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Based on univariate correlation and coherence analyses and considering the physical basis of the relationships, a simple multiforced (multiple) statistical concept is used which correlates observational climatic time series simultaneously with volcanic, solar, ENSO, and the anthropogenic greenhouse gases forcing. This is appropriate to remove some natural climate noise in the observed data and to evaluate the components (signals) possibly due to the anthropogenic greenhouse gas forcing (CO2, or “equivalent” CO2 implying additional gases) during industrial time. In this paper, we apply this technique to 100 global “box” data time series 1890–1985, of the surface air temperature, using observed data from Hansen and Lebedeff. The results are presented in terms of latitudinal-seasonal and regional trends, where the observed trend patterns are compared with the hypothetical signals (statistical assessments) possibly due to anthropogenic greenhouse forcing. These latter signals can be amplified to enable a comparison with corresponding results from general circulation model (GCM) CO2 doubling experiments. These observed-statistical assessments lead to results which are, at least qualitatively and in respect to the zonal mean temperatures, very similar to some GCM experiments indicating the maximum CO2 doubling signals (statistical assessment > 12 K) in the arctic winter. However, these signals are moderate in the tropics and in the Southern Hemisphere (global average 2.8–4.4 K). As far as the “industrial” signals are concerned (observed period) these signals are somewhat larger (maximum 7 K, global average 0.5–0.9 K) than the observed trends (maximum 5 K, global average 0.5 K). Phase shifts of cause and effect may amplify these signals but are very uncertain.

Analyses indicate that the Atlantic Ocean SST was considerably colder at the beginning than in the middle of the century. Parallel to this a systematic change of the North Atlantic SLP pattern was observed. To find out whether the analyzed SST and SLP changes are consistent, which would indicate that the SST change was real and not an instrumental artifact, a response experiment with a low resolution (T21) atmospheric GCM was performed. Two perpetual January simulations were conducted which differ solely in Atlantic Ocean (40$ - 60N) SST: the ”cold” simulation utilizes the SSTs for the period 1904-13, the ”warm” simulation uses the SST’s for the period 1951—60. Also a ”control” run, with the model’s standard SST somewhat between the ”cold” and ”warm” SST, was made. For the response analysis a rigorous statistical approach was taken: First the null hypothesis of identical horizontal distributions was subjected to a multivariate significance test. Second, the level of recurrence was estimated. The multivariate statistical approaches are based on hierarchies of test models. We examined three different hierarchies: a scale dependent hierarchy based on spherical harmonics (S), and two physically motivated ones, one based on the barotropic normal modes of the mean 300 hPa flow (B) and one based on the eigenmodes of the advection diffusion operator at 1000 hPa (A). The intercomparison of the ”cold” and ”warm” experiments indicates a signal in the geostrophic stream function that is in the S-hierarchy significantly nonzero and highly recurrent. In the A—hierarchy, the low level temperature field is identi— fied as being significantly and recurrently affected by the altered SST distribution. The SLP signal is reasonably similar to the observed SLP change. Unexpectedly the upper level streamfunction signal does not appear significantly nonzero in the B—hierarchy. If, however the pairs of experiments ”warm vs. control” and ”cold vs. control” are examined in the B—hierarchy, a highly significant and recurrent signal emerges. We conclude that the ”cold vs. warm” response is not a ”small disturbance” which would allow the signal to be described by eigenmodes of the linearized system. An analysis of the three—dimensional structure of the signal leads to the hy— pothesis that two different mechanisms are acting to modify the model’s mean state. At low levels, the local heating and advection are dominant, but at upper levels the extratropical signal is a remote response to modifications of the tropical convection.