Comment on “The phase relation between atmospheric carbon dioxide and global temperature” Humlum et al. [Glob. Planet. Change 100: 51–69.]: Isotopes ignored

A recent study relying purely on statistical analysis of relatively short time series suggested substantial re-thinking of the traditional view about causality explaining the detected rising trend of atmospheric CO2 (atmCO2) concentrations. If these results are well-justified then they should surely compel a fundamental scientific shift in paradigms regarding both atmospheric greenhouse warming mechanism and global carbon cycle. However, the presented work suffers from serious logical deficiencies such as, 1) what could be the sink for fossil fuel CO2 emissions, if neither the atmosphere nor the ocean – as suggested by the authors – plays a role? 2) What is the alternative explanation for ocean acidification if the ocean is a net source of CO2 to the atmosphere? Probably the most provocative point of the commented study is that anthropogenic emissions have little influence on atmCO2 concentrations. The authors have obviously ignored the reconstructed and directly measured carbon isotopic trends of atmCO2 (both δ13C, and radiocarbon dilution) and the declining O2/N2 ratio, although these parameters provide solid evidence that fossil fuel combustion is the major source of atmCO2 increase throughout the Industrial Era.

The sleeper effect: Artifact or phenomenon—A brief comment on Bell et al. (2013)

OBJECTIVE: Bell, Marcus, and Goodlad (2013) recently conducted a meta-analysis of randomized controlled additive trials and found that adding an additional component to an existing treatment vis-à-vis the existing treatment produced larger effect sizes on targeted outcomes at 6-months follow-up than at termination, an effect they labeled as a sleeper effect. One of the limitations with Bell et al.'s detection of the sleeper effect was that they did not conduct a statistical test of the size of the effect at follow-up versus termination. METHOD: To statistically test if the differences of effect sizes between the additive conditions and the control conditions at follow-up differed from those at termination, we used a restricted maximum-likelihood random-effect model with known variances to conduct a multilevel longitudinal meta-analysis (k = 30). RESULTS: Although the small effects at termination detected by Bell et al. were replicated (ds = 0.17-0.23), none of the analyses of growth from termination to follow-up produced statistically significant effects (ds < 0.08; p > .20), and when asymmetry was considered using trim-and-fill procedure or the studies after 2000 were analyzed, magnitude of the sleeper effect was negligible (d = 0.00). CONCLUSION: There is no empirical evidence to support the sleeper effect.