Climatic Change

, Volume 138, Issue 1–2, pp 55–69 | Cite as

Observation-based blended projections from ensembles of regional climate models

  • Esther Salazar
  • Dorit Hammerling
  • Xia Wang
  • Bruno Sansó
  • Andrew O. Finley
  • Linda O. Mearns


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.

Supplementary material

10584_2016_1722_MOESM1_ESM.pdf (209 kb)
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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Esther Salazar
    • 1
  • Dorit Hammerling
    • 2
  • Xia Wang
    • 3
  • Bruno Sansó
    • 4
  • Andrew O. Finley
    • 5
  • Linda O. Mearns
    • 2
  1. 1.U.S. Food and Drug AdministrationCenter for Tobacco ProductsSilver SpringUSA
  2. 2.Institute for Mathematics Applied to GeosciencesNational Center for Atmospheric ResearchBoulderUSA
  3. 3.Department of Mathematical SciencesUniversity of CincinnatiCincinnatiUSA
  4. 4.Department of Applied Mathematics and StatisticsUniversity of CaliforniaSanta CruzUSA
  5. 5.Departments of Forestry and GeographyMichigan State UniversityLansingUSA

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