Abstract
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.
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Notes
If there is significant feedback, then it is not possible to determine whether the variable explains the hiatus or simply adjusts to the variable of interest.
Non-stationary refers to a non-time-invariant joint distribution.
The divergence of model errors consistent with a deterministic trend appears to be a phenomenon across coupled climate models over the time period. For an abrupt failure of a model during the hiatus, a step-shift change may be more appropriate. Section 3 tests for both trend and step-shift changes.
Multiple breaks in a deterministic trend make it possible to assess whether model failure similar to the hiatus have occurred prior to the hiatus. The presence of breaks is not imposed a-priori, if multiple breaks are detected, this leads to a piece-wise linear representation where later breaks can off-set earlier ones.
This list is non-exhaustive and additional methods are available (see e.g., Perron 2006 for an overview).
Multiple breaks (m) in the intercept alone can be estimated using Bai and Perron (1998) and Bai and Perron (2003). Given that the focus here lies on breaks in the trend, we restrict the analysis to at most one breakpoint in the intercept when using the LS approach. Multiple breaks are covered here using the IS methodology.
False positives are easily controlled in the IS approach. For a sample of T observations, IS for a break in either the trend or intercept implies that T variables are selected over. At the chosen level of significance α one can expect to spuriously retain αT break indicators. When testing for both breaks in the trend and step shifts, we include a full set of step indicators together with our full set of trend functions. To account for the higher number of observations when step shifts and trend changes are allowed, the significance level can be tightened further.
Trimming also limits the maximum number of breaks.
Corresponding code is available on the website of P. Perron: http://people.bu.edu/perron
For the least squares procedure the model with or without breakpoint is determined using the Bayesian Information Criterion (BIC). The trimming factor is set to 0.1, which corresponds to a minimum break length of 6 years.
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Acknowledgments
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.
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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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Pretis, F., Mann, M.L. & Kaufmann, R.K. Testing competing models of the temperature hiatus: assessing the effects of conditioning variables and temporal uncertainties through sample-wide break detection. Climatic Change 131, 705–718 (2015). https://doi.org/10.1007/s10584-015-1391-5
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DOI: https://doi.org/10.1007/s10584-015-1391-5