Abstract

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

Details

Title
Inferring causation from time series in Earth system sciences
Author
Runge, Jakob 1   VIAFID ORCID Logo  ; Bathiany, Sebastian 2 ; Bollt, Erik 3 ; Camps-Valls, Gustau 4 ; Coumou, Dim 5 ; Deyle, Ethan 6 ; Clark Glymour 7 ; Kretschmer, Marlene 8 ; Mahecha, Miguel D 9   VIAFID ORCID Logo  ; Muñoz-Marí, Jordi 4 ; van Nes, Egbert H 10 ; Peters, Jonas 11 ; Quax, Rick 12 ; Reichstein, Markus 9 ; Scheffer, Marten 10 ; Schölkopf, Bernhard 13 ; Spirtes, Peter 7 ; Sugihara, George 6 ; Sun, Jie 14   VIAFID ORCID Logo  ; Zhang, Kun 7 ; Zscheischler, Jakob 15   VIAFID ORCID Logo 

1  German Aerospace Center, Institute of Data Science, Jena, Germany; Grantham Institute, Imperial College, London, UK 
2  Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht, Hamburg, Germany; Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands 
3  Department of Mathematics, Clarkson Center for Complex Systems Science (C3S2), Clarkson University, Potsdam, NY, USA 
4  Image Processing Laboratory, Universitat de València, Paterna (València), Spain 
5  Department of Water and Climate Risk, Institute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, The Netherlands; Potsdam Institute for Climate Impact Research, Earth System Analysis, Potsdam, Germany 
6  Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA 
7  Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA 
8  Potsdam Institute for Climate Impact Research, Earth System Analysis, Potsdam, Germany 
9  Max Planck Institute for Biogeochemistry, Jena, Germany 
10  Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands 
11  Department of Mathematical Sciences, University of Copenhagen, København, Denmark 
12  Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands; Institute of Advanced Studies, University of Amsterdam, Amsterdam, The Netherlands 
13  Max Planck Institute for Intelligent Systems, Tübingen, Germany 
14  Department of Mathematics, Clarkson Center for Complex Systems Science (C3S2), Clarkson University, Potsdam, NY, USA; Department of Physics and Department of Computer Science, Clarkson University, Potsdam, NY, USA 
15  Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland; Climate and Environmental Physics, University of Bern, Bern, Switzerland; Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland 
Pages
1-13
Publication year
2019
Publication date
Jun 2019
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41467-019-10105-3
ProQuest document ID
2240137686
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.