Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties
Computer models of ice sheet behavior are important tools for projecting future sea level rise. The simulated modern ice sheets generated by these models differ markedly as input parameters are varied. To ensure accurate ice sheet mass loss projections, these parameters must be constrained using obs...
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ftdoajarticles:oai:doaj.org/article:6b719a96e2bc43fbaf45496021ba22ba 2023-05-15T16:28:21+02:00 Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties W. Chang P. J. Applegate M. Haran K. Keller 2014-09-01T00:00:00Z https://doi.org/10.5194/gmd-7-1933-2014 https://doaj.org/article/6b719a96e2bc43fbaf45496021ba22ba EN eng Copernicus Publications http://www.geosci-model-dev.net/7/1933/2014/gmd-7-1933-2014.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 1991-959X 1991-9603 doi:10.5194/gmd-7-1933-2014 https://doaj.org/article/6b719a96e2bc43fbaf45496021ba22ba Geoscientific Model Development, Vol 7, Iss 5, Pp 1933-1943 (2014) Geology QE1-996.5 article 2014 ftdoajarticles https://doi.org/10.5194/gmd-7-1933-2014 2022-12-30T21:35:02Z Computer models of ice sheet behavior are important tools for projecting future sea level rise. The simulated modern ice sheets generated by these models differ markedly as input parameters are varied. To ensure accurate ice sheet mass loss projections, these parameters must be constrained using observational data. Which model parameter combinations make sense, given observations? Our method assigns probabilities to parameter combinations based on how well the model reproduces the Greenland Ice Sheet profile. We improve on the previous state of the art by accounting for spatial information and by carefully sampling the full range of realistic parameter combinations, using statistically rigorous methods. Specifically, we estimate the joint posterior probability density function of model parameters using Gaussian process-based emulation and calibration. This method is an important step toward calibrated probabilistic projections of ice sheet contributions to sea level rise, in that it uses data–model fusion to learn about parameter values. This information can, in turn, be used to make projections while taking into account various sources of uncertainty, including parametric uncertainty, data–model discrepancy, and spatial correlation in the error structure. We demonstrate the utility of our method using a perfect model experiment, which shows that many different parameter combinations can generate similar modern ice sheet profiles. This result suggests that the large divergence of projections from different ice sheet models is partly due to parametric uncertainty. Moreover, our method enables insight into ice sheet processes represented by parameter interactions in the model. Article in Journal/Newspaper Greenland Ice Sheet Directory of Open Access Journals: DOAJ Articles Greenland Geoscientific Model Development 7 5 1933 1943 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Geology QE1-996.5 |
spellingShingle |
Geology QE1-996.5 W. Chang P. J. Applegate M. Haran K. Keller Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
topic_facet |
Geology QE1-996.5 |
description |
Computer models of ice sheet behavior are important tools for projecting future sea level rise. The simulated modern ice sheets generated by these models differ markedly as input parameters are varied. To ensure accurate ice sheet mass loss projections, these parameters must be constrained using observational data. Which model parameter combinations make sense, given observations? Our method assigns probabilities to parameter combinations based on how well the model reproduces the Greenland Ice Sheet profile. We improve on the previous state of the art by accounting for spatial information and by carefully sampling the full range of realistic parameter combinations, using statistically rigorous methods. Specifically, we estimate the joint posterior probability density function of model parameters using Gaussian process-based emulation and calibration. This method is an important step toward calibrated probabilistic projections of ice sheet contributions to sea level rise, in that it uses data–model fusion to learn about parameter values. This information can, in turn, be used to make projections while taking into account various sources of uncertainty, including parametric uncertainty, data–model discrepancy, and spatial correlation in the error structure. We demonstrate the utility of our method using a perfect model experiment, which shows that many different parameter combinations can generate similar modern ice sheet profiles. This result suggests that the large divergence of projections from different ice sheet models is partly due to parametric uncertainty. Moreover, our method enables insight into ice sheet processes represented by parameter interactions in the model. |
format |
Article in Journal/Newspaper |
author |
W. Chang P. J. Applegate M. Haran K. Keller |
author_facet |
W. Chang P. J. Applegate M. Haran K. Keller |
author_sort |
W. Chang |
title |
Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
title_short |
Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
title_full |
Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
title_fullStr |
Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
title_full_unstemmed |
Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
title_sort |
probabilistic calibration of a greenland ice sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
publisher |
Copernicus Publications |
publishDate |
2014 |
url |
https://doi.org/10.5194/gmd-7-1933-2014 https://doaj.org/article/6b719a96e2bc43fbaf45496021ba22ba |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Geoscientific Model Development, Vol 7, Iss 5, Pp 1933-1943 (2014) |
op_relation |
http://www.geosci-model-dev.net/7/1933/2014/gmd-7-1933-2014.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 1991-959X 1991-9603 doi:10.5194/gmd-7-1933-2014 https://doaj.org/article/6b719a96e2bc43fbaf45496021ba22ba |
op_doi |
https://doi.org/10.5194/gmd-7-1933-2014 |
container_title |
Geoscientific Model Development |
container_volume |
7 |
container_issue |
5 |
container_start_page |
1933 |
op_container_end_page |
1943 |
_version_ |
1766017995695456256 |