title: 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.
authors: Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany.
authors: Jochem
authors: J
authors: Marotzke
authors: School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK.
authors: Piers M
authors: PM
authors: Forster
mesh_terms: D015982:Bias; D005246:Feedback; D057232:Global Warming / Q000266:history / Q000706:statistics & numerical data*; D049673:History, 20th Century; D049674:History, 21st Century; D008962:Models, Theoretical; D015999:Multivariate Analysis; D012044:Regression Analysis; D015203:Reproducibility of Results; D013696:Temperature; D013997:Time Factors
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