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Author: van Nes, Egbert H. × Publisher: [London] : Nature Publishing Group UK ×

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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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    Hysteresis of tropical forests in the 21st century
    ([London] : Nature Publishing Group UK, 2020) Staal, Arie; Fetzer, Ingo; Wang-Erlandsson, Lan; Bosmans, Joyce H. C.; Dekker, Stefan C.; van Nes, Egbert H.; Rockström, Johan; Tuinenburg, Obbe A.
    Tropical forests modify the conditions they depend on through feedbacks at different spatial scales. These feedbacks shape the hysteresis (history-dependence) of tropical forests, thus controlling their resilience to deforestation and response to climate change. Here, we determine the emergent hysteresis from local-scale tipping points and regional-scale forest-rainfall feedbacks across the tropics under the recent climate and a severe climate-change scenario. By integrating remote sensing, a global hydrological model, and detailed atmospheric moisture tracking simulations, we find that forest-rainfall feedback expands the geographic range of possible forest distributions, especially in the Amazon. The Amazon forest could partially recover from complete deforestation, but may lose that resilience later this century. The Congo forest currently lacks resilience, but is predicted to gain it under climate change, whereas forests in Australasia are resilient under both current and future climates. Our results show how tropical forests shape their own distributions and create the climatic conditions that enable them.