Microclimates can be accurately predicted across ecologically important remote ecosystems
Microclimates can be accurately predicted across ecologically important remote ecosystems
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可以准确预测具有重要生态意义的偏远生态系统的微气候
DOI：
10.1101/2021.01.14.426699
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Baker D
中科院分区：
文献类型：
--
作者：
Baker D
小气候信息对于理解生态模式和过程，包括气候变化下的生态模式和过程往往至关重要，但由于难以获得小气候数据，生态学和地理学研究通常缺乏小气候信息。然而，微气候建模的最新进展表明，现在可以使用混合物理和物理模型在任何时间任何地点预测微气候条件。在这里，我们第一次测试这种方法在偏远，交通不便，气候变化威胁的极地岛屿生态系统在生态相关的尺度上的效用。小气候预测是在全岛100 × 100米的颗粒（高度为4厘米）上生成的，模型参数化使用岛上气象站（AWS）的气象观测数据或气候再分析数据（CRA）。自动气象站模型的误差率较低，并且与观测到的季节和每日温度高度相关（预测的季节平均T平均值的均方根误差≤ 0.6 °C;每日T平均值的皮尔逊相关系数（r）≥ 0.86）。相比之下，CRA模式在所有季节都有轻微的暖偏置，夏末的日较差比原位观测小。尽管存在这些差异，受威胁的地方性垫植物Azorella macquariensi和小气候的覆盖率之间的关系与小气候数据的来源变化不大（r = 0.97），这表明两个模型parameterisation捕获类似的模式，在整个岛屿生态系统的小气候条件的空间变化。在这里，我们已经表明，在生态相关的空间和时间尺度上的小气候条件的准确预测，现在可以使用混合的物理和物理为基础的模型，即使是最偏远和气候极端的环境。这些进展将有助于增加生态和地理研究的小气候层面，这对于在气候变化暴露的生态系统中提供气候变化适应性保护规划至关重要。
Microclimate information is often crucial for understanding ecological patterns and processes, including under climate change, but is typically absent from ecological and biogeographic studies owing to difficulties in obtaining microclimate data. Recent advances in microclimate modelling, however, suggest that microclimate conditions can now be predicted anywhere at any time using hybrid physically- and empirically-based models. Here, for the first time, we test the utility of this approach across a remote, inaccessible, and climate change threatened polar island ecosystem at ecologically relevant scales. Microclimate predictions were generated at a 100 × 100 m grain (at a height of 4 cm) across the island, with models parameterised using either meteorological observations from the island’s weather station (AWS) or climate reanalysis data (CRA). AWS models had low error rates and were highly correlated with observed seasonal and daily temperatures (root mean squared error of predicted seasonal average Tmean≤ 0.6 °C; Pearson’s correlation coefficient (r) for the daily Tmean≥ 0.86). By comparison, CRA models had a slight warm bias in all seasons and a smaller diurnal range in the late summer period thanin situobservations. Despite these differences, the modelled relationship between the percentage cover of the threatened endemic cushion plantAzorella macquariensisand microclimate varied little with the source of microclimate data (r = 0.97), suggesting that both model parameterisations capture similar patterns of spatial variation in microclimate conditions across the island ecosystem. Here, we have shown that the accurate prediction of microclimate conditions at ecologically relevant spatial and temporal scales is now possible using hybrid physically- and empirically-based models across even the most remote and climatically extreme environments. These advances will help add the microclimate dimension to ecological and biogeographic studies, which could be critical for delivering climate change-resilient conservation planning in climate-change exposed ecosystems.
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