Abstract
Abstract. Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.
Highlights
Predicting the Earth system’s future trajectory given ongoing human intervention into the climate system and land surface transformations requires a deep understanding of its functioning (Schellnhuber, 1999; Intergovernmental Panel on Climate Change (IPCC), 2013)
We binned the subsignals into short-term, seasonal, and long-term modes of variability, which leads to an extended data cube as we have shown in Eq (12)
We describe the insights gained during the development of the concept and the implementation of the Earth System Data Lab (ESDL), addressing issues arising and critiques expressed during our community consultation processes
Summary
Predicting the Earth system’s future trajectory given ongoing human intervention into the climate system and land surface transformations requires a deep understanding of its functioning (Schellnhuber, 1999; IPCC, 2013). Diagnostic models that encode basic process knowledge, but which are essentially driven by observations, produce highly relevant data products (see, e.g. Duveiller and Cescatti, 2016; Jiang and Ryu, 2016a; Martens et al, 2017; Ryu et al, 2018) Many of these derived data streams are essential for monitoring the climate system including land surface dynamics (see, for instance, the essential climate variables, ECVs; Hollmann et al, 2013; Bojinski et al, 2014), oceans at different depths (essential ocean variables, EOVs; Miloslavich et al, 2018), or the various aspects of biodiversity (essential biodiversity variables, EBVs; Pereira et al, 2013). These essential variables describe the state of the planet at a given moment in time and are indispensable for evaluating Earth system models (Eyring et al, 2019)
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