TY - JOUR

T1 - Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

AU - Förster, Kristian

AU - Hanzer, Florian

AU - Stoll, Elena

AU - Scaife, Adam A.

AU - MacLachlan, Craig

AU - Schöber, Johannes

AU - Huttenlau, Matthias

AU - Achleitner, Stefan

AU - Strasser, Ulrich

N1 - Funding Information: The publication of this article was funded by the open-access fund of Leibniz Universität Hannover. Funding Information: Acknowledgements. This work was carried out as part of the W01 MUSICALS II – Multiscale Snow/Ice Melt Discharge Simulation for Alpine Reservoirs project at alpS – Centre for Climate Change Adaptation in Innsbruck, Austria. The K1-Centre alpS is funded through the Federal Ministry of Transport, Innovation and Technology (BMVIT), the Federal Ministry of Science, Research and Economy (BMWFW), and the Austrian federal states of Tyrol and Vorarlberg within the scope of COMET – Competence Centers for Excellent Technologies. The COMET programme is managed by the Austrian Research Promotion Agency (FFG). We want to thank Tiroler Wasserkraft AG (TIWAG) for the collaboration and for co-funding the project. Additional thanks go to the NOAA (National Oceanic and Atmospheric Administration) National Centers for Environmental Prediction (NCEP) for the provision the CFSv2 data. The retrospective forecasts of the GloSea5 model were kindly provided by the SPECS project (Seasonal-to-decadal climate Prediction for the improvement of European Climate Services; http://www.specs-fp7.eu/). We would like to thank Felix Oesterle, who wrote the script to automatically retrieve and convert CFSv2 data. Assistance with HISTALP data provided by Anna-Maria Tilg and Barbara Chimani is greatly appreciated. Adam A. Scaife and Craig MacLachlan were supported by the joint DECC/Defra MetOffice Hadley Centre Programme (GA01101). The publication of this article was funded by the Open Access fund of Leibniz Universität Hannover. We wish to thank two anonymous reviewers for their helpful comments that helped to improve the manuscript.

PY - 2018/2/9

Y1 - 2018/2/9

N2 - 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 D0.57. 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 D0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.

AB - 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 D0.57. 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 D0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.

UR - http://www.scopus.com/inward/record.url?scp=85041849444&partnerID=8YFLogxK

U2 - 10.5194/hess-22-1157-2018

DO - 10.5194/hess-22-1157-2018

M3 - Article

AN - SCOPUS:85041849444

VL - 22

SP - 1157

EP - 1173

JO - Hydrology and Earth System Sciences

JF - Hydrology and Earth System Sciences

SN - 1027-5606

IS - 2

ER -