Snow representation over Siberia in operational seasonal forecasting systems

  • Seasonal forecasting systems still have difficulties predicting temperature over continental regions, while their performance is better over some maritime regions. On the other hand, the land surface is a substantial source of (sub-)seasonal predictability. A crucial land surface component in focus here is the snow cover, which stores water and modulates the surface radiation balance. This paper’s goal is to attribute snow cover seasonal forecasting biases and lack of skill to either initialization or parameterization errors. For this purpose, we compare the snow representation in five seasonal forecasting systems (from DWD, ECMWF, Météo-France, CMCC, and ECCC) and their performances in predicting snow and 2-m temperature over a Siberian region against ERA5 reanalysis and station data. Although all systems use similar atmospheric and land initialization approaches and data, their snow and temperature biases differ in sign and amplitude. Too-large initial snow biases persist over the forecast period, delaying and prolonging the melting phase. The simplest snow scheme (used in DWD’s system) shows too-early and fast melting in spring. However, systems including multi-layer snow schemes (Météo-France and CMCC) do not necessarily perform better. Both initialization and parameterization are causes of snow biases, but, depending on the system, one can be more dominant.

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Author:Danny RistoORCiD, Kristina FröhlichORCiDGND, Bodo AhrensORCiDGND
Parent Title (English):Atmosphere
Publisher:MDPI AG
Place of publication:Basel
Document Type:Article
Date of Publication (online):2022/06/22
Date of first Publication:2022/06/22
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/01/24
Tag:land–atmosphere; seasonal forecasting; snow
Issue:7, art. 1002
Article Number:1002
Page Number:13
First Page:1
Last Page:13
The Open-Access was funded by Goethe University Frankfurt.
ERA5 and the seasonal forecasting data are available at, accessed on 4 April 2022, provided by the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The Russian SYNOP station data are available at, accessed on 16 July 2021, provided by the All-Russian Research Institute of Hydrometeorological Information—World Data Centre (RIHMI-WDC).
Institutes:Geowissenschaften / Geographie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International