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Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows us to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitized numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA: 10.1594/PANGAEA.925860 (Imfeld et al., 2021). [ABSTRACT FROM AUTHOR]

Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitised numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA https://www.pangaea.de/tok/9e5b464ba828e931103d3794d9ae1d4576f7a03d (Imfeld et al, in review). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years a number of reanalysis datasets have been published that cover the past century or more, including the 'Twentieth Century Reanalysis' 20CRv2 and the European Reanalysis of the twentieth century ERA-20C. These datasets are widely used, showing the need for, and possible benefit of, reanalysis data products designed for climate applications. The twentieth-century reanalyses so far have assimilated only surface observations, and rely on independent estimates of monthly averaged sea-surface temperatures and sea ice concentrations as boundary conditions. While 20CRv2 uses only observations of surface and sea-level pressure, ERA-20C additionally assimilates marine winds. Here we describe an experimental reanalysis, referred to as ERA-PreSAT, which covers the period 1939-1967 and also assimilates historical upper-air data. Assessments of this dataset including comparisons with independent data show that (i) temperature biases in the Northern Hemisphere are largely reduced compared to reanalyses that assimilate surface data only, (ii) concentration of 1940s upper-air data in the northern extratropics created a strong interhemispheric asymmetry which is likely not realistic, (iii) the forecast skill in the Northern Hemisphere has increased substantially compared to reanalyses that assimilate surface data only, (iv) day-to-day and (in the northern extratropics) month-to-month correlations with independent observations (of total column ozone, upper-air data) increase over time, (v) interannual variability is well captured in the reanalysis, (vi) a signature of the stratospheric Quasi-Biennial Oscillation is present as far back as the 1940s, and (vii) tropical cyclones are not well represented. The generally encouraging results from the experimental ERA-PreSAT reanalysis underline that early upper-air data greatly contribute to our knowledge on the troposphere and lower stratosphere over the twentieth century. [ABSTRACT FROM AUTHOR]

Snow cover variability has significant effects on local and global climate evolution. By changing surface energy fluxes and hydrological conditions, changes in snow cover can alter atmospheric circulation and lead to remote climate effects. To document such multi-scale climate effects, atmospheric reanalysis and derived products offer the opportunity to analyze snow variability in great detail far back to the early 20th century. So far only little is know about their quality. Comparing snow depth in four long-term reanalysis datasets with Russian in situ snow depth data, we find a moderately high daily correlation (around 0.6-0.7), which is comparable to correlations for the recent era (1981-2010), and a good representation of sub-decadal variability. However, the representation of pre-1950 inter-decadal snow variability is questionable, since reanalysis products divert towards different base states. Limited availability of independent long-term snow data makes it difficult to assess the exact cause for this bifurcation in snow states, but initial investigations point towards representation of the atmosphere rather than differences in assimilated data or snow schemes. This study demonstrates the ability of long-term reanalysis to reproduce snow variability accordingly. [ABSTRACT FROM AUTHOR]