A gridded monthly upper-air data set from 1918 to 1957

Abstract

Significant efforts have been devoted in recent years towards extending observation-based three-dimensional atmospheric data sets back in time. Such data sets form an important basis for a better understanding of the climate system. Here we present a new monthly three-dimensional global data set that is based on historical upper-air data and surface data. We use statistical reconstruction techniques, calibrated using ERA-40 data, to obtain gridded data from the numerous, but scattered and heterogeneous historical upper-air observations. In contrast to previous work, in which we used hemispheric principal components on both the predictor and the predictand side to reconstruct spatially complete fields back to 1880, we restrict the procedure to places and times where upper-air observations are available. Each grid column (consisting of four variables at six levels) is then reconstructed independently using only predictor variables in the vicinity (i.e., only local stationarity is required rather than stationary large-scale patterns). The product, termed REC2, is a gridded, global monthly data set of geopotential height, temperature, and u and v wind from 850 to 100 hPa back to 1918. The data set is sparse (i.e., many grid cells are empty), but provides an alternative to large-scale reconstructions as it allows for non-stationary teleconnections. We show the results of several validation experiments, compare our new data set with a number of existing data sets, and demonstrate that it is suitable for analysing large-scale climate variability on interannual time-scales.