English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Earth system data cubes unravel global multivariate dynamics

Authors

Mahecha,  M. D.
External Organizations;

Gans,  F.
External Organizations;

Brandt,  G.
External Organizations;

Christiansen,  R.
External Organizations;

Cornell,  S. E.
External Organizations;

Fomferra,  N.
External Organizations;

Kraemer,  G.
External Organizations;

Peters,  J.
External Organizations;

Bodesheim,  P.
External Organizations;

Camps-Valls,  G.
External Organizations;

/persons/resource/Donges

Donges,  Jonathan Friedemann
Potsdam Institute for Climate Impact Research;

Dorigo,  W.
External Organizations;

Estupinan-Suarez,  L. M.
External Organizations;

Gutierrez-Velez,  V. H.
External Organizations;

Gutwin,  M.
External Organizations;

Jung,  M.
External Organizations;

Londono,  M. C.
External Organizations;

Miralles,  D. G.
External Organizations;

Papastefanou,  P.
External Organizations;

Reichstein,  M.
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)

8622oa.pdf
(Publisher version), 9MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Mahecha, M. D., Gans, F., Brandt, G., Christiansen, R., Cornell, S. E., Fomferra, N., Kraemer, G., Peters, J., Bodesheim, P., Camps-Valls, G., Donges, J. F., Dorigo, W., Estupinan-Suarez, L. M., Gutierrez-Velez, V. H., Gutwin, M., Jung, M., Londono, M. C., Miralles, D. G., Papastefanou, P., Reichstein, M. (2020): Earth system data cubes unravel global multivariate dynamics. - Earth System Dynamics, 11, 1, 201-234.
https://doi.org/10.5194/esd-11-201-2020


Cite as: https://publications.pik-potsdam.de/pubman/item/item_23372
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.