Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-1959-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-1959-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Apr 2024
Research article |
| 26 Apr 2024
| 26 Apr 2024
Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?
Benjamin Poschlod
CORRESPONDING AUTHOR
Research Unit Sustainability and Climate Risk, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, 20144 Hamburg, Germany
Anne Sophie Daloz
Center for International Climate Research (CICERO), 0349 Oslo, Norway
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Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
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Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
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We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Anne Sophie Daloz, Marian Mateling, Tristan L'Ecuyer, Mark Kulie, Norm B. Wood, Mikael Durand, Melissa Wrzesien, Camilla W. Stjern, and Ashok P. Dimri
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The total of snow that falls globally is a critical factor governing freshwater availability. To better understand how this resource is impacted by climate change, we need to know how reliable the current observational datasets for snow are. Here, we compare five datasets looking at the snow falling over the mountains versus the other continents. We show that there is a large consensus when looking at fractional contributions but strong dissimilarities when comparing magnitudes.
Cited articles
Anderson, E. A.: A point energy and mass balance model of a snow cover, Tech. Rep. NWQ 19, NOAA, Office of Hydrology, National Weather Service, Silver Spring, MD, USA, https://repository.library.noaa.gov/view/noaa/6392 (last access: 19 July 2023), 1976.
ARD: Winterchaos in Bayern Tausende Stromausfälle – Retter im Dauereinsatz, https://www.tagesschau.de/inland/innenpolitik/bayern-winterchaos-stromausfaelle-100.html (last access: 27 February 2024), 2023.
Arduini, G., Balsamo, G., Dutra, E., Day, J. J., Sandu, I., Boussetta, S., and Haiden, T.: Impact of a Multi-Layer Snow Scheme on Near-Surface Weather Forecasts, J. Adv. Model. Earth Sy., 11, 4687–4710, https://doi.org/10.1029/2019MS001725, 2019.
Aschauer, J., Michel, A., Jonas, T., and Marty, C.: An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0, Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, 2023.
Ban, N., Schmidli, J., and Schär, C.: Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations, J. Geophys. Res.-Atmos., 119, 7889–7907, https://doi.org/10.1002/2014JD021478, 2014.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Bausch, T. and Unseld, C.: Winter tourism in Germany is much more than skiing! consumer motives and implications to Alpine destination marketing, J. Vacat. Mark., 24, 203–217, https://doi.org/10.1177/1356766717691806, 2018.
Bednorz, E.: Heavy snow in Polish–German lowlands: large-scale synoptic reasons and economic impacts, Weather and Climate Extremes, 2, 1–6, https://doi.org/10.1016/j.wace.2013.10.007, 2013.
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.: The relative importance of different flood-generating mechanisms across Europe, Water Resour. Res., 55, 4582–4593, https://doi.org/10.1029/2019WR024841, 2019.
Blume-Werry, G., Kreyling, J., Laudon, H., and Milbau, A.: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants, J. Ecol., 104, 1638–1648, https://doi.org/10.1111/1365-2745.12636, 2016.
Bocharov, G.: pyextremes – Extreme Value Analysis (EVA) in Python, https://georgebv.github.io/pyextremes/ (last access: 19 July 2023), 2022.
Boussetta, S., Balsamo, G., Arduini, G., Dutra, E., McNorton, J., Choulga, M., Agustí-Panareda, A., Beljaars, A., Wedi, N., Muñoz Sabater, J., de Rosnay, P., Sandu, I., Hadade, I., Carver, G., Mazzetti, C., Prudhomme, C., Yamazaki, D., and Zsoter, E.: ECLand: The ECMWF Land Surface Modelling System, Atmosphere, 12, 723, https://doi.org/10.3390/atmos12060723, 2021.
Braun, L. N.: Simulation of snowmelt-runoff in lowland and lower alpine regions of Switzerland, PhD thesis, ETH Zurich, https://doi.org/10.3929/ethz-a-000334295, 1984.
Brienen, S., Haller, M., Brauch, J., and Früh, B.: HoKliSim-De COSMO-CLM climate model simulation data version V2022.01, DWD [data set], https://doi.org/10.5676/DWD/HOKLISIM_V2022.01, 2022.
Chen, J., Li, C., Brissette, F. P., Chen, H., Wang, M., and Essou, G. R. C.: Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling, J. Hydrol., 560, 326–341, https://doi.org/10.1016/j.jhydrol.2018.03.040, 2018.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Hreinsson, E. O., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011wr010745, 2011.
Coles, S.: An introduction to statistical modeling of extreme values, Springer, London, UK, https://doi.org/10.1007/978-1-4471-3675-0, 2001.
Collier, E.: BAYWRF, OSFHOME [data set], https://doi.org/10.17605/OSF.IO/AQ58B, 2020.
Collier, E. and Mölg, T.: BAYWRF: a high-resolution present-day climatological atmospheric dataset for Bavaria, Earth Syst. Sci. Data, 12, 3097–3112, https://doi.org/10.5194/essd-12-3097-2020, 2020.
Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B., Anders, I., Ban, N., Bastin, S., Belda, M., Belusic, D., Caldas-Alvarez, A., Cardoso, R. M., Davolio, S., Dobler, A., Fernandez, J., Fita, L., Fumiere, Q., Giorgi, F., Goergen, K., Güttler, I., Halenka, T., Heinzeller, D., Hodnebrog, Q., Jacob, D., Kartsios, S., Katragkou, E., Kendon, E., Khodayar, S., Kunstmann, H., Knist, S., Lavín-Gullón, A., Lind, P., Lorenz, T., Maraun, D., Marelle, L., van Meijgaard, E., Milovac, J., Myhre, G., Panitz, H. J., Piazza, M., Raffa, M., Raub, T., Rockel, B., Schär, C., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Stocchi, P., Tölle, M. H., Truhetz, H., Vautard, R., de Vries, H., and Warrach-Sagi, K.: A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean, Clim. Dynam., 43, 3–34, https://doi.org/10.1007/s00382-018-4521-8, 2020.
Croce, P., Formichi, P., Landi, F., Mercogliano, P., Bucchignani, E., Dosio, A., and Dimova, S.: The snow load in Europe and the climate change, Climate Risk Management, 20, 138–154, https://doi.org/10.1016/j.crm.2018.03.001, 2018.
Daloz, A. S., Mateling, M., L'Ecuyer, T., Kulie, M., Wood, N. B., Durand, M., Wrzesien, M., Stjern, C. W., and Dimri, A. P.: How much snow falls in the world's mountains? A first look at mountain snowfall estimates in A-train observations and reanalyses, The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, 2020.
Daloz, A. S., Schwingshackl, C., Mooney, P., Strada, S., Rechid, D., Davin, E. L., Katragkou, E., de Noblet-Ducoudré, N., Belda, M., Halenka, T., Breil, M., Cardoso, R. M., Hoffmann, P., Lima, D. C. A., Meier, R., Soares, P. M. M., Sofiadis, G., Strandberg, G., Toelle, M. H., and Lund, M. T.: Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect, The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, 2022.
Daudt, R. C., Wulf, H., Hafner, E. D., Bühler, Y., Schindler, K., and Wegner, J. D.: Snow depth estimation at country-scale with high spatial and temporal resolution, ISPRS J. Photogramm., 197, 105–121, https://doi.org/10.1016/j.isprsjprs.2023.01.017, 2023.
DIN: DIN 1055-5, 2005-07, Einwirkungen auf Tragwerke – Teil 5: Schnee- und Eislasten, Deutsches Institut für Normung e.V., Beuth-Verlag, 24 pp., 2005.
Doll, C., Trinks, C., Sedlacek, N., Pelikan, V., Comes, T., and Schultmann, F.: Adapting rail and road networks to weather extremes: case studies for southern Germany and Austria, Nat. Hazards, 72, 63–85, https://doi.org/10.1007/s11069-013-0969-3, 2014.
Doms, G., Förstner, J., Heise, E., Herzog, H. J., Mironov, D., Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P., and Vofel, G.: A Description of the Nonhydrostatic Regional COSMO-Model – Part II: Physical Parameterizations, Deutscher Wetterdienst DWD, Offenbach, 2021.
Dong C. and Menzel, L.: Recent snow cover changes over central European low mountain ranges, Hydrol. Process., 34, 321–338, https://doi.org/10.1002/hyp.13586, 2020.
Durre, I. and Squires, M. F.: White Christmas? An Application of NOAA's 1981–2010 Daily Normals, B. Am. Meteorol. Soc., 96, 1853–1858, https://doi.org/10.1175/BAMS-D-15-00038.1, 2015.
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M., Beljaars, A., Schär, C., and Elder, K.: An improved snow scheme for the ECMWF land surface model: description and offline validation, J. Hydrometeorol., 11, 899–916, https://doi.org/10.1175/2010JHM1249.1, 2010.
DWD (German Weather Service): Weiße Weihnachten – eine Frage des Standortes, https://www.dwd.de/DE/wetter/thema_des_tages/2020/12/24.html (last access: 19 July 2023), 2020.
DWD (German Weather Service): Daily climate data, DWD [data set], https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/kl/historical/, last access: 13 June 2023.
ECMWF (Ed.): IFS documentation Cy45r1 Operational implementation 5 June 2018 PART IV: physical processes, Shinfield Park, Reading, England, UK, 223, https://doi.org/10.21957/4whwo8jw0, 2018.
Essery, R., Morin, S., Lejeune, Y., and Ménard, C.: 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.
Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J.: emcee: The MCMC Hammer, Publ. Astron. Soc. Pac., 125, 306–312, https://doi.org/10.1086/670067, 2013.
Frei, P., Kotlarski, S., Liniger, M. A., and Schär, C.: Future snowfall in the Alps: projections based on the EURO-CORDEX regional climate models, The Cryosphere, 12, 1–24, https://doi.org/10.5194/tc-12-1-2018, 2018.
Frese, M. and Blaß, H. J.: Statistics of damages to timber structures in Germany, Eng. Struct., 33, 2969–2977, https://doi.org/10.1016/j.engstruct.2011.02.030, 2011.
Gavazov, K., Ingrisch, J., Hasibeder, R., Mills, R. T. E., Buttler, A., Gleixner, G., Pumpanen, J., and Bahn, M.: Winter ecology of a subalpine grassland: effects of snow removal on soil respiration, microbial structure and function, Sci. Total Environ., 590–591, 316–324, https://doi.org/10.1016/j.scitotenv.2017.03.010, 2017.
Gerhold, L., Wahl, S., and Dombrowsky, W. R.: Risk perception and emergency food preparedness in Germany, Int. J. Disast. Risk Re., 37, 101183, https://doi.org/10.1016/j.ijdrr.2019.101183, 2019.
Girons Lopez, M., Vis, M. J. P., Jenicek, M., Griessinger, N., and Seibert, J.: Assessing the degree of detail of temperature-based snow routines for runoff modelling in mountainous areas in central Europe, Hydrol. Earth Syst. Sci., 24, 4441–4461, https://doi.org/10.5194/hess-24-4441-2020, 2020.
Hagen, P. and Mese, O.: Schneefälle: Wer zahlt für die Schäden?, https://www.sueddeutsche.de/wirtschaft/schneechaos-deutschland-versicherung-schaeden-zahlen-1.6314022 (last access: 27 February 2024), 2023.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG, Version 61, NASA NSIDC DAAC, Boulder, Colorado, USA [data set], https://doi.org/10.5067/MODIS/MOD10C1.061, 2021.
Hammer, H. L.: Statistical models for short- and long-term forecasts of snow depth, J. Appl. Stat., 45, 1133–1156, https://doi.org/10.1080/02664763.2017.1357683, 2018.
Hanzer, F., Helfricht, K., Marke, T., and Strasser, U.: Multilevel spatiotemporal validation of snow/ice mass balance and runoff modeling in glacierized catchments, The Cryosphere, 10, 1859–1881, https://doi.org/10.5194/tc-10-1859-2016, 2016.
Hanzer, F., Förster, K., Nemec, J., and Strasser, U.: Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach, Hydrol. Earth Syst. Sci., 22, 1593–1614, https://doi.org/10.5194/hess-22-1593-2018, 2018.
Harley, T. A.: Nice weather for the time of year: the British obsession with the weather, in: Weather, climate, culture, edited by: Strauss, S. and Orlove, B., Routledge, 103–120, https://doi.org/10.4324/9781003103264, 2003.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P. de, Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
HND (Hochwassernachrichtendienst Bayern): Station München Stadt, https://www.hnd.bayern.de/schnee/donau_bis_kelheim/muenchen-stadt-10865?addhr=false&begin=25.11.2023&end=12.12.2023, last access: 27 February 2024.
Hodeck, A. and Hovemann, G.: Destination Choice In German Winter Sport Tourism: Empirical Findings, Pol. J. Sport Tour., 22, 114–117, https://doi.org/10.1515/pjst-2015-0019, 2015.
Jin, J., Gao, X., Yang, Z.-L., Bales, R. C., Sorooshian, S., Dickinson, R. E., Sun, S. F., and Wu, G. X.: Comparative Analyses of Physically Based Snowmelt Models for Climate Simulations, J. Climate, 12, 2643–2657, https://doi.org/10.1175/1520-0442(1999)012<2643:CAOPBS>2.0.CO;2, 1999.
Jordan, R.: A one-dimensional temperature model for a snow cover: Technical documentation for SNTHERM 89, Tech. rep., Hanover, NH, https://hdl.handle.net/11681/11677 (last access: 19 July 2023), 1991.
Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W.: Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay, Water Resour. Res., 55, 4465–4487, https://doi.org/10.1029/2018WR024431, 2019.
Kouki, K., Räisänen, P., Luojus, K., Luomaranta, A., and Riihelä, A.: Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014, The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, 2022.
Kouki, K., Luojus, K., and Riihelä, A.: Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018, The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, 2023.
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.
Lee, W. Y., Gim, H. J., and Park, S. K.: Parameterizations of Snow Cover, Snow Albedo and Snow Density in Land Surface Models: A Comparative Review, Asia-Pac. J. Atmos. Sci., https://doi.org/10.1007/s13143-023-00344-2, 2023.
Liu, L., Ma, Y., Menenti, M., Su, R., Yao, N., and Ma, W.: Improved parameterization of snow albedo in Noah coupled with Weather Research and Forecasting: applicability to snow estimates for the Tibetan Plateau, Hydrol. Earth Syst. Sci., 25, 4967–4981, https://doi.org/10.5194/hess-25-4967-2021, 2021.
Luque, A., Carrasco, A., Martín, A., and de Las Heras, A.: The impact of class imbalance in classification performance metrics based on the binary confusion matrix, Pattern Recogn., 91, 216–231, https://doi.org/10.1016/j.patcog.2019.02.023, 2019.
Lüthi, S., Ban, N., Kotlarski, S., Steger, C. R., Jonas, T., and Schär, C.: Projections of Alpine Snow-Cover in a High-Resolution Climate Simulation, Atmosphere, 10, 463, https://doi.org/10.3390/atmos10080463, 2019.
Lynch-Stieglitz, M.: The Development and Validation of a Simple Snow Model for the GISS GCM, J. Climate, 7, 1842–1855, https://doi.org/10.1175/1520-0442(1994)007<1842:TDAVOA>2.0.CO;2, 1994.
Mathew, J. K., Liu, M., and Bullock, D. M.: Impact of Weather on Shared Electric Scooter Utilization, in: Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27–30 October 2019, IEEE, 4512–4516, https://doi.org/10.1109/ITSC.2019.8917121, 2019.
Matiu, M. and Hanzer, F.: Bias adjustment and downscaling of snow cover fraction projections from regional climate models using remote sensing for the European Alps, Hydrol. Earth Syst. Sci., 26, 3037–3054, https://doi.org/10.5194/hess-26-3037-2022, 2022.
Matthews, B. W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme, BBA – Protein Struct., 405, 442–451, https://doi.org/10.1016/0005-2795(75)90109-9, 1975.
Meromy, L., Molotch, N. P., Link, T. E., Fassnacht, S. R., and Rice, R.: Subgrid variability of snow water equivalent at operational snow stations in the western USA, Hydrol. Process., 27, 2383–2400, https://doi.org/10.1002/hyp.9355, 2012.
Meyer, J., Kohn, I., Stahl, K., Hakala, K., Seibert, J., and Cannon, A. J.: Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments, Hydrol. Earth Syst. Sci., 23, 1339–1354, https://doi.org/10.5194/hess-23-1339-2019, 2019.
Monteiro, D. and Morin, S.: Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets, The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, 2023.
Moody, E. G., King, M. D., Schaaf, C. B., Hall, D. K., and Platnick, S.: Northern Hemisphere five-year average (2000–2004) spectral albedos of surfaces in the presence of snow: Statistics computed from Terra MODIS land products, Remote Sens. Environ., 111, 337–345, https://doi.org/10.1016/j.rse.2007.03.026, 2007.
Mooney, P. A., Rechid, D., Davin, E. L., Katragkou, E., de Noblet-Ducoudré, N., Breil, M., Cardoso, R. M., Daloz, A. S., Hoffmann, P., Lima, D. C. A., Meier, R., Soares, P. M. M., Sofiadis, G., Strada, S., Strandberg, G., Toelle, M. H., and Lund, M. T.: Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation, The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, 2022.
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Musselman, K. N., Lehner, F., Ikeda, K., Clark, M. P., Prein, A. F., Liu, C., Barlage, M., and Rasmussen, R.: Projected increases and shifts in rain-on-snow flood risk over western north america, Nat. Clim. Change, 8, 808–812, https://doi.org/10.1038/s41558-018-0236-4, 2018.
Niu, G.-Y. and Yang, Z.-L.: Effects of vegetation canopy processes on snow surface energy and mass balances, J. Geophys. Res.-Atmos., 109, D23111, https://doi.org/10.1029/2004JD004884, 2004.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Orr, H. G., Ekström, M., Charlton, M. B., Peat, K. L., and Fowler, H. J.: Using high-resolution climate change information in water management: A decision-makers' perspective. Philos. T. Roy. Soc. A, 379, 2195, 20200219, https://doi.org/10.1098/rsta.2020.0219, 2021.
Pham, T. V., Steger, C., Rockel, B., Keuler, K., Kirchner, I., Mertens, M., Rieger, D., Zängl, G., and Früh, B.: ICON in Climate Limited-area Mode (ICON release version 2.6.1): a new regional climate model, Geosci. Model Dev., 14, 985–1005, https://doi.org/10.5194/gmd-14-985-2021, 2021.
Poschlod, B., Hodnebrog, Ø., Wood, R. R., Alterskjær, K., Ludwig, R., Myhre, G., and Sillmann, J.: Comparison and evaluation of statistical rainfall disaggregation and high-resolution dynamical downscaling over complex terrain, J. Hydrometeorol., 19, 1973–1982, https://doi.org/10.1175/JHM-D-18-0132.1, 2018.
Poschlod, B., Willkofer, F., and Ludwig, R.: Impact of climate change on the hydrological regimes in Bavaria, Water, 12, 1599, https://doi.org/10.3390/w12061599, 2020a.
Poschlod, B., Zscheischler, J., Wood, R., Sillmann, J., and Ludwig, R.: Climate Change Effects on hydrometeorological compound events over Southern Norway, Weather and Climate Extremes, 28, 100253, https://doi.org/10.1016/j.wace.2020.100253, 2020b.
Qi, W., Feng, L., Liu, J., and Yang, H.: Snow as an important natural reservoir for runoff and soil moisture in Northeast China, J. Geophys. Res.-Atmos., 125, e2020JD033086, https://doi.org/10.1029/2020jd033086, 2020.
Quante, L., Willner, S. N., Middelanis, R., and Levermann, A.: Regions of intensification of extreme snowfall under future warming, Sci. Rep., 11, 1–9, https://doi.org/10.1038/s41598-021-95979-4, 2021.
Räisänen, J.: Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change, The Cryosphere, 15, 1677–1696, https://doi.org/10.5194/tc-15-1677-2021, 2021.
Richter, D.: Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Meßfehlers des Hellmann-Niederschlagsmessers, Tech. Rep., Deutscher Wetterdienst, Offenbach a. M., Germany, ISSN: 2194-5969, 1995.
Rybka, H., Haller, M., Brienen, S., Brauch, J., Früh, B., Junghänel, T., Lengfeld, K., Walter, A., and Winterrath, T.: Convection-permitting climate simulations with COSMO-CLM for Germany: Analysis of present and future daily and sub-daily extreme precipitation, Meteorol. Z., 32, 91–111, https://doi.org/10.1127/metz/2022/1147, 2022.
Saigger, M., M”olg, T., Schmid, C., and Sauter, T.: Simulating snow drift in WRF – First results and future plans of a novel module, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12384, https://doi.org/10.5194/egusphere-egu23-12384, 2023.
Sasai, T., Kawase, H., Kanno, Y., Yamaguchi, J., Sugimoto, S., Yamazaki, T., Sasaki, H., Fujita, M., and Iwasaki, T.: Future projection of extreme heavy snowfall events with a 5 km large ensemble regional climate simulation, J. Geophys. Res.-Atmos., 124, 13975–13990, https://doi.org/10.1029/2019JD030781, 2019.
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global 0.05Deg CMG V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43C3.061, 2021.
Schulz, J.-P. and Vogel, G.: Improving the Processes in the Land Surface Scheme TERRA: Bare Soil Evaporation and Skin Temperature, Atmosphere, 11, 5, https://doi.org/10.3390/atmos11050513, 2020.
Schulz, J.-P., Vogel, G., Becker, C., Kothe, S., Rummel, U., and Ahrens, B.: Evaluation of the ground heat flux simulated by a multi-layer land surface scheme using high-quality observations at grass land and bare soil, Meteorol. Z., 25, 607–620, https://doi.org/10.1127/metz/2016/0537, 2016.
Sharma, V., Gerber, F., and Lehning, M.: Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling, Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, 2023.
Simpkins, G.: Snow-related water woes, Nat. Clim. Change, 8, 945, https://doi.org/10.1038/s41558-018-0330-7, 2018.
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Liu, Z., Berner, J., Wang, W., Powers, G., Duda, G., Barker, D., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4, NCAR, Boulder, Colorado, USA, https://doi.org/10.5065/1dfh-6p97, 2019.
Slatyer, R. A., Umbers, K. D. L., and Arnold, P. A.: Ecological responses to variation in seasonal snow cover, Conserv. Biol., 36, e13727, https://doi.org/10.1111/cobi.13727, 2022.
Sørland, S. L., Brogli, R., Pothapakula, P. K., Russo, E., Van de Walle, J., Ahrens, B., Anders, I., Bucchignani, E., Davin, E. L., Demory, M.-E., Dosio, A., Feldmann, H., Früh, B., Geyer, B., Keuler, K., Lee, D., Li, D., van Lipzig, N. P. M., Min, S.-K., Panitz, H.-J., Rockel, B., Schär, C., Steger, C., and Thiery, W.: COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review, Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, 2021.
Steger, C., Kotlarski, S., Jonas, T., and Schär, C.: Alpine snow cover in a changing climate: a regional climate model perspective, Clim. Dynam., 41, 735–754, https://doi.org/10.1007/s00382-012-1545-3, 2013.
Steiger, R., Scott, D., Abegg, B., Pons, M., and Aall, C.: A critical review of climate change risk for ski tourism, Curr. Issues Tour., 22, 1343–1379, https://doi.org/10.1080/13683500.2017.1410110, 2017.
Strasser, U.: Modelling of the mountain snow cover in the Berchtesgaden National Park, Tech. Rep. 55, Berchtesgaden National Park, Berchtesgaden, ISBN 978-3-922325-62-8, 2008a.
Strasser, U.: Snow loads in a changing climate: new risks?, Nat. Hazards Earth Syst. Sci., 8, 1–8, https://doi.org/10.5194/nhess-8-1-2008, 2008b.
Sun, S., Jin, J., and Xue, Y.: A simple snow-atmosphere-soil transfer (SAST) model, J. Geophys. Res., 55, 1206–1216, https://doi.org/10.1007/s11430-011-4328-5, 1999.
Tanniru, S. and Ramsankaran, R.: Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions, Remote Sens.-Basel, 15, 1052, https://doi.org/10.3390/rs15041052, 2023.
Taszarek, M., Kendzierski, S., and Pilguj, N.: Hazardous weather affecting European airports: Climatological estimates of situations with limited visibility, thunderstorm, low-level wind shear and snowfall from ERA5, Weather and Climate Extremes, 28, 100243, https://doi.org/10.1016/j.wace.2020.100243, 2020.
Tomasi, E., Giovannini, L., Zardi, D., and de Franceschi, M.: Optimization of Noah and Noah_MP WRF land surface schemes in snow-melting conditions over complex terrain, Mon. Weather Rev., 145, 4727–4745, https://doi.org/10.1175/MWR-D-16-0408.1, 2017.
Trinks, C., Hiete, M., Comes, T., and Schultmann, F.: Extreme weather events and road and rail transportation in Germany, Int. J. Emerg. Manag., 8, 207–227, https://doi.org/10.1504/IJEM.2012.047525, 2012.
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., Borah, F., and Xu, X.: Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, 2022.
Vassiljev, P., Timo, P., Kull, Ai., Külvik, M., Bell, S., Kull, An., and Mander, Ü.: Forest landscape assessment for cross country skiing in declining snow conditions: The case of Haanja Upland, Estonia, Balt. For., 16, 280–295, 2010.
Verseghy, D. L.: Class – A Canadian land surface scheme for GCMS. I. Soil model, Int. J. Climatol., 11, 111–133, https://doi.org/10.1002/joc.3370110202, 1991.
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021.
Warren, S. G.: Optical properties of ice and snow, Philos. T. Roy. Soc. A, 377, 20180161, https://doi.org/10.1098/rsta.2018.0161, 2019.
Warscher, M., Wagner, S., Marke, T., Laux, P., Smiatek, G., Strasser, U., and Kunstmann, H.: A 5 km Resolution Regional Climate Simulation for Central Europe: Performance in High Mountain Areas and Seasonal, Regional and Elevation-Dependent Variations, Atmosphere, 10, 682, https://doi.org/10.3390/atmos10110682, 2019.
Warscher, M., Hanzer, F., Becker, C., and Strasser, U.: Monitoring snow processes in the Ötztal Alps (Austria) and development of an open source snow model framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9101, https://doi.org/10.5194/egusphere-egu21-9101, 2021.
Winter, K. J. P. M., Kotlarski, S., Scherrer, S. C., and Schär, C.: The Alpine snow-albedo feedback in regional climate models, Clim. Dynam., 48, 1109–1124, https://doi.org/10.1007/s00382-016-3130-7, 2017.
Witting, M. and Schmude, J.: Impacts of climate and demographic change on future skier demand and its economic consequences – Evidence from a ski resort in the German alps, J. Outdoor Recreat. Tour., 26, 50–60, https://doi.org/10.1016/j.jort.2019.03.002, 2019.
Witting, M., Bischof, M., and Schmude, J.: Behavioural change or “business as usual”? Characterising the reaction behaviour of winter (sport) tourists to climate change in two German destinations, Int. J. Tour. Res., 23, 110–122, https://doi.org/10.1002/jtr.2399, 2021.
Xue, Y., Sun, S., Kahan, D. S., and Jiao, Y.: Impact of parameterizations in snow physics and interface processes on the simulation of snow cover and runoff at several cold region sites, J. Geophys. Res., 108, 8859, https://doi.org/10.1029/2002JD003174, 2003.
Yang, H., Xie, K., Ozbay, K., Ma, Y., and Wang, Z.: Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems, Transp. Res. Record, 2672, 92–102, https://doi.org/10.1177/0361198118801354, 2018.
Short summary
Information about snow depth is important within climate research but also many other sectors, such as tourism, mobility, civil engineering, and ecology. Climate models often feature a spatial resolution which is too coarse to investigate snow depth. Here, we analyse high-resolution simulations and identify added value compared to a coarser-resolution state-of-the-art product. Also, daily snow depth extremes are well reproduced by two models.
Information about snow depth is important within climate research but also many other sectors,...
