期刊
JOURNAL OF GLACIOLOGY
卷 67, 期 263, 页码 385-403出版社
CAMBRIDGE UNIV PRESS
DOI: 10.1017/jog.2020.112
关键词
Basal ice; glacier hydrology; ice-sheet modeling; ice velocity; subglacial processes
资金
- NASA [NNX17AG65G]
The study highlights the importance of basal motion in Greenland ice flux, but notes the challenges in predicting it. By using a Bayesian approach and coupling models with observations, the study was able to infer parameter distributions. The findings show that surface velocity observations provide strong constraints on model parameters, but further data collection and model development are necessary for comprehensive understanding.
Basal motion is the primary mechanism for ice flux in Greenland, yet a widely applicable model for predicting it remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, characterization of these distributions using classical Markov Chain Monte Carlo sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据