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A comparison of U.S. precipitation extremes under RCP8.5 and RCP4.5 with an application of pattern scaling

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Abstract

Precipitation extremes are expected to increase in a warming climate, which may have serious societal impacts. This study uses two initial condition ensembles conducted with the Community Earth System Model (CESM) to investigate potential changes in extreme precipitation under two climate change scenarios over the contiguous United States. We fit non-stationary generalized extreme value (GEV) models to annual maximum daily precipitation simulated from a 30-member ensemble under the RCP8.5 scenario and a 15-member ensemble under the RCP4.5 scenario. We then compare impacts using the 1 % annual exceedance probability (AEP) level, which is the amount of daily rainfall with only a 1 % chance of being exceeded in a given year. Under RCP8.5 between 2005 and 2080, the 1 % AEP level is projected to increase by 17 % on average across the U.S., and up to 36 % for some grid cells. Compared to RCP8.5 in the year 2080, RCP4.5 is projected to reduce the 1 % AEP level by 7 % on average, with reductions as large as 18 % for some grid cells. We also investigate a pattern scaling approach in which we produce predictive GEV distributions of annual precipitation maxima under RCP4.5 given only global mean temperatures for this scenario. We compare results from this less computationally intensive method to those obtained from our GEV model fitted directly to the CESM RCP4.5 output and find that pattern scaling produces reasonable projections.

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References

  • Alexeeff S, Sain SR, Nychka D, Tebaldi C (in preparation) Patterns of mean temperature and variability across and within scenarios: using the RCP8.5 large ensemble to emulate RCP4.5

  • Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419(6903):224–232

    Article  Google Scholar 

  • Beniston M, Stephenson DB, Christensen OB, Ferro CAT, Frei C, Goyette S, Halsnaes K, Holt T, Jylhä K, Koffi B, et al. (2007) Future extreme events in European climate: an exploration of regional climate model projections. Clim Change 81(1):71–95

    Article  Google Scholar 

  • Brown S J, Murphy J M, Sexton D M H, Harris G R (2014) Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases. Clim Dyn 43(9-10):2681–2705

    Article  Google Scholar 

  • Chen C-T, Knutson T (2008) On the verification and comparison of extreme rainfall indices from climate models. J Climate 21(7):1605–1621

    Article  Google Scholar 

  • Coles SG (2001) An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag London Ltd., London, p xiv+208. Springer Series in Statistics

    Book  Google Scholar 

  • Cooley D, Sain SR (2010) Spatial hierarchical modeling of precipiation extremes from a regional climate model. J Agric Biol Environ Stat 15:381–402

    Article  Google Scholar 

  • Cooley D, Hsu K, Schubert S (2012) Return Periods Under Climate Change. In: AghaKouchak A, Easterling D, Sorooshian S (eds) Extremes in a Changing Climate: Detection, Analysis and Uncertainty. Water Science and Technology Series, Vol 65. Springer, Netherlands, Dordrecht, pp 97–114

    Google Scholar 

  • Cooley D, Nychka D, Naveau P (2007) Bayesian spatial modeling of extreme precipitation return levels. J Am Stat Assoc 102(479):824–840

    Article  Google Scholar 

  • Fowler HJ, Cooley D, Sain SR, Thurston M (2010) Detecting change in UK extreme precipitation using results from the climateprediction.net BBC Climate Change Experiment. Extremes 13(2):241–267

    Article  Google Scholar 

  • Fowler HJ, Ekström M, Blenkinsop S, Smith A P (2007) Estimating change in extreme European precipitation using a multimodel ensemble. In: Journal of Geophysical Research: Atmospheres, vol 112. Wiley Online Library

  • Gilleland E, Katz RW (2011) New software to analyze how extremes change over time. Eos Trans AGU 92(2):13–14

    Article  Google Scholar 

  • Hanel M, Buishand TA (2011) Analysis of precipitation extremes in an ensemble of transient regional climate model simulations for the Rhine basin. Clim Dyn 36(5-6):1135–1153

    Article  Google Scholar 

  • Hosking JRM, Wallis J R (1997) Regional Frequency Analysis: An approach based on L-Moments. Cambridge, University Press, Cambridge., U.K.

    Book  Google Scholar 

  • Hurrell JW, Holland MM, Gent PR, Ghan S, Kay JE, Kushner PJ, Lamarque J-F, Large WG, Lawrence D, Lindsay K, et al. (2013) The community earth system model: a framework for collaborative research. Bull Am Meteorol Soc 94(9):1339–1360

    Article  Google Scholar 

  • IPCC (2012). In: Field CB, Barros V, Stocker TF, D Qin, DJ Dokken, KL Ebi, MD Mastrandrea, KJ Mach, G-K Plattner, SK Allen, M Tignor, PM Midgley (eds) Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change, summary for policymakers. Cambridge University Press, Cambridge, UK

  • Kay JE, Deser C, Phillips A, Mai A, Hannay C, Strand G, Arblaster JM, Bates SC, Danabasoglu G, Edwards J, et al. (2014) The Community Earth System Model (CESM) Large Ensemble Project: A community resource for studying climate change in the presence of internal climate variability. Bull Am Meteorol Soc 96 (8):1333–1349

    Article  Google Scholar 

  • Kharin VV, Zwiers FW (2005) Estimating extremes in transient climate change simulations. J Clim 18(8):1156–1173

    Article  Google Scholar 

  • Kharin VV, Zwiers FW, Zhang X, Wehner M (2013) Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Change 119(2):345–357

    Article  Google Scholar 

  • Kharin VV, Zwiers FW, Zhang XB, Hegerl GC (2007) Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J Clim 20:1419–1444

    Article  Google Scholar 

  • Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23(10):2739–2758

    Article  Google Scholar 

  • Leadbetter MR (1974) On Extreme Values in Stationary Processes. Z Wahrsch verw Gebiete 28(4):289–303

    Article  Google Scholar 

  • Maraun D, Osborn TJ, Rust HW (2011) The influence of synoptic airflow on UK daily precipitation extremes. Part I: Observed spatio-temporal relationships. Clim Dyn 36(1):261–275

    Article  Google Scholar 

  • O’Neill BC, Gettelman A (in preparation) Introduction to the special issue

  • Pausader M, Bernie D, Parey S, Nogaj M (2012) Computing the distribution of return levels of extreme warm temperatures for future climate projections. Clim Dyn 38(5-6):1003–1015

    Article  Google Scholar 

  • Rootzén H, Katz RW (2013) Design Life Level: Quantifying risk in a changing climate. Water Resour Res 49(9):5964–5972

    Article  Google Scholar 

  • Sanderson BM, Oleson KW, Strand WG, Lehner F, O’Neill BC (2015) A new ensemble of GCM simulations to assess avoided impacts in a climate mitigation scenario. Clim Change:1–16. doi:10.1007/s10584-015-1567-z. Issn 1573–1480

  • Sang H, Gelfand AE (2010) Continuous spatial process models for spatial extreme values. J Agric Biol Environ Stat 15(1):49–65

    Article  Google Scholar 

  • Seneviratne SI, Donat MG, Pitman AJ, Knutti R, Wilby RL (2016) Allowable CO2 emissions based on regional and impact-related climate targets. Nature. doi:10.1038/nature16542

    Google Scholar 

  • Santer BD, Wigley TML, Schlesinger ME, Mitchell JFB (1990) Developing climate scenarios from equilibrium GCM results. Max-Plank Institut, Hamburg

    Google Scholar 

  • Schliep EM, Cooley D, Sain SR, Hoeting JA (2010) A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling. Extremes 13(2):219–239

    Article  Google Scholar 

  • Sillmann J, Croci-Maspoli M, Kallache M, Katz RW (2011) Extreme cold winter temperatures in Europe under the influence of North Atlantic atmospheric blocking. J Clim 24(22):5899–5913

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. American Meteorological Society. Bull Am Meteorol Soc 93 (4):485–498

    Article  Google Scholar 

  • Tebaldi C, Arblaster JM (2014) Pattern scaling: Its strengths and limitations, and an update on the latest model simulations. Clim Change 122(3):459–471

    Article  Google Scholar 

  • Tye MR, Cooley D (2015) A spatial model to examine rainfall extremes in Colorado’s Front Range. J Hydrol 530:15–23

    Article  Google Scholar 

  • Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, et al. (2011) The representative concentration pathways: an overview. Clim Change 109:5–31

    Article  Google Scholar 

  • Westra S, Alexander LV, Zwiers FW (2013) Global increasing trends in annual maximum daily precipitation. J Clim 26(11):3904–3918

    Article  Google Scholar 

  • Wilks DS (2006) Statistical methods in the atmospheric sciences: an introduction, 2nd. Academic Press, San Diego

    Google Scholar 

  • Wehner MF (2013) Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections. Clim Dyn 40(1-2):59–80

    Article  Google Scholar 

  • Zhang X, Zwiers FW, Li G (2004) Monte carlo experiments on the detection of trends in extreme values. J Clim 17(10):1945–1952

    Article  Google Scholar 

Download references

Acknowledgments

M. Fix was partially supported by a Center for Interdisciplinary Mathematics and Statistics student fellowship at Colorado State University. D. Cooley was partially supported by NSF grant DMS-1243102. S. Sain undertook this work as a Scientist in the Institute for Mathematics Applied to Geosciences at the National Center for Atmospheric Research. C. Tebaldi is supported by the Regional and Global Climate Modeling program of the US Department of Energy, Office of Science (BER), through Cooperative Agreement DE-FC02-97ER62402. We thank B. O’Neill and three anonymous reviewers for comments on an earlier draft of this paper. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by the National Center for Atmospheric Research’s Computational and Information Systems Laboratory, sponsored by NSF. In addition, we would also like to acknowledge the modeling groups who created the CESM model and generated the initial condition ensembles used in this analysis.

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Authors and Affiliations

  1. Department of Statistics, Colorado State University, Fort Collins, CO, USA

    Miranda J. Fix & Daniel Cooley

  2. The Climate Corporation, San Francisco, CA, USA

    Stephan R. Sain

  3. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Claudia Tebaldi

Authors
  1. Miranda J. Fix
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  2. Daniel Cooley
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  3. Stephan R. Sain
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  4. Claudia Tebaldi
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Corresponding author

Correspondence to Miranda J. Fix.

Additional information

This article is part of a Special Issue on ”Benefits of Reduced Anthropogenic Climate ChangE (BRACE)” edited by Brian O’Neill and Andrew Gettelman.

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Fix, M.J., Cooley, D., Sain, S.R. et al. A comparison of U.S. precipitation extremes under RCP8.5 and RCP4.5 with an application of pattern scaling. Climatic Change 146, 335–347 (2018). https://doi.org/10.1007/s10584-016-1656-7

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  • DOI: https://doi.org/10.1007/s10584-016-1656-7

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