Simulated versus Satellite Retrieval Distribution Patterns of the Snow Water Equivalent over Southeast Europe

The snow is a very important component of the climate system which controls surface energy and water balances and is the largest transient feature of the land surface Yang et al., 2001). It has an effect on atmospheric circulation through changes to the surface albedo, thermal conductivity, heat capacity and aerodynamic roughness as has been documented in numerous observational and modelling studies (e.g., Barnet et al., 1989 Gong et al., 2003). According Cliford et al., (2010), the snow properties of surface water storage control the availability of water in many ecosystems and to a sixth of the world’s population. Therefore it is vital that snow is properly represented in geophysical models if we want to understand and make predictions of weather, climate, the carbon cycle, flooding and drought.

 The various properties characterizing snow are highly variable and so have to be determined as dynamically active components of climate. These include the snow depth (hs), SWE, density, and snow cover area (SCA). The SWE is a measure of the amount of water contained in snow pack and is the product of snow depth and snow density. Unfortunately, from the four snow metrics listed above, only extent (i.e., SCA) is easily monitored using satellites. SCA, however, is only an indirect measure of the world’s snow water resources (e.g., Brown, 2000 Brown et al. 2000). To understand global snow water trends in the necessary depth, the most fundamental metric to assess is SWE, with hs a close second. However, on large spatial scales the properties of snow are not easily quantified either from modelling or observations. For example, station based snow measurements often lack spatial representativeness, especially in regions where the topography, vegetation and overlaying atmosphere produce considerable heterogeneity of the snow-pack distribution (Liston, 2004).

 Of the two fundamental parameters, depth is quicker and easier to measure than SWE. No detailed estimates of the total number of depth and SWE measurements made worldwide is available, but what is available suggests that considerably more depths are collected than SWE measurements. So, for example, following the directives of the World Meteorological Organization (WMO), hs is measured in every station of the network of the Bulgarian National Institute of Meteorology and Hydrology at the Bulgarian Academy of Sciences (NIMH-BAS) every day, at 06 UTC and SWE - usually only five times monthly. It is clear that data-sets with such time gaps are highly insufficient for any comprehensive snow climatology. This fact is strengthened by the already mentioned spatial heterogeneity of the snow cover parameters. Satellite Earth observation (SEO) and RCM provides spatially and temporally consistent data regularly; especially as many snow-affected areas are covered with sparse ground-based measurement networks. Despite the weaknesses of both methods, data from these information sources should be combined with conventional data in optimal way in order to produce comprehensive representation of the snow-pack distribution and its long-term dynamics. Hence, due to these weaknesses, which will be addressed further, not RCM-, nor SEO-products can be treated solely as 'ultimate true', every evaluation of the model performance (i.e. “model verification”), respectively the satellite data quality, based only upon the comparison with the other, would be incorrect. It is possible and necessary, however, to asses the concordance between them over certain area for climatologically long enough (i.e. more the decade) period. 

The presented work is part of common effort in NIMH-BAS elaborate more reliable picture of snow- pack distribution and its long-term dynamics over Bulgaria and the surrounding territories, involving all available information. Thus, subject of previous paper of Chervenkov et al. (2015) was the comparison of RCM output for SWE with measurements and, therefore, the presented study can be treated as possible continuation. 

Main aim is to compare the gridded digital maps of SWE, resulting from the Globsnow SEO-product, which, as will be shown further, are practically only one reasonable possibility, with the output of the well-known in the climatological community regional climate model RegCM for 14 consecutive winters in the period 2000-2014 for the region of Southeast Europe, searching, in particular, systematic disagreement. 

The paper is organized as follows: The considered two information sources and the corresponding datasets are described briefly in Section 1. Explanation of the methodology of the performed comparisons is placed in Section 2. Core of the paper is Section 3, where the results are presented and commented. The conclusions and concise summary and the are briefly stated in Section 4.