Effects of climate model interdependency on the uncertainty quantification of extreme rainfall projections

M. A. Sunyer, Dan Rosbjerg, Karsten Arnbjerg-Nielsen, H. Madsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    The inherent uncertainty in climate models is one of the most important uncertainties in climate change impact studies. In recent years, several uncertainty quantification methods based on multi-model ensembles have been suggested. Most of these methods assume that the climate models are independent. This study investigates the validity of this assumption and its effects on the estimated probabilistic projections of the changes in the 95% quantile of wet days. The methodology is divided in two main parts. First, the interdependency of the ENSEMBLES RCMs is estimated using the methodology developed by Pennell and Reichler (2011). The results show that the projections from the ENSEMBLES RCMs cannot be assumed independent. This result is then used to estimate the uncertainty in climate model projections. A Bayesian approach has been developed using the procedure suggested by Tebaldi et al. (2005) in order to quantify the uncertainty.
    Original languageEnglish
    Title of host publication9th International Workshop on Precipitation in Urban Areas: Urban Challenges in Rainfall Analysis, Urbanrain 2012
    PublisherETH Zurich
    Publication date2017
    Pages284-289
    ISBN (Electronic)9783906031217
    Publication statusPublished - 2017
    Event9th International Workshop on Precipitation in Urban Areas - St. Moritz, Switzerland
    Duration: 6 Dec 20129 Dec 2012
    Conference number: 9

    Workshop

    Workshop9th International Workshop on Precipitation in Urban Areas
    Number9
    Country/TerritorySwitzerland
    CitySt. Moritz
    Period06/12/201209/12/2012

    Keywords

    • Environmental Engineering
    • Interdependency
    • Multi-model ensemble
    • Rainfall
    • RCM
    • Uncertainty
    • Aluminum alloys
    • Bayesian networks
    • Climate change
    • Precipitation (meteorology)
    • Rain
    • Uncertainty analysis
    • Bayesian approaches
    • Climate change impact
    • Extreme rainfall
    • Probabilistic projections
    • Uncertainty quantifications
    • Climate models

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