Bayesian calibration of firn densification models

Vincent Verjans,C Max Stevens,Jan Melchior Van Wessem,Peter Kuipers Munneke,Amber A Leeson,Christopher Nemeth,Brice Noël

doi:10.5194/tc-14-3017-2020

Vincent Verjans, C Max Stevens + Show 5 more

Open Access

https://doi.org/10.5194/tc-14-3017-2020

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Abstract

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.

Highlights

On the Antarctic and Greenland ice sheets (AIS and GrIS), snow falling at the surface progressively compacts into ice, passing through an intermediary stage called firn
We present the results of the calibration process after 15 000 algorithm iterations and compare the maximum a posteriori (MAP) and original models’ performances against the 22 evaluation cores
We evaluate the uncertainty of the posterior distributions and compare performances between the different MAP models

Summary

Introduction

On the Antarctic and Greenland ice sheets (AIS and GrIS), snow falling at the surface progressively compacts into ice, passing through an intermediary stage called firn. Errors in the firn-related correction can lead to over- or underestimation of mass changes related to surface processes and lead to misinterpreting elevation change signals as changes in mass balance and in ice flow dynamics. Model estimates of current and future surface mass balance of the AIS and GrIS are dependent on accurate models of firn evolution. The densification rate determines the firn age at which air bubbles are trapped in the ice matrix. Knowing this age is crucial for precisely linking samples of past atmospheric composition, which are preserved in these bubbles, to paleo-temperature indicators, which come from the water isotopes in the ice (Buizert et al, 2014)

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