Identification of anthropogenic climate change using a second-generation reanalysis

2.1. Reanalysis Data

[18] 2.1 Full descriptions of the ERA-15, NCEP-50, and ERA-40 reanalysis projects are given by Gibson et al. [1997], Kalnay et al. [1996], Kistler et al. [2001], Simmons and Gibson [2000], and the reanalysis websites (http://wesley.wwb.noaa.gov (NCEP-50), http://www.ecmwf.int/research/era/ERA-15 (ERA-15), and http://www.ecmwf.int/research/era (ERA-40)). Our aim here is to highlight some of the principal differences between ERA-40 and earlier reanalyses. The first key difference is that ERA-40 has higher horizontal and vertical resolution. The operational numerical weather prediction models in NCEP-50 and ERA-15 were run at T62 and T106 spectral truncation (respectively), while ERA-40 was run at T159 spectral truncation. ERA-40 employs a hybrid sigma-pressure coordinate system with 60-level vertical resolution; the top model level is at 0.1 hPa (ca. 65 km). Both NCEP-50 (28 levels) and ERA-15 (31 levels) had substantially fewer levels in the vertical than ERA-40, lower top levels (ca. 3 hPa for NCEP-50 and 10 hPa for ERA-15), and a less extensive representation of the stratosphere. The enhanced vertical resolution of ERA-40 is useful for exploring the sensitivity of estimated p_LRT changes to the number of model levels used in calculating p_LRT (see section 3.1).

[19] There are also important differences in the data assimilation systems. ERA-40 directly incorporates raw satellite radiances through a three-dimensional variational data assimilation system (3D-Var) [Andersson et al., 1998]. The 3D-Var scheme is global, multivariate, and nonlinear. Implementation and subsequent refinement of the variational assimilation scheme in ECMWF operations has yielded pronounced increases in analysis accuracy and forecast skill [Andersson et al., 1998; Simmons and Hollingsworth, 2002]. ERA-15 assimilated retrievals based on a less-extensive set of satellite radiances that had undergone several preprocessing steps [McNally et al., 2000]. These steps are not required in the case of direct assimilation of raw radiances.

[20] Like ERA-40, NCEP-50 also used a 3D-Var scheme [Kalnay et al., 1996], but satellite data were assimilated through temperature retrievals rather than radiances. The retrievals were generated by the National Environmental Satellite Data and Information Service (NESDIS), and incorporate information from several different satellite-borne sensors: MSU, the Stratospheric Sounding Unit (SSU), and the High-Resolution Infrared Radiation Sounder (HIRS). The NESDIS retrievals have documented biases in the temperature of the tropical stratosphere [Mo et al., 1995] and in the temperature and static stability of the troposphere [Kelly et al., 1991]. These biases, together with changes in the retrieval algorithms themselves, can induce spurious temporal variability [Basist and Chelliah, 1997; Santer et al., 2004]. The assimilation of satellite data is therefore fundamentally different in the three reanalyses, as are procedures for bias correction of satellite and radiosonde information [Gibson et al., 1997; Kalnay et al., 1996; Harris and Kelly, 2001; Andrae et al., 2004].

[21] ERA-40 atmospheric temperature data required for p_LRT and equivalent MSU calculations were available on the model Gaussian grid at each of the 60 full model levels. ECMWF also interpolated model-level fields to 23 discrete pressure levels and archived temperature data at this reduced vertical resolution [Kållberg et al., 2004]. We made use of both the 60- and 23-level temperature data sets for computing p_LRT, but calculated synthetic MSU temperatures with 23-level data only. Most calculations employed monthly mean data for the period January 1958 through December 2001, although some six-hourly data were used to test the sensitivity of p_LRT changes to the temporal resolution of the input temperature data (see section 3.1).

[22] NCEP-50 atmospheric temperature data were interpolated from the T62 Gaussian grid and 28 model levels to a regular 2.5° × 2.5° latitude-longitude grid and 17 discrete pressure levels (spanning 1000–10 hPa). NCEP-50 data were available in the form of monthly means for the period January 1948 through December 2001. ERA-15 data were not used for the present study given the relatively short duration of this reanalysis.

2.2. Satellite Data

[23] To evaluate whether atmospheric temperature changes in ERA-40 are more reliable than those in NCEP-50 (and hence whether tropopause height detection times estimated from ERA-40 temperature data are more credible), we compare synthetic MSU temperatures calculated from both reanalyses with the MSU temperatures processed by Mears et al. [2003] of Remote Sensing Systems (RSS) and by Christy et al. [2003] at the University of Alabama in Huntsville (UAH). Our focus is on MSU channels 4 and 2, which provide information on layer-average stratospheric and tropospheric temperatures. (The maxima of the weighting functions for MSU channels 4 and 2 are at roughly 74 and 595 hPa.) We refer to these temperatures as T4 and T2, respectively. We use the most recent versions of the RSS and UAH T4 and T2 data (versions 1.2 for RSS and 5.1 for UAH). Both data sets were available in the form of monthly means on a regular 2.5° × 2.5° latitude-longitude grid, and span the period from January 1979 through December 2003.

[24] RSS and UAH use different procedures to adjust the raw MSU radiances for intersatellite biases, uncertainties in instrument calibration coefficients, changes in instrument body temperature, and drift in sampling of the diurnal cycle [Mears et al., 2003; Christy et al., 2003]. These processing differences lead to divergent estimates of T2 changes over 1979–2001: the troposphere warms by 0.09°C/decade in RSS, while the T2 trend in UAH is close to zero (Table 1). Discrepancies between the RSS and UAH T2 changes influence the detectability of model-predicted T2 fingerprints [Santer et al., 2003c].

2.3. Climate Model Data

[25] Both our original SAN03 tropopause height detection study and the present work employ data from the Department of Energy Parallel Climate Model (PCM) developed by NCAR and Los Alamos National Laboratory [Washington et al., 2000]. In addition to climate change detection work [Santer et al., 2003c], PCM has been used for a wide range of applications, including studies of forced changes in decadal variability [Meehl et al., 2000], factors affecting the amplitude of simulated ENSO variability [Meehl et al., 2001], the climate response to volcanic forcing [Ammann et al., 2003; T. M. L. Wigley et al., Effect of climate sensitivity on the response to volcanic forcing, submitted to Journal of Geophysical Research, 2004] and the differential responses to solar and greenhouse-gas forcing [Meehl et al., 2003].

[26] We analyze two PCM experiments here. The first (ANTHRO) involves combined changes in three anthropogenic forcings: well-mixed greenhouse gases, the direct scattering effects of sulphate aerosols, and tropospheric and stratospheric ozone. The second (ALL) additionally includes the effects of changes in solar irradiance and volcanic aerosols. Our earlier study considered only the ALL experiment. Here we use ANTHRO for detection purposes, while ALL is more relevant for direct visual comparison with observations. ALL commences in 1890, while ANTHRO starts in 1872. Both end in 1999. Four realizations of each experiment were performed. Further details of the model and imposed forcing changes are given in Appendix A.

[27] Our fingerprint study requires model-based estimates of internally generated climate noise for assessing statistical significance (Appendix B). These were obtained from two 300-year control integrations performed with PCM and the ECHAM4/OPYC model (‘ECHAM’). Technical details of the ECHAM model are provided in Roeckner et al. [1999] and in Appendix A. Previous work with ECHAM has shown that tropopause height increases in anthropogenic climate-change experiments are large and readily identifiable relative to the unforced variability of p_LRT in the model control run [Sausen and Santer, 2003; Santer et al., 2003b].

3.1. Tropopause Height

[28] We diagnose changes in p_LRT from reanalysis and model data by interpolation of the lapse rate in a coordinate system, where p denotes pressure, κ = R/c_p, and R and c_p are the gas constant for dry air and the specific heat capacity of dry air at constant pressure [Reichler et al., 2003]. The algorithm identifies the threshold model level at which the lapse rate falls below 2°C/km, and then remains less than this critical value for a vertical distance of 2 km [World Meteorological Organization (WMO), 1957]. The exact pressure at which the lapse rate attains the critical value is determined by linear interpolation of lapse rates in the layers immediately above and below the threshold level. This definition of tropopause height is robust under most conditions. Exceptions include situations where the atmosphere is relatively isothermal or where multiple stable layers are present [Reichler et al., 2003]. To avoid unrealistically high or low p_LRT values, search limits are restricted to pressure levels between roughly 600 and 75 hPa; the search proceeds upward from 600 hPa. The algorithm is applied in a consistent way to monthly mean profiles of atmospheric temperature in PCM, ECHAM, ERA-40, and NCEP-50.

[29] Our previous work [Santer et al., 2003b] showed that calculations performed with the 28- and 17-level temperature data from NCEP-50 yielded similar decadal-timescale changes in global- and tropical-mean p_LRT. Concerns remain, however, regarding the reliability of p_LRT changes estimated from temperature data with coarse vertical resolution [Ramaswamy et al., 2001]. To address these concerns, p_LRT trends in ERA-40 were calculated from temperatures archived at the reduced set of 23 pressure levels (‘L23’) and the full 60 model levels (‘L60’). The latter data set has higher vertical resolution in the vicinity of the tropopause. Note that in the L60 case, model-level pressures were calculated from hybrid sigma-pressure coordinates using monthly mean values of surface pressure and the vertical coordinate definition specified by Kållberg et al., [2004]. Model-level pressures near the tropopause depend only weakly on surface pressure.

[30] The L60 and L23 calculations yield similar estimates of global mean p_LRT changes in ERA-40, with linear trends of −2.36 hPa/decade and −2.66 hPa/decade (respectively) over 1979–2001 (Figure 1a and Table 1). (A decrease in p_LRT signifies an increase in tropopause height.) Both trends are significantly different from zero when temporal autocorrelation effects are properly accounted for [Santer et al., 2000b]. The spatial fields of p_LRT trends over this 23-year period are also highly similar in the two calculations (Figures 2a and 2b), with a pattern correlation of r_L60:L23 = 0.94. This is an encouraging result, particularly for our pattern-based climate change detection work, since it illustrates that the large-scale pattern of recent tropopause height change is relatively insensitive to the vertical resolution of the temperature data used in p_LRT calculations (at least in ERA-40). The most pronounced pattern differences are in the tropics, where the L23 results have small but spatially coherent decreases in p_LRT, while small p_LRT trends of both sign occur in the L60 case (Figure 2). The mean height of the tropical tropopause (102.7 hPa for L60 and 105.7 hPa for L23) differs by less than 3% in the two calculations. (These values were computed using climatological annual-mean p_LRT data over 1979 to 2001, averaged over 20°N–20°S.)

[31] We note that the primary detection conclusions described in section 5 are insensitive to our choice of L23 or L60 p_LRT data. For consistency with p_LRT calculations involving the (low vertical resolution) NCEP-50, PCM, and ECHAM data (see Table 1), all ERA-40 p_LRT results employed in our detection work are from L23 calculations.

[32] Highwood et al. [2000] and Reichler et al. [2003] have pointed out that p_LRT is often difficult to define at high latitudes in the Southern Hemisphere (SH), particularly during SH winter. In the observation-sparse early years of ERA-40, this problem is compounded by the model's wintertime stratospheric cold bias in polar regions of the SH, which can lead to an unrealistically high lapse-rate tropopause at individual Antarctic grid points (see, e.g., the August result in Figure 3). The ‘high tropopause’ problem becomes less severe in the satellite era, when improved observational data constraints are introduced. The time-varying nature of the problem results in an annual cycle with spuriously large p_LRT anomalies in SH winter in the initial two decades of the reanalysis (Figure 1a). For this reason, data poleward of 60S were excluded from the tropopause height fingerprint analysis (section 5) and from all subsequent calculations of spatially averaged p_LRT changes. This removes the spurious annual cycle (see Figures 1a and 1b), thereby reducing the variance of the time series and the standard error of the p_LRT trend (Table 1). It also decreases differences between the L23 and L60 results early in the period. Averaging over 90°N–60°S has the additional effect of decreasing the global mean p_LRT trends themselves, since large p_LRT decreases occur poleward of 60°S (Figure 2).

[33] Finally, we examined the sensitivity of ERA-40's decadal-timescale p_LRT trends to the temporal resolution of the input temperature data. As is the case with data from radiosondes [Highwood and Hoskins, 1998] and NCEP-50 [Santer et al., 2003b], ERA-40 p_LRT trends computed from monthly mean temperature data are very similar to trends calculated from six-hourly data. This justifies our use of monthly mean data for determining tropopause height changes.

3.2. Synthetic MSU Temperatures

[34] We use a static global mean weighting function to compute synthetic MSU T4 and T2 temperatures from both climate model and reanalysis data [Santer et al., 1999]. This procedure facilitates the ‘like with like’ comparison of synthetic MSU temperatures with the MSU data processed by RSS and UAH. The appropriate weighting function is applied to grid point profiles of monthly mean pressure-level temperatures in PCM, ERA-40, and NCEP-50. For global and hemispheric means, this approach yields results similar to those obtained with a complex radiative transfer code [Santer et al., 1999].

[35] Locally, surface emissivity effects and large temporal changes in atmospheric moisture can yield differences between the equivalent T2 temperatures estimated with the static weighting function and full radiative transfer approaches. This is not a significant problem for our comparison of models and reanalyses, since we use a consistent approach for calculating synthetic MSU temperatures from these two types of data. However, comparisons of the synthetic and processed MSU T2 data should be made with caution over elevated terrain, particularly over the ice-covered surface of Antarctica.

4.1. Global Mean Changes

[36] The height of the tropopause shows a sustained multidecadal increase since the early 1960s (Figure 4). This overall increase is evident in ERA-40, NCEP-50, and the PCM ALL experiment. (Note that ERA-40 diverges markedly from NCEP-50 prior to roughly 1975, during the period when observational coverage is relatively sparse and does not provide as strong a constraint on the reanalyses.) Superimposed on this increase are short-term height decreases in response to explosive volcanic eruptions. The p_LRT changes after the eruptions of Mt. Agung (1963), El Chichón (1982), and Pinatubo (1991) are invariably larger in ALL than in either reanalysis, primarily due to the excessive stratospheric warming responses in PCM (Figure 5). This is a common deficiency in models with coarse vertical resolution in the stratosphere [see, e.g., Bengtsson et al., 1999].

[37] Another relevant factor in the comparison of volcanic p_LRT responses is that ALL includes estimates of volcanic aerosol forcing and explicitly considers the aerosol's radiative effects [Ammann et al., 2003], while NCEP-50 and ERA-40 do not incorporate observed estimates of volcanic aerosol properties. In both reanalyses, information on the climate signatures of volcanic eruptions is obtained indirectly through the assimilated satellite and in situ data. During eruptions in the presatellite era, such as that of Agung in 1963, the sparse coverage of available radiosonde data may bias reanalysis-based estimates of volcanically induced climate signals, thus contributing to differences between reanalyses and PCM.

[38] ERA-40 shows a pronounced lower stratospheric warming in 1975 (Figure 5). Although a slight warming may have taken place in reality due to the eruption of Mt. Fuego in October 1974, the stratospheric warming in ERA-40 stems primarily from an error in the bias correction of radiances from the Vertical Temperature Profiler Radiometer (VTPR) on the NOAA-4 satellite, which were assimilated for the period 1975 through mid-1976. Bias correction coefficients computed for VTPR data from the NOAA-3 satellite were inadvertently applied in adjusting NOAA-4 VTPR data. The error had largest impact in the Southern Hemisphere stratosphere, but is also evident in the global mean of the synthetic T2 data for ERA-40 (see Figure 9 below).

[39] Figure 6 provides a simple conceptual model for interpreting the low- and high-frequency p_LRT changes shown in Figure 4 (see also Highwood et al. [2000], who use a similar conceptual model). Calculations performed with radiative-convective models and more complex atmospheric GCMs illustrate that stratospheric cooling and tropospheric warming are robust signals of increases in atmospheric CO₂ [e.g., Hansen et al., 1984, 2002; Manabe and Wetherald, 1987; Ramaswamy et al., 1996, 2001]. These temperature changes tend to increase tropopause height. Anthropogenically induced depletion of stratospheric ozone also causes a net increase in tropopause height through strong cooling of the stratosphere. (Depletion of stratospheric ozone cools the stratosphere and the troposphere. These changes have effects of opposite sign on tropopause height. The stratospheric cooling influence dominates, so the net effect of ozone depletion is to raise tropopause height. In PCM, tropospheric ozone increases warm the troposphere and also contribute to tropopause height increases.) In contrast, volcanic aerosols injected into the stratosphere absorb incoming solar radiation and outgoing longwave radiation, thus warming the stratosphere and cooling the troposphere. Both of these changes decrease tropopause height (Figure 6).

[40] The actual temperature perturbations associated with these three forcings are more complex as a function of latitude and altitude than the idealized changes illustrated in Figure 6 [e.g., Bengtsson et al., 1999; Hansen et al., 2002; Santer et al., 2003b]. We note, however, that the global-scale p_LRT changes in PCM, NCEP-50, and ERA-40 are qualitatively consistent with this simple conceptual model.

[41] Our quantitative comparisons of p_LRT changes in ERA-40 and NCEP-50 focus on 1979–2001. This period is characterized by a relatively stable observing system, and by higher quantity and quality of assimilated observations. PCM ALL results are given for the period 1979–1999. (Recall that the ALL experiment ends in 1999.) In ERA-40, p_LRT decreases by 2.12 hPa/decade over 1979–2001 in the L23 calculation, corresponding to an overall increase in global mean tropopause height of roughly 200 m. The p_LRT decrease of 1.79 hPa/decade in NCEP-50 corresponds to a height increase of approximately 170 m. Both trends are significantly different from zero at the 1% level (Table 1), and are consistent with height increases inferred directly from radiosondes [Highwood et al., 2000; Seidel et al., 2001]. The PCM trend of −1.13 hPa/decade over 1979–1999 is smaller than in either reanalysis.

4.2. Spatial Patterns

[42] Despite their use of very different assimilation systems, input satellite data, and bias correction schemes, ERA-40 and NCEP-50 have striking similarities in their spatial patterns of tropopause height change (Figures 7a–7d). Both show increases in height over most of the globe. These increases are small in the tropics, and are largest at high latitudes in the Southern Hemisphere. It is notable that pattern similarities are not restricted to 1979–2001 (Figures 7c and 7d), but are also evident over the longer (and observationally less well-constrained) 1958–2001 period, particularly between 30°N–60°N, where radiosonde coverage is relatively dense (Figures 7a and 7b). The largest differences between ERA-40 and NCEP-50 are poleward of 45°S, where radiosonde coverage is poor; height increases here are more coherent in ERA-40 than in NCEP-50 (compare Figures 7a and 7b and Figures 7c and 7d). There are also prominent differences off the coast of California (Figures 7c and 7d).

[43] The large-scale patterns of p_LRT change over 1979–1999 in the PCM ALL and ANTHRO experiments are qualitively similar to those in the two reanalyses, with coherent height increases over most of the globe (compare Figures 7e and 7f and Figures 7c and 7d). As in ERA-40 and NCEP-50, increases are small and relatively unstructured in the tropics, and largest poleward of 45°S. In both model and reanalysis results, p_LRT changes tend to be noisy at the transition from the tropical to the extratropical tropopause. The similarity between the spatial fields of p_LRT change in the ALL and ANTHRO experiments (Figures 7e and 7f) arises because both patterns are driven primarily by anthropogenic forcing, at least in PCM (see SAN03 and section 5).

6.1. Synthetic MSU Temperatures in ERA-40 and PCM

[49] As discussed above, both stratospheric cooling and tropospheric warming tend to increase tropopause height (Figure 6 and section 4.1). In SAN03, we found that the height increase in NCEP-50 over 1979–2001 was driven by stratospheric cooling only, whereas recent height increases in PCM and ERA-15 were due to the combined effects of tropospheric warming and stratospheric cooling. We speculated that the tropopause height increase in NCEP-50 was partly the result of compensating errors, with excessive stratospheric cooling (related to the assimilation of biased temperature retrievals) offsetting the height decrease induced by a spurious cooling of NCEP's troposphere [Santer et al., 2004]. It is important, therefore, to determine whether our positive identification of the PCM tropopause height fingerprint in ERA-40 arises from partly compensating errors (as in the NCEP-50 case) or from real similarities in model and reanalysis profiles of atmospheric temperature change.

[50] We address this issue by comparing synthetic MSU temperature trends in ERA-40 and PCM ALL. Over 1979–2001, the stratosphere cools by 0.30°C/decade in ERA-40; its troposphere warms by 0.08°C/decade (Table 1 and Figures 5 and 9). These results are similar to (and statistically consistent with) T4 and T2 trends in the PCM ALL experiment. This confirms that recent tropopause height increases in PCM and ERA-40 are being driven by similar global-scale atmospheric temperature changes both above and below the tropopause.

[51] It is also instructive to compare the low-frequency variability of T2 changes in ERA-40 and the ALL experiment (Figure 10). The four realizations of ALL represent four different manifestations of natural internal variability, each superimposed on the underlying climate response to combined anthropogenic and natural forcings. The ALL realizations define an ‘envelope’ of possible changes in tropospheric temperature. The low-frequency T2 changes in ERA-40 are generally contained within this envelope. This constitutes a more stringent test of PCM's performance than comparison of trends alone. Note that during times of major El Niño or La Niña events, high-frequency T2 changes in ERA-40 are often outside PCM's envelope of inter-realization variability (Figure 10). This is because the phasing of El Niño and La Niña events is not the same in the real world and in a coupled model experiment, except by chance. ERA-40 is also outside the PCM variability envelope for most of 1975, when it was affected by the VTPR bias correction error noted earlier (section 4.1).

6.2. Comparison With Processed MSU Temperatures

[52] To evaluate whether T4 and T2 changes in ERA-40 are more reliable than those in NCEP-50 (and hence whether tropopause height detection times estimated from ERA-40 are more credible), we compare synthetic MSU temperatures calculated from both reanalyses with the RSS and UAH MSU temperatures processed by Mears et al. [2003] and Christy et al. [2003] (section 2.2). We analyze both temporal correlations between time series of global mean temperature changes, and spatial correlations between patterns of temperature trends. For related comparisons of the statistical properties of atmospheric temperature changes in RSS, UAH, and various radiosonde data sets, refer to Seidel et al. [2004].

6.2.2. Spatial Correlations

[55] Patterns of linear trends in T2 are qualitatively similar in ERA-40, RSS, and UAH (Figures 11a–11c). All show coherent warming over most of the Northern Hemisphere and cooling over the central Pacific and northern Siberia. Tropospheric temperature trends in these three data sets differ poleward of 45°S, where UAH cools markedly, RSS cools moderately, and ERA-40 has no net cooling. These differences are not fully understood, although differences in the treatment of surface emissivity effects over snow- and ice-covered surfaces are likely to be a contributory factor. The large-scale patterns of stratospheric cooling are similar in ERA-40, RSS, and UAH, with maximum cooling at high latitudes in the Southern Hemisphere, and cooling minima (or even slight warming) over the central Pacific, Alaska, and the South Indian Basin and Ross Sea (Figures 12a–12c). NCEP's T2 and T4 changes (Figures 11d and 12d) are distinctly different from those in ERA-40 and the two satellite data sets, with more coherent tropospheric cooling, and stronger cooling of the tropical and subtropical stratosphere. Possible reasons for this behavior were discussed in section 6.2.1. PCM's patterns of T2 and T4 trends (Figures 11e and 12e) are more similar to those in ERA-40, RSS and UAH than to NCEP's trend patterns.

[56] Pattern correlations help to quantify these comparisons (Table 3). Correlations are calculated both with and without inclusion of the spatial means; these statistics are referred to as c and r, respectively [Barnett and Schlesinger, 1987]. Removal of spatial means can reveal smaller-scale pattern similarities and/or differences that may be obscured by global mean trend differences between two data sets. This is the case with T2 changes in the two reanalyses, for which c_{NCEP:ERA} = −0.08, while r_{NCEP:ERA} = 0.74. The lower value for c arises from large differences in the global mean trends. In contrast, removal of spatial-mean T4 changes in NCEP-50 and ERA-40 degrades pattern similarity, pointing towards differences in the smaller-scale spatial structure of their stratospheric temperature trends (c_{NCEP:ERA} = 0.86, r_{NCEP:ERA} = 0.41; Figures 12a and 12d).

Table 3. Correlations Between the Spatial Patterns of Atmospheric Temperature Change in Reanalysis and Satellite Data Sets^a

	UAH	RSS	NCEP-50	ERA-40
T4 Results
UAH	1.00	0.99	0.91	0.98
RSS	0.97	1.00	0.91	0.98
NCEP-50	0.30	0.47	1.00	0.86
ERA-40	0.97	0.96	0.41	1.00
T2 Results
UAH	1.00	0.80	0.27	0.42
RSS	0.95	1.00	−0.15	0.73
NCEP-50	0.46	0.51	1.00	−0.08
ERA-40	0.50	0.55	0.74	1.00

• a Pattern correlations were calculated with the linear trend data in Figures 11 and 12 (for T2 and T4, respectively). All data sets were transformed to the RSS grid and masked with RSS coverage. Two forms of correlation are given: with spatial means included (c) and spatial means subtracted (r [Barnett and Schlesinger, 1987]). The latter are underlined.

[57] For ‘observed’ (RSS and UAH) and synthetic MSU temperatures, correlations between the spatial patterns of trends yield three key results: (1) Despite fundamental differences in their satellite data adjustment procedures, atmospheric temperature changes in RSS and UAH are more similar to each other than to changes in either reanalysis data set; (2) temperature changes in the two reanalyses are more similar to changes in observed satellite data products than they are to each other; (3) temperature changes in RSS and UAH are consistently more highly correlated with those in ERA-40 than with changes in NCEP-50. All three findings hold for both T2 and T4, and for ‘mean included’ and ‘mean removed’ correlations.