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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Mar 2019

Research article | 19 Mar 2019

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal The Cryosphere (TC).

Evaluation of snow depth and snow-cover over the Tibetan Plateau in global reanalyses using in-situ and satellite remote sensing observations

Yvan Orsolini1, Martin Wegmann2,a, Emanuel Dutra3, Boqi Liu5, Gianpaolo Balsamo4, Kun Yang6,7, Patricia de Rosnay4, Congwen Zhu5, Wenli Wang6,7, and Retish Senan4 Yvan Orsolini et al.
  • 1NILU – Norwegian Institute for Air Research, Norway
  • 2Alfred-Wegener Institute, Germany
  • 3Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, Portugal
  • 4European Centre for Medium-Range Weather Forecasts (ECMWF), UK
  • 5Institute of Climate System, Chinese Academy of Meteorological Sciences, China
  • 6Department of Earth System Science, Tsinghua University, China
  • 7Institute of Tibetan Plateau Research of Chinese Academy of Sciences, China
  • aformerly at: Institut des Geosciences de l’Environnement, University of Grenoble, Grenoble, France

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole and, is the world highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impacts on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric re-analyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global re-analyses with satellite and in-situ data. Yet, snow in re-analyses provides critical surface information for forecast systems from the medium to sub-seasonal time scales.

Here, snow depth and snow cover from 5 recent global reanalysis products are inter-compared over the TP region, and evaluated against a set of 33 in-situ station observations, as well as against the Interactive Multi-sensor Snow and Ice Mapping System (or IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in-situ observations provides confidence in the station data despite the relative paucity of in-situ measurement sites and the harsh operating conditions.

While several re-analyses show a systematic over-estimation of the snow depth or snow cover, the reanalyses that assimilate local in-situ observations or IMS snow-cover are better capable of representing the shallow, transient snowpack over the TP region. The later point is clearly demonstrated by examining the family of re-analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF), of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. One missing process in the re-analyses is the blown snow sublimation, which seems important in the dry, windy and cold conditions of the TP. By incorporating a simple parametrisation of this process in the ECMWF land re-analysis, the positive snow bias is somewhat alleviated. Future snow reanalyses that optimally combine the use of satellite snow cover and in-situ snow-depth observations over the Tibetan Plateau region in the assimilation and analysis cycles, along with improved representation of snow processes, have the potential to substantially improve weather and climate prediction and water resources applications.

Yvan Orsolini et al.
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Yvan Orsolini et al.
Yvan Orsolini et al.
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Short summary
The Tibetan Plateau region exerts a considerable influence on regional climate, yet the snowpack over that region is poorly represented in both climate and forecast models due a large precipitation and snowfall bias. We evaluate the snowpack in state-of-the-art atmospheric re-analyses against in-situ observations and satellite remote sensing products. Improved snow initialisation through better use of snow observations in re-analyses may improve medium-range to seasonal weather forecasts.
The Tibetan Plateau region exerts a considerable influence on regional climate, yet the snowpack...