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      Inferring causation from time series in Earth system sciences

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Abstract

          Questions of causality are ubiquitous in Earth system sciences and beyond, yet correlation techniques still prevail. This Perspective provides an overview of causal inference methods, identifies promising applications and methodological challenges, and initiates a causality benchmark platform.

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          Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

          By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.
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            Investigating Causal Relations by Econometric Models and Cross-spectral Methods

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              Complex brain networks: graph theoretical analysis of structural and functional systems.

              Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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                Author and article information

                Contributors
                jakob.runge@dlr.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 June 2019
                14 June 2019
                2019
                : 10
                : 2553
                Affiliations
                [1 ]German Aerospace Center, Institute of Data Science, Mälzer Str. 3, 07745 Jena, Germany
                [2 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Grantham Institute, , Imperial College, ; London, SW7 2AZ UK
                [3 ]ISNI 0000 0004 0541 3699, GRID grid.24999.3f, Climate Service Center Germany (GERICS), , Helmholtz-Zentrum Geesthacht, ; Fischertwiete 1, 20095 Hamburg, Germany
                [4 ]ISNI 0000 0001 0791 5666, GRID grid.4818.5, Department of Environmental Sciences, , Wageningen University, ; P.O. Box 47, NL-6700 AA Wageningen, The Netherlands
                [5 ]ISNI 0000 0001 0741 9486, GRID grid.254280.9, Department of Mathematics, Clarkson Center for Complex Systems Science (C3S2), , Clarkson University, ; 8 Clarkson Ave., Potsdam, NY 13699-5815 USA
                [6 ]ISNI 0000 0001 2173 938X, GRID grid.5338.d, Image Processing Laboratory, , Universitat de València, ; ES-46980 Paterna (València), Spain
                [7 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Department of Water and Climate Risk, Institute for Environmental Studies (IVM), , VU University Amsterdam, ; De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands
                [8 ]ISNI 0000 0004 0493 9031, GRID grid.4556.2, Potsdam Institute for Climate Impact Research, Earth System Analysis, ; Telegraphenberg A62, 14473 Potsdam, Germany
                [9 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Scripps Institution of Oceanography, , University of California, San Diego, ; 9500 Gilman Drive, La Jolla, CA 92093 USA
                [10 ]ISNI 0000 0001 2097 0344, GRID grid.147455.6, Department of Philosophy, , Carnegie Mellon University, ; 5000 Forbes Ave, Pittsburgh, PA 15213 USA
                [11 ]ISNI 0000 0004 0491 7318, GRID grid.419500.9, Max Planck Institute for Biogeochemistry, ; PO Box 100164, 07701 Jena, Germany
                [12 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Department of Mathematical Sciences, , University of Copenhagen, ; Universitetsparken 5, 2100 København, Denmark
                [13 ]ISNI 0000000084992262, GRID grid.7177.6, Institute for Informatics, , University of Amsterdam, ; PO Box 94323, 1090 GH Amsterdam, The Netherlands
                [14 ]ISNI 0000000084992262, GRID grid.7177.6, Institute of Advanced Studies, , University of Amsterdam, ; Oude Turfmarkt 147, 1012 GC Amsterdam, The Netherlands
                [15 ]ISNI 0000 0001 1015 6533, GRID grid.419534.e, Max Planck Institute for Intelligent Systems, ; Max Planck Ring 4, 72076 Tübingen, Germany
                [16 ]ISNI 0000 0001 0741 9486, GRID grid.254280.9, Department of Physics and Department of Computer Science, , Clarkson University, ; 8 Clarkson Ave., Potsdam, NY 13699-5815 USA
                [17 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, Institute for Atmospheric and Climate Science, ETH Zurich, ; Universitätstrasse 16, 8092 Zurich, Switzerland
                [18 ]ISNI 0000 0001 0726 5157, GRID grid.5734.5, Climate and Environmental Physics, , University of Bern, ; Sidlerstrasse 5, 3012 Bern, Switzerland
                [19 ]ISNI 0000 0001 0726 5157, GRID grid.5734.5, Oeschger Centre for Climate Change Research, , University of Bern, ; Bern, 3012 Switzerland
                Author information
                http://orcid.org/0000-0002-0629-1772
                http://orcid.org/0000-0003-3031-613X
                http://orcid.org/0000-0003-2553-1804
                http://orcid.org/0000-0001-6045-1629
                Article
                10105
                10.1038/s41467-019-10105-3
                6572812
                31201306
                5b204231-5d5f-4024-9dfa-d7cf7ae4b366
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 February 2018
                : 17 April 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000913, James S. McDonnell Foundation (McDonnell Foundation);
                Funded by: FundRef https://doi.org/10.13039/100000006, United States Department of Defense | United States Navy | Office of Naval Research (ONR);
                Award ID: N00014-15-1-2093
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000183, United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO);
                Award ID: W911NF-16-1-0081
                Award ID: W911NF-16-1-0081
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 647423
                Award ID: 640176
                Award ID: 640176
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research);
                Award ID: 01LN1304A
                Award ID: 01LN1304A
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research);
                Award ID: 016.Vidi.171011
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: NSFDEB-1655203
                Award ID: NSFDEB-1655203
                Award ID: DBI-1667584
                Award ID: IIS-1829681
                Award Recipient :
                Funded by: DoD-Strategic Environmental Research and Development Program, grant number 15 RC-2509
                Funded by: FundRef https://doi.org/10.13039/100000093, U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health);
                Award ID: FAINR01EB022858
                Award ID: NIH-1R01LM012087
                Award ID: NIH5U54HG008540-02
                Award ID: FAIN-U54HG008540
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100008398, Villum Fonden (Villum Foundation);
                Award ID: 18968
                Award Recipient :
                Funded by: Netherlands Earth System Science Centre (NESSC)
                Funded by: FundRef https://doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 318812
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100006831, United States Department of Defense | U.S. Air Force (United States Air Force);
                Award ID: FA8650-17-C-7715
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: PZ00P2-179876
                Award Recipient :
                Categories
                Perspective
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                climate sciences,environmental sciences,computational science,statistical physics, thermodynamics and nonlinear dynamics,databases

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