Sval_Imp: a gridded forcing dataset for climate change impact research on Svalbard

• Published in: Earth System Science Data
• Main Authors: T. V. Schuler, T. I. Østby
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: info; geo; Arctic; Svalbard; Climate change
• Online Access: https://doi.org/10.5194/essd-12-875-2020; https://www.earth-syst-sci-data.net/12/875/2020/essd-12-875-2020.pdf; https://doaj.org/article/27ac6b7016f4442abccdba614db21710

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).