Skip to main content

Assessing the Reliability of Climate Models, CMIP5

Abstract

In spite of the yet incomplete subsample of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) model ensemble to date, evaluation of these models is underway. Novel diagnostics and analysis methods are being utilized in order to explore the skill of particular processes, the degree to which models have improved since CMIP3, and particular features of the hindcasts, decadal and centennial projections. These assessments strongly benefit from the increasing availability of state-of-the-art data sets and model output processing techniques. Also paleo-climate analysis proves to be useful for demonstrating the ability of models to simulate climate conditions that are different from present day. The existence of an increasingly wide ensemble of model simulations re-emphasizes the need to carefully consider the implications of model spread. Disparity between projected results does imply that model uncertainty exists, but not necessarily reflects a true estimate of this uncertainty. Projections generated by models with a similar origin or utilizing parameter perturbation techniques generally show more mutual agreement than models with different development histories. Weighting results from different models is a potentially useful technique to improve projections, if the purpose of the weighting is clearly identified. However, there is yet no consensus in the community on how to best achieve this.

These findings, discussed at the session “Assessing the reliability of climate models: CMIP5” of the World Climate Research Program (WCRP) Open Science Conference (OSC), illustrate the need for comprehensive and coordinated model evaluation and data collection. The role that WCRP can play in this coordination is summarized at the end of this chapter.

Keywords

  • Climate model assessment
  • Evaluation
  • Model ensembles
  • Process verification
  • CMIP5
  • WCRP coordinations

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-94-007-6692-1_9
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-94-007-6692-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)

Notes

  1. 1.

    https://www.ipcc-wg1.unibe.ch/publications/supportingmaterial/IPCC_EM_MultiModelEvaluation_MeetingReport.pdf

Abbreviations

AMIP:

Atmospheric Model Intercomparison Project

CMIP3:

CMIP5 3rd, 5th Coupled Model Intercomparison Project

ENSO:

El Nino Southern Oscillation

ESM:

Earth System Model

GCM:

General Circulation Model

IGBP:

International Geosphere-Biosphere Program

IHDP:

International Human Dimensions Program

IPCC:

Intergovernmental Panel on Climate Change

ISCCP:

International Satellite Cloud Climatology Project

MME:

Multi Model Ensemble

OSC:

Open Science Conference

PCMDI:

Program for Climate Model Diagnosis and Intercomparison

PPE:

Perturbed Physics Ensemble

WCRP:

World Climate Research Program

References

  • Allen RJ, Norris JR, Wild M (2012) Evaluation of multidecadal variability in CMIP5 surface solar radiation and inferred underestimation of aerosol direct effects. Submitted to J Geophys Res

    Google Scholar 

  • Anav A, Friedlingstein P, Kidston M, Bopp L, Ciais P, Cox P, Jones C, Jung M, Myneni R, Zhu Z (2013) Evaluating the land and ocean components of the carbon cycle in the CMIP5 Earth System Models. J Climate. doi:10.1175/JCLI-D-12-00417.1 (in press)

  • Bates SC, Fox-Kemper B, Jayne SR, Large WG, Stevenson S, Yeager SG (2012) Mean biases, variability, and trends in air-sea fluxes and SST in the CCSM4. J Climate 25:7781–7801. doi:10.1175/JCLI-D-11-00442.1

    CrossRef  Google Scholar 

  • Bodas-Salcedo A and Coauthors (2011) COSP: satellite simulation software for model assessment. Bull Am Meteorol Soc 92:1023–1043. doi:10.1175/2011BAMS2856.1

  • Branstator G, Teng H (2010) Two limits of initial-value decadal predictability in a CGCM. J Clim 23(23):6292–6311. doi:10.1175/2010JCLI3678.1

    CrossRef  Google Scholar 

  • CLIVAR (2011) WCRP Coupled Model Intercomparison Project – Phase 5 – CMIP5 –, CLIVAR exchanges, Special issue no 56, vol 16(2), May 2011

    Google Scholar 

  • Cox PM, Pearson D, Booth BB, Friedlingstein P, Huntingford C, Jones CD, Luke CM (2012) Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494:341–344. doi:10.1038/nature11882

    CrossRef  Google Scholar 

  • Dufresne J-L, Bony S (2008) An assessment of the primary sources of spread of global warming estimates from coupled atmosphere–ocean models. J Clim 21:5135–5144

    CrossRef  Google Scholar 

  • Dwyer JG, Norris JR, Ruckstuhl C (2010) Do climate models reproduce observed solar dimming and brightening over China and Japan? J Geophys Res 115:D00K08. doi:10.1029/2009JD012945

    CrossRef  Google Scholar 

  • Friedlingstein P et al (2006) Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison’. J Clim 19(15):3337–3353

    CrossRef  Google Scholar 

  • Guilyardi E, Cai W, Collins M, Fedorov A, Jin F-F, Kumar A, Sun D-Z, Wittenberg A (2011) New strategies for evaluating ENSO processes in climate models. BAMS. doi:10.1175/BAMS-D-11-00106.1

    Google Scholar 

  • Hall A, Qu X (2006) Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys Res Lett 33:L03502. doi:10.1029/2005GL025127

    CrossRef  Google Scholar 

  • Hargreaves HC, Paul A, Ohgait R, Abe-Ouchi A, Annan JD (2011) Are paleoclimate model ensembles consistent with the MARGO data synthesis? Clim Past Discuss 7:775–807. doi:10.5194/cpd-7-775-2011

    CrossRef  Google Scholar 

  • Hawkins E, Sutton RT (2009) The potential to narrow uncertainty in regional climate predictions. BAMS 90:1095. doi:10.1175/2009BAMS2607.1

    CrossRef  Google Scholar 

  • Huber M, Mahlstein I, Wild M, Fasullo J, Knutti R (2011) Constraints on climate sensitivity from radiation patterns in climate models. J Clim 24:1034–1052. doi:10.1175/2010JCLI3403.1

    CrossRef  Google Scholar 

  • Jacob C (2011) From regional weather to global climate; oral presentation at OSC. http://conference2011.wcrp-climate.org/abstracts/jackob_A4.pdf

  • Jiang JH, Su H, Zhai C, Perun VS et al (2012) Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA A-train satellite observations. J Geophys Res 117(D1410):24 pp. doi:10.1029/2011JD017237

    Google Scholar 

  • Jung M, Reichstein M, Bondeau A (2009) Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences 6:2001–2013

    CrossRef  CAS  Google Scholar 

  • Knutti R (2008) Should we believe model predictions of future climate change? Trienn Issue Earth Sci Philos Trans R Soc A 366:4647–4664. doi:10.1098/rsta.2008.0169

    CrossRef  Google Scholar 

  • Knutti R et al (2010) Good practice guidance paper on assessing and combining multi model climate projections. In: Stocker TF, Qin D, Plattner G.-K, Tignor M, Midgley PM (eds) Meeting report of the Intergovernmental Panel on Climate Change expert meeting on assessing and combining multi model climate Projections, IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland

    Google Scholar 

  • MARGO Project Members (2009) Constraints on the magnitude and patterns of ocean cooling at the Last Glacial Maximum. Nat Geosci 2:127–132. doi:10.1038/ngeo411

    CrossRef  Google Scholar 

  • Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38:L08703. doi:10.1029/2011GL046864

    CrossRef  Google Scholar 

  • Matei D, Baehr J, Jungclaus JH, Haak H, Müller WA, Marotzke J (2012) Multiyear prediction of monthly mean atlantic meridional overturning circulation at 26.5°N. Science 335:76–79. doi:10.1126/science.1210299

    CrossRef  CAS  Google Scholar 

  • Msadek R (2011) Comparing the meridional heat transport at 26.5ºN and its relationship with the MOC in two CMIP5 coupled models and in RAPID-array observations (oral presentation WCRP OSC Denver, Oct 2011)

    Google Scholar 

  • Reichler T, Kim J (2008) How well do coupled models simulate today’s climate? Bull Am Meteorol Soc 89:303–311

    CrossRef  Google Scholar 

  • Sakaguchi K, Xubin Z, Brunke MA (2012) Temporal- and spatial-scale dependence of three CMIP3 climate models in simulating the surface temperature trend in the twentieth century. J Clim 25:2456–2470. doi:10.1175/JCLI-D-11-00106.1, http://dx.doi.org/

    CrossRef  Google Scholar 

  • Schmittner A, Urban NM, Shakun JD, Mahowald NM, Clark PU, Bartlein PJ, Mix AC, Rosell-Melé A (2011) Climate ensitivity estimated from temperature reconstructions of the last glacial maximum. Science 334(6061):1385–1388. doi:10.1126/science.1203513

    CrossRef  CAS  Google Scholar 

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

    CrossRef  Google Scholar 

  • Teixeira J, Waliser D, Ferraro R, Gleckler P, Potter G (2011) Satellite observations for CMIP5 simulations. CLIVAR Exchanges No. 56, 16(2) May 2011

    Google Scholar 

  • Williams KD, Webb MJ (2009) A quantitative performance assessment of cloud regimes in climate models. Clim Dyn 33:141–157. doi:10.1007/s00382-008-0443-1

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bart van den Hurk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

van den Hurk, B. et al. (2013). Assessing the Reliability of Climate Models, CMIP5. In: Asrar, G., Hurrell, J. (eds) Climate Science for Serving Society. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6692-1_9

Download citation