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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorDijkstra, H.A.
dc.contributor.authorNijsse, F.J.M.M.
dc.date.accessioned2017-07-19T17:01:36Z
dc.date.available2017-07-19T17:01:36Z
dc.date.issued2017
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/26195
dc.description.abstractEmergent constraints are one of the tools to reduce uncertainty in climate model projec-tions. These are physically explainable empirical relationships between characteristicsof the current climate and long-term behavior that emerge in ensembles of climatemodels, where the long-term behavior is constrained using observations. So far, nogeneral mathematical framework describing emergent constraints has been proposed.In this work, we introduce a classification for emergent constraints, depending on theprocess under consideration: we distinguish between emergent constraints that modelvariability, mean state or feedback strength. In addition we present a mathematicalframework making use of linear response theory and apply this description to a setof conceptual (climate) models. We specifically focus on an emergent constraint that waspreviously found for the snow-albedo feedback. We conclude by discussing if and howthis framework can be applied to GCMs.
dc.description.sponsorshipUtrecht University
dc.format.extent924833
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleA mathematical approach to understandingemergent constraints
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsemergent constraints;linear response theory;climate
dc.subject.courseuuMeteorology, Physical Oceanography and Climate


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