These abstracts don't resolve the puzzle. I confess, I haven't read the papers, just the abs, but it looks to me like they simply confirm the continued existence of the puzzle. What would resolve it? One of the MSU groups finding a major error in their methods or code, perhaps. The realisation that sfc and upper air don't have to follow each other on decadal timescales - this would reconcile sfc and lowest upper air estimates, but would leave the various MSU records still conflicting. Something else? We shall see.
Causes of differing temperature trends in radiosonde upper air data sets; Free M, Seidel DJ, JGR-A, 110 (D7): art. no. D07101 APR 6 2005
Differences between trends in different radiosonde temperature products resulting from the varying choices made by the developers of the data sets create obstacles for use of those products in climate change detection and attribution. To clarify the causes of these differences, one must examine results using a common subset of locations to minimize spatial sampling effects. When this is done for the Lanzante-Klein-Seidel (LKS) and Hadley Center (HadRT) radiosonde data sets, differences are reduced by at least one third. Differing homogeneity adjustment methods and differences in the source data are both important factors contributing to the remaining discrepancies. In contrast, subsampling the microwave sounding unit (MSU) satellite data sets according to the radiosonde coverage does not generally bring the trends in the satellite data closer to those in the radiosonde data so that adjustments and other processing differences appear to be the predominant sources of satellite-radiosonde discrepancies. Experiments in which we subsample globally complete data sets provide additional insight into the role of sampling errors. In the troposphere, spatial sampling errors are frequently comparable to the trends for 1979 1997, while in the stratosphere the errors are generally small relative to the trends. Sampling effects estimated from National Centers for Environmental Prediction reanalysis and MSU satellite data for seven actual radiosonde networks show little consistent relation between sampling error and network size. These results may have significant implications for the design of future climate monitoring networks. However, estimates of sampling effects using the reanalysis and the satellite data sets differ noticeably from each other and from effects estimated from actual radiosonde data, suggesting that these globally complete data sets may not fully reproduce actual sampling effects.
And another one, with a star cast of authors from the Tropospheric world, including Christy and Wentz, Angell and Parker; Free as above...
Uncertainty in signals of large-scale climate variations in radiosonde and satellite upper-air temperature datasets ; Seidel DJ, Angell JK, Christy J, Free M, Klein SA, Lanzante JR, Mears C, Parker D, Schabel M, Spencer R, Sterin A, Thorne P, Wentz F, J. Climate, 17 (11): 2225-2240 JUN 2004
There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976-77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset.
The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upper-air temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted ( although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data.