Earth system data cubes unravel global multivariate dynamics

dc.bibliographicCitation.firstPage201eng
dc.bibliographicCitation.issue1eng
dc.bibliographicCitation.journalTitleEarth System Dynamics : ESDeng
dc.bibliographicCitation.lastPage234eng
dc.bibliographicCitation.volume11eng
dc.contributor.authorMahecha, Miguel D.
dc.contributor.authorGans, Fabian
dc.contributor.authorBrandt, Gunnar
dc.contributor.authorChristiansen, Rune
dc.contributor.authorCornell, Sarah E.
dc.contributor.authorFomferra, Normann
dc.contributor.authorKraemer, Guido
dc.contributor.authorPeters, Jonas
dc.contributor.authorBodesheim, Paul
dc.contributor.authorCamps-Valls, Gustau
dc.contributor.authorDonges, Jonathan F.
dc.contributor.authorDorigo, Wouter
dc.contributor.authorEstupinan-Suarez, Lina M.
dc.contributor.authorGutierrez-Velez, Victor H.
dc.contributor.authorGutwin, Martin
dc.contributor.authorJung, Martin
dc.contributor.authorLondoño, Maria C.
dc.contributor.authorMiralles, Diego G.
dc.contributor.authorPapastefanou, Phillip
dc.contributor.authorReichstein, Markus
dc.date.accessioned2021-09-29T05:47:42Z
dc.date.available2021-09-29T05:47:42Z
dc.date.issued2020
dc.description.abstractUnderstanding 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<span idCombining double low line"page202"/> 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. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6933
dc.identifier.urihttps://doi.org/10.34657/5980
dc.language.isoengeng
dc.publisherGöttingen : Copernicus Publ.eng
dc.relation.doihttps://doi.org/10.5194/esd-11-201-2020
dc.relation.essn2190-4987
dc.relation.issn2190-4979
dc.rights.licenseCC BY 4.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subject.ddc550eng
dc.subject.otherClimate modelseng
dc.subject.otherData streamseng
dc.subject.otherGeometryeng
dc.subject.otherData interoperabilityeng
dc.subject.otherDisciplinary boundarieseng
dc.subject.otherEarth system scienceeng
dc.subject.otherIntrinsic dimensionalitieseng
dc.subject.otherLarge-scale modelingeng
dc.subject.otherMultiple timescaleseng
dc.subject.otherSpatial and temporal scaleeng
dc.subject.otherUser Defined Functionseng
dc.subject.otherData integrationeng
dc.subject.otheracademic researcheng
dc.subject.otherconceptual frameworkeng
dc.subject.otherdata processingeng
dc.subject.otherEarth scienceeng
dc.subject.otherensemble forecastingeng
dc.subject.otherinterdisciplinary approacheng
dc.subject.othermachine learningeng
dc.subject.othermultivariate analysiseng
dc.subject.otherresearch workeng
dc.titleEarth system data cubes unravel global multivariate dynamicseng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccesseng
wgl.contributorPIKeng
wgl.subjectGeowissenschafteneng
wgl.typeZeitschriftenartikeleng
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