Testing competing models of the temperature hiatus: assessing the effects of conditioning variables and temporal uncertainties through sample-wide break detection
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Explaining the recent slowdown in the rise of global mean surface temperature (the hiatus in warming) has become a major focus of climate research. Efforts to identify the causes of the hiatus that compare simulations from experiments run by climate models raise several statistical issues. Specifically, it is necessary to identify whether an experiment’s inability to simulate the hiatus is unique to this period or reflects a more systematic failure throughout the sample period. Furthermore, efforts to attribute the hiatus to a particular factor by including that mechanism in an experimental treatment must improve the model’s performance in a statistically significant manner at the time of the hiatus. Sample-wide assessments of simulation errors can provide an accurate assessment of whether or not the control experiment uniquely fails at the hiatus, and can identify its causes using experimental treatments. We use this approach to determine if the hiatus constitutes a unique failure in simulated climate models and to re-examine the conclusion that the hiatus is uniquely linked to episodes of La Niña-like cooling (Kosaka and Xie 2013). Using statistical techniques that do not define the hiatus a priori, we find no evidence that the slowdown in temperature increases are uniquely tied to episodes of La Niña-like cooling.
KeywordsSample Period Model Error Conditioning Variable Hist Experiment Couple Climate Model
Financial support from the Open Society Foundations and the Oxford Martin School is gratefully acknowledged. We are thankful to David F. Hendry, Max Roser, and Andrew Martinez for helpful comments on an earlier version. We thank Yu Kosaka and Shang-Ping Xie for providing model temperature data.
FP conducted the statistical estimation procedures. MM created the Figures. RK conceived the analysis. All three authors contributed to writing the paper.
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