This paper examines in detail the statement in the 2007 IPCC Fourth Assessment Report that “Most of the observed increase in global average temperatures since the mid-twentieth century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations”. We use a quantitative probabilistic analysis to evaluate this IPCC statement, and discuss the value of the statement in the policy context. For forcing by greenhouse gases (GHGs) only, we show that there is a greater than 90 % probability that the expected warming over 1950–2005 is larger than the total amount (not just “most”) of the observed warming. This is because, following current best estimates, negative aerosol forcing has substantially offset the GHG-induced warming. We also consider the expected warming from all anthropogenic forcings using the same probabilistic framework. This requires a re-assessment of the range of possible values for aerosol forcing. We provide evidence that the IPCC estimate for the upper bound of indirect aerosol forcing is almost certainly too high. Our results show that the expected warming due to all human influences since 1950 (including aerosol effects) is very similar to the observed warming. Including the effects of natural external forcing factors has a relatively small impact on our 1950–2005 results, but improves the correspondence between model and observations over 1900–2005. Over the longer period, however, externally forced changes are insufficient to explain the early twentieth century warming. We suggest that changes in the formation rate of North Atlantic Deep Water may have been a significant contributing factor.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
P.J. Michaels: “A rational discussion of climate change: The science, the evidence, the response”. Hearing before the Subcommittee on Energy and Environment, Committee on Science and Technology, House of Representatives, 111th Congress, Second Session, November 17, 2010. Serial No. 111–114, pp. 85–99. Available at: http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_house_hearings&docid=f:62618.pdf.
Allen MR (2011) In defense of the traditional null hypothesis: remarks on the Trenberth and Curry WIREs opinion articles. WIREs Clim Change 2011(2):931–934. doi:10.1002/wcc145
Anderson TL, Charlson RJ, Schwartz SE, Knutti R, Boucher O, Rohde H, Heintzenberg J (2003) Climate forcing by aerosols—a hazy picture. Science 300:1103–1104
Andronova NG, Schlesinger ME (2001) Objective estimation of the probability density function for climate sensitivity. J Geophys Res 106:22605–22611
Bjerknes J (1964) Atlantic air–sea interaction. Adv Geophys 10:1–82
Brohan P, Kennedy J, Harris I, Tett SFB, Jones PD (2006) Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J Geophys Res 111:D12106. doi:10.1029/2005JD006548
Charlson RJ, Lovelock JE, Andreae MO, Warren SG (1987) Oceanic phytoplankton, atmospheric sulfur, cloud albedo and climate. Nature 326:655–661
Cunningham SA, Marsh R (2010) Observing and modeling changes in the Atlantic MOC. WIREs Clim Change 1:180–191. doi:10.1002/wcc22
Cunningham SA, Kaznow T, Rayner D, Baringer MO, Johns WE, Marotzke J, Longworth HR, Grant EM, Hirschi JJ-M, Beal LM, Meinen CS, Bryden HL (2007) Temporal variability of the Atlantic meridional overturning circulation at 26.5°N. Science 317:935–938
Curry JA, Webster PJ (2011) Climate science and the uncertainty monster. Bull Am Meteorol Soc 92:1667–1682. doi:10.1175/2011BAMS3139.1
Delworth TL, Manabe S, Stouffer RJ (1993) Interdecadal variations of the thermohaline circulation in a coupled ocean–atmosphere model. J Clim 6:1993–2011
Forest CE, Stone PH, Sokolov AP (2006) Estimated PDFs of climate system properties including natural and anthropogenic forcings. Geophys Res Lett 33:L01705. doi:10.1029/2005GL023977
Foukal P (2002) A comparison of variable solar total and ultraviolet outputs in the 20th century. Geophys Res Lett 29:4377–4380. doi:10.1029/2002GL015474
Foukal P, North G, Wigley TML (2004) A stellar view on solar variations and climate. Science 306:68–69
Foukal P, Fröhlich C, Spruit H, Wigley TML (2006) Physical mechanisms of solar luminosity variation, and its effect on climate. Nature 443:161–166
Foukal P, Ortiz A, Schnerr R (2011) Dimming of the 17th century sun. Ap J Lett 733:L38. doi:10.1088/2041-8205/733/2/L38
Fröhlich C (2006) Solar irradiance variability since 1978. Space Sci Rev 125:53–65. doi:10.1007/s11214-006-9046-5
Gregory JM, Dixon KW, Stouffer RJ, Weaver AJ, Driesschaert E, Eby M, Fichefet T, Hasumi H, Hu A, Jungclaus JH, Kamenkovich IV, Levermann A, Montoya M, Murakami S, Nawrath S, Oka A, Sokolov AP, Thorpe RB (2005) A model intercomparison of changes in the Atlantic thermohaline circulation in response to increasing atmospheric CO2 concentration. Geophys Res Lett 32:L12703. doi:10.1029/2005GL023209
Hansen J, Ruedy R, Glascoe J, Sato M (1999) GISS analysis of surface temperature change. J Geophys Res 104:30997–31022. doi:10.1029/1999JD900835
Hansen JE, Ruedy R, Sato M, Imhoff M, Lawrence W, Easterling D, Peterson T, Karl T (2001) A closer look at United States and global surface temperature change. J Geophys Res 106:23947–23963. doi:10.1029/2001JD000354
Hansen J, Ruedy R, Glascoe J, Sato M (2006) GISS analysis of surface temperature change. Proc Natl Acad Sci 103:14288–14293
IPCC (Intergovernmental Panel on Climate Change) (2001) Climate change 2001: the physical science basis, contribution of working group I to the third assessment report of the intergovernmental panel on climate change. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, Meehl G, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) Cambridge University Press, Cambridge, UK
IPCC (Intergovernmental Panel on Climate Change) (2007) Summary for policymakers. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis, contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, pp 1–18
Jones PD, Wigley TML (2010) Estimation of global temperature trends: what’s important and what isn’t. Clim Change 100:59–69. doi:10.1007/s10584-010-9836-3
Katsman CA, van Oldenborgh GJ (2011) Tracing the ocean’s “missing heat”. Geophys Res Lett 38:L14610. doi:10.1029/2011GL048417
Knight JR, Allan RJ, Folland CK, Vellinga M, Mann ME (2005) A signature of persistent natural thermohaline circulation cycles in observed climate. Geophys Res Lett 32:L20708. doi:10.1029/2005GL024233
Knutti R, Stocker TF, Joos F, Plattner G-K (2002) Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature 416:719–723
Knutti R, Stocker TF, Joos F, Plattner G-K (2003) Probabilistic climate change projections using neural networks. Clim Dyn 21:257–272
Lean J (2000) Evolution of the Sun’s spectral irradiance since the Maunder Minumum. Geophys Res Lett 27:2425–2428
Medhaug I, Langehaug HR, Eldevik T, Furevik T, Bentsen M (2012) Mechanisms for decadal scale variability in a simulated Atlantic meridional overturning circulation. Clim Dyn 39:77–93. doi:10:1007/s00382-011-1124-z
Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao Z-C (2007) Global climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis, contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, pp 747–845
Meinshausen M, Meinshausen N, Hare W, Raper SCB, Frieler K, Knutti R, Frame DJ, Allen MR (2009–supplementary information) Greenhouse-gas emission targets for limiting global warming to 2°C. Nature 458:1158–1162
Meinshausen M, Raper SCB, Wigley TML (2011) Emulating coupled atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6—part I: model description and calibration. Atmos Chem Phys 11:1417–1456
Menary MB, Park W, Lohmann K, Vellinga M, Palmer MD, Latif M, Jungclaus JH (2012) A multimodel comparison of centennial Atlantic meridional overturning circulation variability. Clim Dyn 38:2377–2388. doi:10.1007/s00382-011-1172-4
Michaels PJ (1992) Global warming: beyond the popular vision. In: Majumdar SK, Kalkstein LS, Yarnal B, Miller EW, Rosenfeld LM (eds) Global climate change: implications, challenges and mitigation measures. The Pennsylvania Academy of Science, Pennsylvania, pp 100–116
Mitchell JM Jr (1971) The effect of atmospheric aerosols on climate with special reference to temperature near the Earth’s surface. J Appl Meteorol 10:703–714
Morice CP, Kennedy JJ, Rayner NA, Jones PD (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J Geophys Res 117:D08101. doi:10.1029/2011JD017187
Parker DE, Folland CK, Scaife AA, Knight JR, Colman A, Baines P, Dong B (2007) Decadal to multidecadal variability and the climate change background. J Geophys Res 112:D18115. doi:10.1029/2007JD008411.`
Raper SCB, Gregory JM, Osborn TJ (2001) Use of an upwelling-diffusion energy balance climate model to simulate and diagnose A/OGCM results. Clim Dyn 17:601–613
Santer BD, Taylor KE, Wigley TML, Penner JE, Jones PD, Cubasch U (1995) Towards the detection and attribution of an anthropogenic effect on climate. Clim Dyn 12:77–100
Santer BD, Wigley TML, Doutriaux C, Boyle JS, Hansen JE, Jones PD, Meehl GA, Roeckner E, Sengupta S, Taylor KE (2001) Accounting for the effects of volcanoes and ENSO in comparisons of modeled and observed temperature trends. J Geophys Res 106:28033–28059
Santer BD, Mears C, Doutriaux C, Caldwell P, Gleckler PJ, Wigley TML, Solomon S, Gillett NP, Ivanova D, Karl TR, Lanzante JR, Meehl GA, Stott PA, Taylor KE, Thorne PW, Wehner MF, Wentz FJ (2011) Separating signal and noise in atmospheric temperature changes: the importance of timescale. J Geophys Res 116:D22105. doi:10.1029/2011JD016263
Sato M, Hansen JE, McCormick MP, Pollack JB (1993) Stratospheric aerosol optical depth, 1850–1990. J Geophys Res 98:22987–22994
Schaeffer M, Selten FM, Opsteegh JD, Goosse H (2004) The influence of ocean convection patterns on high-latitude climate projections. J Clim 17:4316–4329
Schlesinger ME, Ramankutty N (1994) An oscillation in the global climate system of period 65–70 years. Nature 367:723–726
Schlesinger ME, Ramankutty N (1995) Is the recently reported 65- to 70-year surface-temperature oscillation the result of climatic noise? J Geophys Res 100:13,767–13,774
Schlesinger ME, Yin J, Yohe G, Andronova NG, Malyshev S, Li B (2006) Assessing the risk of a collapse of the Atlantic thermohaline circulation. In: Schellnhuber HJ, Cramer W, Nakićenović N, Wigley TML, Yohe G (eds) Avoiding dangerous climate change. Cambridge University Press, Cambridge, pp 37–47
Smith TM, Reynolds RW (2005) A global merged land and sea surface temperature reconstruction based on historical observations (1880–1997). J Clim 18:2021–2036
Smith SJ, Pitcher H, Wigley TML (2001) Global and regional anthropogenic sulfur dioxide emissions. Glob Planet Change 29:99–119
Smith TM, Reynolds RW, Peterson TC, Lawrimore J (2008) Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J Clim 21:2283–2293
Smith SJ, van Aardenne J, Klimont Z, Andres RJ, Volke A, Delgado Arias S (2011) Anthropogenic sulfur dioxide emissions: 1850–2005. Atmos Chem Phys 11:1101–1116. doi:10.5194/acp-11-1101-2011
Stott PA, Mitchell JFB, Allen MR, Delworth TL, Gregory JM, Meehl GA, Santer BD (2006) Observational constraints on past attributable warming and predictions of future global warming. J Clim 19:3055–3069
Stouffer RJ, Yin J, Gregory JM, Dixon KW, Spelman MJ, Hurlin W, Weaver AJ, Eby M, Flato GM, Hasumi H, Hu A, Jungclaus JH, Kamenkovich IV, Levermann A, Montoya M, Murakami S, Nawrath S, Oka A, Peltier WD, Robitaille DY, Sokolov A, Vettoretti G, Weber SL (2006) Investigating the causes of the response of the thermohaline circulation to past and future climate changes. J Clim 19:1365–1387
Swanson KL, Sugihara G, Tsonis AA (2009) Long-term natural variability and 20th century climate change. Proc Natl Acad Sci 106:16120–16123. doi:10.1073/pnas.0908699106
Trenberth KE, Shea DJ (2006) Atlantic hurricanes and natural variability in 2005. Geophys Res Lett 33:L12704. doi:10.1029/2006GL026894
Vernier J-P, Thomason LW, Pommereau J-P, Bourassa A, Pelon J, Garnier A, Hauchecorne A, Blanot L, Trepte C, Degenstein D, Vargas F (2011) Major influence of tropical volcanic eruptions on the stratospheric aerosol layer during the last decade. Geophys Res Lett 38:L12807. doi:10.1029/2011GL047563
Wang Y-M, Lean J, Sheeley NR Jr (2005) Modeling the Sun’s magnetic field and irradiance since 1713. Astrophys J 625:522–538
Wigley TML (1989) Possible climatic change due to SO2-derived cloud condensation nuclei. Nature 339:365–367
Wigley TML (2000) ENSO, volcanoes and record breaking temperatures. Geophys Res Lett 27:4101–4104
Wigley TML, Raper SCB (1987) Thermal expansion of sea water associated with global warming. Nature 330:127–131
Wigley TML, Raper SCB (2001) Interpretation of high projections for global-mean warming. Science 293:451–454
Wigley TML, Clarke LE, Edmonds JA, Jacoby HD, Paltsev S, Pitcher H, Reilly JM, Richels R, Sarofim MC, Smith SJ (2009) Uncertainties in climate stabilization. Clim Change 97:85–121. doi:10.1007/s10584-009-9585-3
Wu Z, Huang NE, Long SR, Peng C-K (2007) On the trend, detrending and variability of nonlinear and non-stationary time series. Proc Natl Acad Sci 104:14889–14894
Wu Z, Huang NE, Wallace JM, Smoliak BV, Chen X (2011) On the time-varying trend in global-mean surface temperature. Clim Dyn 37:759–773. doi:10.1007/s00382-011-1128-8
Yin J, Schlesinger ME, Andronova NG, Malyshev S, Li B (2006) Is a shutdown of the thermohaline circulation irreversible? J Geophys Res 111:D12104. doi:10.1029/2005JD006562
Zhang R, Delworth TL, Held IM (2007) Can the Atlantic Ocean drive the observed multidecadal variability in Northern Hemisphere mean temperature? Geophys Res Lett 34:L02709. doi:10.1029/2006GL028683
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendix 1: Aerosol forcing uncertainties
The 2005 reference-level aerosol forcings used in our analyses and their associated uncertainties are taken from the AR4. Table 4 summarizes this information. Although these values were based on both observational estimates and model results, there was no attempt by IPCC to assess their overall consistency (or lack thereof) with observed changes in climate. As noted in the main text, when the temperature implications of the indirect aerosol uncertainty range are considered, the very large 95th percentile value for indirect aerosol forcing (−1.8 W/m2) appears highly unlikely, unless compensated for by missing (or underestimated) positive forcings (or by an unusual manifestation of low-frequency internally generated variability).
The AR4 range for indirect aerosol forcing is essentially a compromise between observationally based studies (see main text), which invariably give a much lower value for the upper bound (e.g., around −1 W/m2 in Stott et al. 2006), and estimates derived from forward calculations, which range up to −4 W/m2 for total sulfate aerosol forcing (Anderson et al. 2003). Why, then, is −1.8 W/m2 for indirect aerosol forcing highly unlikely? We can answer this question by performing another set of sensitivity studies where we consider only the effects of indirect forcing uncertainties. To do so we assume that all anthropogenic forcings except indirect aerosol forcing are given their central values, and consider a range of values for the reference (2005) indirect forcing amount.
If the AR4 95th percentile value of −1.8 W/m2 is accepted rather than the best estimate (−0.7 W/m2), then the total anthropogenic forcing in 2005 would be reduced by 1.1 W/m2 (i.e., 1.8 minus 0.7) relative to the estimates of total forcing in Table 4 (rows 13 or 14). In other words, total anthropogenic forcing in 2005 would be reduced to 0.62 W/m2 using the row-13 result, or to only 0.5 W/m2 if we use the central AR4 total forcing value (row 14). (The MAGICC estimate for this case is 0.52 W/m2, see Fig. 7). The implied historical development of total anthropogenic forcing then would show negative forcing until the late twentieth century, and the implied global-mean temperature record would show no net warming at all over the twentieth century (see Fig. 8)—indeed, it would show a considerable cooling from 1860 until the early 1970s. In the absence of large, currently unknown positive forcings, or a very large contribution from internally generated variability to observed warming, these are extremely unlikely results, so one must conclude that a negative indirect aerosol forcing as large as 1.8 W/m2 is also extremely unlikely (i.e., probability much less than 1 %).
If the 95th percentile for indirect aerosol forcing given in the AR4 is so improbable, then we need to produce an improved estimate of the uncertainty in aerosol forcing to use in our probabilistic calculations. For total aerosol forcing using the AR4 uncertainty ranges for individual components, the 90 % confidence range, by quadrature, is ±0.86 W/m2 (see Table 4). This is based on an uncertainty range of ±0.75 W/m2 for indirect forcing (i.e., half the 90 % confidence interval range). Changing the 95th percentile value for indirect forcing from −1.8 to −1.1 W/m2 produces more realistic histories of past forcing and temperature change (see Figs. 7, 8). Note, however, that even −1.1 W/m2 would require high end values for the positive forcing components. If the indirect aerosol forcing uncertainty is reduced to ±0.4 W/m2 (i.e., a 90 % range of −0.3 to −1.1 W/m2) then the overall aerosol forcing uncertainty (i.e., the 90 % confidence interval) is reduced to ±0.57 W/m2. This is what we assume for our probabilistic calculations.
Appendix 2: Linear trend results
In the main text we have presented results using the robust trend as the metric for change. Here we give results using the linear trend. Figure 9, the equivalent of Fig. 4, shows cdfs for temperature changes over 1900–2005 and 1950–2005 for (a) GHG forcing only and (b) full anthropogenic forcing. Figure 10, the equivalent of Fig. 6, compares results for anthropogenic forcing with those for anthropogenic-plus-natural forcing over (a) 1900–2005 and (b) 1950–2005.
Consider Fig. 9 first. For GHG forcing only, the linear trend results are very similar to those using the robust trend. They show, as before, that the observed warming over either period is very likely less than that expected from GHG forcing. The probability that this is so is about 93 %.
For full anthropogenic forcing, the linear trend results are qualitatively the same as the robust trend results. Over 1950–2005, the observed warming trend is slightly greater than the model expectation: a probability of 57 % for the linear trend compared with 61 % for the robust trend. Over 1900–2005 the observed trend is substantially greater than the model expectation: a probability of 87 % for the linear trend compared with 78 % for the robust trend. Compared with the 1950–2005 results, the larger model versus observed difference over 1900–2005 is due to a much larger observed warming in the early twentieth century than the model expectation. We have attributed this additional warming, at least in part, to an increase in the AMOC.
That the model-observed discrepancy over 1900–2005 cannot be attributed to natural (solar plus volcanic) external forcing is demonstrated in Fig. 10a. If the linear trend is used as the change metric, the cdf for anthropogenic forcing is virtually the same as that for anthropogenic-plus-natural forcing: adding in natural forcing has almost no effect on model estimates of linear trend over 1900–2005. In both the anthropogenic only and anthropogenic-plus-natural forcing cases, the probability that the observed warming is greater than the model expectation is high, about 87 %. This is in contrast to the robust trend result where adding in natural forcing improved (slightly) the trend fits between model and observations. These results are further illustrated in Fig. 11.
Over 1950–2005 (Fig. 10b), observed warming is also greater than the model expectation, both for anthropogenic forcing only (probability of 57 %) and for anthropogenic-plus-natural forcing (79 %)—but note that neither of these model-observed differences is statistically significant. Adding in the effects of natural forcing increases the trend discrepancy between model and observations. This is the case for both the linear trend and robust trend change metrics. This does not, however, mean that the overall fit is degraded by adding in the effects of natural forcing. Table 5 shows correlations between median model results for both forcing cases and the four observed temperature time series. Over both 1900–2005 and 1950–2005 the correlations are improved by the addition of natural forcing.
Figure 11 complements Fig. 10 and shows the median model time series for the responses to anthropogenic and anthropogenic-plus-natural forcing, together with the observed (NOAA/NCDC) data (with ENSO removed). It can be seen that the model trends over 2000–2005 are very similar for both forcing cases, and that the observed trend is substantially greater.
About this article
Cite this article
Wigley, T.M.L., Santer, B.D. A probabilistic quantification of the anthropogenic component of twentieth century global warming. Clim Dyn 40, 1087–1102 (2013). https://doi.org/10.1007/s00382-012-1585-8
- Global warming
- Probabilistic calculations
- Human influences
- Solar forcing
- Aerosol forcing