Why models run hot: results from an irreducibly simple climate model

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  • ReceivedAug 27, 2014
  • AcceptedNov 12, 2014
  • PublishedJan 8, 2015


An irreducibly simple climate-sensitivity model is designed to empower even non-specialists to research the question how much global warming we may cause. In 1990, the First Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) expressed "substantial confidence" that near-term global warming would occur twice as fast as subsequent observation. Given rising CO2 concentration, few models predicted no warming since 2001. Between the pre-final and published drafts of the Fifth Assessment Report, IPCC cut its near-term warming projection substantially, substituting "expert assessment" for models' near-term predictions. Yet its long-range predictions remain unaltered. The model indicates that IPCC's reduction of the feedback sum from 1.9 to 1.5 W m−2 K−1 mandates a reduction from 3.2 to 2.2 K in its central climate-sensitivity estimate; that, since feedbacks are likely to be net-negative, a better estimate is 1.0 K; that there is no unrealized global warming in the pipeline; that global warming this century will be <1 K; and that combustion of all recoverable fossil fuels will cause <2.2 K global warming to equilibrium. Resolving the discrepancies between the methodology adopted by IPCC in its Fourth and Fifth Assessment Reports that are highlighted in the present paper is vital. Once those discrepancies are taken into account, the impact of anthropogenic global warming over the next century, and even as far as equilibrium many millennia hence, may be no more than one-third to one-half of IPCC's current projections.


[1] IPCC FAR (1990) In: Houghton JT, Jenkins GJ, Ephraums JJ (eds) Climate change-the IPCC assessment (1990): report prepared for Intergovernmental panel on climate change by working group I. Cambridge University Press, Cambridge, New York. Google Scholar

[2] IPCC SAR (1995) In: Houghton JT, Meira Filho LG, Callander BA et al (eds) Climate change 1995-the science of climate change: contribution of WG1 to the second assessment report. Cambridge University Press, Cambridge, New York. Google Scholar

[3] IPCC TAR (2001) Climate change 2001: the scientific basis. In: Houghton JT, Ding Y, Griggs DJ et al (eds) Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, New York. Google Scholar

[4] IPCC AR4 (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M et al (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change, 2007. Cambridge University Press, Cambridge, New York. Google Scholar

[5] IPCC AR5 (2013) Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K et al (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, New York. Google Scholar

[6] Garnaut R (2011) The Garnaut climate change review: final report. Cambridge University Press, Cambridge. Google Scholar

[7] Hoyle F (1996) The great greenhouse controversy. In: Emsley J (ed) The global warming debate. The European Science and Environment Forum, London, pp 179-189. Google Scholar

[8] Legates DR, Soon WW-H, Briggs WM et al (2013) Climate consensus and "misinformation": a rejoinder to "agnotology, scientific consensus, and the teaching and learning of climate change". Sci Educ. doi:10.1007/s11191-013-9647-9. Google Scholar

[9] RSS (2014) Satellite-derived monthly global mean lower-troposphere temperature anomaly dataset: www.remss.com/data/msu/monthly_time_series/RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_3.txt. Accessed 1 July 2014. Google Scholar

[10] UAH (University of Alabama at Huntsville) (2014) Satellite MSU monthly global mean lower-troposphere temperature anomalies. http://vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc_lt_5.6.txt. Accessed 1 July 2014. Google Scholar

[11] NCDC (2014) National Climatic Data Center monthly global mean land and ocean surface temperature anomalies, 1880-2013. https://www.ncdc.noaa.gov/cag/time-series/global/globe/land_ocean/p12/12/1880-2014.csv. Accessed 1 July 2014. Google Scholar

[12] Morice CP, Kennedy JJ, Rayner N et al (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J Geophys Res 117:D08101. Google Scholar

[13] GISS (2014) Goddard Institute for Space Studies monthly global mean land and sea surface temperature anomalies, 1880-2014. http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt. Accessed 1 July 2014. Google Scholar

[14] Soon WW-H, Baliunas S, Idso SB et al (2001) Modeling climate effects of anthropogenic carbon dioxide emissions: unknowns and uncertainties. Clim Res 18:259-275. CrossRef Google Scholar

[15] Douglass DH, Pearson BD, Singer SF (2004) Altitude dependence of atmospheric temperature trends: climate models versus observation. Geophys Res Lett 31:L13208. CrossRef Google Scholar

[16] Douglass DH, Christy JR, Pearson BD et al (2007) A comparison of tropical temperature trends with model predictions. Int J Climatol. doi:10.1002/joc.1651. Google Scholar

[17] McKitrick RR, Michaels PJ (2007) Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data. J Geophys Res (Atmos.) 112:D24S09. CrossRef Google Scholar

[18] Tsonis AA, Swanson KL, Kravtsov S (2007) A new dynamical mechanism for major climate shifts. Geophys Res Lett. doi:10.1029/2007GL030288. Google Scholar

[19] Wentz FJ, Ricciardulli L, Hilburn K et al (2007) How much more rain will global warming bring? Science 317:233-235. CrossRef Google Scholar

[20] Monckton of Brenchley C (2008) Climate sensitivity reconsidered. Phys Soc 37:6-19. Google Scholar

[21] Monckton of Brenchley C (2011) Global brightening and climate sensitivity. In: Zichichi A, Ragaini R (eds) Proceedings of the 45th annual international seminar on nuclear war and planetary emergencies, World Federation of Scientists. World Scientific, London. Google Scholar

[22] Paltridge G (2009) The climate caper. Connor Court, Sydney. Google Scholar

[23] Douglass DH, Christy JR (2009) Limits on CO2 climate forcing from recent temperature data of earth. Energy Environ 20:1-2. CrossRef Google Scholar

[24] Douglass DH, Christy JR (2013) Reconciling observations of global temperature change. Energy Environ 24:415-419. CrossRef Google Scholar

[25] Lindzen RS, Choi Y-S (2011) On the observational determination of climate sensitivity and its implications. Asia Pac J Atmos Sci 47:377-390. CrossRef Google Scholar

[26] Loehle C, Scafetta N (2011) Climate change attribution using empirical decomposition of climatic data. Open Atmos Sci J 5:74-86. CrossRef Google Scholar

[27] Spencer RW, Braswell WD (2011) On the misdiagnosis of surface temperature feedbacks from variations in Earth's radiant-energy balance. Remote Sens 3:1603-1613. CrossRef Google Scholar

[28] Tsonis AA (2011) Cycles in the major ocean oscillations. Lecture at the annual seminar on planetary emergencies, World Federation of Scientists. Erice, Sicily, August 23. Google Scholar

[29] Essex C (2013) Does laboratory-scale physics obstruct the development of a theory for climate? J Geophys Res. doi:10.1029/jgrd.50195. Google Scholar

[30] Zhang D, Lou T, Liu D et al (2014) Spatial scales of altocumulus clouds observed with collocated CALIPSO and CloudSat measurements. Atmos Res 148:58-69. CrossRef Google Scholar

[31] Jiang DB, Lang XM, Tian ZP et al (2012) Considerable model-data mismatch in temperature over China during the mid-Holocene: results of PMIP simulations. J Clim 25:4135-4153. CrossRef Google Scholar

[32] Myhre G, Highwood EJ, Shine KP et al (1998) New estimates of radiative forcing due to well-mixed greenhouse gases. Geophys Res Lett 25:2715-2718. CrossRef Google Scholar

[33] Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean-atmosphere models. J Clim 19:3354-3360. CrossRef Google Scholar

[34] Hansen J, Lacis A, Rind D et al (1984) Climate sensitivity: analysis of feedback mechanisms. Meteorol Monogr 29:130-163. Google Scholar

[35] Kiehl JT (1992) Atmospheric general circulation modeling. In: Trenberth KE (ed) Climate system modeling. Cambridge University Press, Cambridge, pp 319-369. Google Scholar

[36] Hartmann DL (1994) Global physical climatology. Academic Press, San Diego. Google Scholar

[37] Colman RA (2003) A comparison of climate feedbacks in general-circulation models. Clim Dyn 20:865-873. Google Scholar

[38] Roe G (2009) Feedbacks, timescales, and seeing red. Ann Rev Earth Planet Sci 37:93-115. CrossRef Google Scholar

[39] Solomon S, Plattner GK, Knutti R et al (2009) Irreversible climate change due to carbon dioxide emissions. Proc Natl Acad Sci USA 106:74-79. CrossRef Google Scholar

[40] Jouzel J, Masson-Delmotte V, Cattani O et al (2007) Orbital and millennial Antarctic climate variability over the past 800,000 years. Science 317:793-796. CrossRef Google Scholar

[41] Petit JR, Jouzel J, Raynaud D et al (1999) Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399:429-436. CrossRef Google Scholar

[42] Scotese CR, Boucot AJ, McKerrow WS (1999) Gondwanan paleogeography and paleoclimatology. J Afr Earth Sci 28:99-114. CrossRef Google Scholar

[43] Zachos J, Pagani M, Sloan L et al (2001) Trends, rhythms and aberrations in global climate 65 Ma to present. Science 292:686-693. CrossRef Google Scholar

[44] Schlesinger ME, Mitchell JFB (1987) Climate model simulations of the equilibrium climatic response to increased carbon dioxide. Rev Geophys 25:760-798. CrossRef Google Scholar

[45] Kirk-Davidoff DB, Lindzen RS (2000) An energy balance model based on potential vorticity homogenization. J Clim 13:431-448. CrossRef Google Scholar

[46] Hourdin F, Foujols M-A, Codron F et al (2013) Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Clim Dyn 40:2167-2192. CrossRef Google Scholar

[47] Schmidt GA, Kelley M, Nazarenko L (2014) Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J Adv Model Earth Syst 6:141-184. CrossRef Google Scholar

[48] Bode HW (1945) Network analysis and feedback amplifier design. Van Nostrand Reinhold, New York. Google Scholar

[49] Bódai T, Lucarini V, Lunkeit F et al (2014) Global instability in the Ghil-Sellers model. Clim Dyn. doi:10.1007/s00382-014-2206-5. Google Scholar

[50] Budyko MI (1969) The effect of solar radiation variations on the climate of the Earth. Tellus 21:611-619. CrossRef Google Scholar

[51] Sellers WD (1969) A global climatic model based on the energy balance of the earth-atmosphere system. J Appl Meteorol 8:392-400. CrossRef Google Scholar

[52] North GR (1975) Theory of energy-balance climate models. J Atmos Sci 32:2033-2043. CrossRef Google Scholar

[53] Ghil M (1976) Climate stability for a Sellers-type model. J Atmos Sci 33:3-20. CrossRef Google Scholar

[54] North GR, Cahalan RF, Coakley JA Jr (1981) Energy balance climate models. Rev Geophys Space Phys 19:91-121. CrossRef Google Scholar

[55] North GR (1984) The small ice cap instability in diffusive climate models. J Atmos Sci 41:3390-3395. CrossRef Google Scholar

[56] Lindzen RS, Farrell B (1977) Some realistic modifications of simple climate models. J Atmos Sci 34:1487-1501. CrossRef Google Scholar

[57] Schneider EK, Lindzen RS (1977) Axially symmetric steady-state models of the basic state for instability and climate studies, part I, linearized calculations. J Atmos Sci 34:263-279. CrossRef Google Scholar

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