Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models
Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models
复制标题
冰川质量和能量平衡模型时空可传递性的鲁棒不确定性评估
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
L. Nicholson
中科院分区：
文献类型：
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作者：
Tobias Zolles;F. Maussion;Stephan Galos;W. Gurgiser;L. Nicholson
抽象的。冰川的能量和质量平衡模型是气候影响的关键工具
研究未来的冰川活动。通过将许多物理
负责表面积累和消融的过程，它们提供了更多
比简单的统计模型更有洞察力，并且被认为受到的影响更小。
在变化的气候条件下应用时的稳定性问题。
然而，参数化的广泛使用对这一观点提出了挑战
对于引入统计校准步骤的某些物理过程。
我们认为，报告的不确定性建模质量平衡（和
相关的能量通量分量）可能在建模中被低估
研究不使用时空交叉验证，并使用一个单一的
模型优化的性能指标。为了证明
这些原则，我们提出了严格的敏感性和不确定性，
评估工作流程适用于对2002年至2004年期间的两个冰川进行的建模研究。
欧洲阿尔卑斯山，扩展经典的最佳猜测方法。的程序
首先，使用全局
敏感性评估，用于确定模型适用的参数
反应最灵敏。我们发现模型对个体的敏感性
参数在空间和时间上变化很大，表明单个
所述模型敏感度值不太可能是现实的。该模型是最
对与雪覆盖度和垂直梯度有关的参数敏感，
气象强迫数据。然后，我们应用蒙特卡罗多目标
基于三个性能指标的优化：模型偏差和平均值
绝对偏差的上部和下部冰川部分，与冰川学
在单个桩位置测量的质量平衡数据用作参考。
该过程生成最优参数解的集合，
同样有效。与这些集合成员相关的参数范围
用于估计模型输出的交叉验证不确定性，
计算能量成分。最优解的参数值
差异很大，考虑到较长的校准周期，
系统地导致更好的约束参数选择。所得
质量平衡的不确定性达到1300 kg m−2，
具有相同数量级的空间和时间传递误差。的
点上集合上表面能通量分量的不确定性
比例达到计算通量的50%。最大绝对
不确定性来源于短波辐射，
参数化，其次是湍流通量。我们的研究强调了
在应用这种方法时，需要适当的谨慎和现实的误差量化，
区域冰川建模工作，或冰川预测
物质平衡在气候环境中有很大的不同，
模型优化的条件。
Abstract. Energy and mass-balance modelling of glaciers is a key tool for climate impact
studies of future glacier behaviour. By incorporating many of the physical
processes responsible for surface accumulation and ablation, they offer more
insight than simpler statistical models and are believed to suffer less from
problems of stationarity when applied under changing climate conditions.
However, this view is challenged by the widespread use of parameterizations
for some physical processes which introduces a statistical calibration step.
We argue that the reported uncertainty in modelled mass balance (and
associated energy flux components) are likely to be understated in modelling
studies that do not use spatio-temporal cross-validation and use a single
performance measure for model optimization. To demonstrate the importance of
these principles, we present a rigorous sensitivity and uncertainty
assessment workflow applied to a modelling study of two glaciers in the
European Alps, extending classical best guess approaches. The procedure
begins with a reduction of the model parameter space using a global
sensitivity assessment that identifies the parameters to which the model
responds most sensitively. We find that the model sensitivity to individual
parameters varies considerably in space and time, indicating that a single
stated model sensitivity value is unlikely to be realistic. The model is most
sensitive to parameters related to snow albedo and vertical gradients of the
meteorological forcing data. We then apply a Monte Carlo multi-objective
optimization based on three performance measures: model bias and mean
absolute deviation in the upper and lower glacier parts, with glaciological
mass balance data measured at individual stake locations used as reference.
This procedure generates an ensemble of optimal parameter solutions which are
equally valid. The range of parameters associated with these ensemble members
are used to estimate the cross-validated uncertainty of the model output and
computed energy components. The parameter values for the optimal solutions
vary widely, and considering longer calibration periods does not
systematically result in better constrained parameter choices. The resulting
mass balance uncertainties reach up to 1300 kg m−2, with the
spatial and temporal transfer errors having the same order of magnitude. The
uncertainty of surface energy flux components over the ensemble at the point
scale reached up to 50 % of the computed flux. The largest absolute
uncertainties originate from the short-wave radiation and the albedo
parameterizations, followed by the turbulent fluxes. Our study highlights the
need for due caution and realistic error quantification when applying such
models to regional glacier modelling efforts, or for projections of glacier
mass balance in climate settings that are substantially different from the
conditions in which the model was optimized.
影响因子：
5.2
作者：
Sauter, Tobias;Galos, Stephan Peter
通讯作者：
Galos, Stephan Peter