Climate Dynamics

, Volume 48, Issue 9–10, pp 3035–3045 | Cite as

The influence of model resolution on temperature variability

  • Jeremy M. Klavans
  • Andrew Poppick
  • Shanshan Sun
  • Elisabeth J. MoyerEmail author


Understanding future changes in climate variability, which can impact human activities, is a current research priority. It is often assumed that a key part of this effort involves improving the spatial resolution of climate models; however, few previous studies comprehensively evaluate the effects of model resolution on variability. In this study, we systematically examine the sensitivity of temperature variability to horizontal atmospheric resolution in a single model (CCSM3, the Community Climate System Model 3) at three different resolutions (T85, T42, and T31), using spectral analysis to describe the frequency dependence of differences. We find that in these runs, increased model resolution is associated with reduced temperature variability at all but the highest frequencies (2–5 day periods), though with strong regional differences. (In the tropics, where temperature fluctuations are smallest, increased resolution is associated with increased variability.) At all resolutions, temperature fluctuations in CCSM3 are highly spatially correlated, implying that the changes in variability with model resolution are driven by alterations in large-scale phenomena. Because CCSM3 generally overestimates temperature variability relative to reanalysis output, the reductions in variability associated with increased resolution tend to improve model fidelity. However, the resolution-related variability differences are relatively uniform with frequency, whereas the sign of model bias changes at interannual frequencies. This discrepancy raises questions about the mechanisms underlying the improvement at subannual frequencies. The consistent response across frequencies also implies that the atmosphere plays a significant role in interannual variability.


Climate variability Variability Model resolution CCSM3 Spectral analysis 



We thank Matthew Huber, Robert Jacob, Ben Kirtman, Leonard Smith, and Michael Stein for helpful comments on this paper. This research was performed as part of the Center for Robust Decision-making on Climate and Energy Policy (RDCEP) at the University of Chicago, funded by a grant from the National Science Foundation (NSF) Decision Making Under Uncertainty program (SES-0951576). Model runs were completed by NCAR and are available publicly on This work was completed in part with resources provided by the University of Chicago Research Computing Center.

Supplementary material

382_2016_3249_MOESM1_ESM.pdf (11 mb)
Supplementary material 1 (pdf 11271 KB)


  1. Alexander LV, Zhang X, Peterson TC et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res Atmos 111(D5):D05,109CrossRefGoogle Scholar
  2. Bacmeister JT, Wehner MF, Neale RB et al (2013) Exploratory high-resolution climate simulations using the community atmosphere model (CAM). J Clim 27(9):3073–3099CrossRefGoogle Scholar
  3. Clement A, Bellomo K, Murphy LN et al (2015) The Atlantic multidecadal oscillation without a role for ocean circulation. Science 350(6258):320–324CrossRefGoogle Scholar
  4. Collier JC, Zhang GJ (2007) Effects of increased horizontal resolution on simulation of the North American monsoon in the NCAR CAM3: an evaluation based on surface, satellite, and reanalysis data. J Clim 20(9):1843–1861CrossRefGoogle Scholar
  5. Collins WD, Bitz CM, Blackmon ML et al (2006) The community climate system model version 3 (CCSM3). J Clim 19(11):2122–2143CrossRefGoogle Scholar
  6. Delworth TL, Rosati A, Anderson W et al (2012) Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. J Clim 25(8):2755–2781CrossRefGoogle Scholar
  7. Deser C, Capotondi A, Saravanan R et al (2006) Tropical Pacific and Atlantic climate variability in CCSM3. J Clim 19(11):2451–2481CrossRefGoogle Scholar
  8. DeWeaver E, Bitz CM (2006) Atmospheric circulation and its effect on Arctic Sea Ice in CCSM3 simulations at medium and high resolution. J Clim 19(11):2415–2436CrossRefGoogle Scholar
  9. Gent PR, Yeager SG, Neale RB et al (2010) Improvements in a half degree atmosphere/land version of the CCSM. Clim Dyn 34(6):819–833CrossRefGoogle Scholar
  10. Gualdi S, Navarra A, von Storch H (1997) Tropical intraseasonal oscillation appearing in operational analyses and in a family of general circulation models. J Atmos Sci 54(9):1185–1202CrossRefGoogle Scholar
  11. Guemas V, Codron F (2011) Differing impacts of resolution changes in latitude and longitude on the midlatitudes in the LMDZ atmospheric GCM. J Clim 24(22):5831–5849CrossRefGoogle Scholar
  12. Guilyardi E, Gualdi S, Slingo J et al (2004) Representing El Niño in coupled ocean–atmosphere GCMs: the dominant role of the atmospheric component. J Clim 17(24):4623–4629CrossRefGoogle Scholar
  13. Hack JJ, Caron JM, Danabasoglu G et al (2006) CCSM–CAM3 climate simulation sensitivity to changes in horizontal resolution. J Clim 19(11):2267–2289CrossRefGoogle Scholar
  14. Hansen J, Sato M, Ruedy R (2012) Perception of climate change. Proc Natl Acad Sci 109(37):E2415–E2423CrossRefGoogle Scholar
  15. Holmes CR, Woollings T, Hawkins E et al (2015) Robust future changes in temperature variability under greenhouse gas forcing and the relationship with thermal advection. J Clim 2015:2221–2236Google Scholar
  16. Huntingford C, Jones PD, Livina VN et al (2013) No increase in global temperature variability despite changing regional patterns. Nature 500(7462):327–330CrossRefGoogle Scholar
  17. Iorio JP, Duffy PB, Govindasamy B et al (2004) Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental United States. Clim Dyn 23(3–4):243–258Google Scholar
  18. IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (eds). Cambridge University Press, Cambridge, UK and New York, USA, p 582Google Scholar
  19. IPCC (2013) Evaluation of climate models. In: Flato G, Marotzke J, Abiodun B et al (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK and New York, USA, pp 741–866Google Scholar
  20. IPCC (2014) Human health: impacts, adaptation, and co-benefits. In: Smith KR, Woodward A, et al. (eds) Climate change 2014: impacts, adaptation, and vulnerability. Cambridge University Press, Cambridge, UK and New York, USA, pp 709–754Google Scholar
  21. Jones GS, Stott PA, Christidis N (2013) Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. J Geophys Res Atmos 118(10):4001–4024CrossRefGoogle Scholar
  22. Jung T, Miller MJ, Palmer TN et al (2012) High-resolution global climate simulations with the ECMWF model in project Athena: experimental design, model climate, and seasonal forecast skill. J Clim 25(9):3155–3172CrossRefGoogle Scholar
  23. Kanamitsu M, Ebisuzaki W, Woollen J et al (2002) NCEP–DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83(11):1631–1643CrossRefGoogle Scholar
  24. Kinter JL, Cash B, Achuthavarier D et al (2013) Revolutionizing climate modeling with project Athena A: multi-institutional, international collaboration. Bull Am Meteorol Soc 94(2):231–245CrossRefGoogle Scholar
  25. Kirtman BP, Bitz C, Bryan F et al (2012) Impact of ocean model resolution on CCSM climate simulations. Clim Dyn 39(6):1303–1328CrossRefGoogle Scholar
  26. Kobayashi C, Sugi M (2004) Impact of horizontal resolution on the simulation of the Asian summer monsoon and tropical cyclones in the JMA global model. Clim Dyn 23(2):165–176CrossRefGoogle Scholar
  27. Laepple T, Huybers P (2014a) Global and regional variability in marine surface temperatures. Geophys Res Lett 41(7):2528–2534CrossRefGoogle Scholar
  28. Laepple T, Huybers P (2014b) Ocean surface temperature variability: large model-data differences at decadal and longer periods. Proc Natl Acad Sci USA 111(47):16,682–16,687CrossRefGoogle Scholar
  29. Leeds WB, Moyer EJ, Stein ML (2015) Simulation of future climate under changing temporal covariance structures. Adv Stat Climatol Meteorol Oceanogr 1(1):1–14CrossRefGoogle Scholar
  30. Marti O, Braconnot P, Dufresne JL et al (2010) Key features of the IPSL ocean atmosphere model and its sensitivity to atmospheric resolution. Clim Dyn 34(1):1–26CrossRefGoogle Scholar
  31. Meehl GA, Zwiers F, Evans J et al (2000) Trends in extreme weather and climate events: issues related to modeling extremes in projections of future climate change. Bull Am Meteorol Soc 81(3):427–436CrossRefGoogle Scholar
  32. Morak S, Hegerl GC, Christidis N (2013) Detectable changes in the frequency of temperature extremes. J Clim 26(5):1561–1574CrossRefGoogle Scholar
  33. Navarra A, Gualdi S, Masina S et al (2008) Atmospheric horizontal resolution affects tropical climate variability in coupled models. J Clim 21(4):730–750CrossRefGoogle Scholar
  34. Neale RB, Richter JH, Jochum M (2008) The impact of convection on ENSO: from a delayed oscillator to a series of events. J Clim 21(22):5904–5924CrossRefGoogle Scholar
  35. Otto-Bliesner BL, Tomas R, Brady EC et al (2006) Climate sensitivity of moderate- and low-resolution versions of CCSM3 to preindustrial forcings. J Clim 19(11):2567–2583CrossRefGoogle Scholar
  36. Poppick A, McInerney DJ, Moyer EJ et al (2016) Temperatures in transient climates: improved methods for simulations with evolving temporal covariances. Ann Appl Stat 10(1):477–505CrossRefGoogle Scholar
  37. Reichler T, Kim J (2008) How well do coupled models simulate today’s climate? Bull Am Meteorol Soc 89(3):303–311CrossRefGoogle Scholar
  38. Roeckner E, Brokopf R, Esch M et al (2006) Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J Clim 19(16):3771–3791CrossRefGoogle Scholar
  39. Saha S, Moorthi S, Pan HL et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91(8):1015–1057CrossRefGoogle Scholar
  40. Schneider T, Bischoff T, Plotka H (2014) Physics of changes in synoptic midlatitude temperature variability. J Clim 28(6):2312–2331CrossRefGoogle Scholar
  41. Thornton PK, Ericksen PJ, Herrero M et al (2014) Climate variability and vulnerability to climate change: a review. Glob Chang Biol 20(11):3313–3328CrossRefGoogle Scholar
  42. Wehner MF, Smith RL, Bala G et al (2010) The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model. Clim Dyn 34(2–3):241–247CrossRefGoogle Scholar
  43. Yeager SG, Shields CA, Large WG et al (2006) The low-resolution CCSM3. J Clim 19(11):2545–2566CrossRefGoogle Scholar
  44. Zhang H, Clement A, Di Nezio P (2014) The South Pacific meridional mode: a mechanism for ENSO-like variability. J Clim 27(2):769–783CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jeremy M. Klavans
    • 1
    • 2
  • Andrew Poppick
    • 3
  • Shanshan Sun
    • 4
  • Elisabeth J. Moyer
    • 4
    Email author
  1. 1.Center for Robust Decision-making on Climate and Energy PolicyUniversity of ChicagoChicagoUSA
  2. 2.Rosenstiel School of Marine and Atmospheric Science, University of MiamiMiamiUSA
  3. 3.Department of StatisticsUniversity of ChicagoChicagoUSA
  4. 4.Department of the Geophysical SciencesUniversity of ChicagoChicagoUSA

Personalised recommendations