Attributes of several methods for detecting discontinuities in mean temperature series.

The procedure explicitly looks for both abrupt jumps and local, unrepresentative trends in temperature series.

“Last, the procedure explicitly looks for both abrupt ''jumps'' as well as local, unrepresentative trends in the temperature series (DeGaetano 2006).”

Of the nine shifts not identified, six had a magnitude of less than 0.3σ.

“Of the nine shifts not identified, six had a magnitude of less than 0.3s, which is below the sensitivity of most tests for undocumented changepoints (DeGaetano 2006;Ducre ´-Robitaille et al. 2003).”

For target series under M3 (step change with no trend), the overall power of detection is a function of the magnitude of the step.

“For target series under M3 (step change with no trend), the overall power of detection is a function of the magnitude of the step, as shown in previous investigations (e.g., DeGaetano 2006).”

The standard normal homogeneity test (SNHT) has superior accuracy in identifying the position of a step change under a wide variety of step and trend inhomogeneity scenarios relative to other commonly used methods.

“While no one test clearly outperforms others under all circumstances, the standard normal homogeneity test (SNHT; Alexandersson 1986) has been shown to have superior accuracy in identifying the position of a step change under a wide variety of step and trend inho-mogeneity scenarios relative to other commonly used methods (DeGaetano 2006; R07).”

Use of such tests in the presence of trends can lead to falsely detected step changes as well as inaccurate estimates of the magnitude of a shift when it occurs within a general trend.

“Use of such tests in the presence of trends can lead to falsely detected step changes as well as to inaccurate estimates of the magnitude of a shift when it occurs within a general trend (DeGaetano 2006;Pielke et al. 2007).”

Misclassification removes some of the unique target series trend as a step adjustment, bringing the target series more in line with the regional background climate trend captured by the neighbors.

“The impact of this type of misclassification is to inadvertently remove some of the unique target series trend as a step adjustment, thereby bringing the target series more in line with the regional background climate trend captured by the neighbors (DeGaetano 2006;Pielke et al. 2007).”

The biased estimate brings the adjusted target more in agreement with the background trend captured by the neighbors.

“Much like a false alarm when the target follows M2, the biased estimate would bring the adjusted target more in agreement with the background trend captured by the neighbors (DeGaetano 2006;Pielke et al. 2007).”

Preserving local trends (trend inhomogeneities) can often result in undesirable adjusted series.

“Given that preserving local trends (i.e., trend inhomogeneities) can often result in undesirable adjusted series, the pairwise algorithm was modified to employ the more commonly used M3 adjustment for all step changes (DeGaetano 2006).”

The correlation between temperature series in the United States is typically higher than in the simulations used here.

“As noted by DeGaetano (2006), the correlation between temperature series in the United States is typically higher than in the simulations used here.”