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Forcing, feedback and internal variability in global temperature trends

Abstract

Most present-generation climate models simulate an increase in global-mean surface temperature (GMST) since 1998, whereas observations suggest a warming hiatus. It is unclear to what extent this mismatch is caused by incorrect model forcing, by incorrect model response to forcing or by random factors. Here we analyse simulations and observations of GMST from 1900 to 2012, and show that the distribution of simulated 15-year trends shows no systematic bias against the observations. Using a multiple regression approach that is physically motivated by surface energy balance, we isolate the impact of radiative forcing, climate feedback and ocean heat uptake on GMST—with the regression residual interpreted as internal variability—and assess all possible 15- and 62-year trends. The differences between simulated and observed trends are dominated by random internal variability over the shorter timescale and by variations in the radiative forcings used to drive models over the longer timescale. For either trend length, spread in simulated climate feedback leaves no traceable imprint on GMST trends or, consequently, on the difference between simulations and observations. The claim that climate models systematically overestimate the response to radiative forcing from increasing greenhouse gas concentrations therefore seems to be unfounded.

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Figure 1: Simulated and observed 15-year GMST trends since 1900.
Figure 2: Regression-based and observed 15-year GMST trends since 1900.
Figure 3: Regression-based and observed 62-year GMST trends since 1900.

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Acknowledgements

We are indebted to J. Fyfe for making his CMIP5 GMST data set available to us, and to D. Notz, J. Risbey and B. Santer for comments on the manuscript. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (names of models listed in Extended Data Table 1) for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was supported by the Max Planck Society for the Advancement of Science (J.M.) and by a Royal Society Wolfson Merit Award and EPSRC grant EP/1014721/1 (P.M.F.).

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Authors

Contributions

The authors jointly designed the study. J.M. analysed the data and wrote the manuscript. Both authors discussed the results and the manuscript.

Corresponding author

Correspondence to Jochem Marotzke.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Observed and simulated time series of the anomalies in annually averaged GMST, from 1900 to 2012.

All anomalies are differences from the 1961–1990 temporal mean of each individual time series. GMST is the globally averaged merged surface temperature (2 m height over land and surface temperature over the ocean). The figure shows single simulations for the CMIP5 models (thin lines), the multimodel ensemble mean (thick red line) and the HadCRUT427 observations (thick black line). All model results have been subsampled using the HadCRUT4 observational data mask11. a, 114 realizations from the CMIP5 archive, obtained with 36 different models. b, Subset of 75 realizations with the 18 different models for which information on ERF is available35 (Extended Data Table 1). The two model ensembles are nearly indistinguishable.

Extended Data Figure 2 Time series of trends in ERF, as a function of start year.

a, 15-year trends; b, 62-year trends. Thin coloured lines show individual models as diagnosed previously35; if multiple realizations were available for a model, then the ensemble average of the individual diagnosed ERF time series for that model was given35 and is shown here. The thick red line shows the ensemble average over all models. The thick black line shows the best estimate from AR546, including, for illustration, the 5–95% uncertainty range for the periods 1984–1998 (a) and 1951–2011 (b), taken from fig. 8.19 in ref. 46. These uncertainty ranges, both of which are around 0.2 W m−2 per decade, do not take into account observational biases such as those diagnosed in ref. 48. Despite the scatter of the CMIP5 ensemble trends, the ensemble mean is in good agreement with the AR5 best estimate for almost all start years. The AR5 best-estimate ERF sums time series of forcing across individual forcing terms. Individual time series of AR5 ERF were derived in different ways. Greenhouse gas concentrations (observed or inferred), stratospheric aerosol optical depth and total solar irradiance were used to derive estimates of radiative forcing using simple formulae. Surface albedo forcing was derived from estimated anthropogenic vegetation trends. Ozone and aerosol forcings were derived from chemical transport model results with aspects of the forcing constrained by other modelling approaches or observations, or both. ERF sums rapid adjustments with traditional radiative forcings. Most time series in AR5 were based on traditional radiative forcings, and only CO2 and aerosol forcings included an assessment of the rapid adjustment. In other cases ERF and radiative forcings were assumed to be the same. The AR5 ERF for the most recent 2000–2011 period included updated estimates of volcanic and solar forcing, taking into account the broader 2008–2009 solar minimum and post-2000 volcanic activity46. These two cooling influences are not included in the CMIP5 ERF; it is hence surprising and unexplained why the CMIP5 ensemble-mean of 15-year ERF trends lies below the best-estimate AR5 ERF trend for the latest start years in a.

Extended Data Figure 3 Joint relative frequency distribution as a function of GMST trend and ERF trend, for the reduced 75-member ensemble for which forcing information is available and all start years.

a, 15-year trends; bin sizes are 0.025 °C per decade and 0.05 W m−2 per decade for GMST and ERF trend, respectively. b, 62-year trends; bin sizes are 0.0125 °C per decade and 0.025 W m−2 per decade for GMST and ERF trend, respectively. The ‘climate resistance’, ρ, is given by ρ = α + κ (refs 35, 36, 37). Each joint distribution is normalized such that its area integral is unity. Note the different axes, reflecting the much tighter correlation of the 62-year trends.

Extended Data Figure 4 Regression-based 15-year GMST trends since 1900.

a, Joint relative frequency distribution of regression result (equation (4) minus the ensemble-mean trend) as a function of start year and trend size. The P values of the regression have a median across start years of 0.075, based on the null hypothesis that all regression coefficients are zero. b, Joint relative frequency distribution of regression contribution from the trend in ERF. c, Joint relative frequency distribution of regression contribution from the climate feedback parameter α. d, Joint relative frequency distribution of regression contribution from the ocean heat uptake efficiency κ. In all joint relative frequency distributions, GMST trend is collected in bins of 0.025 °C per decade, and each vertical cross section is normalized such that its area integral is unity.

Extended Data Table 1 CMIP5 models used in this study

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Marotzke, J., Forster, P. Forcing, feedback and internal variability in global temperature trends. Nature 517, 565–570 (2015). https://doi.org/10.1038/nature14117

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