As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

中文翻译：

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评估阿拉斯加北极苔原植物群落的动态植被模型参数不确定性

随着北极地区在气候变暖下进入未知领域，改进有助于我们理解和预测变化的陆地生物圈模型 (TBM) 非常重要。TBM 的一个基本不确定性与模型参数、模型内部的配置变量有关，其值可以从数据中估计。我们将针对北极生态系统开发的陆地生态系统模型 (TEM) 版本整合到预测生态系统分析器 (PEcAn) 框架中。PEcAn 将模型参数视为概率分布，根据可用现场数据的综合估计参数，然后量化模型对给定参数或一组参数的敏感性和不确定性。我们研究了总初级生产方程中 21 个参数的变化如何影响模型的敏感性和不确定性，这取决于两种碳通量（净初级生产力和异养呼吸）和两个碳（C）库（植被 C 和土壤 C）。我们在阿拉斯加北部的一系列苔原类型（草丛苔原、荒地苔原、湿莎草苔原和灌木苔原）沿从 Utqiaġvik 附近的沿海平原延伸到布鲁克斯南部山麓的纬度样带设置了不同的 TEM 参数化范围，到苏厄德半岛。TEM对与光合作用温度调节相关的参数最为敏感。模型的不确定性主要是由于与叶面积、光合作用的温度调节和气孔对环境光条件的反应相关的参数。我们的分析还表明，对给定参数的敏感性和不确定性在空间上有所不同。在某些地点，模型的敏感性和不确定性往往与更广泛的参数相关联，强调了跨环境梯度或地理位置评估苔原群落过程的重要性。一般来说，在不同地点，净初级生产力 (NPP) 的通量和植被 C 库的不确定性大致相同，而异养呼吸的不确定性高于土壤 C 库。我们的研究说明了评估高度异质性参数不确定性所固有的复杂性北极苔原植物群落。它还提供了一个框架，用于迭代测试新收集的与关键参数相关的现场数据如何导致更有效地预测北极变化。