Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets

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 upperair 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

Absolute Proper Motions to B~22.5: IV. Faint, Low Velocity White Dwarfs and the White Dwarf Population Density Law

The reduced proper motion diagram (RPMD) for a complete sample of faint stars with high accuracy proper motions in the North Galactic Pole field SA57 is investigated. Eight stars with very large reduced proper motions are identified as faint white dwarf candidates. We discriminate these white dwarf candidates from the several times more numerous QSOs based on proper motion and variability. We discuss the implausibility that these stars could be any kind of survey contaminant. If {\it bona fide} white dwarfs, the eight candidates found here represent a portion of the white dwarf population hitherto uninvestigated by previous surveys by virtue of the faint magnitudes and low proper motions. The newly discovered stars suggest a disk white dwarf scaleheight larger than the values of 250-350 pc typically assumed in assessments of the local white dwarf density. Both a <V/V_{max}> and a more complex maximum likelihood analysis of the spatial distribution of our likely thin disk white dwarfs yield scaleheights of 400-600 pc while at the same time give a reasonable match to the local white dwarf volume density found in other surveys. Our results could have interesting implications for white dwarfs as potential MACHO objects. We can place some direct constraints (albeit weak ones) on the contribution of halo white dwarfs to the dark matter of the Galaxy. Moreover, the elevated scale height that we measure for the thin disk could alter the interpretation of microlensing results to the extent of making white dwarfs untenable as the dominant MACHO contributor. (Abridged)Comment: 38 pages, 5 figures, to appear in April Ap

Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets

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 upperair 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