当前位置: X-MOL 学术J. Glaciol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia
Journal of Glaciology ( IF 3.4 ) Pub Date : 2020-01-27 , DOI: 10.1017/jog.2019.91
David R. Rounce , Tushar Khurana , Margaret B. Short , Regine Hock , David E. Shean , Douglas J. Brinkerhoff

The response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.

中文翻译:

使用贝叶斯推理量化大规模冰川演化模型中的参数不确定性:在亚洲高山地区的应用

冰川对气候变化的反应对全球海平面变化和水资源具有重大影响。大规模冰川演化模型用于预测冰川径流和质量损失,但受到有限观测的约束,导致模型过度参数化。最近的系统大地质量平衡观测为改进冰川演化模型的校准提供了机会。在这项研究中,我们使用贝叶斯逆模型和大地质量平衡观测开发了冰川演化模型的校准方案,这使我们能够量化模型参数的不确定性。使用马尔可夫链蒙特卡罗方法将贝叶斯模型应用于亚洲高山的每个冰川。10,000 步后,这些链生成足够数量的独立样本,以从联合后验分布估计模型参数的属性。它们的空间分布显示出明显的地形效应,表明气候数据的分辨率过于粗糙,无法分辨高海拔地区的温度和降水。鉴于冰川演化模型被过度参数化,特别关注可识别性和未来工作整合额外观测的需要,以便更好地约束模型参数的合理集合。
更新日期:2020-01-27
down
wechat
bug