DOI: 10.5194/tc-14-3017-2020
论文题名: Bayesian calibration of firn densification models
作者: Verjans V. ; Leeson A.A. ; Nemeth C. ; Max Stevens C. ; Kuipers Munneke P. ; Noël B. ; Van Wessem J.M.
刊名: Cryosphere
ISSN: 19940416
出版年: 2020
卷: 14, 期: 9 起始页码: 3017
结束页码: 3032
语种: 英语
英文关键词: Bayesian analysis
; calibration
; detection method
; model
英文摘要: 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. © 2020 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164612
Appears in Collections: 气候变化与战略
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作者单位: Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4QY, United Kingdom; Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, United Kingdom; Department of Earth and Space Sciences, University of Washington, Seattle, WA, United States; Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, Netherlands
Recommended Citation:
Verjans V.,Leeson A.A.,Nemeth C.,et al. Bayesian calibration of firn densification models[J]. Cryosphere,2020-01-01,14(9)