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Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models

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  • Jianting Zhu
  • William Forsee
  • Rina Schumer
  • Mahesh Gautam

Abstract

Changes in climate are expected to lead to changes in the characteristics extreme rainfall frequency and intensity. In this study, we propose an integrated approach to explore potential changes in intensity-duration-frequency (IDF) relationships. The approach incorporates uncertainties due to both the short simulation periods of regional climate models (RCMs) and the differences in IDF curves derived from multiple RCMs in the North American Regional Climate Change Assessment Program (NARCCAP). The approach combines the likelihood of individual RCMs according to the goodness of fit between the extreme rainfall intensities from the RCMs’ historic runs and those from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data set and Bayesian model averaging (BMA) to assess uncertainty in IDF predictions. We also partition overall uncertainties into within-model uncertainty and among-model uncertainty. Results illustrate that among-model uncertainty is the dominant source of the overall uncertainty in simulating extreme rainfall for multiple locations in the U.S., pointing to the difficulty of predicting future climate, especially extreme rainfall regimes. For all locations a more intense extreme rainfall occurs in future climate; however the rate of increase varies among locations. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Jianting Zhu & William Forsee & Rina Schumer & Mahesh Gautam, 2013. "Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models," Climatic Change, Springer, vol. 118(2), pages 469-485, May.
    • Handle: RePEc:spr:climat:v:118:y:2013:i:2:p:469-485
    DOI: 10.1007/s10584-012-0639-6
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    References listed on IDEAS

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    1. James M. Murphy & David M. H. Sexton & David N. Barnett & Gareth S. Jones & Mark J. Webb & Matthew Collins & David A. Stainforth, 2004. "Quantification of modelling uncertainties in a large ensemble of climate change simulations," Nature, Nature, vol. 430(7001), pages 768-772, August.
    2. T. N. Palmer & J. Räisänen, 2002. "Quantifying the risk of extreme seasonal precipitation events in a changing climate," Nature, Nature, vol. 415(6871), pages 512-514, January.
    3. D. A. Stainforth & T. Aina & C. Christensen & M. Collins & N. Faull & D. J. Frame & J. A. Kettleborough & S. Knight & A. Martin & J. M. Murphy & C. Piani & D. Sexton & L. A. Smith & R. A. Spicer & A. , 2005. "Uncertainty in predictions of the climate response to rising levels of greenhouse gases," Nature, Nature, vol. 433(7024), pages 403-406, January.
    4. Nadja A. Leith & Richard E. Chandler, 2010. "A framework for interpreting climate model outputs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 279-296, March.
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    Cited by:

    1. Muhammad Fraz Ismail & Bibi S. Naz & Michel Wortmann & Markus Disse & Laura C. Bowling & Wolfgang Bogacki, 2020. "Comparison of two model calibration approaches and their influence on future projections under climate change in the Upper Indus Basin," Climatic Change, Springer, vol. 163(3), pages 1227-1246, December.
    2. Yog Aryal & Jianting Zhu, 2017. "On bias correction in drought frequency analysis based on climate models," Climatic Change, Springer, vol. 140(3), pages 361-374, February.

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