Resolving the Antarctic contribution to sea-level rise: a hierarchical modelling framework†
Resolving the Antarctic contribution to sea-level rise: a hierarchical modelling framework†
复制标题
解决南极对海平面上升的影响：分层建模框架†
作者：
A. Zammit‐Mangion;J. Rougier;J. Bamber;N. Schoen
根据观测数据确定南极洲对海平面上升的贡献是一个复杂的问题。所涉及的物理过程（例如冰动力学和地表气候）的数量超过了可观测的数量，其中一些的空间定义非常差。一般来说，这导致了利用强先验假设或基于物理的确定性模型来简化问题的解决方案。在这里，我们提出了一种估计南极贡献的新方法，该方法仅在分析和统计方式中纳入基于物理的模型的描述性方面。通过将物理见解与现代空间统计建模技术相结合，我们能够提供被认为在观测数据和海平面上升贡献中发挥作用的所有过程的概率分布。具体来说，我们使用随机偏微分方程及其与地质统计场的关系来捕获我们的物理理解，并采用高斯马尔可夫随机场方法进行高效计算。该方法是贝叶斯分层建模的实例，自然地包含不确定性，以便揭示所有估计量的可信区间。使用这种方法估计的海平面上升贡献证实了使用统计独立方法发现的结果。 © 2013 作者。环境计量学由 John Wiley & Sons, Ltd. 出版
Determining the Antarctic contribution to sea‐level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to solutions that utilise strong prior assumptions or physically based deterministic models to simplify the problem. Here, we present a new approach for estimating the Antarctic contribution, which only incorporates descriptive aspects of the physically based models in the analysis and in a statistical manner. By combining physical insights with modern spatial statistical modelling techniques, we are able to provide probability distributions on all processes deemed to play a role in both the observed data and the contribution to sea‐level rise. Specifically, we use stochastic partial differential equations and their relation to geostatistical fields to capture our physical understanding and employ a Gaussian Markov random field approach for efficient computation. The method, an instantiation of Bayesian hierarchical modelling, naturally incorporates uncertainty in order to reveal credible intervals on all estimated quantities. The estimated sea‐level rise contribution using this approach corroborates those found using a statistically independent method. © 2013 The Authors. Environmetrics Published by John Wiley & Sons, Ltd.
影响因子：
18.3
作者：
Rignot, Eric;Bamber, Jonathan L.;Van Meijgaard, Erik
通讯作者：
Van Meijgaard, Erik
影响因子：
5.3
作者：
R. Riva;B. Gunter;T. Urban;B. Vermeersen;R. Lindenbergh;M. Helsen;J. Bamber;R. Wal;M. Broeke;B. Schutz
通讯作者：
R. Riva;B. Gunter;T. Urban;B. Vermeersen;R. Lindenbergh;M. Helsen;J. Bamber;R. Wal;M. Broeke;B. Schutz