Abstract. Land surface models (LSMs) should have reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are considered the benchmarking data for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets lack site-observed attribute data, limiting their use as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, external disturbances, and energy balance closure (EBC), leading to 90 high-quality sites. For these sites, we collected vegetation, soil, topography information, and wind speed measurement height from literature, regional networks, and Biological, Ancillary, Disturbance, and Metadata (BADM) files. Then we obtained the final flux tower attribute dataset by global data product complement and plant functional types (PFTs) classification. This dataset is provided in NetCDF format complete with necessary descriptions and reference sources. Model simulations revealed substantial disparities in output between the attribute data observed at the site and the defaults of the model, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of site attribute data to some extent, reduces uncertainty in LSMs data source, and aids in diagnosing parameter as well as process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.10939725 (Shi et al., 2024).

中文翻译：

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用于地表建模的通量塔站点属性数据集

摘要。陆地表面模型（LSM）应具有可靠的强迫、验证和表面属性数据，作为有效模型开发和改进的基础。涡流协方差通量塔数据被认为是 LSM 的基准数据。然而，当前可用的通量塔数据集通常需要进行多个方面的处理，以确保在应用于 LSM 之前的数据质量。更重要的是，这些数据集缺乏现场观察的属性数据，限制了它们作为基准数据的使用。在这里，我们对现有的再处理通量塔数据集进行了全面的质量筛选，包括间隙填充数据的比例、外部干扰和能量平衡闭合（EBC），最终得到了 90 个高质量站点。对于这些站点，我们从文献、区域网络以及生物、辅助、干扰和元数据 (BADM) 文件中收集了植被、土壤、地形信息和风速测量高度。然后通过全局数据产品补充和植物功能类型（PFT）分类获得最终的通量塔属性数据集。该数据集以 NetCDF 格式提供，并附有必要的描述和参考源。模型模拟揭示了现场观测到的属性数据与模型默认值之间的输出存在巨大差异，强调了现场观测到的属性数据的关键作用，并增加了 LSM 社区对通量塔属性数据的重视。该数据集在一定程度上解决了场地属性数据的缺乏，减少了LSM数据源的不确定性，有助于诊断参数和流程缺陷。该数据集可在 https://doi.org/10.5281/zenodo.10939725 上获取（Shi et al., 2024）。