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Thomas Griesser, Stefan Brönnimann, Andrea Grant, Tracy Ewen, Alexander Stickler, and Joey Comeaux


This work presents statistically reconstructed global monthly mean fields of temperature and geopotential height (GPH) up to 100 hPa for the period 1880–1957. For the statistical model several thousand predictors were used, comprising a large amount of historical upper-air data as well as data from the earth’s surface. In the calibration period (1958–2001), the statistical models were fit using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as the predictand. After the weighting of the predictors, principal component (PC) analyses were performed on both the predictand and predictor dataset. Multiple linear regression models relate each principal component time series from the predictand with an optimal subset of principal component time series from the predictor. To assess the quality of the reconstructions, statistical split-sample validation (SSV) experiments were performed within the calibration period. Furthermore, the reconstructions were compared with independent historical upper-air and total ozone data. Based on the SSV experiment, this study obtained good reconstructions for temperature and GPH in the Northern Hemisphere; however, the skill in the tropics and the Southern Hemisphere was much lower. The reconstruction skill shows a clear annual cycle with the highest values in January. The lower levels were better reconstructed except in the tropics where the highest levels showed the best skill. With the inclusion of a considerable amount of upper-air data after 1939 the skill increased substantially. The fields were analyzed for selected months in the 1920s and 1930s to demonstrate the usefulness of the reconstructions. It is shown that the reconstructions are able to capture regional-to-global dynamical features.

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Stefan Brönnimann, Rob Allan, Christopher Atkinson, Roberto Buizza, Olga Bulygina, Per Dahlgren, Dick Dee, Robert Dunn, Pedro Gomes, Viju O. John, Sylvie Jourdain, Leopold Haimberger, Hans Hersbach, John Kennedy, Paul Poli, Jouni Pulliainen, Nick Rayner, Roger Saunders, Jörg Schulz, Alexander Sterin, Alexander Stickler, Holly Titchner, Maria Antonia Valente, Clara Ventura, and Clive Wilkinson


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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