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The influence of resolution and topographic uncertainty on melt modelling using hypsometric sub-grid parameterization

机译:分辨率和形貌不确定性对使用高渗亚网格参数化的熔体建模的影响

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摘要

Modelling of physical processes such as ablation or runoff at continental or global scales provides a key challenge: a high degree of abstraction is required in order to minimize computational demands, while spatial and temporal variability of key processes, often at the sub-scale level, need to be adequately captured and reproduced within a lower resolution model. For some approaches, such as temperature index models, downscaling to lower resolutions is straightforward. However a key issue when using these downscaled models is to assess the impact of scaling on model behaviour and results, including the associated uncertainties. We assess the impact of scaling on both a simple and an enhanced temperature index melt model from 100 m to 1, 5 and 10 km resolutions. Different sub-grid parameterization approaches are applied to both models across all resolutions and tested for their suitability against high-resolution reference data, with the aim of developing a robust, scalable and computationally undemanding parameterization. Results show patterns of over- and underestimation of potential melt rates for both models, with clear dependencies on scale, terrain roughness and variations of temperature thresholds, among other quantities. The sub-grid parameterizations tested in this article are found to effectively compensate these effects at little additional computational cost.
机译:在大陆或全球范围内对诸如消融或径流之类的物理过程进行建模提出了一个关键挑战:为了最大限度地减少计算需求,需要进行高度抽象,而关键过程的时空变异性通常在子规模级别上,需要在较低分辨率的模型中充分捕获和复制。对于某些方法,例如温度指数模型,将比例尺缩小到较低的分辨率非常简单。但是,使用这些缩减模型时的关键问题是评估缩放对模型行为和结果(包括相关的不确定性)的影响。我们评估了缩放对从100 m到1、5和10 km分辨率的简单和增强温度指数融解模型的影响。将不同的子网格参数化方法应用于所有分辨率的两个模型,并测试了它们对高分辨率参考数据的适用性,目的是开发强大,可扩展且计算上不需要的参数化。结果表明,两种模型的潜在熔化速率都被高估和低估了,与规模,地形粗糙度和温度阈值的变化以及其他数量有着明显的相关性。发现本文中测试的子网格参数化可以以很少的额外计算成本有效地补偿这些影响。

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