Sval_Imp_v1: A gridded forcing dataset for climate change impact research on Svalbard
We present Sval_Imp_v1, 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 multi-dimen...
| Main Authors: | , |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/essd-2019-180 https://www.earth-syst-sci-data-discuss.net/essd-2019-180/ |
| Summary: | We present Sval_Imp_v1, 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 multi-dimensional interpolation with simple dynamical modelling, 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 as well as project-based measurements. The quality of the downscaled dataset is found to vary in space and time, but generally represents an improvement compared to unscaled values, especially for precipitation. Whereas operational records are biased to low-elevations around the fringes of the archipelago, we stress the hitherto under-used potential of project-based measurements 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, thus providing added value. |
|---|