Understanding the complex interdependencies of processes in our climate system has become one of the most critical challenges for society with our main current tools being cli- mate modeling and observational data analysis, in particular observational causal discovery. Causal discovery is still in its infancy in Earth sciences and a major issue is that current methods are not well adapted to climate data challenges. We here present an overview of a NeurIPS 2019 competition on causal discovery for climate time series. The Causality 4 Climate (C4C) competition was hosted on the benchmark platform www.causeme.net. C4C offers an extensive number of climate model-based time series datasets with known causal ground truth that incorporate the main challenges of causal discovery in climate research. We give an overview over the benchmark platform, the challenges modeled, how datasets were generated, and implementation details. The goal of C4C is to spur more focused methodological research on causal discovery for understanding our climate system.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    The Causality for Climate Challenge


    Beteiligte:

    Kongress:



    Erscheinungsdatum :

    2020


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    TRAFFIC CAUSALITY

    SCOFIELD CHRISTOPHER L | Europäisches Patentamt | 2017

    Freier Zugriff

    Traffic causality

    SCOFIELD CHRISTOPHER L | Europäisches Patentamt | 2017

    Freier Zugriff

    Will Climate Change Challenge BizAv?

    Gormley, M. | British Library Online Contents | 2007


    Symmetry, Causality, Mind

    Leyton, M. / University of Naples / National Research Council of Italy | British Library Conference Proceedings | 1997