scholarly journals Giant natural fluctuation models and anthropogenic warming

2016 ◽  
Vol 43 (16) ◽  
pp. 8670-8676 ◽  
Author(s):  
S. Lovejoy ◽  
L. del Rio Amador ◽  
R. Hébert ◽  
I. de Lima
Keyword(s):  
Natural Fluctuation

What have we done?

2019 ◽  
Author(s):  
Shaun Lovejoy
Keyword(s):  
Reasonable Doubt ◽  
Statistical Hypothesis ◽  
Fluctuation Analysis ◽  
Natural Fluctuation ◽  
Statistical Hypothesis Testing ◽  
Political Consequences ◽  
The Hours ◽  
Mcgill University ◽  
The Media ◽  
Set Up

It was April 11, 2014, and the McGill University press release went online at 1:30 in the afternoon. Although I’d published many articles, they were on fundamental geoscience; the release summarized the first one that had significant social and political consequences. Its title, “Scaling Fluctuation Analysis and Statistical Hypothesis Testing of Anthropogenic Warming,” was arcane, but the release was clear enough: “Statistical analysis rules out natural- warming hypothesis with more than 99% certainty” (the article, published in Climate Dynamics, is hereafter referred to as CD). It had been fifteen months since the original submission went to peer review, but now the pace picked up dramatically. Within hours, the tone was set by the skeptic majordomo Viscount Christopher Monckton of Brenchely, who displayed his Oxbridge classics erudition by deliciously qualifying the paper as a “mephitically ectoplasmic emanation from the Forces of Darkness.” Three days later, with the release getting 12,000 hits per day, the “Friends of Science” sent an aggressive missive to the McGill chancellor asking that it be removed from McGill’s site. The Calgary- based group with its Orwellian name was set up in 2002 to promote the theory that “The sun is the driver of climate change. Not you. Not CO2.” (Fig. 6.1). One could understand their thunder. Rather than trying to prove that the warming was anthropogenic— something that is impossible to do “beyond reasonable doubt”— the new paper closed the debate2 by doing something far simpler: by disproving the “Friends” Giant Natural Fluctuation (GNF) hypothesis. If we exclude either divine or extraterrestrial intervention, then the warming is natural or it is human; there is no third alternative. The skeptics were stuck. To add insult to injury, their prepackaged sermons on the inadequacies of computer models or their speculations about solar variability were irrelevant. Provoked by the media attention and several Op- Eds in the hours, days, and weeks that followed, in email, blogs, and Twitter, I was treated to a deluge of abuse: “atheist,” “Marxist,” “hippy name,” and so on— everything, it seemed, short of death threats.

scholarly journals Within-day biological variation and hour-to-hour reference change values for hematological parameters

2017 ◽  
Vol 55 (7) ◽  
Author(s):  
Judith M. Hilderink ◽  
Lieke J.J. Klinkenberg ◽  
Kristin M. Aakre ◽  
Norbert C.J. de Wit ◽  
Yvonne M.C. Henskens ◽  
...  
Keyword(s):  
Hematological Parameters ◽  
Biological Variation ◽  
Natural Fluctuation ◽  
Sample Collection ◽  
Blood Samples ◽  
Variation Data ◽  
Random Fluctuations ◽  
Analytical Variation ◽  
Variability Data

AbstractBackground:Middle- and long-term biological variation data for hematological parameters have been reported in the literature. Within-day 24-h variability profiles for hematological parameters are currently lacking. However, comprehensive hour-to-hour variability data are critical to detect diurnal cyclical rhythms, and to take into account the ‘time of sample collection’ as a possible determinant of natural fluctuation. In this study, we assessed 24-h variation profiles for 20 hematological parameters.Methods:Blood samples were collected under standardized conditions from 24 subjects every hour for 24 h. At each measurement, 20 hematological parameters were determined in duplicate. Analytical variation (CVResults:All parameters showed higher CVConclusions:We present complete 24-h variability profiles for 20 hematological parameters. Hour-to-hour reference changes values may help to better discriminate between random fluctuations and true changes in parameters with rhythmic diurnal oscillations.

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