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A Minimal, Statistical Model for the Surface Albedo of Vestfonna Ice Cap, Svalbard : Volume 6, Issue 2 (12/03/2012)

By Möller, M.

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Book Id: WPLBN0004022801
Format Type: PDF Article :
File Size: Pages 33
Reproduction Date: 2015

Title: A Minimal, Statistical Model for the Surface Albedo of Vestfonna Ice Cap, Svalbard : Volume 6, Issue 2 (12/03/2012)  
Author: Möller, M.
Volume: Vol. 6, Issue 2
Language: English
Subject: Science, Cryosphere, Discussions
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2012
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Möller, M. (2012). A Minimal, Statistical Model for the Surface Albedo of Vestfonna Ice Cap, Svalbard : Volume 6, Issue 2 (12/03/2012). Retrieved from http://www.ebooklibrary.org/


Description
Description: Department of Geography, RWTH University Aachen, Templergraben 55, Aachen, Germany. The ice cap Vestfonna is located in Northeastern Svalbard and forms one of the largest ice bodies of the Eurasian Arctic. Its surface albedo plays a key role in understanding and modelling of its energy and mass balance. The principle governing factors for albedo evolution, i.e. precipitation and air temperature and therewith snowdepth and melt duration, were found to vary almost exclusively with terrain elevation throughout the ice cap. Hence, surface albedo can be expected to develop a comparable pattern. A new statistical model is presented that estimates this mean altitudinal albedo profile of the ice cap on the basis of a minimal set of meteorological variables on a monthly resolution. Model calculations are based on a logistic function of the artificial quantity rain-snow ratio and a linear function of cumulative snowfall and cumulative positive degree days. Surface albedo fields of the MODIS snow product MOD10A1 of the period March to October of the years 2001–2008 serve as a basis for both calibration and cross-validation of the model. The meteorological model input covers the period September 2000 until October 2008 and is based on ERA-Interim data of a grid point located close to the ice cap. The albedo model shows a good performance. The root mean square error between observed and modelled albedo values along the altitudinal profile is 0.057 ± 0.028 (mean ± one standard deviation). The area weighted mean even reduces to a value of 0.053. Distinctly higher deviations (0.07–0.09) are only present throughout the very lowest and uppermost parts of the ice cap that are either small in area or hardly affected by surface melt. Thus, the new, minimal, statistical albedo model presented in this study is found to reproduce the albedo evolution on Vestfonna ice cap on a high level of accuracy and is thus suggested to be fully suitable for further application in broader energy or mass-balance studies of the ice cap.

Summary
A minimal, statistical model for the surface albedo of Vestfonna ice cap, Svalbard

Excerpt
Braun, M., Pohjola, V., Pettersson, R., Möller, M., Finkelnburg, R., Falk, U., Scherer, D., and Schneider, C.: Changes of glacier frontal positions of Vestfonna (Nordaustlandet, Svalbard), Geogr. Ann., 93, 301–310, 2011.; Brock, B., Willis, I., and Sharp, M.: Measurements and parameterization of albedo variations at Haut Glacier d'Arolla, Switzerland, J. Glaciol., 46, 675–688, 2000.; Essery, R., Blyth, E., Harding, R., and Lloyd, C.: Modelling albedo and distributed snowmelt across a low hill in Svalbard, Nord. Hydrol., 36, 207–218, 2005.; Førland, E., Hanssen-Bauer, I., and Nordli, P.: Climate Statistics and Longterm Series of Temperatures and Precipitation at Svalbard and Jan Mayen, Det Norske Meteorologiske Institutt, Oslo, 1997.; Arendt, A.: Approaches to modelling the surface albedo of a High Arctic glacier, Geogr. Ann. A, 81, 477–487, 1999.; Beaudon, E., Arppe, L., Jonsell, U., Martma, T., Möller, M., Pohjola, V., Scherer, D., and Moore, J.: Spatial and temporal variability of net accumulation from shallow cores from Vestfonna ice cap (Nordaustlandet, Svalbard), Geogr. Ann., 93, 287–299, 2011.; Braithwaite, R.: Calculation of degree-days for glacier-climate research, Z. Gletscherk. Glazialgeol., 20, 1–8, 1984.; Hagen, J., Liestøl, O., Roland, E., and Jørgensen, T.: Glacier Atlas of Svalbard and Jan Mayen, Meddelelser Nr. 129, Norsk Polarinstitutt, Oslo, 1993.; Hall, D. and Riggs, G.: Accuracy assessment of the MODIS snow products, Hydrol. Process., 21, 1534–1547, 2007.; Hall, D., Riggs, G., Salomonson, V., DiGirolamo, N., and Bayr, K.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, 2002.; Huth, R.: Statistical downscaling in Central Europe: evaluation of methods and potential predictors, Clim. Res., 13, 91–101, 1999.; Jordan, R., Albert, M., and Brun, E.: Physical processes within the snow cover and their parameterization, in: Snow and Climate, Physical Processes, Surface Energy Exchange and Modeling, edited by: Armstrong, R. and Brun, E., Cambridge University Press, Cambridge, 12–69, 2008.; Karl, T., Wang, W., Schlesinger, M., Knight, R., and Portman, D.: A method of relating general circulation model simulated climate to observed local climate. Part I: Seasonal statistics, J. Climate, 3, 1053–1079, 1990.; Kaser, G., Cogley, J., Dyurgerov, M., Meier, M., and Ohmura, A.: Mass balance of glaciers and ice caps: consensus estimates for 1961–2004, Geophys. Res. Lett., 33, L19501, doi:10.1029/2006GL027511, 2006.; Loeng, H.: Features of the physical oceanographic conditions of the Barents Sea, Polar Res., 10, 5–18, 1991.; Marzeion, B., Hofer, M., Jarosch, A. H., Kaser, G., and Mölg, T.: A minimal model for reconstructing interannual mass balance variability of glaciers in the European Alps, The Cryosphere, 6, 71–84, doi:10.5194/tc-6-71-2012, 2012.; Möller, M. and Schneider, C.: Calibration of glacier volume-area relations from surface extent fluctuations and application to future glacier change, J. Glaciol., 56, 33–40, 2010.; Möller, M., Finkelnburg, R., Braun, M., Hock, R., Jonsell, U., Pohjola, V., Scherer, D., and Schneider, C.: Climatic mass balance of Vestfonna ice cap, Svalbard: a spatially distributed assessment using ERA-Interim and MODIS data, J. Geophys. Res., 116, F03009, doi:10.1029/2010JF001905, 2011

 

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