Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system processes at different spatiotemporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. Here, we propose a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatiotemporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatiotemporal continuum. The proposed cross-scale framework for integrating multi-source data in Earth system sciences is already developed as a stand-alone R package that is freely available to download.

整合来自各种来源的地球系统数据是一项艰巨的任务。除了其质的异质性外，还存在不同的数据记录，用于描述不同时空尺度上的相似地球系统过程。数据比对和验证通常在单个空间或时间尺度上进行，这可能会妨碍其他尺度上潜在差异的识别。在这里，我们提出了一种简单而有效的图形方法，用于合成和比较跨时空范围的观测数据和建模数据。我们没有关注特定的尺度，例如年度平均值或原始网格分辨率，而是研究了它们的统计属性在时空连续性中的变化。