Climatic Change

, Volume 146, Issue 3–4, pp 319–333 | Cite as

Emulating mean patterns and variability of temperature across and within scenarios in anthropogenic climate change experiments

  • Stacey E. Alexeeff
  • Doug Nychka
  • Stephan R. Sain
  • Claudia Tebaldi


There are many climate change scenarios that are of interest to explore by climate models, but computational power limits the total number of model runs. Pattern scaling is a useful approach to approximate mean changes in climate model projections, and we extend this methodology to build a climate model emulator that also accounts for variability of temperature projections at the seasonal scale. Using 30 runs from the NCAR/DOE CESM1 large initial condition ensemble for RCP8.5 from 2006 to 2080, we fit a pattern scaling model to grid-specific seasonal average temperature change. We then use this fitted model to emulate seasonal average temperature change for the RCP4.5 scenario based on its global average temperature trend. By using a linear mixed-effects model and carefully resampling the residuals from the RCP8.5 model, we emulate the variability of RCP4.5 and allow the variability to depend on global average temperature. Specifically, we emulate both the internal variability affecting the long-term trends across initial condition ensemble members, and the variability superimposed on the long-term trend within individual ensemble members. The 15 initial condition ensemble members available for RCP4.5 from the same climate model are then used to validate the emulator. We view this approach as a step forward in providing relevant climate information for avoided impacts studies, and more broadly for impact models, since we allow both forced changes and internal variability to play a role in determining future impact risks.


Initial condition ensemble Internal variability Pattern scaling Emulator 



The authors of this paper acknowledge the large contribution provided by the modeling groups to create the CESM model and to generate the initial condition ensembles used in this analysis. This work was supported in part by grants from the National Science Foundation (NSF). NCAR is managed by the University Corporation for Atmospheric Research under the sponsorship of the NSF. S. A. was supported by NSF Mathematical Sciences Postdoctoral Research Fellowship Award Number 1304321. C. T. was supported by the Regional and Global Climate Modeling Program (RGCM) of the U.S. Department of Energy’s, Office of Science (BER), Cooperative Agreement DE-FC02-97ER62402.

Supplementary material

10584_2016_1809_MOESM1_ESM.docx (1000 kb)
ESM 1 (DOCX 999 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Stacey E. Alexeeff
    • 1
  • Doug Nychka
    • 1
  • Stephan R. Sain
    • 1
  • Claudia Tebaldi
    • 2
  1. 1.Institute for Mathematics Applied to GeosciencesNational Center for Atmospheric ResearchBoulderUSA
  2. 2.Climate and Global Dynamics LaboratoryNational Center for Atmospheric ResearchBoulderUSA

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