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Multivariate spatio-temporal modelling for assessing Antarcticas present-day contribution to sea-level rise

机译:用于评估南极洲目前对海平面上升的贡献的多元时空模型

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摘要

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.
机译:南极洲是世界上最大的淡水水库,有可能使海平面上升约60 m。当冰盖的排冰速度和/或表面融化超过降雪造成的积聚时,它会导致海平面上升(SLR)。限制冰盖对当今单反的贡献对于沿海发展和规划以及气候预测都至关重要。可以从几个遥感数据集以及诸如全球定位系统数据之类的现场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,时间采样和感测特性,因此将它们全部组合在一个框架中以评估SLR贡献和评估控制与海洋的质量交换过程是有利的。 ,我们预测了由于南极的显着地球物理过程而引起的高度变化率,并使用这些数据来提供对SLR贡献的估计以及相关的不确定性。我们采用多元时空模型,近似为高斯马尔可夫随机场,以利用过程的不同时空特性来区分观察到的变化的原因。过程参数是从地球物理模型估算的,而其余参数是使用马尔可夫链蒙特卡洛方案估算的,该方案设计用于在多个节点上的高性能计算环境中运行。我们针对单独的数据集验证了我们的方法,并将结果与​​始终采用数字模型输出的研究结果进行了比较。我们得出结论,无需明确使用数值模型就可以评估南极洲的贡献。此外,此处获得的结果可用于测试难以获得原位数据的地球物理数值模型。 ©2015作者。 John Wiley&Sons Ltd.发布的环境计量学。

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