Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Hydrol. Earth Syst. Sci., 22, 1157-1173, 2018
https://doi.org/10.5194/hess-22-1157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
09 Feb 2018
Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)
Kristian Förster1,2,3, Florian Hanzer3,4, Elena Stoll2, Adam A. Scaife5,6, Craig MacLachlan5, Johannes Schöber7, Matthias Huttenlau2, Stefan Achleitner8, and Ulrich Strasser3 1Leibniz Universität Hannover, Institute of Hydrology and Water Resources Management, Hanover, Germany
2alpS – Centre for Climate Change Adaptation, Innsbruck, Austria
3Institute of Geography, University of Innsbruck, Innsbruck, Austria
4Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
5Met Office Hadley Centre, Exeter, Devon, UK
6College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
7TIWAG, Tiroler Wasserkraft AG, Innsbruck, Austria
8Unit of Hydraulic Engineering, Institute of Infrastructure, University of Innsbruck, Innsbruck, Austria
Abstract. This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months' lead time from two coupled atmosphere–ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As snowpack is hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. Even though predictions for precipitation may not be significantly more skilful than for temperature, the predictive skill achieved for precipitation is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r  =  0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2-AWARE, the corresponding values are r  =  0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.
Citation: Förster, K., Hanzer, F., Stoll, E., Scaife, A. A., MacLachlan, C., Schöber, J., Huttenlau, M., Achleitner, S., and Strasser, U.: Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps), Hydrol. Earth Syst. Sci., 22, 1157-1173, https://doi.org/10.5194/hess-22-1157-2018, 2018.
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This article presents predictability analyses of snow accumulation for the upcoming winter season. The results achieved using two coupled atmosphere–ocean general circulation models and a water balance model show that the tendency of snow water equivalent anomalies (i.e. the sign of anomalies) is correctly predicted in up to 11 of 13 years. The results suggest that some seasonal predictions may be capable of predicting tendencies of hydrological model storages in parts of Europe.
This article presents predictability analyses of snow accumulation for the upcoming winter...
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