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Bayesian calibration of firn densification models

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Bayesian calibration of firn densification models. / Verjans, Vincent; Leeson, Amber; Nemeth, Christopher; Stevens, C. Max; Munneke, Peter Kuipers; Noël, Brice; Wessem, Jan Melchior van.

In: Cryosphere, Vol. 14, 15.09.2020, p. 3017-3032.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Verjans, V, Leeson, A, Nemeth, C, Stevens, CM, Munneke, PK, Noël, B & Wessem, JMV 2020, 'Bayesian calibration of firn densification models', Cryosphere, vol. 14, pp. 3017-3032. https://doi.org/10.5194/tc-14-3017-2020

APA

Verjans, V., Leeson, A., Nemeth, C., Stevens, C. M., Munneke, P. K., Noël, B., & Wessem, J. M. V. (2020). Bayesian calibration of firn densification models. Cryosphere, 14, 3017-3032. https://doi.org/10.5194/tc-14-3017-2020

Vancouver

Verjans V, Leeson A, Nemeth C, Stevens CM, Munneke PK, Noël B et al. Bayesian calibration of firn densification models. Cryosphere. 2020 Sep 15;14:3017-3032. https://doi.org/10.5194/tc-14-3017-2020

Author

Verjans, Vincent ; Leeson, Amber ; Nemeth, Christopher ; Stevens, C. Max ; Munneke, Peter Kuipers ; Noël, Brice ; Wessem, Jan Melchior van. / Bayesian calibration of firn densification models. In: Cryosphere. 2020 ; Vol. 14. pp. 3017-3032.

Bibtex

@article{cb0cd04457044054869be43febb65c0d,
title = "Bayesian calibration of firn densification models",
abstract = "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.",
author = "Vincent Verjans and Amber Leeson and Christopher Nemeth and Stevens, {C. Max} and Munneke, {Peter Kuipers} and Brice No{\"e}l and Wessem, {Jan Melchior van}",
year = "2020",
month = sep,
day = "15",
doi = "10.5194/tc-14-3017-2020",
language = "English",
volume = "14",
pages = "3017--3032",
journal = "Cryosphere",
issn = "1994-0416",
publisher = "Copernicus Gesellschaft mbH",

}

RIS

TY - JOUR

T1 - Bayesian calibration of firn densification models

AU - Verjans, Vincent

AU - Leeson, Amber

AU - Nemeth, Christopher

AU - Stevens, C. Max

AU - Munneke, Peter Kuipers

AU - Noël, Brice

AU - Wessem, Jan Melchior van

PY - 2020/9/15

Y1 - 2020/9/15

N2 - 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.

AB - 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.

U2 - 10.5194/tc-14-3017-2020

DO - 10.5194/tc-14-3017-2020

M3 - Journal article

VL - 14

SP - 3017

EP - 3032

JO - Cryosphere

JF - Cryosphere

SN - 1994-0416

ER -