1 Nature Climate Change 2013 Vol: 3(9):767-769. DOI: 10.1038/nclimate1972

Overestimated global warming over the past 20 years

Recent observed global warming is significantly less than that simulated by climate models. This difference might be explained by some combination of errors in external forcing, model response and internal climate variability.

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Figures
Figure 1: Trends in global mean surface temperature. a, 1993–2012. b, 1998–2012. Histograms of observed trends (red hatching) are from 100 reconstructions of the HadCRUT4 dataset1. Histograms of model trends (grey bars) are based on 117 simulations of the models, and black curves are smoothed versions of the model trends. The ranges of observed trends reflect observational uncertainty, whereas the ranges of model trends reflect forcing uncertainty, as well as differences in individual model responses to external forcings and uncertainty arising from internal climate variability. Figure 2: Global mean surface temperature trends and p values. a,b, 20-year (a) and 15-year (b) running trends. Black curves are ensemble-averaged trends over the 37 sets of model simulations. Dark-grey shading indicates the 2.5–97.5% ranges of the simulated estimates. Light-grey shading shows the 95% uncertainty ranges of the ensemble means, derived by dividing the 2.5–97.5% ranges by the square root of the number of models. Red curves are the observed trends averaged over 100 realizations and the horizontal red lines show the observed 1900–2012 trends averaged over 100 realizations. Black cross-hatchings are the 95% uncertainty ranges for simulated 1900–2012 ensemble mean trends. Note that the observed and simulated long-term trends are very similar to one another, and are smaller than the short-term trends. c,d, 20-year (c) and 15-year (d) p values on trend differences between the simulations and observations for assumption (1) (purple curves), or assumption (2) (green curves). The horizontal dashed lines indicate the threshold below which we reject the null hypothesis. Figure 3: Trends in global mean surface temperature and in associated natural and residual time series. a, 1993–2012. b, 1998–2012. The 2.5–97.5% ranges for observed estimates are shown by the red boxes. The 2.5–97.5% ranges for simulated estimates are represented by the open black boxes, with the 95% ranges on ensemble mean trends indicated by grey shading. The estimated natural signals shown are associated with the El Niño-Southern Oscillation (ENSO), dynamically induced atmospheric variability (cold ocean–warm Earth; COWL) and major explosive volcanic eruptions (Volcano). Trends in global mean surface temperature without these estimated natural signals are shown at the bottom (Residual).
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References
  1. Morice, C. P.; Kennedy, J. J.; Rayner, N. A.; Jones, P. D. J. Geophys. Res. 117, D08101 (2012) .
    • . . . Histograms of observed trends (red hatching) are from 100 reconstructions of the HadCRUT4 dataset1 . . .
    • . . . Global mean surface temperature over the past 20 years (1993–2012) rose at a rate of 0.14 ± 0.06 °C per decade (95% confidence interval)1 . . .
  2. Easterling, D. R.; Wehner, M. F. Geophys. Res. Lett. 36, L08706 (2009) .
    • . . . It is worth noting that the observed trend over this period — not significantly different from zero — suggests a temporary 'hiatus' in global warming2, 3, 4 . . .
  3. Knight, J. Bull. Am. Meteorol. Soc. 90, S75-S79 (2009) .
    • . . . It is worth noting that the observed trend over this period — not significantly different from zero — suggests a temporary 'hiatus' in global warming2, 3, 4 . . .
  4. Fyfe, J. C. Geophys. Res. Lett. 38, L22801 (2011) .
    • . . . It is worth noting that the observed trend over this period — not significantly different from zero — suggests a temporary 'hiatus' in global warming2, 3, 4 . . .
  5. Thompson, W. J.; Wallace, J. M.; Jones, P. D.; Kennedy, J. J. J. Clim. 22, 6120-6141 (2009) .
    • . . . Thus we employ here an established technique to estimate the impact of ENSO on global mean temperature, and to incorporate the effects of dynamically induced atmospheric variability and major explosive volcanic eruptions5, 6 . . .
  6. Fyfe, J. C.; Gillett, N. P.; Thompson, D. W. J. Geophys. Res. Lett. 37, L16802 (2010) .
    • . . . Thus we employ here an established technique to estimate the impact of ENSO on global mean temperature, and to incorporate the effects of dynamically induced atmospheric variability and major explosive volcanic eruptions5, 6 . . .
  7. Schlesinger, M. E.; Ramankutty, N. Nature 367, 723-726 (1994) .
    • . . . Another source of internal climate variability that may contribute to the inconsistency is the Atlantic multidecadal oscillation7 (AMO) . . .
  8. Solomon, S. Science 333, 866-870 (2011) .
    • . . . Results from several independent datasets show that stratospheric aerosol abundance has increased since the late 1990s, owing to a series of comparatively small tropical volcanic eruptions8 . . .
    • . . . Although none of the CMIP5 simulations take this into account, two independent sets of model simulations estimate that increasing stratospheric aerosols have had a surface cooling impact of about 0.07 °C per decade since 19988, 9 . . .
  9. Fyfe, J. C.; von Salzen, K.; Cole, J. N. S.; Gillett, N. P.; Vernier, J-P. Geophys. Res. Lett. 40, 584-588 (2013) .
    • . . . Although none of the CMIP5 simulations take this into account, two independent sets of model simulations estimate that increasing stratospheric aerosols have had a surface cooling impact of about 0.07 °C per decade since 19988, 9 . . .
  10. Solomon, S. Science 327, 1219-1223 (2010) .
    • . . . Other factors that contribute to the discrepancy could include a missing decrease in stratospheric water vapour10 (whose processes are not well represented in current climate models), errors in aerosol forcing in the CMIP5 models, a bias in the prescribed solar irradiance trend, the possibility that the transient climate sensitivity of the CMIP5 models could be on average too high11, 12 or a possible unusual episode of internal climate variability not considered above13, 14 . . .
  11. Stott, P.; Good, P.; Jones, G.; Gillett, N.; Hawkins, E. Environ. Res. Lett. 8, 014024 (2013) .
    • . . . Other factors that contribute to the discrepancy could include a missing decrease in stratospheric water vapour10 (whose processes are not well represented in current climate models), errors in aerosol forcing in the CMIP5 models, a bias in the prescribed solar irradiance trend, the possibility that the transient climate sensitivity of the CMIP5 models could be on average too high11, 12 or a possible unusual episode of internal climate variability not considered above13, 14 . . .
  12. Otto, A. Nature Geosci. 6, 415-416 (2013) .
    • . . . Other factors that contribute to the discrepancy could include a missing decrease in stratospheric water vapour10 (whose processes are not well represented in current climate models), errors in aerosol forcing in the CMIP5 models, a bias in the prescribed solar irradiance trend, the possibility that the transient climate sensitivity of the CMIP5 models could be on average too high11, 12 or a possible unusual episode of internal climate variability not considered above13, 14 . . .
  13. Meehl, G. A.; Arblaster, J. M.; Fasullo, J. T.; Hu, A.; Trenberth, K. E. Nature Clim. Change 1, 360-364 (2011) .
    • . . . Other factors that contribute to the discrepancy could include a missing decrease in stratospheric water vapour10 (whose processes are not well represented in current climate models), errors in aerosol forcing in the CMIP5 models, a bias in the prescribed solar irradiance trend, the possibility that the transient climate sensitivity of the CMIP5 models could be on average too high11, 12 or a possible unusual episode of internal climate variability not considered above13, 14 . . .
  14. Guemas, V.; Doblas-Reyes, F. J.; Andreu-Burillo, I.; Asif, M. Nature Clim. Change 3, 649-653 (2013) .
    • . . . Other factors that contribute to the discrepancy could include a missing decrease in stratospheric water vapour10 (whose processes are not well represented in current climate models), errors in aerosol forcing in the CMIP5 models, a bias in the prescribed solar irradiance trend, the possibility that the transient climate sensitivity of the CMIP5 models could be on average too high11, 12 or a possible unusual episode of internal climate variability not considered above13, 14 . . .
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