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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/esd-2019-62
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2019-62
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 09 Oct 2019

Submitted as: research article | 09 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Dynamics (ESD).

Earth system data cubes unravel global multivariate dynamics

Miguel D. Mahecha1,2,3, Fabian Gans1, Gunnar Brandt4, Rune Christiansen5, Sarah E. Cornell6, Normann Fomferra4, Guido Kraemer1,7, Jonas Peters5, Paul Bodesheim1,8, Gustau Camps-Valls7, Jonathan F. Donges6,9, Wouter Dorigo10, Lina Estupiñan-Suarez1, Victor H. Gutierrez-Velez11, Martin Gutwin1,12, Martin Jung1, Maria C. Londoño13, Diego G. Miralles14, Phillip Papastefanou15, and Markus Reichstein1,2,3 Miguel D. Mahecha et al.
  • 1Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
  • 2German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany
  • 3Michael Stifel Center Jena for Data-Driven and Simulation Science, 07743 Jena, Germany
  • 4Brockmann Consult GmbH, 21029 Hamburg, Germany
  • 5Department of Mathematical Sciences, University of Copenhagen, Denmark
  • 6Stockholm Resilience Center, Stockholm University, SE-106 91 Stockholm, Sweden
  • 7Image Processing Lab, Universitat de València, 46980 Paterna (València), Spain
  • 8Computer Vision Group, Computer Science, Friedrich Schiller University, Jena, Germany
  • 9Earth System Analysis, Potsdam Institute for Climate Impact Research, PIK, Germany
  • 10Department of Geodesy and Geo-Information, TU Wien, Austria
  • 11Department of Geography and Urban Studies, Temple University, Philadelphia, USA
  • 12Department of Geography, Friedrich Schiller University, Jena, Germany
  • 13Alexander von Humboldt Biological Resources Research Institute, Bogotá, Colombia
  • 14Laboratory of Hydrology and Water Management, Ghent Unversity, Ghent 9000, Belgium
  • 15TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85356 Freising, Germany

Abstract. Understanding Earth system dynamics in the 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 inter-disciplinary 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. 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, spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple time-scales; and (3) data-model integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. 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.

Miguel D. Mahecha et al.
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Short summary
Earth system sciences today needs to meet the growing interdisciplinary challenges of data-rich world. Here, we introduce the concept of Earth system data cubes for exploiting the complex dynamics in the Earth system.
Earth system sciences today needs to meet the growing interdisciplinary challenges of data-rich...
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