Statistical significance of trends and trend differences in layer-average atmospheric temperature time series

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A Climatology of the Tropical Tropopause Layer (2002, background): “Significance of linear correlations, unless otherwise noted, is reported assuming a 95% confidence level and a 2-sided T-test with appropriate degrees of freedom (Santer et al. 2000), which for the trend analyses is typically one degree of freedom per year.”
Variability and trends in the global tropopause estimated from radiosonde data (2006, background): “…linear regression, and their ±2-sigma confidence D21101 SEIDEL AND RANDEL: GLOBAL TROPOPAUSE VARIABILITY AND TRENDS 8 of 17 D21101 intervals (taking into account the effect of temporal autocorrelation on the number of independent values in each series [Santer et al., 2000]), are shown in Figure 8.”
Assessment of global warming on the island of Tenerife, Canary Islands (Spain). Trends in minimum, maximum and mean temperatures since 1944 (2012, methodology): “The statistical significance of the linear trends was assessed, accounting for temporal autocorrelation following the method described by Santer et al. (2000).”
Nonlinear Trends, Long-Range Dependence, and Climate Noise Properties of Surface Temperature (2012, methodology): “Most climate studies use a linear least squares regression fit (e.g., Santer et al. 2000, 2008; Feldstein 2002; Turner et al. 2005), though recently the EMD/ EEMD method has become popular in climate research (e.g., Huang et al. 1998; Huang and Wu 2008; Wu et al. 2007; Franzke 2010).”