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Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation

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Abstract

Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy.

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References

  • Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4:1147–1167

    Article  Google Scholar 

  • Adler RF, Gu G, Huffman GJ (2012) Estimating climatological bias errors for the global precipitation climatology project (GPCP). J Appl Meteor Climatol 51:84–99

    Article  Google Scholar 

  • Blázquez J, Nuñez MN (2013) Analysis of uncertainties in future climate projections for South America: comparison of WCRP-CMIP3 and WCRP-CMIP5 models. Clim Dyn 41:1039–1056

    Google Scholar 

  • Brands S, Herrera S, Fernández J, Gutiérrez JM (2013) How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa? Clim Dyn 41:803–817

    Article  Google Scholar 

  • Branstator G, Teng H (2012) Potential impact of initialization on decadal predictions as assessed for CMIP5 models. Geophys Res Lett 39:L12703. doi:10.1029/2012GL051974

    Google Scholar 

  • Brohan P, Kennedy JJ, Harris I, Tett SFB, Jones PD (2006) Uncertainty estimates in regional and global observed temperature changes: a new data set from 1850. J Geophys Res 111:D12106. doi:10.1029/2005JD006548

    Article  Google Scholar 

  • Cattiaux J, Douville H, Peings Y (2013) European temperatures in CMIP5: origins of present-day biases and future uncertainties. Clim Dyn 41:2889–2907

    Google Scholar 

  • Deser C, Knutti R, Solomon S, Phillips AS (2012) Communication of the role of natural variability in future North American climate. Nat Clim Chang 2:775–779

    Article  Google Scholar 

  • Dessai S, Lu X, Hulme M (2005) Limited sensitivity analysis of regional climate change probabilities for the twenty first century. J Geophy Res 110:D19108. doi:10.1029/2005JD005919

    Article  Google Scholar 

  • Giorgi F, Francisco R (2000) Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Clim Dyn 16:169–182

    Article  Google Scholar 

  • Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:D06104. doi:10.1029/2007JD008972

    Google Scholar 

  • Hulme M, Pielke R Jr, Dessai S (2009) Keeping prediction in perspective. Nat Rep Clim Chang. doi:10.1038/climate.2009.110

    Google Scholar 

  • Joetzjer E, Douville H, Delire C, Ciais P (2013) Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim Dyn 41:2921–2936

    Article  Google Scholar 

  • Kalnay EM et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteor Soc 77:437–471

    Article  Google Scholar 

  • Kanamitsu M et al (2002) NCEP-DOE AMIP-II Reanalysis (R-2). Bull Am Meteor Soc 83:1631–1643

    Article  Google Scholar 

  • Kao SC, Ganguly A (2011) Intensity, duration, frequency of precipitation extremes under twenty first century warming scenarios. J Geophy Res 116:D16119. doi:10.1029/2010JD015529

    Article  Google Scholar 

  • Kharin VV, Zweris FW (2002) Climate predictions with multimodel ensembles. J Clim 15:793–799. doi:10.1175/1520-0442(2002

    Article  Google Scholar 

  • Kharin VV, Zwiers FW, Zhang X, Hegrel GC (2007) Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J Clim 20:1419–1444. doi:10.1175/JCLI4066.1

    Article  Google Scholar 

  • Knutti R, Sedlacek J (2012) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Chang 3:369–373

    Article  Google Scholar 

  • Knutti R, Abramowitz G, Collins M et al (2010) Good practice guidance paper on assessing and combining multi model climate projections. In: meeting report of the intergovernmental panel on climate change expert meeting on assessing and combining multi model climate projections [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, and P. M. Midgley (eds.)]. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland

  • Kodra E, Steinhaeuser K, Ganguly A (2011) Persisting cold extremes under twenty first century warming scanarios. Geophys Res Lett 38:L08705. doi:10.1029/2011GL047103

    Google Scholar 

  • Kug J-S, Ham Y-G, Lee J-Y, Jin F–F (2012) Improved simulation of two types of El-Nino in CMIP5 models. Environ Res Lett 7:034002. doi:10.1088/1748-9326/7/3/034002

    Article  Google Scholar 

  • Liu C, Allan RP, Huffman GJ (2012) Co-variation of temperature and precipitation in CMIP5 models and satellite observations. Geophys Res Lett 39:L13803. doi:10.1029/2012GL052093

    Google Scholar 

  • Maslin M, Austin P (2012) Uncertainty: climate models at their limit? Nature 486:183–184

    Article  Google Scholar 

  • Meehl GA, Goddard L, Murphy J et al (2009) Decadal prediction: can it be skillful? Bull Am Meteor Soc 90:1467–1485. doi:10.1175/2009BAMS2778.1

    Article  Google Scholar 

  • Monerie PA, Fontaine B, Roucou P (2012) Expected future changes in the African monsoon between 2030 and 2070 using some CMIP3 and CMIP5 models under a medium-low RCP scenario. J Geophys Res. doi:10.1029/2012JD017510

    Google Scholar 

  • Moss R, Babiker M, Brinkman S et al (2008) Towards new scenarios for analysis of emissions, climate change, impacts, and response Strategies. Intergovernmental Panel on Climate Change. Geneva: 132 pp

  • Moss RH, Edmonds JA, Hibbard KA et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756

    Article  Google Scholar 

  • Perkins SE, Pitman AJ, Holbrook NJ, Mcaneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20:4356–4376

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Rogelj J, Meinshausen M, Knutti R (2012) Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat Clim Chang 2:248–253. doi:10.1038/nclimate1385

    Article  Google Scholar 

  • Rupp D, Abatzoglou J, Hegewisch K, Mote P (2013) Evaluation of CMIP5 twentieth century climate simulations for the Pacific Northwest USA. J Geophys Res 118:10884–10906. doi:10.1002/jgrd.50843

    Google Scholar 

  • Sanderson BM, Knutti R (2012) On the interpretation of constrained climate model ensembles. Geophys Res Lett 39:L16708. doi:10.1029/2012GL052665

    Google Scholar 

  • Santer BD, Taylor KE, Glecker PJ et al (2009) Incorporating model quality information in climate change detection and attribution studies. Proc Natl Acad Sci 106:14778–14783. doi:10.1073/pnas.0901736106

    Article  Google Scholar 

  • IPCC Special Report on Emissions Scenarios (eds Nakicenovic, N. & Swart, R.) (Cambridge Univ. Press, 2007)

  • Shukla J, Hagedorn R, Hoskins B et al (2009) Revolution in climate prediction is both necessary and possible: a declaration at the world modelling summit for climate prediction. Bull Am Meteor Soc 2:175–178. doi:10.1175/2008BAMS2759

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zweris FW, Zhang X, Bronaugh D (2013a) Climate extreme indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res Atmos 118:1–18. doi:10.1002/jgrd.50203

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zweris FW, Zhang X, Bronaugh D (2013b) Climate extreme indices in the CMIP5 multimodel ensemble: part 2. Future climate projections. J Geophys Res Atmos 118:2473–2493. doi:10.1002/jgrd.50188

    Article  Google Scholar 

  • Sperber KR, Annamalai H, Kang I-S, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2013) The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Clim Dyn 41:2711–2744

    Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2011a) A summary of the CMIP5 experiment design. (Program for Climate Model Diagnosis and Intercomparison (PCMDI), 2011); available online at http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf

  • Taylor KE, Balaji V, Hankin S, Juckes M, Lawrence B, Pascoe S (2011b) CMIP5 Data Reference Syntax (DRS) and Controlled Vocabularies (Program for Climate Model Diagnosis and Intercomparison (PCMDI); available online at http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experimental design. Bull Am Meteor Soc 93:485–498. doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  • Tebaldi C, Knutti R (2007) The use of the multimodel ensemble in probabilistic climate projections. Philos Trans R Soc 365:2053–2075

    Article  Google Scholar 

  • Wilks, DS (2011) Statistical methods in the atmospheric sciences. Academic Press, 704 pp

Download references

Acknowledgments

The work was funded by the United States (US) National Science Foundation (NSF) Expeditions in Computing Grant # 1029711. The US Nuclear Regulatory Commission and Northeastern University provided partial funding. The climate model datasets were obtained from the PCMDI archive of the US Department of Energy at LLNL.

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Authors and Affiliations

  1. Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, Boston, MA, 02115, USA

    Devashish Kumar, Evan Kodra & Auroop R. Ganguly

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  1. Devashish Kumar
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  2. Evan Kodra
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  3. Auroop R. Ganguly
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Correspondence to Auroop R. Ganguly.

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Kumar, D., Kodra, E. & Ganguly, A.R. Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation. Clim Dyn 43, 2491–2518 (2014). https://doi.org/10.1007/s00382-014-2070-3

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  • DOI: https://doi.org/10.1007/s00382-014-2070-3

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