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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Beniston M, Stephenson D, Christensen O, Ferro CT, Frei C, Goyette S, Halsnaes K, Holt T, Jylhä K, Koffi B, Palutikof J, Schöll R, Semmler T, Woth K (2007) Future extreme events in European climate: an exploration of regional climate model projections. Clim Chang 81(1):71–95
Bondeau A, Smith PC, Zaehle S, Schaphoff S, Lucht W, Cramer W, Gerten D, Lotze-Campen H, MÜLler C, Reichstein M, Smith B (2007) Modelling the role of agriculture for the twentieth century global terrestrial carbon balance. Glob Chang Biol 13(3):679–706
Cabré M, Solman SA, Nuñez MN (2010) Creating regional climate change scenarios over southern South America for the 2020’s and 2050’s using the pattern scaling technique: validity and limitations. Clim Chang 98(3–4):449–469
Deryng D, Sacks WJ, Barford CC, Ramankutty N (2011) Simulating the effects of climate and agricultural management practices on global crop yield. Glob Biogeochem Cycles 25(2):GB2006
Fitzmaurice GM, Laird NM, Ware JH (2011) Applied longitudinal analysis, 2nd Edition, Wiley
Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90(8):1095–1107
Hurrell JW, Marika M, Holland PR, Gent S, Ghan JE, Kay PJ, Kushner J-F, Lamarque (2013) The community earth system model: a framework for collaborative research. Bulletin of the American Meteorological Society 94(9):1339–1360
Izaurralde RC, Williams JR, McGill WB, Rosenberg NJ, Jakas MCQ (2006) Simulating soil C dynamics with EPIC: model description and testing against long-term data. Ecol Model 192(3–4):362–384
Kay JE, Deser C, Phillips A, Mai A, Hannay C, Strand G, Arblaster, JM, Bates SC, Danabasoglu G, Edwards J, Holland M, Kushner P, Lamarque JWF, Lawrence D, Lindsay K, Middleton A, Munoz E, Neale R, Oleson K, Polvani L, Vertenstein M (2014 (to appear)) The community earth system model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society
Maracchi G, Sirotenko O, Bindi M (2005) Impacts of present and future climate variability on agriculture and forestry in the temperate regions: Europe. Clim Chang 70(1–2):117–135
Mitchell T (2003) Pattern scaling: an examination of the accuracy of the technique for describing future climates. Clim Chang 60(3):217–242
NCAR CGD (2016) Climate Variability Diagnostics Package. Retrieved Tue Feb 2, 2016, from http://webext.cgd.ucar.edu/Multi-Case/CVDP_ex/CESM-CAM5-BGC-LE_1920-2014/
Patz JA, Campbell-Lendrum D, Holloway T, Foley JA (2005) Impact of regional climate change on human health. Nature 438(7066):310–317
Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li J, Zhou L, Liu H, Ma Y, Ding Y, Friedlingstein P, Liu C, Tan K, Yu Y, Zhang T, Fang J (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311):43–51
Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh TAM, Schmid E, Stehfest E, Yang H, Jones JW (2014) Assessing agricultural risks of climate change in the twenty-first century in a global gridded crop model intercomparison. Proc Natl Acad Sci 111(9):3268–3273
Ruosteenoja K, Tuomenvirta H, Jylhä K (2007) GCM-based regional temperature and precipitation change estimates for Europe under four SRES scenarios applying a super-ensemble pattern-scaling method. Clim Chang 81(1):193–208
Salinger MJ (2005) Climate variability and change: past, present and future – an overview. Clim Chang 70(1–2):9–29
Schar C, Vidale PL, Luthi D, Frei C, Haberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427(6972):332–336
Tebaldi C, Arblaster JM (2014) Pattern scaling: its strengths and limitations, and an update on the latest model simulations. Clim Chang 122(3):459–471
Watterson IG (2008) Calculation of probability density functions for temperature and precipitation change under global warming. J Geophys Res Atmos 113(D12):D12106
Wilby RL (1997) Non-stationarity in daily precipitation series: implications for gcm down-scaling using atmospheric circulation indices. Int J Climatol 17(4):439–454
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.
This article is part of a Special Issue on “Benefits of Reduced Anthropogenic Climate ChangE (BRACE)” edited by Brian O’Neill and Andrew Gettelman.
Electronic supplementary material
About this article
Cite this article
Alexeeff, S.E., Nychka, D., Sain, S.R. et al. Emulating mean patterns and variability of temperature across and within scenarios in anthropogenic climate change experiments. Climatic Change 146, 319–333 (2018). https://doi.org/10.1007/s10584-016-1809-8
- Initial condition ensemble
- Internal variability
- Pattern scaling