Antarctica is the world's largest fresh-water reservoir, with the potential to raise sea levels by about 60 m. An ice sheet contributes to sea-level rise (SLR) when its rate of ice discharge and/or surface melting exceeds accumulation through snowfall. Constraining the contribution of the ice sheets to present-day SLR is vital both for coastal development and planning, and climate projections. Information on various ice sheet processes is available from several remote sensing data sets, as well as in situ data such as global positioning system data. These data have differing coverage, spatial support, temporal sampling and sensing characteristics, and thus, it is advantageous to combine them all in a single framework for estimation of the SLR contribution and the assessment of processes controlling mass exchange with the ocean. In this paper, we predict the rate of height change due to salient geophysical processes in Antarctica and use these to provide estimates of SLR contribution with associated uncertainties. We employ a multivariate spatio-temporal model, approximated as a Gaussian Markov random field, to take advantage of differing spatio-temporal properties of the processes to separate the causes of the observed change. The process parameters are estimated from geophysical models, while the remaining parameters are estimated using a Markov chain Monte Carlo scheme, designed to operate in a high-performance computing environment across multiple nodes. We validate our methods against a separate data set and compare the results to those from studies that invariably employ numerical model outputs directly. We conclude that it is possible, and insightful, to assess Antarctica's contribution without explicit use of numerical models. Further, the results obtained here can be used to test the geophysical numerical models for which in situ data are hard to obtain. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

中文翻译：

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用于评估南极目前对海平面上升的贡献的多元时空模型

南极洲是世界上最大的淡水水库，有可能将海平面升高约 60 m。当冰盖的冰排放和/或表面融化速度超过通过降雪积累时，冰盖会导致海平面上升 (SLR)。限制冰盖对当今 SLR 的贡献对于沿海开发和规划以及气候预测都至关重要。可以从多个遥感数据集以及全球定位系统数据等原位数据中获得有关各种冰盖过程的信息。这些数据具有不同的覆盖范围、空间支持、时间采样和传感特性，因此，将它们全部组合在一个框架中是有利的，用于估计 SLR 贡献和评估控制与海洋质量交换的过程。在本文中，我们预测由于南极洲突出的地球物理过程引起的高度变化率，并使用这些来提供具有相关不确定性的 SLR 贡献的估计。我们采用多元时空模型，近似为高斯马尔可夫随机场，利用过程的不同时空特性来分离观察到的变化的原因。过程参数是根据地球物理模型估计的，而其余参数是使用马尔可夫链蒙特卡罗方案估计的，该方案旨在在跨多个节点的高性能计算环境中运行。我们针对单独的数据集验证我们的方法，并将结果与​​那些总是直接使用数值模型输出的研究的结果进行比较。我们得出的结论是，评估南极洲是可能的，而且是有见地的” 没有明确使用数值模型的贡献。此外，这里获得的结果可用于测试现场数据难以获得的地球物理数值模型。© 2015 作者。John Wiley & Sons Ltd. 出版的Environmetrics