Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5645-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5645-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
COSIPY v1.3 – an open-source coupled snowpack and ice surface energy and mass balance model
Department of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 15, 91058 Erlangen, Germany
Anselm Arndt
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Christoph Schneider
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Ecohydrological processes in heavily managed catchments are often incorrectly represented in models. We applied a tracer-aided model STARR in an ET-dominated region (the Middle Spree, NE Germany) with major management impacts. Water isotopes were useful in identifying runoff contributions and partitioning ET even at sparse resolution. Trade-offs between discharge- and isotope-based calibrations could be partially mitigated by integrating more process-based conceptualizations into the model.
Pedro Henrique Lima Alencar, Saskia Arndt, Kei Namba, Márk Somogyvári, Frederik Bart, Fabio Brill, Juan Dueñas, Peter Feindt, Daniel Johnson, Nariman Mahmoodi, Christoph Merz, Subham Mukherjee, Katrin Nissen, Eva Nora Paton, Tobias Sauter, Dörthe Tetzlaff, Franziska Tügel, Thomas Vogelpohl, Stenka Valentinova Vulova, Behnam Zamani, and Hui Hui Zhang
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Glacial lake outburst floods (GLOFs) continue to threaten high-mountain communities in Nepal. We simulate potential GLOF events from five glacial lakes in the Everest region during the 21st century using a 3D flood model and several breach and SSP scenarios. Large GLOFs could extend over 100 km and inundate 80 to 100 km of roads, 735 to 1,989 houses and between 0.85 and 3.52 km2 of agricultural land. The results help to assess the changing GLOF impacts and support more accurate risk assessments.
Annelies Voordendag, Brigitta Goger, Rainer Prinz, Tobias Sauter, Thomas Mölg, Manuel Saigger, and Georg Kaser
The Cryosphere, 18, 849–868, https://doi.org/10.5194/tc-18-849-2024, https://doi.org/10.5194/tc-18-849-2024, 2024
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Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
Franziska Temme, David Farías-Barahona, Thorsten Seehaus, Ricardo Jaña, Jorge Arigony-Neto, Inti Gonzalez, Anselm Arndt, Tobias Sauter, Christoph Schneider, and Johannes J. Fürst
The Cryosphere, 17, 2343–2365, https://doi.org/10.5194/tc-17-2343-2023, https://doi.org/10.5194/tc-17-2343-2023, 2023
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Calibration of surface mass balance (SMB) models on regional scales is challenging. We investigate different calibration strategies with the goal of achieving realistic simulations of the SMB in the Monte Sarmiento Massif, Tierra del Fuego. Our results show that the use of regional observations from satellite data can improve the model performance. Furthermore, we compare four melt models of different complexity to understand the benefit of increasing the processes considered in the model.
Hanwu Zheng, Doerthe Tetzlaff, Christian Birkel, Songjun Wu, Tobias Sauter, and Chris Soulsby
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Short summary
Short summary
Ecohydrological processes in heavily managed catchments are often incorrectly represented in models. We applied a tracer-aided model STARR in an ET-dominated region (the Middle Spree, NE Germany) with major management impacts. Water isotopes were useful in identifying runoff contributions and partitioning ET even at sparse resolution. Trade-offs between discharge- and isotope-based calibrations could be partially mitigated by integrating more process-based conceptualizations into the model.
Pedro Henrique Lima Alencar, Saskia Arndt, Kei Namba, Márk Somogyvári, Frederik Bart, Fabio Brill, Juan Dueñas, Peter Feindt, Daniel Johnson, Nariman Mahmoodi, Christoph Merz, Subham Mukherjee, Katrin Nissen, Eva Nora Paton, Tobias Sauter, Dörthe Tetzlaff, Franziska Tügel, Thomas Vogelpohl, Stenka Valentinova Vulova, Behnam Zamani, and Hui Hui Zhang
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As climate change escalates, the Berlin-Brandenburg region faces new challenges. Climate change-induced extreme events are expected to cause new conflicts to emerge and aggravate existing ones. To guide future research, we co-develop a list of key questions on climate and water challenges in the region. Our findings highlight the need for new research approaches. We expect this list to provide a roadmap for actionable knowledge production to address climate and water challenges in the region.
Wilhelm Furian and Tobias Sauter
EGUsphere, https://doi.org/10.5194/egusphere-2025-50, https://doi.org/10.5194/egusphere-2025-50, 2025
Short summary
Short summary
Glacial lake outburst floods (GLOFs) continue to threaten high-mountain communities in Nepal. We simulate potential GLOF events from five glacial lakes in the Everest region during the 21st century using a 3D flood model and several breach and SSP scenarios. Large GLOFs could extend over 100 km and inundate 80 to 100 km of roads, 735 to 1,989 houses and between 0.85 and 3.52 km2 of agricultural land. The results help to assess the changing GLOF impacts and support more accurate risk assessments.
Annelies Voordendag, Brigitta Goger, Rainer Prinz, Tobias Sauter, Thomas Mölg, Manuel Saigger, and Georg Kaser
The Cryosphere, 18, 849–868, https://doi.org/10.5194/tc-18-849-2024, https://doi.org/10.5194/tc-18-849-2024, 2024
Short summary
Short summary
Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
Franziska Temme, David Farías-Barahona, Thorsten Seehaus, Ricardo Jaña, Jorge Arigony-Neto, Inti Gonzalez, Anselm Arndt, Tobias Sauter, Christoph Schneider, and Johannes J. Fürst
The Cryosphere, 17, 2343–2365, https://doi.org/10.5194/tc-17-2343-2023, https://doi.org/10.5194/tc-17-2343-2023, 2023
Short summary
Short summary
Calibration of surface mass balance (SMB) models on regional scales is challenging. We investigate different calibration strategies with the goal of achieving realistic simulations of the SMB in the Monte Sarmiento Massif, Tierra del Fuego. Our results show that the use of regional observations from satellite data can improve the model performance. Furthermore, we compare four melt models of different complexity to understand the benefit of increasing the processes considered in the model.
Mohamed H. Salim, Sebastian Schubert, Jaroslav Resler, Pavel Krč, Björn Maronga, Farah Kanani-Sühring, Matthias Sühring, and Christoph Schneider
Geosci. Model Dev., 15, 145–171, https://doi.org/10.5194/gmd-15-145-2022, https://doi.org/10.5194/gmd-15-145-2022, 2022
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Radiative transfer processes are the main energy transport mechanism in urban areas which influence the surface energy budget and drive local convection. We show here the importance of each process to help modellers decide on how much detail they should include in their models to parameterize radiative transfer in urban areas. We showed how the flow field may change in response to these processes and the essential processes needed to assure acceptable quality of the numerical simulations.
Guisella Gacitúa, Christoph Schneider, Jorge Arigony, Inti González, Ricardo Jaña, and Gino Casassa
Earth Syst. Sci. Data, 13, 231–236, https://doi.org/10.5194/essd-13-231-2021, https://doi.org/10.5194/essd-13-231-2021, 2021
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We performed the first successful ice thickness measurements using terrestrial ground-penetrating radar in the ablation area of Schiaparelli Glacier (Cordillera Darwin, Tierra del Fuego, Chile). Data are fundamental to understand glaciers dynamics, constrain ice dynamical modelling, and predict glacier evolution. Results show a valley-shaped bedrock below current sea level; thus further retreat of Schiaparelli Glacier will probably lead to an enlarged and strongly over-deepened proglacial lake.
Cited articles
Anderson, E. A.: Development and testing of snow pack energy balance equations,
Water Resour. Res., 4, 19–37, https://doi.org/10.1029/WR004i001p00019, 1968. a
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swis
avalanche warning: Part I: numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5,
2002. a, b, c, d
Bintanja, R. and Van Den Broeke, M. R.: The Surface Energy Balance of
Antarctic Snow and Blue Ice, J. Appl. Meteorol., 34,
902–926, https://doi.org/10.1175/1520-0450(1995)034<0902:TSEBOA>2.0.CO;2, 1995. a
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux-profile
relationships in the atmospheric surface layer, J. Atmos. Sci., 28, 181–189,
https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2,
1971. a
Cogley, J. G., Hock, R., Rasmussen, L. A., Arendt, A., Bauder, A.,
Braithwaite, R. J., Jansson, P., Kaser, G., Möller, M., Nicholson, L., and
Zemp, M.: Glossary of glacier mass balance and related terms, International
Association of Cryospheric Sciences, IHP-VII Technical Documents in Hydrology No. 86,
IACS Contribution No. 2,
UNESCO, Paris,
https://doi.org/10.5167/uzh-53475, 2011. a
Coléou, C. and Lesaffre, B.: Irreducible water saturation in snow:
experimental results in a cold laboratory, Ann. Glaciol., 26, 64–68,
https://doi.org/10.3189/1998AoG26-1-64-68, 1998. a
Conway, J. and Cullen, N.: Constraining turbulent heat flux parameterization
over a temperate maritime glacier in New Zealand, Ann. Glaciol.,
54, 41–51, https://doi.org/10.3189/2013AoG63A604, 2013. a, b
Dask Development Team: Dask: Library for dynamic task scheduling, DASK,
available at: https://dask.org (last access: 20 June 2020), 2016. a
Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay.
Meteorol., 7, 363–372, https://doi.org/10.1007/BF00240838, 1974. a
Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of 1701
snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013. a, b
Ferziger, J. H. and Perić, M.: Computational Methods for Fluid Dynamics,
Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-56026-2, 2002. a
Fletcher, R.: Practical Methods of Optimization, John Wiley & Sons,
Chichester, West Sussex England, https://doi.org/10.1002/9781118723203, 2000. a
Foken, T.: Micrometeorology, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-74666-9, 2008. a, b, c, d
Galos, S. P., Klug, C., Maussion, F., Covi, F., Nicholson, L., Rieg, L., Gurgiser, W., Mölg, T.,
and Kaser, G.: Reanalysis of a 10-year record (2004–2013) of seasonal mass balances at
Langenferner/Vedretta Lunga, Ortler Alps, Italy, The Cryosphere, 11, 1417–1439, https://doi.org/10.5194/tc-11-1417-2017, 2017. a
Gurgiser, W., Marzeion, B., Nicholson, L., Ortner, M., and Kaser, G.: Modeling energy and mass balance of Shallap Glacier, Peru, The Cryosphere, 7, 1787–1802, https://doi.org/10.5194/tc-7-1787-2013, 2013. a
Hantel, M., Ehrendorfer, M., and Haslinger, A.: Climate sensitivity of snow
cover duration in Austria, Int. J. Climatol., 20, 615–640,
https://doi.org/10.1002/(SICI)1097-0088(200005)20:6<615::AID-JOC489>3.0.CO;2-0,
2000. a
Hersbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF
Newsletter, Tech. rep., European Centre for Medium-Range Weather Forecasts, available at: https://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production (last access: 11 November 2020),
vol. 147, 2016. a
Hock, R. and Holmgren, B.: A distributed surface energy-balance model for
complex topography and its application to Storglaciären, Sweden, J. Glaciol., 51, 25–36, https://doi.org/10.3189/172756505781829566, 2005. a, b
Hoyer, S. and Hamman, J. J.: xarray: N-D labeled Arrays and Datasets in
Python, Journal of Open Research Software, 5, p. 10, https://doi.org/10.5334/jors.148, 2017. a
Huintjes, E.: Energy and mass balance modelling for glaciers on the Tibetan
Plateau – Extension, validation and application of a coupled snow and
energy balance model, Ph.D. thesis, Rheinisch-Westfälischen Technischen
Hochschule Aachen, Aachen, 2014. a
Huintjes, E., Neckel, N., Hochschild, V., and Schneider, C.: Surface energy and
mass balance at Purogangri ice cap, central Tibetan Plateau,
2001–2011, J. Glaciol., 61, 1048–1060,
https://doi.org/10.3189/2015JoG15J056, 2015a. a
Huintjes, E., Sauter, T., Schröter, B., Maussion, F., Yang, W., Kropáček,
J., Buchroithner, M., Scherer, D., Kang, S., and Schneider, C.: Evaluation of
a Coupled Snow and Energy Balance Model for Zhadang Glacier,
Tibetan Plateau, Using Glaciological Measurements and
Time-Lapse Photography, Arctic, Antarctic, and Alpine Research, 47,
573–590, https://doi.org/10.1657/AAAR0014-073, 2015b. a, b, c, d
Klok, E. and Oerlemans, J.: Model study of the spatial distribution of the
energy and mass balance of Morteratschgletscher, Switzerland, J. Glaciol., 48, 505–518, https://doi.org/10.3189/172756502781831133, 2002. a, b, c, d
Kraus, H.: An energy balance model for ablation in mountainous areas, Proceedings of the Moscow Symposium, August 1971; Actes du Colloque de Moscou, août 1971, IAHS-AISH Publ. No. 104, 1975. a
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a, b, c
Kuhn, M.: On the Computation of Heat Transfer Coefficients from
Energy-Balance Gradients on a Glacier, J. Glaciol., 22,
263–272, https://doi.org/10.3189/S0022143000014258, 1979. a
Kuhn, M.: Micro-Meteorological Conditions for Snow Melt, J. Glaciol., 33, 24–26, https://doi.org/10.3189/S002214300000530X, 1987. a
Machguth, H., Paul, F., Hoelzle, M., and Haeberli, W.: Distributed glacier
mass-balance modelling as an important component of modern multi-level
glacier monitoring, Ann. Glaciol., 43, 335–343,
https://doi.org/10.3189/172756406781812285, 2006. a
Machguth, H., Paul, F., Kotlarski, S., and Hoelzle, M.: Calculating distributed
glacier mass balance for the Swiss Alps from regional climate model
output: A methodical description and interpretation of the results, J.
Geophys. Res., 114, D19106, https://doi.org/10.1029/2009JD011775, 2009. a
Male, D. H. and Granger, R. J.: Snow surface energy exchange, Water Resour.
Res., 17, 609–627, https://doi.org/10.1029/WR017i003p00609, 1981. a
Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and Finkelnburg,
R.: Precipitation Seasonality and Variability over the Tibetan
Plateau as Resolved by the High Asia Reanalysis, J.
Climate, 27, 1910–1927, https://doi.org/10.1175/JCLI-D-13-00282.1, 2014. a
Maussion, F., Gurgiser, W., Großhauser, M., Kaser, G., and Marzeion, B.: ENSO influence on surface energy and mass balance at Shallap Glacier, Cordillera Blanca, Peru, The Cryosphere, 9, 1663–1683, https://doi.org/10.5194/tc-9-1663-2015, 2015. a
Ménard, C. B. and Essery, R.: ESM-SnowMIP meteorological and evaluation
datasets at ten reference sites (in situ and bias corrected reanalysis data), dataset, PANGAEA, https://doi.org/10.1594/PANGAEA.897575,
2019. a, b
Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N.: Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, 2019. a
Michlmayr, G., Lehning, M., Koboltschnig, G., Holzmann, H., Zappa, M., Mott,
R., and Schöner, W.: Application of the Alpine 3D model for glacier mass balance and glacier runoff studies at Goldbergkees, Austria, Hydrol. Process, 22, 3941–3949, https://doi.org/10.1002/hyp.7102, 2008. a
Microsoft: Bing Maps, available at: https://www.bing.com/maps/, last access: 10 November 2020. a
Mölg, T. and Hardy, D. R.: Ablation and associated energy balance of a
horizontal glacier surface on Kilimanjaro, J. Geophys. Res.,
109, D16104, https://doi.org/10.1029/2003JD004338,2004. a
Mölg, T., Cullen, N. J., Hardy, D. R., Kaser, G., and Klok, L.: Mass balance
of a slope glacier on Kilimanjaro and its sensitivity to climate,
Int. J. Climatol., 28, 881–892, https://doi.org/10.1002/joc.1589, 2008. a
Mölg, T., Cullen, N. J., Hardy, D. R., Winkler, M., and Kaser, G.: Quantifying
Climate Change in the Tropical Midtroposphere over East Africa
from Glacier Shrinkage on Kilimanjaro, J. Climate, 22,
4162–4181, https://doi.org/10.1175/2009JCLI2954.1, 2009. a, b
Mölg, T., Maussion, F., Yang, W., and Scherer, D.: The footprint of Asian monsoon dynamics in the mass and energy balance of a Tibetan glacier, The Cryosphere, 6, 1445–1461, https://doi.org/10.5194/tc-6-1445-2012, 2012. a, b
Mölg, T., Maussion, F., and Scherer, D.: Mid-latitude westerlies as a driver
of glacier variability in monsoonal High Asia, Nat. Clim. Change, 4, 68–73, https://doi.org/10.1038/nclimate2055, 2014. a
Morris, E.: Turbulent transfer over snow and ice, J. Hydrol., 105,
205–223, https://doi.org/10.1016/0022-1694(89)90105-4, 1989. a
Morris, E. M.: Physics-Based Models of Snow, in: Recent Advances in the
Modeling of Hydrologic Systems, edited by: Bowles, D. S. and
O’Connell, P. E., Springer Netherlands, Dordrecht, 85–112
https://doi.org/10.1007/978-94-011-3480-4_5,
1991. a
Munro, D. S.: Surface Roughness and Bulk Heat Transfer on a Glacier: Comparison with Eddy Correlation, J. Glaciol., 35, 343–348,
https://doi.org/10.3189/S0022143000009266, 1989. a, b, c
Munro, D. S.: A surface energy exchange model of glacier melt and net mass
balance, Int. J. Climatol., 11, 689–700,
https://doi.org/10.1002/joc.3370110610, 1991. a
Nicholson, L. I., Prinz, R., Mölg, T., and Kaser, G.: Micrometeorological conditions and surface mass and energy fluxes on Lewis Glacier, Mt Kenya, in relation to other tropical glaciers, The Cryosphere, 7, 1205–1225, https://doi.org/10.5194/tc-7-1205-2013, 2013. a
Obleitner, F. and Lehning, M.: Measurement and simulation of snow and
superimposed ice at the Kongsvegen glacier, Svalbard (Spitzbergen),
J. Geophys. Res.-Atmos., 109, D04106,
https://doi.org/10.1029/2003JD003945, 2004. a
Oerlemans, J.: Glaciers and climate change, A.A. Balkema Publishers, Lisse;
Exton, (PA), 2001. a
Oerlemans, J. and Knap, W. H.: A 1 year record of global radiation and albedo
in the ablation zone of Morteratschgletscher, Switzerland, J. Glaciol., 44, 231–238, https://doi.org/10.1017/S0022143000002574, 1998. a
Østby, T. I., Schuler, T. V., Hagen, J. O., Hock, R., Kohler, J., and Reijmer, C. H.: Diagnosing the decline in climatic mass balance of glaciers in Svalbard over 1957–2014, The Cryosphere, 11, 191–215, https://doi.org/10.5194/tc-11-191-2017, 2017. a
Qu, B., Ming, J., Kang, S.-C., Zhang, G.-S., Li, Y.-W., Li, C.-D., Zhao, S.-Y., Ji, Z.-M., and Cao, J.-J.: The decreasing albedo of the Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities, Atmos. Chem. Phys., 14, 11117–11128, https://doi.org/10.5194/acp-14-11117-2014, 2014. a
Radić, V. and Hock, R.: Modeling future glacier mass balance and volume
changes using ERA-40 reanalysis and climate models: A sensitivity study
at Storglaciären, Sweden, J. Geophys. Res.-Earth, 111, F03003, https://doi.org/10.1029/2005JF000440, 2006. a
Radić, V., Menounos, B., Shea, J., Fitzpatrick, N., Tessema, M. A., and Déry, S. J.: Evaluation of different methods to model near-surface turbulent fluxes for a mountain glacier in the Cariboo Mountains, BC, Canada, The Cryosphere, 11, 2897–2918, https://doi.org/10.5194/tc-11-2897-2017, 2017. a
Reijmer, C. H. and Hock, R.: Internal accumulation on Storglaciären,
Sweden, in a multi-layer snow model coupled to a distributed energy-and
mass-balance model, J. Glaciol., 54, 61–72,
https://doi.org/10.3189/002214308784409161, 2008. a
Rye, C. J., Willis, I. C., Arnold, N. S., and Kohler, J.: On the need for
automated multiobjective optimization and uncertainty estimation of glacier
mass balance models, J. Geophys. Res.-Earth, 117,
F02005, https://doi.org/10.1029/2011JF002184, 2012. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model (Version v1.3), Zenodo, https://doi.org/10.5281/zenodo.3902191, 2020a. a, b
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model, GitHub repository, https://github.com/cryotools/cosipy, last access: 20 June 2020b. a
Sauter, T. and Arndt, A.: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model – Read the Docs documentation, available at: https://cosipy.readthedocs.io/en/latest/index.html, last access: 20 June 2020c. a
Sauter, T. and Arndt, A.: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model – Slack comunity platform for user communication, available at: https://cosipy.slack.com, last access: 20 June 2020d. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – General DOI pointing to the newest release, Zenodo, https://doi.org/10.5281/zenodo.2579668, 2020e. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – Travis CI repository, available at: https://travis-ci.org/cryotools/cosipy, last access: 20 June 2020f. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – CodeCov repository, available at: https://codecov.io/github/cryotools/cosipy/, last access: 20 June 2020g. a
Sauter, T. and Galos, S. P.: Effects of local advection on the spatial sensible heat flux variation on a mountain glacier, The Cryosphere, 10, 2887–2905, https://doi.org/10.5194/tc-10-2887-2016, 2016. a
Sauter, T. and Obleitner, F.: Assessing the uncertainty of glacier mass-balance simulations in the European Arctic based on variance decomposition, Geosci. Model Dev., 8, 3911–3928, https://doi.org/10.5194/gmd-8-3911-2015, 2015. a
Sauter, T., Möller, M., Finkelnburg, R., Grabiec, M., Scherer, D., and Schneider, C.: Snowdrift modelling for the Vestfonna ice cap, north-eastern Svalbard, The Cryosphere, 7, 1287–1301, https://doi.org/10.5194/tc-7-1287-2013, 2013. a
Schuler, T. V., Hock, R., Jackson, M., Elvehøy, H., Braun, M., Brown, I., and
Hagen, J.-O.: Distributed mass-balance and climate sensitivity modelling of
Engabreen, Norway, Ann. Glaciol., 42, 395–401,
https://doi.org/10.3189/172756405781812998,
2005. a
Sicart, J. E., Hock, R., Ribstein, P., Litt, M., and Ramirez, E.: Analysis of
seasonal variations in mass balance and meltwater discharge of the tropical
Zongo Glacier by application of a distributed energy balance model,
J. Geophys. Res., 116, D13105, https://doi.org/10.1029/2010JD015105, 2011. a, b
Siemer, A. H.: Ein eindimensionales Energie-Massenbilanzmodell einer
Schneedecke unter Berücksichtigung der Flüssigwassertransmission,
Berichte des Institutes für Meteorologie und Klimatologie der
Universität Hannover, 34, Universität Hannover, Hannover, 1988. a
Smeets, C. J. P. P. and van den Broeke, M. R.: Temporal and Spatial
Variations of the Aerodynamic Roughness Length in the Ablation
Zone of the Greenland Ice Sheet, Bound.-Lay. Meteorol., 128,
315–338, https://doi.org/10.1007/s10546-008-9291-0, 2008. a
Van Den Broeke, M., Reijmer, C., Van As, D., and Boot, W.: Daily cycle of the
surface energy balance in Antarctica and the influence of clouds,
International J. Climatol., 26, 1587–1605, https://doi.org/10.1002/joc.1323, 2006.
a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A
Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
van Pelt, W. J. J., Oerlemans, J., Reijmer, C. H., Pohjola, V. A., Pettersson, R., and van Angelen, J. H.: Simulating melt, runoff and refreezing on Nordenskiöldbreen, Svalbard, using a coupled snow and energy balance model, The Cryosphere, 6, 641–659, https://doi.org/10.5194/tc-6-641-2012, 2012. a, b
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b, c
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and Contributors, S.: SciPy
1.0–Fundamental Algorithms for Scientific Computing in Python, Nature Methods 17, 261,
arXiv [preprint], arXiv:1907.10121, 2019. a
Wagnon, P., Ribstein, P., Kaser, G., and Berton, P.: Energy balance and runoff
seasonality of a Bolivian glacier, Global Planet. Change, 22, 49–58,
https://doi.org/10.1016/S0921-8181(99)00025-9, 1999. a
Weidemann, S. S., Sauter, T., Malz, P., Jaña, R., Arigony-Neto, J., Casassa,
G., and Schneider, C.: Glacier Mass Changes of Lake-Terminating
Grey and Tyndall Glaciers at the Southern Patagonia Icefield
Derived From Geodetic Observations and Energy and Mass Balance
Modeling, Front. Earth Sci., 6, 81, https://doi.org/10.3389/feart.2018.00081,
2018. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a
Whitaker, J., Khrulev, C., Huard, D., Paulik, C., Hoyer, S., and Kinoshita, B. P.: Unidata/netcdf4-python: version 1.5.1.2 release (Version v1.5.1.2rel), Zenodo, https://doi.org/10.5281/zenodo.2669496, 2019. a
Wohlfahrt, G., Hammerle, A., Niedrist, G., Scholz, K., Tomelleri, E., and Zhao,
P.: On the energy balance closure and net radiation in complex terrain,
Agr. Forest Meteorol., 226–227, 37–49,
https://doi.org/10.1016/j.agrformet.2016.05.012, 2016. a, b
Short summary
Glacial changes play a key role from a socioeconomic, political, and scientific point of view. Here, we present the open-source coupled snowpack and ice surface energy and mass balance model, which provides a lean, flexible, and user-friendly framework for modeling distributed snow and glacier mass changes. The model provides a suitable platform for sensitivity, detection, and attribution analyses for glacier changes and a tool for quantifying inherent uncertainties.
Glacial changes play a key role from a socioeconomic, political, and scientific point of view....