Ground heat flux (G₀) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G₀ is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G₀ across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G₀ data (measured or modeled) for snow-free period as a reference. When observed G₀ is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G₀ reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

中文翻译：

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使用贝叶斯不确定性量化机制和代理建模重建地面热通量

地面热通量（G ₀）是高纬度地区地表能量平衡的重要组成部分。尽管 G 0 在控制全球变暖导致的永久冻土退化方面发挥着至关重要的作用，但G ₀的测量很少，并且在全球尺度模型模拟的输出中没有得到很好的体现。在本研究中，测试了分析传热模型，以使用现场测量的土壤温度序列、全球气候模型和气候再分析输出来重建跨季节的G _{0 。}使用无雪期可用的G ₀数据（测量或建模）作为参考，推断出地热通量和模型参数的概率密度函数。当观察到的G ₀不可用时，使用表面热通量的估计（取决于参数）作为顶部边界条件来应用数值模型。通过比较多个深度的模拟和测量土壤温度的分布来验证这些估计（以及相应的参数）。在最先进的不确定性量化方法的帮助下，开发的G ₀重建方法为评估区域永久冻土变化研究的地热通量的概率结构提供了新的方法。