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  1.  44
    Applying big data beyond small problems in climate research.Benedikt Knüsel, Marius Zumwald, Christoph Baumberger, Gertrude Hirsch Hadorn, Erich M. Fischer, Reto Knutti & David M. Bresch - 2019 - Nature Climate Change 9 (March 2019):196-202.
    Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if (...)
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  2.  2
    Uncertainty Quantification Using Multiple Models—Prospects and Challenges.Reto Knutti, Christoph Baumberger & Gertrude Hirsch Hadorn - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Springer. pp. 835-855.
    Model evaluation for long-term climate predictions must be done on quantities other than the actual prediction, and a comprehensive uncertainty quantificationUncertainty quantification is impossible. An ad hoc alternative is provided by coordinated model intercomparisonsModel intercomparisons which typically use a “one model one vote” approach. The problem with such an approach is that it treats all models as independent and equally plausible. Reweighting all models of the ensemble for performance and dependence seems like an obvious way to improve on model democracy, (...)
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  3.  25
    Understanding and assessing uncertainty of observational datasets for model evaluation using ensembles.Marius Zumwald, Benedikt Knüsel, Christoph Baumberger, Gertrude Hirsch Hadorn, David Bresch & Reto Knutti - 2020 - WIREs Climate Change 10:1-19.
    In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the (...)
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    Uncertainty quantification using multiple models - Prospects and challenges.Reto Knutti, Christoph Baumberger & Gertrude Hirsch Hadorn - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Cham, Switzerland: pp. 835-855.
    Model evaluation for long term climate predictions must be done on quantities other than the actual prediction, and a comprehensive uncertainty quantification is impossible. An ad hoc alternative is provided by coordinated model intercomparisons which typically use a “one model one vote” approach. The problem with such an approach is that it treats all models as independent and equally plausible. Reweighting all models of the ensemble for performance and dependence seems like an obvious way to improve on model democracy, yet (...)
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