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Observation-based blended projections from ensembles of regional climate models

Author

Listed:
  • Esther Salazar

    (Center for Tobacco Products)

  • Dorit Hammerling

    (National Center for Atmospheric Research)

  • Xia Wang

    (University of Cincinnati)

  • Bruno Sansó

    (University of California)

  • Andrew O. Finley

    (Michigan State University)

  • Linda O. Mearns

    (National Center for Atmospheric Research)

Abstract

We consider the problem of projecting future climate from ensembles of regional climate model (RCM) simulations using results from the North American Regional Climate Change Assessment Program (NARCCAP). To this end, we develop a hierarchical Bayesian space-time model that quantifies the discrepancies between different members of an ensemble of RCMs corresponding to present day conditions, and observational records. Discrepancies are then propagated into the future to obtain high resolution blended projections of 21st century climate. In addition to blended projections, the proposed method provides location-dependent comparisons between the different simulations by estimating the different modes of spatial variability, and using the climate model-specific coefficients of the spatial factors for comparisons. The approach has the flexibility to provide projections at customizable scales of potential interest to stakeholders while accounting for the uncertainties associated with projections at these scales based on a comprehensive statistical framework. We demonstrate the methodology with simulations from the Weather Research & Forecasting regional model (WRF) using three different boundary conditions. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the Special Report on Emissions Scenarios (SRES) A2 emissions scenario, covering 2041 to 2070. We investigate and project yearly mean summer and winter temperatures for a domain in the South West of the United States.

Suggested Citation

  • Esther Salazar & Dorit Hammerling & Xia Wang & Bruno Sansó & Andrew O. Finley & Linda O. Mearns, 2016. "Observation-based blended projections from ensembles of regional climate models," Climatic Change, Springer, vol. 138(1), pages 55-69, September.
    • Handle: RePEc:spr:climat:v:138:y:2016:i:1:d:10.1007_s10584-016-1722-1
    DOI: 10.1007/s10584-016-1722-1
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    References listed on IDEAS

    as
    1. Peter Guttorp & Stephan R. Sain & Christopher K. Wikle & Francisco Beltrán & Bruno Sansó & Ricardo T. Lemos & Roy Mendelssohn, 2012. "Joint projections of North Pacific sea surface temperature from different global climate models," Environmetrics, John Wiley & Sons, Ltd., vol. 23(5), pages 451-465, August.
    2. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    3. Claudia Tebaldi & Bruno Sansó, 2009. "Joint projections of temperature and precipitation change from multiple climate models: a hierarchical Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 83-106, January.
    4. L. Mearns & S. Sain & L. Leung & M. Bukovsky & S. McGinnis & S. Biner & D. Caya & R. Arritt & W. Gutowski & E. Takle & M. Snyder & R. Jones & A. Nunes & S. Tucker & D. Herzmann & L. McDaniel & L. Sloa, 2013. "Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)," Climatic Change, Springer, vol. 120(4), pages 965-975, October.
    5. Smith, Richard L. & Tebaldi, Claudia & Nychka, Doug & Mearns, Linda O., 2009. "Bayesian Modeling of Uncertainty in Ensembles of Climate Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 97-116.
    6. Jonathan Rougier & Michael Goldstein & Leanna House, 2013. "Second-Order Exchangeability Analysis for Multimodel Ensembles," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 852-863, September.
    7. Peter Guttorp & Stephan R. Sain & Christopher K. Wikle & David B. Stephenson & Matthew Collins & Jonathan C. Rougier & Richard E. Chandler, 2012. "Statistical problems in the probabilistic prediction of climate change," Environmetrics, John Wiley & Sons, Ltd., vol. 23(5), pages 364-372, August.
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