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dc.contributor.authorRypdal, Kristoffer
dc.date.accessioned2016-02-10T08:22:09Z
dc.date.available2016-02-10T08:22:09Z
dc.date.issued2015-09-18
dc.description.abstractA simple conceptual model for the global mean surface temperature (GMST) response to CO2 emissions is presented and analysed. It consists of linear long-memory models for the GMST anomaly response ∆T to radiative forcing and atmospheric CO2 - 5 concentration response ∆C to emission rate. The responses are connected by the standard logarithmic relation between CO2 concentration and its radiative forcing. The model depends on two sensitivity parameters, αT and αC, and two “inertia parameters”, the memory exponents βT and βC. Based on observation data, and constrained by results from the Climate Model Intercomparison Project Phase 5 (CMIP5), the likely 10 values and range of these parameters are estimated, and projections of future warming for the parameters in this range are computed for various idealised, but instructive, emission scenarios. It is concluded that delays in the initiation of an effective global emission reduction regime is the single most important factor that influences the magnitude of global warming over the next two centuries. The main value of this study is 15 the simplicity and transparency of the conceptual model, which makes it a useful tool for communicating the issue to non-climate scientists, students, policy-makers, and the general public.en_US
dc.identifier.citationEarth System Dynamics Discussions 6(2015) s. 1789-1813en_US
dc.identifier.cristinIDFRIDAID 1287716
dc.identifier.doi10.5194/esdd-6-1789-2015
dc.identifier.issn1866-3591
dc.identifier.urihttps://hdl.handle.net/10037/8455
dc.identifier.urnURN:NBN:no-uit_munin_8024
dc.language.isoengen_US
dc.publisherCopernicus GmbHen_US
dc.rights.accessRightsopenAccess
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Meteorologi: 453en_US
dc.subjectVDP::Mathematics and natural science: 400::Geosciences: 450::Meteorology: 453en_US
dc.titleGlobal warming projections derived from an observation-based minimal modelen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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