Keeping it simple: the value of an irreducibly simple climate model

More info
  • ReceivedJun 15, 2015
  • AcceptedJun 30, 2015
  • PublishedAug 6, 2015



[1] IPCC FAR (1990) Climate change-the IPCC Assessment (1990): report prepared for Intergovernmental Panel on Climate Change by Working Group I. In: Houghton JT, Jenkins GJ, Ephraums JJ (eds) Cambridge University Press, Cambridge. Google Scholar

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

[3] IPCC TAR (2001) Climate Change 2001: the scientific basis. Contribution of working group I to the third assessment report of the Intergovernmental Panel on Climate Change. Houghton JT, Ding Y, Griggs DJ et al (eds) Cambridge University Press, Cambridge. Google Scholar

[4] IPCC AR4 (2007) Climate change 2007: the physical science basis. Contribution of Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate Change, 2007. In: Solomon S, Qin D, Manning M et al (eds) Cambridge University Press, Cambridge. Google Scholar

[5] IPCC AR5 (2013) Climate change 2013: the physical science basis. Contribution of Working Group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. In: Stocker TF, Qin D, Plattner G-K et al (eds) Cambridge University Press, Cambridge. Google Scholar

[6] Monckton of Brenchley CW, Soon WW-H, Legates DR et al (2015) Why models run hot: results from an irreducibly simple model. Sci Bull 60:122-135. doi:10.1007/s11434-014-0699-2. CrossRef Google Scholar

[7] Michaels PJ, Knappenberger PC, Frauenfeld OW et al (2002) Revised 21st century temperature projections. Clim Res 23:1-9. CrossRef Google Scholar

[8] Douglass DH, Pearson BD, Singer SF (2004) Altitude dependence of atmospheric temperature trends: climate models versus observation. Geophys Res Lett 31:L13208. doi:10.1029/2004GL020103. CrossRef Google Scholar

[9] Landscheidt T (2003) New Little Ice Age instead of global warming? Energy Environ 14:327-350. CrossRef Google Scholar

[10] Chylek P, Lohmann U (2008) Aerosol radiative forcing and climate sensitivity deduced from the Last Glacial Maximum to Holocene transition. Geophys Res Lett 35:L04804. doi:10.1029/2007GL032759. Google Scholar

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

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

[13] Lindzen RS, Choi Y-S (2009) On the determination of climate feedbacks from ERBE data. Geophys Res Lett 36:L16705. doi:10.1029/2009GL039628. CrossRef Google Scholar

[14] Spencer RW, Braswell WD (2010) On the diagnosis of radiative feedback in the presence of unknown radiative forcing. J Geophys Res 115:D16109. doi:10.1029/2009JD013371. CrossRef Google Scholar

[15] Annan JD, Hargreaves JC (2011) On the generation and interpretation of probabilistic estimates of climate sensitivity. Clim Change 104:324-436. CrossRef Google Scholar

[16] 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

[17] 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

[18] Schmittner A, Urban NM, Shakun JD et al (2011) Climate sensitivity estimated from temperature reconstructions of the last glacial maximum. Science 334:1385-1388. doi:10.1126/science.1203513. CrossRef Google Scholar

[19] 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. doi:10.3390/rs3081603. CrossRef Google Scholar

[20] Aldrin M, Holden M, Guttorp P et al (2012) Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperature and global ocean heat content. Environmetrics 23:253-271. doi:10.1002/env.2140. CrossRef Google Scholar

[21] Hargreaves JC, Annan JD, Yoshimori M et al (2012) Can the Last Glacial Maximum constrain climate sensitivity? Geophys Res Lett 39:L24702. doi:10.1029/2012GL053872. CrossRef Google Scholar

[22] Ring MJ, Lindner D, Cross EF et al (2012) Causes of the global warming observed since the 19th century. Atmos Clim Sci 2:401-415. doi:10.4236/acs.2012.24035. Google Scholar

[23] van Hateren JH (2012) A fractal climate response function can simulate global average temperature trends of the modern era and the past millennium. Clim Dyn 40:2651-2670. doi:10.1007/s00382-012-1375-3. CrossRef Google Scholar

[24] Lewis N (2013) An objective Bayesian improved approach for applying optimal fingerprint techniques to estimate climate sensitivity. J Clim 26:7414-7429. doi:10.1175/JCLI-D-12-00473.1. CrossRef Google Scholar

[25] Masters T (2013) Observational estimates of climate sensitivity from changes in the rate of ocean heat uptake and comparison to CMIP5 models. Clim Dyn 42:2173-2181. doi:10.1007/s00382-013-1770-4. CrossRef Google Scholar

[26] Otto A, Otto FEL, Boucher O et al (2013) Energy budget constraints on climate response. Nat Geosci 6:415-416. CrossRef Google Scholar

[27] Spencer RW, Braswell WD (2013) The role of ENSO in global ocean temperature changes during 1955-2011 simulated with a 1D climate model. Asia-Pac J Atmos Sci 50:229-237. doi:10.1007/s13143-014-0011-z. CrossRef Google Scholar

[28] Lewis N, Curry JA (2014) The implications for climate sensitivity of AR5 forcing and heat uptake estimates. Clim Dyn. doi:10.1007/s00382-014-2342-y. Google Scholar

[29] Loehle C (2014) A minimal model for estimating climate sensitivity. Ecol Model 276:80-84. doi:10.1016/j.ecolmodel.2014.01.006. CrossRef Google Scholar

[30] McKitrick R (2014) HAC-robust measurement of the duration of a trendless subsample in a global climate time series. Open J Stat 4:527-535. doi:10.4236/ojs.2014.47050. CrossRef Google Scholar

[31] Monckton of Brenchley C (2014) Political science: drawbacks of apriorism in intergovernmental climatology. Energy Environ 25:1177-1204. CrossRef Google Scholar

[32] Skeie RB, Berntsen T, Aldrin M et al (2014) A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series. Earth Syst Dyn 5:139-175. doi:10.5194/esd-5-139-2014. CrossRef Google Scholar

[33] Lewis N (2015) Implications of recent multimodel attribution studies for climate sensitivity. Clim Dyn. doi:10.1007/s00382-015-2653-7RSS. Google Scholar

[34] 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 4 April 2015. Google Scholar

[35] NOAA (2015) Atmospheric CO2 concentration expressed as a mole fraction in dry air (μmol mol−1). ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt. Accessed 29 April 2015. Google Scholar

[36] 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

[37] Richardson M, Hausfather Z, Nuccitelli D et al (2015) Misdiagnosis of earth climate sensitivity based on energy balance model results. Sci Bull. doi:10.1007/s11434-015-0806-z. Google Scholar

[38] Connolly R, Connolly M (2014) Urbanization bias I: is it a negligible problem for global temperature estimates? Open Peer Rev J 28 (Clim Sci) version 0.1. http://oprj.net/articles/climate-science/28. Accessed 27 December 2014. Google Scholar

[39] Connolly R, Connolly M (2014) Urbanization bias III: estimating the extent of bias in the Historical Climatology Network datasets. Open Peer rev J 28 (Clim Sci) version 0.1. http://oprj.net/articles/climate-science/34. Accessed 27 December 2014. Google Scholar

[40] Le Quéré C, Moriarty R, Andrew RM et al (2014) Global carbon budget 2014. Earth Syst Sci Data Discuss 7:521-610. doi:10.5194/essdd-7-521-2014. CrossRef Google Scholar

[41] Lewis N (2015) The implications for climate sensitivity of Bjorn Stevens' new aerosol forcing paper, http://climateaudit.org/2015/03/19/the-implications-for-climate-sensitivity-of-bjorn-stevens-new-aerosol-forcing-paper. Accessed 2 May 2015. Google Scholar

[42] Stevens B (2015) Rethinking the lower bound on aerosol radiative forcing. J Clim. doi:10.1175/CLI-D-14-00656.1. Google Scholar

[43] 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

[44] Mears CA, Wentz FJ (2009) Construction of the RSS V3.2 lower tropospheric dataset from the MSU and AMSU microwave sounders. J Atmos Ocean Technol 26:1493-1509. CrossRef Google Scholar

[45] McKitrick RR, Michaels PJ (2007) Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded surface climate data. J Geophys Res (Atmos) 112:D24S09. doi:10.1029/2007JD008465. CrossRef Google Scholar

[46] National Climatic Data Center (2015) Differences between raw and adjusted global temperature anomalies. www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif. Accessed 8 June 2015. Google Scholar

[47] Charney JG, Arakawa A, Baker DJ et al (1979) Carbon dioxide and climate: a scientific assessment: report of an ad-hoc study group on carbon dioxide and climate. Climate Research Board, Assembly of Mathematical and Physical Sciences, National Research Council, National Academy of Sciences, Washington, DC. Google Scholar

[48] Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. JASA 102:359-378. CrossRef Google Scholar

[49] Seely S (1950) Electron-tube circuits, 2nd edn. McGraw Hill Book Co Inc, New York, pp 148-149. Google Scholar

[50] Bates JR (2007) Some considerations of the concept of climate feedback. Q J R Meteorol Soc 133:545-560. doi:10.1002/qj.62. CrossRef Google Scholar

[51] 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

[52] 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

[53] Lindzen RS (1994) Climate dynamics and global change. Ann Rev Fluid Mech 26:353-378. CrossRef Google Scholar

[54] Soon WW-H, Legates DR (2013) Solar irradiance modulation of equator-to-pole (Arctic) temperature gradients: empirical evidence for climate variation on multi-decadal timescales. J Atmos Solar-Terr Phys 93:45-56. doi:10.1016/j.jastp.2012.11.015. CrossRef Google Scholar

[55] Kaiho K, Arinobu T, Ishiwatari R et al (1996) Latest Paleocene benthic foraminiferal extinction and environmental changes at Tawanui, New Zealand. Paleooceanography 11:447. doi:10.1029/96PA01021. CrossRef Google Scholar

[56] Bralower TJ, Thomas DC, Zachos JC et al (1997) High-resolution records of the late Paleocene thermal maximum and circum-Caribbean volcanism: Is there a causal link? Geology 25:963-966. CrossRef Google Scholar

[57] Katz ME, Pak DK, Dickens GR et al (1999) The source and fate of massive carbon input during the latest Paleocene thermal maximum. Science 286:1531-1533. CrossRef Google Scholar

[58] Loeb NG, Lyman JM, Johnson GC et al (2012) Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nat Geosci 5:110-113. doi:10.1038/ngeo1375. CrossRef Google Scholar

[59] Hadfield RE, Wells NC, Josey SA et al (2007) On the accuracy of North Atlantic temperature and heat storage fields from Argo. J Geophys Res 112:C01009. doi:10.1029/2006JC003825. Google Scholar

[60] Hare S (1996) The Pacific decadal oscillation. http://research.jisao.washington.edu/pdo/. Accessed 8 June 2014. Google Scholar

[61] Christy JR, Spencer RW, Norris WB (2011) The role of remote sensing in monitoring global bulk tropospheric temperatures. Int J Remote Sens 32:671-685. doi:10.1080/01431161.2010.517803. CrossRef Google Scholar

[62] Lanzante JR, Klein SA, Seidel DJ (2003) Temporal homogenization of monthly radiosonde temperature data. Part I: methodology. J Clim 16:224-240. CrossRef Google Scholar

[63] Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130-141. CrossRef Google Scholar

[64] Giorgi F (2005) Climate change prediction. Clim Change 73:239-265. doi:10.1007/s10584-005-6857-4. CrossRef Google Scholar

[65] Mauritsen T, Stevens B (2015) Missing IRIS effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nat Geosci 8:346-351. CrossRef Google Scholar

[66] Tan J, Jakob C, Rossow WB et al (2015) Increases in tropical rainfall driven by changes in frequency of organized deep convection. Nature 519:451-454. CrossRef Google Scholar

[67] Lindzen RS, Chou MD, Hou AY et al (2001) Does the earth have an adaptive infrared iris? Bull Am Meteorol Soc 82:4117-4432. CrossRef Google Scholar

[68] Peterson TC, Baringer MO (eds) (2009) State of the climate in 2008. Bull Am Meteorol Soc 90:8 (Supplement). Google Scholar

[69] Knight J, Kennedy JJ, Folland C et al (2009) Do global temperature trends over the last decade falsify climate predictions? Bull Am Meterol Soc 90:S22-S23. Google Scholar

[70] Santer BD, Mears C, Doutriaux C et al (2011) Separating signal and noise in atmospheric temperature changes: the importance of timescale. J Geophys Res. doi:10.1029/2011JD016263. Google Scholar

[71] Fyfe JC, Gillett NP, Zwiers FW (2013) Overestimated global warming over the past 20 years. Nat Clim Change 3:767-769. CrossRef Google Scholar

[72] Swart NC, Fyfe JC, Hawkins E et al (2015) Influence of internal variability on Arctic sea-ice trends. Nat Clim Change 5:86-89. CrossRef Google Scholar

[73] Legates DR, McCabe CJ Jr (1999) Evaluating the use of "goodness of fit" measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233-241. CrossRef Google Scholar

[74] Legates DR, McCabe CJ Jr (2013) A refined index of model performance: a rejoinder. Int J Climatol 33:1053-1056. CrossRef Google Scholar

[75] Willmott CJ, Ackleson SG, Davis RE et al (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90:8995-9005. CrossRef Google Scholar


Contact and support