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A multi-approach strategy in climate attribution studies: Is it possible to apply a robustness framework?
Institution:1. CNR, Institute of Atmospheric Pollution Research, Via Salaria Km 29,300, I-00015 Monterotondo St., Rome, Italy;2. CNR, Institute for Complex Systems, Via Salaria Km 29,300, I-00015 Monterotondo St., Rome, Italy;1. Centre for the Study of Governance Innovation, Department of Political Sciences, University of Pretoria, Pretoria, South Africa;2. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK;1. Área de Ecología, Facultad de Ciencias, Campus A Zapateira, Universidad de A Coruña, 15008 A Coruña, Spain;2. Área de Ecología, Facultad de Biología, Campus Vida, Universidad de Santiago de Compostela, Santiago de Compostela, Spain;1. Applied Mathematics 1, Department of Mathematics, University of Erlangen-Nürnberg, Germany;2. Department of Computer Science 10 (System Simulation), University of Erlangen-Nürnberg, Germany;1. NERC Centre for Ecology and Hydrology, Edinburgh, UK;2. UFZ Helmholtz Centre for Environmental Research, Department for Conservation Biology, Leipzig, Germany;3. SYKE, Helsinki, Finland;4. UFZ Helmholtz Centre for Environmental Research, Department for Environmental Politics, Leipzig, Germany;1. Rutgers, The State University of New Jersey, USA;2. DNV GL Energy and Sustainability, USA
Abstract:Attribution studies investigate the causes of recent global warming. For a few decades the scientific community generally adopted dynamical models – the so-called Global Climate Models (GCMs) – for such an investigation. These models show the essential role of anthropogenic forcings in driving the temperature behaviour of the last half century. In the last period even other (data-driven) methodological approaches were adopted for attribution studies. This allows the scientific community to compare the results coming from these different approaches and to possibly increase their robustness. For such a purpose, the paper explores the possibility of applying a robustness framework, so far used only in the case of multi-model GCM ensembles, to a strategy including models from different methodological orientations, assessing such an application especially in the light of the independence issue.
Keywords:Climate change  Climate modelling  Attribution  Scientific uncertainty  Robustness analysis  Dynamical modelling  Multi-model ensembles  Data-driven modelling  Neural networks  Granger causality  Complex systems
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