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Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps)

机译:实验和物理模型在评估圣索林冰川(法国阿尔卑斯山)的季节性和年度冰川表面质量平衡方面的相对表现

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This study focuses on simulations of the seasonal and annual surface mass balance?(SMB) of Saint-Sorlin Glacier (French Alps) for the period 1996–2015 using the detailed SURFEX/ISBA-Crocus snowpack model. The model is forced by SAFRAN meteorological reanalysis data, adjusted with automatic weather station (AWS) measurements to ensure that simulations of all the energy balance components, in particular turbulent fluxes, are accurately represented with respect to the measured energy balance. Results indicate good model performance for the simulation of summer SMB when using meteorological forcing adjusted with in situ measurements. Model performance however strongly decreases without in situ meteorological measurements. The sensitivity of the model to meteorological forcing indicates a strong sensitivity to wind speed, higher than the sensitivity to ice albedo. Compared to an empirical approach, the model exhibited better performance for simulations of snow and firn melting in the accumulation area and similar performance in the ablation area when forced with meteorological data adjusted with nearby AWS measurements. When such measurements were not available close to the glacier, the empirical model performed better. Our results suggest that simulations of the evolution of future mass balance using an energy balance model require very accurate meteorological data. Given the uncertainties in the temporal evolution of the relevant meteorological variables and glacier surface properties in the future, empirical approaches based on temperature and precipitation could be more appropriate for simulations of glaciers in the future.
机译:这项研究的重点是使用详细的SURFEX / ISBA-Crocus积雪模型模拟圣索林冰川(法国阿尔卑斯山)1996年至2015年的季节和年度表面质量平衡(SMB)。该模型由SAFRAN气象再分析数据强制进行,并通过自动气象站(AWS)测量进行了调整,以确保相对于所测得的能量平衡准确地表示所有能量平衡分量(尤其是湍流)的模拟。结果表明,当使用通过原位测量调整的气象强迫时,夏季SMB的模拟具有良好的模型性能。但是,如果没有原位气象测量,模型的性能会大大降低。该模型对气象强迫的敏感性表明,其对风速的敏感性强于对冰反照率的敏感性。与经验方法相比,当用附近的AWS测量值调整的气象数据强迫时,该模型在模拟积聚区域的积雪和杉木融化方面表现出更好的性能,而在消融区域表现出类似的性能。当无法在冰川附近获得此类测量结果时,经验模型的效果会更好。我们的结果表明,使用能量平衡模型对未来质量平衡演变的模拟需要非常准确的气象数据。考虑到未来相关气象变量和冰川表面特性随时间变化的不确定性,基于温度和降水的经验方法可能更适合于未来的冰川模拟。

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