Abstract
Abstract. We present Sval_Imp, a high-resolution gridded dataset designed for forcing models of terrestrial surface processes on Svalbard. The dataset is defined on a 1 km grid covering the archipelago of Svalbard, located in the Norwegian Arctic (74–82∘ N). Using a hybrid methodology, combining multidimensional interpolation with simple dynamical modeling, the atmospheric reanalyses ERA-40 and ERA-Interim by the European Centre for Medium-Range Weather Forecasting have been downscaled to cover the period 1957–2017 at steps of 6 h. The dataset is publicly available from a data repository. In this paper, we describe the methodology used to construct the dataset, present the organization of the data in the repository and discuss the performance of the downscaling procedure. In doing so, the dataset is compared to a wealth of data available from operational and project-based measurements. The quality of the downscaled dataset is found to vary in space and time, but it generally represents an improvement compared to unscaled values, especially for precipitation. Whereas operational records are biased to low elevations around the fringes, we stress the hitherto underused potential of project-based measurements at higher elevation and in the interior of the archipelago for evaluating atmospheric models. For instance, records of snow accumulation on large ice masses may represent measures of seasonally integrated precipitation in regions sensitive to the downscaling procedure and thus providing added value. Sval_Imp (Schuler, 2018) is publicly available from the Norwegian Research Data Archive NIRD, a data repository (https://doi.org/10.11582/2018.00006).
Highlights
The nonlinearity of many surface processes poses challenges in terms of the appropriateness of atmospheric forcing for impact studies in terms of accuracy and precision (e.g., Liston and Elder, 2006)
Precipitation is often heavily biased in coarsely resolved reanalyses, especially in environments with pronounced topography, where it typically is too low and lacks spatial detail (Schuler et al, 2008). This is associated with the smoothed representation of the actual topography in the large-scale model used for the reanalysis (Fig. 1), leading to an underestimation of orographic precipitation
The shortwave radiation at the surface level of the reanalysis is projected to the highresolution topography in a three-step procedure: first the surface SW flux is separated into direct and diffuse components; second, the direct component is corrected for the elevation difference between the reanalysis surface and the high-resolution topography, considering an effective atmospheric transmissivity that is derived from top-of-atmosphere and surface fluxes; third, a topographic correction is applied to account for effects of slope and aspect of the highresolution topography, as well as shading by surrounding topography
Summary
The nonlinearity of many surface processes poses challenges in terms of the appropriateness of atmospheric forcing for impact studies in terms of accuracy and precision (e.g., Liston and Elder, 2006). We describe the methodology used to derive the dataset, present the organization of the data in the repository and discuss the performance of the downscaling procedure For the latter, the dataset is compared to a wealth of data available from long-term operational and short-term scientific records of meteorological and glaciological measurements. Sval_Imp has been employed entirely or in part by a range of projects for forcing process models of the surface energy and mass balances of glaciers (Østby et al, 2017) and precipitation patterns and meltwater production in the Kongsfjord area (Pramanik et al, 2018), for assimilation of remotely sensed snow cover using a snow distribution model (Aalstad et al, 2018), and assessing growing conditions for fungi (Botnen, 2020). The dataset has been used to assess changes and trends in climate conditions of Svalbard (Hanssen-Bauer et al, 2019)
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