Challenges and opportunities toward improved data-guided handling of global climate model ensembles for regional climate change assessments
نویسنده
چکیده
Global climate models (GCMs) are important tools for addressing climate change questions at a global scale. Recent research has attempted to combine outputs from multiple GCMs to quantify uncertainty in regional climate change, which may ultimately inform regional stakeholders and policy-makers. Using a case study, we illustrate a potential path toward improvement in an existing Bayesian formulation, which may ultimately result in more physically meaningful GCM combination and hence add value to decision-making. This area of research is still nascent and may benefit from innovations in the computational data sciences.
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