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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ftzenodo:oai:zenodo.org:4275053 2024-09-15T17:41:39+00:00 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. 2020-11-16 https://doi.org/10.5281/zenodo.4275053 unknown Zenodo https://doi.org/10.5281/zenodo.4275052 https://doi.org/10.5281/zenodo.4275053 oai:zenodo.org:4275053 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Geoscientific model Parameterisation Parameter uncertainty Parameter optimisation Parallel Ice Sheet Model (PISM) Antarctic Ice Sheet Large ensemble modelling info:eu-repo/semantics/other 2020 ftzenodo https://doi.org/10.5281/zenodo.427505310.5281/zenodo.4275052 2024-07-27T07:16:40Z 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. Other/Unknown Material Antarc* Antarctic Ice Sheet Zenodo |
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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 |
Other/Unknown Material |
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://doi.org/10.5281/zenodo.4275053 |
genre |
Antarc* Antarctic Ice Sheet |
genre_facet |
Antarc* Antarctic Ice Sheet |
op_relation |
https://doi.org/10.5281/zenodo.4275052 https://doi.org/10.5281/zenodo.4275053 oai:zenodo.org:4275053 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.427505310.5281/zenodo.4275052 |
_version_ |
1810487878675857408 |