mcera5: Driving microclimate models with ERA5 global gridded climate data
mcera5: Driving microclimate models with ERA5 global gridded climate data
复制标题
mcera5：利用 ERA5 全球网格气候数据驱动微气候模型
DOI：
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发表时间：
2022
影响因子：
6.6
通讯作者：
I. Maclean
中科院分区：
文献类型：
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作者：
D. Klinges;J. Duffy;M. Kearney;I. Maclean
小气候模型预测温度和其他气象变量的规模有关的个别有机体。小气候模式的广泛应用需要网格化的大气候变量作为输入。然而，这些输入的空间和时间分辨率可能是小气候预测准确性的限制因素。由于其良好的分辨率和准确性，ERA5再分析数据集正在成为全球历史天气和气候数据的首选资源，并具有帮助小气候建模的巨大潜力。在这里，我们描述mcera 5，一个R语言包，它提供了方便的访问和争吵，ERA5气候数据集用于小气候模型。通过这个软件包，我们提供了查询ERA5数据的功能，以获得所需的空间和时间范围，纠正空间偏差和过程输出，以便生态学家进行解释，从而实现更快，更准确的小气候预测。通过对全球多个生物群落的经验观测进行验证，我们证明，与其他全球可用数据相比，通过mcera 5使用ERA5气候强迫提高了土壤水分，气温和相对湿度的预测精度，并在预测土壤温度时提供了可比的性能。通过提供高分辨率的ERA5数据，mcera5软件包融入了一个工具生态系统，以时空明确的方式模拟小气候，提高了我们有效预测地球上任何地方过去、现在或未来小气候的能力。该软件包还为一系列其他应用程序提供了对ERA5数据集的方便访问。
Microclimate models predict temperature and other meteorological variables at scales relevant to individual organisms. The broad application of microclimate models requires gridded macroclimatic variables as input. However, the spatial and temporal resolution of such inputs can be a limiting factor on the accuracy of microclimate predictions. Due to its fine resolution and accuracy, the ERA5 reanalysis dataset is emerging as the favoured resource for global historical weather and climate data and has great potential for aiding microclimate modelling. Here we describe mcera5, an R language package that provides convenient access to, and wrangling of, the ERA5 climate datasets for use in microclimate models. Through this package, we provide functions to query ERA5 data for desired spatial and temporal extents, to correct for spatial biases and process outputs for easy interpretation by ecologists, thereby allowing faster and more accurate microclimate predictions. By validating with empirical observations from multiple biomes globally, we demonstrate that the use of ERA5 climate forcing via mcera5 improves the prediction accuracy of soil moisture, air temperature and relative humidity as compared to forcing with other globally available data and offers comparable performance when predicting soil temperatures. Through the provision of fine‐resolution ERA5 data, the mcera5 package fits into an ecosystem of tools for modelling microclimate in a spatio‐temporally explicit fashion, advancing our ability to efficiently predict microclimate for any place on Earth for the past, present or future. The package also provides convenient access to ERA5 datasets for a range of other applications.