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.
Eurasian fall snow cover changes have been suggested as a driver for changes in the Arctic Oscillation and might provide a link between sea-ice decline in the Arctic during summer and atmospheric circulation in the following winter. However, the mechanism connecting snow cover in Eurasia to seaice decline in autumn is still under debate. Our analysis is based on snow observations from 820 Russian land stations, moisture transport using a Lagrangian approach derived from meteorological re-analyses. We show that declining sea-ice in the Barents and Kara Seas (BKS) acts as moisture source for the enhanced Western Siberian snow depth as a result of changed tropospheric moisture transport. Transient disturbances enter the continent from the BKS region related to anomalies in the planetary wave pattern and move southward along the Ural mountains where they merge into the extension of the Mediterranean storm track.
Newly digitized surface and upper-air data are useful to analyze climate and weather events in the first half of the twentieth century and may help to improve future reanalyses.
T o improve our understanding of global weather and climate variability and its change under the inf luence of global warming, it is vital to extend our knowledge about the atmospheric state and variability in the past. Current reanalysis datasets [the National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) 50-Year Reanalysis (Kistler et al. 2001) and the 40-yr European Centre for MediumRange Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005)] provide detailed information on the atmosphere during the past 60 years. The first half of the twentieth century, however-which features some very prominent climate fluctuations such as A systematic compilation of global upperair data from the first half of the twentieth century has weather and climate applications and may be useful in reanalyses.Two men launching a meteorological kite (a so-called "umbrella kite"). This type of kite amongst others was used during the first half of the twentieth century (e.g., at the observatory of Lindenberg, Germany), to obtain vertical profiles of atmospheric variables like temperature, pressure and humidity. The umbrella kite was also used during field expeditions, for launches on board ships, and as an observational platform for military air weather service and artillery during World War I. A still valid world record is connected to the umbrella kite: a maximum altitude of 9,740 m a.s.l. was reached during an ascent on August 1st 1919 by a combination of several such kites. (Photo courtesy of www.wetterdrachen.de.)
Global dynamical reanalyses of the atmosphere and ocean fundamentally rely on observations, not just for the assimilation (i.e., for the definition of the state of the Earth system components) but also in many other steps along the production chain. Observations are used to constrain the model boundary conditions, for the calibration or uncertainty determination of other observations, and for the evaluation of data products. This requires major efforts, including data rescue (for historical observations), data management (including metadatabases), compilation and quality control, and error estimation. The work on observations ideally occurs one cycle ahead of the generation cycle of reanalyses, allowing the reanalyses to make full use of it. In this paper we describe the activities within ERA-CLIM2, which range from surface, upper-air, and Southern Ocean data rescue to satellite data recalibration and from the generation of snow-cover products to the development of a global station data metadatabase. The project has not produced new data collections. Rather, the data generated has fed into global repositories and will serve future reanalysis projects. The continuation of this effort is first contingent upon the organization of data rescue and also upon a series of targeted research activities to address newly identified in situ and satellite records.
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