Figure 14:   Upper air temperature trends in deg. K/decade from Angell 54 at various troposphere and lower stratosphere altitudes for the Northern Hemisphere, the Southern Hemisphere, the Tropics, and the globe for 1958-2000 (Left), 1979-2000 (Center), and the change from the former to the latter (Right). The horizontal bars show 2-sigma confidence intervals for each trend indicated. Figure taken from Angell, 2003.

Figure 15:   The 87 station network used in the LKS radiosonde analysis (Lanzante et. al., 2003).

Figure 16:   Discrete, and possibly anomalous temperature change points found at any level by the six participating groups participating in the 2002 CARDS Workshop. Each team used a different standard method for detecting possibly anomalous change points in the historical temperature records of the 12 stations shown and suggested corrections for all that were considered anomalous. The LKS methodology (Lanzante et. al., 2003) is denoted by (GFDL), and the HadRT methodology (Parker et. al., 1997) is denoted by UKMO. Symbols on or above the grid line for each station show change points for that station. Countries for each station are shown in Figure 17b. Taken from Free et. al., 2002).

Figure 17:   Comparison of change points identified by the different methods used by each team participating in the 2002 CARDS Workshop (top). The values shown are the percentages of change points that occurred within 6 months of each other for each pair of methods. Where one or both teams found change points, the percent agreement is the number of common change points divided by the total number of change points found by the two teams in question. Stations where neither of them found any change points are considered to be in 100% agreement. Each entry in the last column is the average of the percentages shown in that row. Comparisons with the Met Office and UAH are limited to 1979–97. The stations may not constitute a representative sample of all radiosonde data, and not all groups produced results for all stations. The bottom chart shows the number of change points identified by each team for 1979–97. An X denotes stations for which a group did not provide data, and an 0 denotes stations where a group examined the record but found no break points from 1979 through 1997. Taken from Free et. al., 2002.

Figure 18:   Comparison of change points identified by the different methods used by each team participating in the 2002 CARDS Workshop (top). The values shown are the percentages of change points that occurred within 6 months of each other for each pair of methods. Where one or both teams found change points, the percent agreement is the number of common change points divided by the total number of change points found by the two teams in question. Stations where neither of them found any change points are considered to be in 100% agreement. Each entry in the last column is the average of the percentages shown in that row. Comparisons with the Met Office and UAH are limited to 1979–97. The stations may not constitute a representative sample of all radiosonde data, and not all groups produced results for all stations. The bottom chart shows the number of change points identified by each team for 1979–97. An X denotes stations for which a group did not provide data, and an 0 denotes stations where a group examined the record but found no break points from 1979 through 1997. Taken from Free et. al., 2002.

Figure 19:   Tropospheric (850 hPa) and stratospheric (50 hPa) temperature trends in deg. K/decade at Darwin, Australia as determined by 4 of the 6 teams participating in the 2002 CARDS Workshop. UAH trends represent Darwin data weighted to simulate MSU Channel 4. The difference uncertainties are twice the square root of the sum of the squares of the individual time series standard errors, and represent the 95 percent confidence interval. Taken from Free et. al., 2002.

Figure 20:   Global temperature anomalies for the middle troposphere from MSU/AMSU and 2 radiosonde datasets. The HadRT sonde dataset represents monthly CLIMAT TEMP reports and the LKS sonde dataset is from an 87 station network corrected for temporal inhomogeneities. The bottom curve gives the average trend for all products and the individual product curves give deviations from the average (from Seidel et. al., 2003).

Figure 21:   Global temperature anomalies for the lower stratosphere from MSU/AMSU and 2 radiosonde datasets. The HadRT sonde dataset represents monthly CLIMAT TEMP reports and the LKS sonde dataset is from an 87 station network corrected for temporal inhomogeneities. The bottom curve gives the average trend for all products and the individual product curves give deviations from the average (from Seidel et. al., 2003).

Figure 22:   Multidataset-average monthly anomaly time series for 6 vertical layers compared with time series for the Quasi-Biennial oscillation (QBO) as determined by 50-hPa altitude zonal wind patterns from radiosonde data at Singapore, and the Southern Oscillation Index (SOI) as determined by Trenberth (1984). The datasets shown are global averages of data from LKS, HadRT, RIHMI, Angell 63, Angell 54, and UAH Vers. D and 5.0. All are global average time series except for the 300-100 hPa (tropopause) time series which is for the Tropics only. Taken from Seidel et. al., 2003.

Figure 23:   Comparison of global middle troposphere time series from MSU Channel 2 as determined by RSS and UAH from 1979 to October of 2003. The upper curves are based on an “Ocean Only” merge to characterize hot target calibration coefficients and co-orbiting satellite offsets. The lower curves are based on a “Land and Ocean” merge. The black curves show the difference between RSS and UAH for the merge in question. Taken from Mears et. al. (2003b).

Figure 24:   Comparison of Hot Target Calibration Factors (α_i) as determined by RSS and UAH merge calculations. The constant value of 0.03 used by Prabhakara et. al (2000) to collectively characterize all POES satellite hot target variations and all sources of linear and non-linear error (except diurnal drift and orbital decay) is shown for comparison. Adapted from Mears et. al. (2003b).

Figure 25:   Results of a Monte-Carlo analysis of the covariance matrix of standard deviations of the residuals from the RSS Ocean-Only merge calculation. The covariance matrix was derived by superposing statistical noise on the Ocean-Only merge to create a set of 30,000 “noisy” merge calculations and groups of offsets prior to Monte-Carlo analysis. Results have been adjusted to account for the reduction in degrees of freedom among the residuals caused by a significant lag-1 autocorrelation (0.40). Taken from Mears et. al. (2003).