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Subject: review × WGL Institute: PIK ×

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Now showing 1 - 4 of 4
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    Inferring causation from time series in Earth system sciences
    ([London] : Nature Publishing Group UK, 2019) Runge, Jakob; Bathiany, Sebastian; Bollt, Erik; Camps-Valls, Gustau; Coumou, Dim; Deyle, Ethan; Glymour, Clark; Kretschmer, Marlene; Mahecha, Miguel D.; Muñoz-Marí, Jordi; van Nes, Egbert H.; Peters, Jonas; Quax, Rick; Reichstein, Markus; Scheffer, Marten; Schölkopf, Bernhard; Spirtes, Peter; Sugihara, George; Sun, Jie; Zhang, Kun; Zscheischler, Jakob
    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. © 2019, The Author(s).
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    Interglacials of the last 800,000 years
    (Hoboken, NJ : Blackwell Publishing Ltd, 2016) Berger, B.; Crucifix, M.; Hodell, D.A.; Mangili, C.; McManus, J.F.; Otto-Bliesner, B.; Pol, K.; Raynaud, D.; Skinner, L.C.; Tzedakis, P.C.; Wolff, E.W.; Yin, Q.Z.; Abe-Ouchi, A.; Barbante, C.; Brovkin, V.; Cacho, I.; Capron, E.; Ferretti, P.; Ganopolski, A.; Grimalt, J.O.; Hönisch, B.; Kawamura, K.A.; Landais, A.; Margari, V.; Martrat, B.; Masson-Delmotte, V.; Mokeddem, Z.; Parrenin, F.; Prokopenko, A.A.; Rashid, H.; Schulz, M.; Vazquez Riveiros, N.
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    Articulating the effect of food systems innovation on the Sustainable Development Goals
    (Amsterdam : Elsevier, 2021) Herrero, Mario; Thornton, Philip K.; Mason-D'Croz, Daniel; Palmer, Jeda; Bodirsky, Benjamin L.; Pradhan, Prajal; Barrett, Christopher B.; Benton, Tim G.; Hall, Andrew; Pikaar, Ilje; Bogard, Jessica R.; Bonnett, Graham D.; Bryan, Brett A.; Campbell, Bruce M.; Christensen, Svend; Clark, Michael; Fanzo, Jessica; Godde, Cecile M.; Jarvis, Andy; Loboguerrero, Ana Maria; Mathys, Alexander; McIntyre, C. Lynne; Naylor, Rosamond L.; Nelson, Rebecca; Obersteiner, Michael; Parodi, Alejandro; Popp, Alexander; Ricketts, Katie; Smith, Pete; Valin, Hugo; Vermeulen, Sonja J.; Vervoort, Joost; van Wijk, Mark; van Zanten, Hannah HE; West, Paul C.; Wood, Stephen A.; Rockström, Johan
    Food system innovations will be instrumental to achieving multiple Sustainable Development Goals (SDGs). However, major innovation breakthroughs can trigger profound and disruptive changes, leading to simultaneous and interlinked reconfigurations of multiple parts of the global food system. The emergence of new technologies or social solutions, therefore, have very different impact profiles, with favourable consequences for some SDGs and unintended adverse side-effects for others. Stand-alone innovations seldom achieve positive outcomes over multiple sustainability dimensions. Instead, they should be embedded as part of systemic changes that facilitate the implementation of the SDGs. Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging. Trade-offs with undesirable consequences are manageable through the development of well planned transition pathways, careful monitoring of key indicators, and through the implementation of transparent science targets at the local level.
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    Towards dynamical network biomarkers in neuromodulation of episodic migraine
    (Berlin : De Gruyter, 2013) Dahlem, M.A.; Rode, S.; May, A.; Fujiwara, N.; Hirata, Y.; Aihara, K.; Kurths, J.
    Computational methods have complemented experimental and clinical neurosciences and led to improvements in our understanding of the nervous systems in health and disease. In parallel, neuromodulation in form of electric and magnetic stimulation is gaining increasing acceptance in chronic and intractable diseases. In this paper, we firstly explore the relevant state of the art in fusion of both developments towards translational computational neuroscience. Then, we propose a strategy to employ the new theoretical concept of dynamical network biomarkers (DNB) in episodic manifestations of chronic disorders. In particular, as a first example, we introduce the use of computational models in migraine and illustrate on the basis of this example the potential of DNB as early-warning signals for neuromodulation in episodic migraine.