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Evaluation of ice-stream model sensitivities for parameter estimation

机译:评估参数估计的冰流模型敏感性

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Large-ensemble perturbed-parameter forward ice-flow modeling can provide useful insights to uncertainties in inversions for basal drag or other ice-flow parameters. Inversion and data assimilation provide estimates of poorly known parameters that are essential for accurate prognostic modeling. Because ice flow depends on many such parameters with their associated uncertainties, which may interact in nonlinear ways, full uncertainty assessment for parameter estimates is challenging. With rising computational power, it is increasingly practicable to explore co-dependencies and sensitivities. Here, we use a well characterized higher-order flowline model configured for a well-lubricated ("shelfy") ice stream to run large ensembles, perturbing the magnitude and spatial pattern of basal drag, basal topography, and input flux. We find for steady state that ice-stream velocity and thickness along the entire domain are especially correlated to drag at the downstream end, but with greater local correlation during transients. The modeled effects of basal topographic perturbations on velocity and ice thickness are primarily local. Perturbations of input ice fluxes interact with the others in interesting ways. These insights point to the value of inversions informed by concentrated observations during forced transients such as lake-drainage events, accumulation-rate fluctuations or ice-shelf losses, and to the care needed when interpreting local results of some inversions for basal-drag parameters. (C) 2019 Elsevier B.V. All rights reserved.
机译:大集合扰动参数前进冰流量建模可以为基底阻力或其他冰流量参数的反转中的不确定性提供有用的见解。反演和数据同化提供了估计可怕的参数对于准确的预后建模至关重要。因为冰流程取决于许多这样的参数,其相关的不确定性,这可以以非线性方式互动,参数估计的完全不确定性评估是具有挑战性的。随着计算能力上升,探索共同依赖性和敏感性越来越重要。在这里,我们使用良好的特征化的高阶流线模型,该流线模型配置为润滑(“备属”)冰流,以运行大型集合,从而扰乱基底阻力,基础地形和输入通量的幅度和空间模式。我们发现稳定状态,沿整个域的冰流速度和厚度尤其相关,以拖动下游端,但在瞬态期间具有更大的局部相关性。基础地形扰动对速度和冰厚度的建模效果主要是局部的。输入冰通量的扰动以有趣的方式与其他人相互作用。这些见解指出,由于湖排水事件,累积速率波动或冰货架损失,以及在解释基底拖动参数的某些终端的局部结果时,累积速率波动或冰货架损失,所以通过集中观测所通知的反转值。 (c)2019 Elsevier B.v.保留所有权利。

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