Bayesian calibration of firn densification models

• Published in: The Cryosphere
• Main Authors: V. Verjans, A. A. Leeson, C. Nemeth, C. M. Stevens, P. Kuipers Munneke, B. Noël, J. M. van Wessem
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Greenland; Antarc*; Antarctica; Ice Sheet; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-14-3017-2020; https://doaj.org/article/29fa14b54f554ed195d5b64a307706a0

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.