Articles | Volume 11, issue 2
The Cryosphere, 11, 923–935, 2017
The Cryosphere, 11, 923–935, 2017

Research article 13 Apr 2017

Research article | 13 Apr 2017

Eurasian snow depth in long-term climate reanalyses

Martin Wegmann1,2,3, Yvan Orsolini4, Emanuel Dutra5,6, Olga Bulygina7, Alexander Sterin7, and Stefan Brönnimann2,3 Martin Wegmann et al.
  • 1Institut des Géosciences de l'Environnement, University of Grenoble, Grenoble, France
  • 2Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 3Institute of Geography, University of Bern, Bern, Switzerland
  • 4NILU – Norwegian Institute for Air Research, Kjeller, Norway
  • 5ECMWF – European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 6Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
  • 7All-Russian Research Institute of Hydrometeorological Information – World Data Centre, Obninsk, Russian Federation

Abstract. 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.

Short summary
We investigate long-term climate reanalyses datasets to infer their quality in reproducing snow depth values compared to in situ measured data from meteorological stations that go back to 1900. We found that the long-term reanalyses do a good job in reproducing snow depths but have some questionable snow states early in the 20th century. Thus, with care, climate reanalyses can be a valuable tool to investigate spatial snow evolution in global warming and climate change studies.