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Observation-based blended projections from ensembles of regional climate models

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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.

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References

  • Banerjee S, Gelfand AE, Finley AO, Sang H (2008) Gaussian predictive process models for large spatial data sets. J R Stat Soc B 70:825–848

    Article  Google Scholar 

  • Beltrán F, Sansó B, Lemos R, Mendelssohn R (2012) Joint projections of north pacific sea surface temperature from different global climate models. Environmetrics 23:451–465. doi:10.1002/env.2150

    Article  Google Scholar 

  • Berliner LM, Kim Y (2008) Bayesian design and analysis for Superensemble-Based climate forecasting. J Clim 21(9):1891–1910. doi:10.1175/2007JCLI1619.1

    Article  Google Scholar 

  • Berliner LM, Millif R, Wikle C (2003) Bayesian hierarchical modeling of air-sea interactions. J Geophys Res Oceans. doi:10.1029/2002JC001413

  • Buser CM, Künsch HR, Lüthi D, Wild M, Schär C (2009) Bayesian multi-model projection of climate: bias assumptions and interannual variability. Clim Dyn 33(6):849–868. doi:10.1007/s00382-009-0588-6

    Article  Google Scholar 

  • Chandler RE (2013) Exploiting strength, discounting weakness: combining information from multiple climate simulators. Philos Trans R Soc London A Math Phys Eng Sci 371(1991). doi:10.1098/rsta.2012.0388. http://rsta.royalsocietypublishing.org/content/371/1991/20120388

  • Cressie N, Wikle CK (2011) Statistics for Spatio-Temporal data. Wiley, Hoboken

    Google Scholar 

  • Finley A, Banerjee S, Gelfand A (2015) Spbayes for large univariate and multivariate point-referenced spatio-temporal data models. J Stat Softw 63:1–28

    Article  Google Scholar 

  • Furrer R, Sain SR, Nychka DW, Meehl GA (2007) Multivariate Bayesian analysis of atmosphere-ocean general circulation models. Environ Ecol Stat 14(3):249–266. doi:10.1007/s10651-007-0018-z

    Article  Google Scholar 

  • Gamerman D, Lopes HF (2006) Markov chain Monte Carlo - stochastic simulation for Bayesian inference, 2nd edn. Chapman and Hall, London

    Google Scholar 

  • Giorgi F, Jones C, Asrar G (2009) Addressing climate information needs at the regional level: the CORDEX framework. WMO Bull 58:175–183

    Google Scholar 

  • Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “Reliability Ensemble Averaging (REA)” method. J Clim 15:1141–1158

    Article  Google Scholar 

  • Hannachi A, Jolliffe IT, Stephenson DB (2007) Empirical orthogonal functions and related techniques in atmospheric science: a review. Int J Climatolo 27:1119–1152

    Article  Google Scholar 

  • Harris GR, Sexton DMH, Booth BBB, Collins M, Murphy JM (2013) Probabilistic projections of transient climate change. Clim Dyn 40(11):2937–2972. doi:10.1007/s00382-012-1647-y

    Article  Google Scholar 

  • Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo JJ, Fiorino M, Potter GL (2002) “NCEP-DOE AMIP-II” reanalysis. Bull Am Meteorol Soc 83:1631–1643

    Article  Google Scholar 

  • Mearns L, Gutowski W, Jones R, Leung L, McGinnis S, Nunes A, Qian Y (2009) A regional climate change assessment program for North America. EOS 90:311–312

    Article  Google Scholar 

  • Mearns L, Sain S, Leung L, Bukovsky M, McGinnis S, Biner S, Caya D, Arritt R, Gutowski W, Takle E, Snyder M, Jones R, Nunes A, Tucker S, Herzmann D, McDaniel L, Sloan L (2013) Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP). Clim Chang 120(4):965–975. doi:10.1007/s10584-013-0831-3

    Article  Google Scholar 

  • Meehl G, Covey C, Delworth T, Latif M, McAvaney B, Mitchell J, Stouffer R, Taylor K (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394

    Article  Google Scholar 

  • Rougier J, Goldstein M, House L (2013) Second-order exchangeability analysis for multi-model ensembles. J Am Stat Assoc 108:852–863. doi:10.1080/01621459.2013.802963

    Article  Google Scholar 

  • Salazar E, Sansó B, Finley A, Hammerling D, Steinsland I, Wang X, Delamater P (2011) Comparing and blending regional climate model predictions for the american southwest. J Agric Biol Environ Stat 16:586–605. doi:10.1007/s13253-011-0074-6

    Article  Google Scholar 

  • Sexton DMH, Murphy JM, Collins M, Webb MJ (2011) Multivariate probabilistic projections using imperfect climate models part i: outline of methodology. Clim Dyn 38(11):2513–2542. doi:10.1007/s00382-011-1208-9

    Google Scholar 

  • Smith R, Tebaldi C, Nychka D, Mearns L (2009) Bayesian modeling of uncertainty in ensembles of climate models. J Am Stat Assoc:97–116

  • Stephenson DB, Collins M, Rougier JC, Chandler RE (2012) Statistical problems in the probabilistic prediction of climate change. Environmetrics 23:364–372. doi:10.1002/env.2153

    Article  Google Scholar 

  • Taylor K, Stouffer R, Meehl G (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498

    Article  Google Scholar 

  • Tebaldi C, Arblaster J, Knutti R (2011) Mapping model agreement on future climate projections. Geophys Res Lett 38:L2371. doi:10.1029/2011GL049863

    Article  Google Scholar 

  • Tebaldi C, Sansó B (2009) Joint projections of temperature and precipitation change from multiple climate models: a hierarchical bayes approach. J R Stat Soc A 172:83–106

    Article  Google Scholar 

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Acknowledgments

Dorit Hammerling had partial support from the NSF Research Network on Statistics in the Atmosphere and Ocean Sciences (STATMOS) through grant DMS-1106862. Bruno Sansó had partial support from grant DMS-1513076. We thank Anthony Tracy for his help with specifying the small sub regions. All the data used in this work is available upon request. Andrew Finley was supported by National Science Foundation (NSF) DMS-1513481, EF-1137309, EF-1241874, and EF-1253225, as well as NASA Carbon Monitoring System grants.

Disclaimer

Although Esther Salazar is a FDA/CTP employee, this work was not done as part her official duties. This publication/presentation reflects the views of the author and should not be construed to reflect the FDA/CTP’s views or policies.

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Authors and Affiliations

  1. U.S. Food and Drug Administration, Center for Tobacco Products, Silver Spring, MD, USA

    Esther Salazar

  2. Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA

    Dorit Hammerling & Linda O. Mearns

  3. Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA

    Xia Wang

  4. Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA, USA

    Bruno Sansó

  5. Departments of Forestry and Geography, Michigan State University, Lansing, MI, USA

    Andrew O. Finley

Authors
  1. Esther Salazar
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  2. Dorit Hammerling
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  3. Xia Wang
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  4. Bruno Sansó
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  5. Andrew O. Finley
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  6. Linda O. Mearns
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Corresponding author

Correspondence to Bruno Sansó.

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Salazar, E., Hammerling, D., Wang, X. et al. Observation-based blended projections from ensembles of regional climate models. Climatic Change 138, 55–69 (2016). https://doi.org/10.1007/s10584-016-1722-1

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  • DOI: https://doi.org/10.1007/s10584-016-1722-1

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