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

Abstract

Abstract. This paper examines trend uncertainties in layer-average free atmosphere temperatures arising from the use of different trend estimation methods. It also considers statistical issues that arise in assessing the significance of individual trends and of trend differences between data sets: Possible causes of these trends are not addressed. We use data from satellite and radiosonde measurements and from two reanalysis projects. To facilitate intercomparison, we compute from reanalyses and radiosonde data temperatures equivalent to those from the satellite-based Microwave Sounding Unit (MSU). We compare linear trends based on minimization of absolute deviations (LA) and minimization of squared deviations (LS). Differences are generally less than 0.05øC/decade over 1959-1996. Over 1979-1993, they exceed 0.10øC/decade for lower tropospheric time series and 0.15øC/decade for the lower stratosphere. Trend fitting by the LA method can degrade the lower-tropospheric trend agreement of 0.03øC/decade (over 1979-1996) previously reported for the MSU and radiosonde data. In assessing trend significance we employ two methods to account for temporal autocorrelation effects. With our preferred method, virtually none of the individual 1979-1993 trends in deep-layer temperatures are significantly different from zero. To examine trend differences between data sets we compute 95% confidence intervals for individual trends and show that these overlap for almost all data sets considered. Confidence intervals for lower-tropospheric trends encompass both zero and the model-projected trends due to anthropogenic effects. We also test the significance of a trend in d(t), the time series of differences between a pair of data sets. Use of d(t) removes variability common to both time series and facilitates identification of small trend differences. This more discerning test reveals that roughly 30% of the data set comparisons have significant differences in lower-tropospheric trends, primarily related to differences in measurement system. Our study gives empirical estimates of statistical uncertainties in recent atmospheric temperature trends. These estimates and the simple significance testing framework used here facilitate the interpretation of previous temperature trend comparisons involving satellite, radiosonde, and reanalysis data sets.

An assessment and interpretation of the observed warming of West Antarctica in the austral spring

“…Our calculations of the statistical significance of least-squares linear trends in timeseries are based on the two-sided t-test methodology and adjustment for autocorrelation reviewed and outlined by Santer et al (2000). We adjusted both the sample size and the degrees of freedom for indexing of the critical t-value according to the lag-1 autocorrelation of the regression residuals.…”

Summer melt rates on Penny Ice Cap, Baffin Island: Past and recent trends and implications for regional climate

“…Upper air soundings for southeastern Baffin Island are scarce, making it difficult to seek independent corroborating evidence. As an alternative, we used AWS data, weather station records, and Orvig's [1954] report to compare air temperature lapse rates between Penny Ice Cap summit and the Baffin Bay coast for three different observation periods: summer 1953 (July only), 1992-2000. Presently, the lapse rate is largest in October ($0.75 C 100 m À1 ), and decreases through the winter to reach a minimum in February ($0.15 C 100 m À1 ) when air temperatures are uniformly cold, or nearly so, from sea level to the summit of the ice cap.…”

“…The MF averaged 61% for the entire 65-year period and also for the interval , but the mean of the past 15 years [1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010] [16] For the entire period 1948-2010 (N = 63 years), the zero-lag correlation coefficient between 5-year running means of the MF % and regional summer temperature anomalies is R = 0.55, taking into account serial autocorrelation and the reduced degrees of freedom [Ebisuzaki, 1997;Santer et al, 2000]. The correlation is significant at the 95% confidence level, and the R value compares well with those previously obtained by Holdsworth [1984] (0.55 < R < 0.56).…”

Interannual and interdecadal variations of tropical cyclone activity in the South China Sea

“…The whole season also sees a decreasing trend in N T , N E , and N F of −5.7, −5.2, and −0.2 per 100 years over the study period (Figure 1(a), (b) and (c)), with those in N T and N E being significant at the 95% confidence level. The significance in the trend is determined using the method in Santer et al (2000).…”