Bayesian calibration of firn densification models
Firn densification modelling is key to understanding ice sheet mass balance, ice sheet surface elevation change, and the age difference between ice and the air in enclosed air bubbles. This has resulted in the development of many firn models, all relying to a certain degree on parameter calibration...
Published in: | The Cryosphere |
---|---|
Main Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2020
|
Subjects: | |
Online Access: | https://doi.org/10.5194/tc-14-3017-2020 https://tc.copernicus.org/articles/14/3017/2020/tc-14-3017-2020.pdf https://doaj.org/article/29fa14b54f554ed195d5b64a307706a0 |
id |
fttriple:oai:gotriple.eu:oai:doaj.org/article:29fa14b54f554ed195d5b64a307706a0 |
---|---|
record_format |
openpolar |
spelling |
fttriple:oai:gotriple.eu:oai:doaj.org/article:29fa14b54f554ed195d5b64a307706a0 2023-05-15T13:46:27+02:00 Bayesian calibration of firn densification models V. Verjans A. A. Leeson C. Nemeth C. M. Stevens P. Kuipers Munneke B. Noël J. M. van Wessem 2020-09-01 https://doi.org/10.5194/tc-14-3017-2020 https://tc.copernicus.org/articles/14/3017/2020/tc-14-3017-2020.pdf https://doaj.org/article/29fa14b54f554ed195d5b64a307706a0 en eng Copernicus Publications doi:10.5194/tc-14-3017-2020 1994-0416 1994-0424 https://tc.copernicus.org/articles/14/3017/2020/tc-14-3017-2020.pdf https://doaj.org/article/29fa14b54f554ed195d5b64a307706a0 undefined The Cryosphere, Vol 14, Pp 3017-3032 (2020) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.5194/tc-14-3017-2020 2023-01-22T17:20:54Z Firn densification modelling is key to understanding ice sheet mass balance, ice sheet surface elevation change, and the age difference between ice and the air in enclosed air bubbles. This has resulted in the development of many firn models, all relying to a certain degree on parameter calibration against observed data. We present a novel Bayesian calibration method for these parameters and apply it to three existing firn models. Using an extensive dataset of firn cores from Greenland and Antarctica, we reach optimal parameter estimates applicable to both ice sheets. We then use these to simulate firn density and evaluate against independent observations. Our simulations show a significant decrease (24 % and 56 %) in observation–model discrepancy for two models and a smaller increase (15 %) for the third. As opposed to current methods, the Bayesian framework allows for robust uncertainty analysis related to parameter values. Based on our results, we review some inherent model assumptions and demonstrate how firn model choice and uncertainties in parameter values cause spread in key model outputs. Article in Journal/Newspaper Antarc* Antarctica Greenland Ice Sheet The Cryosphere Unknown Greenland The Cryosphere 14 9 3017 3032 |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
geo envir |
spellingShingle |
geo envir V. Verjans A. A. Leeson C. Nemeth C. M. Stevens P. Kuipers Munneke B. Noël J. M. van Wessem Bayesian calibration of firn densification models |
topic_facet |
geo envir |
description |
Firn densification modelling is key to understanding ice sheet mass balance, ice sheet surface elevation change, and the age difference between ice and the air in enclosed air bubbles. This has resulted in the development of many firn models, all relying to a certain degree on parameter calibration against observed data. We present a novel Bayesian calibration method for these parameters and apply it to three existing firn models. Using an extensive dataset of firn cores from Greenland and Antarctica, we reach optimal parameter estimates applicable to both ice sheets. We then use these to simulate firn density and evaluate against independent observations. Our simulations show a significant decrease (24 % and 56 %) in observation–model discrepancy for two models and a smaller increase (15 %) for the third. As opposed to current methods, the Bayesian framework allows for robust uncertainty analysis related to parameter values. Based on our results, we review some inherent model assumptions and demonstrate how firn model choice and uncertainties in parameter values cause spread in key model outputs. |
format |
Article in Journal/Newspaper |
author |
V. Verjans A. A. Leeson C. Nemeth C. M. Stevens P. Kuipers Munneke B. Noël J. M. van Wessem |
author_facet |
V. Verjans A. A. Leeson C. Nemeth C. M. Stevens P. Kuipers Munneke B. Noël J. M. van Wessem |
author_sort |
V. Verjans |
title |
Bayesian calibration of firn densification models |
title_short |
Bayesian calibration of firn densification models |
title_full |
Bayesian calibration of firn densification models |
title_fullStr |
Bayesian calibration of firn densification models |
title_full_unstemmed |
Bayesian calibration of firn densification models |
title_sort |
bayesian calibration of firn densification models |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-14-3017-2020 https://tc.copernicus.org/articles/14/3017/2020/tc-14-3017-2020.pdf https://doaj.org/article/29fa14b54f554ed195d5b64a307706a0 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Antarc* Antarctica Greenland Ice Sheet The Cryosphere |
genre_facet |
Antarc* Antarctica Greenland Ice Sheet The Cryosphere |
op_source |
The Cryosphere, Vol 14, Pp 3017-3032 (2020) |
op_relation |
doi:10.5194/tc-14-3017-2020 1994-0416 1994-0424 https://tc.copernicus.org/articles/14/3017/2020/tc-14-3017-2020.pdf https://doaj.org/article/29fa14b54f554ed195d5b64a307706a0 |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/tc-14-3017-2020 |
container_title |
The Cryosphere |
container_volume |
14 |
container_issue |
9 |
container_start_page |
3017 |
op_container_end_page |
3032 |
_version_ |
1766242840928583680 |