Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)
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 ...
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doaj-def15a5e3e824e9ea1d9dd5d05e197122020-11-24T21:20:10ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382018-02-01221157117310.5194/hess-22-1157-2018Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)K. Förster0K. Förster1K. Förster2F. Hanzer3F. Hanzer4E. Stoll5A. A. Scaife6A. A. Scaife7C. MacLachlan8J. Schöber9M. Huttenlau10S. Achleitner11U. Strasser12Leibniz Universität Hannover, Institute of Hydrology and Water Resources Management, Hanover, GermanyalpS – Centre for Climate Change Adaptation, Innsbruck, AustriaInstitute of Geography, University of Innsbruck, Innsbruck, AustriaInstitute of Geography, University of Innsbruck, Innsbruck, AustriaWegener Center for Climate and Global Change, University of Graz, Graz, AustriaalpS – Centre for Climate Change Adaptation, Innsbruck, AustriaMet Office Hadley Centre, Exeter, Devon, UKCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UKMet Office Hadley Centre, Exeter, Devon, UKTIWAG, Tiroler Wasserkraft AG, Innsbruck, AustriaalpS – Centre for Climate Change Adaptation, Innsbruck, AustriaUnit of Hydraulic Engineering, Institute of Infrastructure, University of Innsbruck, Innsbruck, AustriaInstitute of Geography, University of Innsbruck, Innsbruck, AustriaThis 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 <i>r</i> = 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 <i>r</i> = 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.https://www.hydrol-earth-syst-sci.net/22/1157/2018/hess-22-1157-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
K. Förster K. Förster K. Förster F. Hanzer F. Hanzer E. Stoll A. A. Scaife A. A. Scaife C. MacLachlan J. Schöber M. Huttenlau S. Achleitner U. Strasser |
spellingShingle |
K. Förster K. Förster K. Förster F. Hanzer F. Hanzer E. Stoll A. A. Scaife A. A. Scaife C. MacLachlan J. Schöber M. Huttenlau S. Achleitner U. Strasser Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps) Hydrology and Earth System Sciences |
author_facet |
K. Förster K. Förster K. Förster F. Hanzer F. Hanzer E. Stoll A. A. Scaife A. A. Scaife C. MacLachlan J. Schöber M. Huttenlau S. Achleitner U. Strasser |
author_sort |
K. Förster |
title |
Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps) |
title_short |
Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps) |
title_full |
Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps) |
title_fullStr |
Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps) |
title_full_unstemmed |
Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps) |
title_sort |
retrospective forecasts of the upcoming winter season snow accumulation in the inn headwaters (european alps) |
publisher |
Copernicus Publications |
series |
Hydrology and Earth System Sciences |
issn |
1027-5606 1607-7938 |
publishDate |
2018-02-01 |
description |
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 <i>r</i> = 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 <i>r</i> = 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. |
url |
https://www.hydrol-earth-syst-sci.net/22/1157/2018/hess-22-1157-2018.pdf |
work_keys_str_mv |
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