TY - JOUR

T1 - Earth system data cubes unravel global multivariate dynamics

AU - Mahecha, Miguel D.

AU - Gans, Fabian

AU - Brandt, Gunnar

AU - Christiansen, Rune

AU - Cornell, Sarah E.

AU - Fomferra, Normann

AU - Kraemer, Guido

AU - Peters, Jonas

AU - Bodesheim, Paul

AU - Camps-Valls, Gustau

AU - F. Donges, Jonathan

AU - Dorigo, Wouter

AU - M. Estupinan-Suarez, Lina

AU - H. Gutierrez-Velez, Victor

AU - Gutwin, Martin

AU - Jung, Martin

AU - C. Londoño, Maria

AU - G. Miralles, Diego

AU - Papastefanou, Phillip

AU - Reichstein, Markus

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85080114281&partnerID=8YFLogxK

U2 - 10.5194/esd-11-201-2020

DO - 10.5194/esd-11-201-2020

M3 - Journal article

AN - SCOPUS:85080114281

VL - 11

SP - 201

EP - 234

JO - Earth System Dynamics

JF - Earth System Dynamics

SN - 2190-4979

IS - 1

ER -