Big change question

2009 ◽  
Vol 10 (2-3) ◽  
pp. 245-248 ◽  
Author(s):  
Juan Manuel Moreno
Keyword(s):  
2005 ◽  
Vol 18 (13) ◽  
pp. 2429-2440 ◽  
Author(s):  
Terry C. K. Lee ◽  
Francis W. Zwiers ◽  
Gabriele C. Hegerl ◽  
Xuebin Zhang ◽  
Min Tsao

Abstract A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.


2009 ◽  
Vol 26 (5) ◽  
pp. 571-583 ◽  
Author(s):  
B. Tranter ◽  
M. Western
Keyword(s):  

2005 ◽  
Vol 6 (4) ◽  
pp. 381-387 ◽  
Author(s):  
Michael Schratz
Keyword(s):  

2019 ◽  
Author(s):  
Farid Anvari ◽  
Daniel Lakens

Effect sizes are an important outcome of quantitative research, but few guidelines exist that explain how researchers can determine which effect sizes are meaningful. Psychologists often want to study effects that are large enough to make a difference to people’s subjective experience. Thus, subjective experience is one way to gauge meaningfulness of an effect. We illustrate how to quantify the minimum subjectively experienced difference—the smallest change in an outcome measure that individuals consider to be meaningful enough in their subjective experience such that they are willing to rate themselves as feeling different—using an anchor-based method with a global rating of change question applied to the positive and negative affect scale. For researchers interested in people’s subjective experiences, this anchor-based method provides one way to specify a smallest effect size of interest, which allows researchers to interpret observed results in terms of their theoretical and practical significance.


2006 ◽  
Vol 7 (1-2) ◽  
pp. 91-92 ◽  
Author(s):  
Tondra L. Loder ◽  
James P. Spillane
Keyword(s):  

2007 ◽  
Vol 8 (1) ◽  
pp. 85-90
Author(s):  
Anne Jasman
Keyword(s):  

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