Description: Das Projekt "Quantifying climate change uncertainty from the CMIP3 ensemble of global coupled climate models" wird vom Umweltbundesamt gefördert und von Eidgenössische Technische Hochschule Zürich, Institut für Atmosphäre und Klima durchgeführt. The Earth's climate is changing as a response to anthropogenic emissions of fossil fuels, and is very likely to do so over the next decades to centuries. The projected changes from multiple numerical climate models differ significantly, because some feedbacks and processes are poorly understood or cannot be resolved in the models. Yet a quantitative picture of the uncertainty associated with the expected changes on regional and global scales is crucial, in order to quantify impacts, and decide on adaptation and mitigation measures. The objective of this project is to assess the uncertainty in large-scale regional to global climate change projections based on the CMIP3 dataset, a collection of simulations of about twenty global climate models coordinated for the recent IPCC report. While the amount of data produced by climate models and the desire for ever more detailed projections and probabilistic information is growing rapidly, there is a lack of understanding on how to define the skill of a climate model for a forecast that in practice cannot be verified. So far, models are often averaged regardless of their ability to satisfy even basic requirements, and the community is reluctant to define a set of metrics that would seem important for a model to be credible. Also, a consensus of uncertainty in climate change projections on regional to global scales is missing. We propose to develop a framework to evaluate climate models for their suitability in predicting changes by a set of metrics that are important to be simulated correctly from a physical point of view, rather than comparing present day simulations to observations. In addition, by treating one model M as observations, statistical methods can be used to find optimal predictors and to define model weight, such that a weighted mean or a probabilistic method using the rest of the models can best predict M. The issue of common model error and model dependence will be addressed. We also propose novel methods to aggregate results in regional averages using clustering techniques, and a more flexible approach to communicating results using climate change indices. As milestones of the project, we will attempt to answer the following questions: How large are the uncertainties of model projections on different spatial and temporal scales? On what spatial scale can the current models provide robust information? How can we quantify model performance for projections, and use that to define model weight? Is a small set of 'good' models more useful than a large number of 'good' and 'bad' models? How do model dependence, common biases, and overconfidence affect projection uncertainties? How can we attach confidence to climate change indices? The proposed work combines efforts in climate research and statistics. The results will be used in improving a Bayesian hierarchical model to generate probabilistic climate change information for the spatial scales on which .
Types:
SupportProgram
Origin: /Bund/UBA/UFORDAT
Tags: Fossiler Brennstoff ? Lack ? Zürich ? Klimatologie ? Statistisches Modell ? Prognose ? Globale Erwärmung ? Globales Klimamodell ? Klimaforschung ? Daten ? Emission ? Hochrechnung ? Klimamodell ? Schutzmaßnahme ? Simulation ? Technik ? Großprojekt ? Wirkung ? Globale Veränderung ? Probabilistische Methode ? Globales Klima ? Anthropogener Einfluss ? Arbeit ? Atmosphäre ? Klima ? Klimawandel ? Statistik ? Globale Aspekte ? Mittelwert ? Klimaszenario ? VERANTWORTUNG ? BEGRENZUNGSMASSNAHME ? WICHTIG ? BEREICH ? BEST ? BETRACHTUNG ? BEWERTEN ? DATENSATZ ? EIN ? EINSATZ ? ERFORDERNIS ? ERGEBNIS ? Erlass [Recht] ? FEHLER ? FRAGE ? GEGENWART ? GEMEINSCHAFT ? GEMITTELT ? GEWICHT ? Koordinate ? LEISTUNGSFAEHIGKEIT ? METHODE ? MOEGLICHKEIT ? Muskelarbeit ? NEU ? PROJEKT ? REGIONAL ? SICHT ? STATISTISCH ? ABHOLEN ? VERARBEITEN ? VERGLEICHEN ? VORSCHLAGEN ?
License: cc-by-nc-nd/4.0
Language: Deutsch
Time ranges: 2008-04-01 - 2011-03-31
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