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. Dataset Antarc* Antarctic Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) Antarctic The Antarctic |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Geoscientific model Parameterisation Parameter uncertainty Parameter optimisation Parallel Ice Sheet Model PISM Antarctic Ice Sheet Large ensemble modelling |
spellingShingle |
Geoscientific model Parameterisation Parameter uncertainty Parameter optimisation Parallel Ice Sheet Model PISM Antarctic Ice Sheet Large ensemble modelling Phipps, Steven J. Roberts, Jason L. King, Matt A. An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 |
topic_facet |
Geoscientific model Parameterisation Parameter uncertainty Parameter optimisation Parallel Ice Sheet Model PISM Antarctic Ice Sheet Large ensemble modelling |
description |
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. |
format |
Dataset |
author |
Phipps, Steven J. Roberts, Jason L. King, Matt A. |
author_facet |
Phipps, Steven J. Roberts, Jason L. King, Matt A. |
author_sort |
Phipps, Steven J. |
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 |
title_short |
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 |
title_full |
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 |
title_fullStr |
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 |
title_full_unstemmed |
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 |
title_sort |
iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the parallel ice sheet model (pism) version 0.7.3 |
publisher |
Zenodo |
publishDate |
2020 |
url |
https://dx.doi.org/10.5281/zenodo.4275052 https://zenodo.org/record/4275052 |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic Ice Sheet |
genre_facet |
Antarc* Antarctic Ice Sheet |
op_relation |
https://dx.doi.org/10.5281/zenodo.4275053 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.4275052 https://doi.org/10.5281/zenodo.4275053 |
_version_ |
1766206084365680640 |