Articles | Volume 18, issue 12
https://doi.org/10.5194/tc-18-6005-2024
© 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-18-6005-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Dec 2024
Research article |
| 19 Dec 2024
| 19 Dec 2024
Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps
Matthew Switanek
CORRESPONDING AUTHOR
Department of Geography and Regional Science, University of Graz, Graz, Austria
Gernot Resch
Department of Geography and Regional Science, University of Graz, Graz, Austria
Andreas Gobiet
GeoSphere Austria, Vienna, Austria
Daniel Günther
GeoSphere Austria, Vienna, Austria
Christoph Marty
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Wolfgang Schöner
Department of Geography and Regional Science, University of Graz, Graz, Austria
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Nat. Hazards Earth Syst. Sci., 18, 2653–2674, https://doi.org/10.5194/nhess-18-2653-2018, https://doi.org/10.5194/nhess-18-2653-2018, 2018
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Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Kristyna Falatkova, Miroslav Šobr, Anton Neureiter, Wolfgang Schöner, Bohumír Janský, Hermann Häusler, Zbyněk Engel, and Vojtěch Beneš
Earth Surf. Dynam., 7, 301–320, https://doi.org/10.5194/esurf-7-301-2019, https://doi.org/10.5194/esurf-7-301-2019, 2019
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In the last 50 years the Adygine glacier has been subject to relatively fast recession comparable to other glaciers in Tien Shan. As a consequence, a three-level cascade of glacial lakes formed, two of which were categorised as having medium outburst susceptibility. By 2050, the glacier is expected to have shrunk to 56–73 % of its 2012 extent. Further development of the site will result in formation of new lakes and probably also increase of outburst susceptibility due to permafrost degradation.
Christian Reszler, Matthew Blaise Switanek, and Heimo Truhetz
Nat. Hazards Earth Syst. Sci., 18, 2653–2674, https://doi.org/10.5194/nhess-18-2653-2018, https://doi.org/10.5194/nhess-18-2653-2018, 2018
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Kay Helfricht, Lea Hartl, Roland Koch, Christoph Marty, and Marc Olefs
Hydrol. Earth Syst. Sci., 22, 2655–2668, https://doi.org/10.5194/hess-22-2655-2018, https://doi.org/10.5194/hess-22-2655-2018, 2018
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We calculated hourly new snow densities from automated measurements. This time interval reduces the influence of settling of the freshly deposited snow. We found an average new snow density of 68 kg m−3. The observed variability could not be described using different parameterizations, but a relationship to temperature is partly visible at hourly intervals. Wind speed is a crucial parameter for the inter-station variability. Our findings are relevant for snow models working on hourly timescales.
Martin Beniston, Daniel Farinotti, Markus Stoffel, Liss M. Andreassen, Erika Coppola, Nicolas Eckert, Adriano Fantini, Florie Giacona, Christian Hauck, Matthias Huss, Hendrik Huwald, Michael Lehning, Juan-Ignacio López-Moreno, Jan Magnusson, Christoph Marty, Enrique Morán-Tejéda, Samuel Morin, Mohamed Naaim, Antonello Provenzale, Antoine Rabatel, Delphine Six, Johann Stötter, Ulrich Strasser, Silvia Terzago, and Christian Vincent
The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, https://doi.org/10.5194/tc-12-759-2018, 2018
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This paper makes a rather exhaustive overview of current knowledge of past, current, and future aspects of cryospheric issues in continental Europe and makes a number of reflections of areas of uncertainty requiring more attention in both scientific and policy terms. The review paper is completed by a bibliography containing 350 recent references that will certainly be of value to scholars engaged in the fields of glacier, snow, and permafrost research.
Matthew B. Switanek, Peter A. Troch, Christopher L. Castro, Armin Leuprecht, Hsin-I Chang, Rajarshi Mukherjee, and Eleonora M. C. Demaria
Hydrol. Earth Syst. Sci., 21, 2649–2666, https://doi.org/10.5194/hess-21-2649-2017, https://doi.org/10.5194/hess-21-2649-2017, 2017
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The commonly used bias correction method called quantile mapping assumes a constant function of error correction values between modeled and observed distributions. Our article finds that this function cannot be assumed to be constant. We propose a new bias correction method, called scaled distribution mapping, that does not rely on this assumption. Furthermore, the proposed method more explicitly accounts for the frequency of rain days and the likelihood of individual events.
Christoph Marty, Sebastian Schlögl, Mathias Bavay, and Michael Lehning
The Cryosphere, 11, 517–529, https://doi.org/10.5194/tc-11-517-2017, https://doi.org/10.5194/tc-11-517-2017, 2017
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We simulate the future snow cover in the Alps with the help of a snow model, which is fed by projected temperature and precipitation changes from a large set of climate models. The results demonstrate that snow below 1000 m is probably a rare guest at the end of the century. Moreover, even above 3000 m the simulations show a drastic decrease in snow depth. However, the results reveal that the projected snow cover reduction can be mitigated by 50 % if we manage to keep global warming below 2°.
Gregor Laaha, Juraj Parajka, Alberto Viglione, Daniel Koffler, Klaus Haslinger, Wolfgang Schöner, Judith Zehetgruber, and Günter Blöschl
Hydrol. Earth Syst. Sci., 20, 3967–3985, https://doi.org/10.5194/hess-20-3967-2016, https://doi.org/10.5194/hess-20-3967-2016, 2016
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We present a framework for assessing climate impacts on future low flows that combines different sources of information termed pillars. To illustrate the framework, three pillars are chosen: low-flow observation, climate observations and climate projections. By combining different sources of information we aim at more robust projections than obtained from each pillar alone. The viability of the framework is illustrated for four example catchments from Austria.
Juraj Parajka, Alfred Paul Blaschke, Günter Blöschl, Klaus Haslinger, Gerold Hepp, Gregor Laaha, Wolfgang Schöner, Helene Trautvetter, Alberto Viglione, and Matthias Zessner
Hydrol. Earth Syst. Sci., 20, 2085–2101, https://doi.org/10.5194/hess-20-2085-2016, https://doi.org/10.5194/hess-20-2085-2016, 2016
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Streamflow estimation during low-flow conditions is important for estimation of environmental flows, effluent water quality, hydropower operations, etc. However, it is not clear how the uncertainties in assumptions used in the projections translate into uncertainty of estimated future low flows. The objective of the study is to explore the relative role of hydrologic model calibration and climate scenarios in the uncertainty of low-flow projections in Austria.
Ursula Weiser, Marc Olefs, Wolfgang Schöner, Gernot Weyss, and Bernhard Hynek
The Cryosphere, 10, 775–790, https://doi.org/10.5194/tc-10-775-2016, https://doi.org/10.5194/tc-10-775-2016, 2016
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Geometric effects induced by tilt errors lead to erroneous measurement of snow albedo. These errors are corrected where tilts of sensors and slopes are unknown. Atmospheric parameters are taken from a nearby reference measurement or a radiation model. The developed model is fitted to the measured data to determine tilts and directions which vary daily due to changing atmospheric conditions and snow cover. The results show an obvious under- or overestimation of albedo depending on the slope direction.
Marc Olefs, Dietmar J. Baumgartner, Friedrich Obleitner, Christoph Bichler, Ulrich Foelsche, Helga Pietsch, Harald E. Rieder, Philipp Weihs, Florian Geyer, Thomas Haiden, and Wolfgang Schöner
Atmos. Meas. Tech., 9, 1513–1531, https://doi.org/10.5194/amt-9-1513-2016, https://doi.org/10.5194/amt-9-1513-2016, 2016
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We present the Austrian RADiation monitoring network (ARAD) that has been established to advance national climate monitoring and to support satellite retrieval, atmospheric modeling and solar energy techniques' development. Measurements cover the downwelling solar and thermal infrared radiation using instruments according to Baseline Surface Radiation Network (BSRN) standards. The paper outlines the aims and scopes of ARAD, its measurement and calibration standards, methods and strategies.
A. Gobiet, M. Suklitsch, and G. Heinrich
Hydrol. Earth Syst. Sci., 19, 4055–4066, https://doi.org/10.5194/hess-19-4055-2015, https://doi.org/10.5194/hess-19-4055-2015, 2015
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The effect of empirical-statistical bias correction methods, like quantile mapping (QM), on the simulated climate change signals (CCS) is currently strongly discussed and is often regarded as deficiency of bias correction methods. We demonstrate that, quite the contrary, QM can lead to an improved CCS and also has the potential to serve as an empirical constraint on model uncertainty in climate projections.
E. Katragkou, M. García-Díez, R. Vautard, S. Sobolowski, P. Zanis, G. Alexandri, R. M. Cardoso, A. Colette, J. Fernandez, A. Gobiet, K. Goergen, T. Karacostas, S. Knist, S. Mayer, P. M. M. Soares, I. Pytharoulis, I. Tegoulias, A. Tsikerdekis, and D. Jacob
Geosci. Model Dev., 8, 603–618, https://doi.org/10.5194/gmd-8-603-2015, https://doi.org/10.5194/gmd-8-603-2015, 2015
S. Kotlarski, K. Keuler, O. B. Christensen, A. Colette, M. Déqué, A. Gobiet, K. Goergen, D. Jacob, D. Lüthi, E. van Meijgaard, G. Nikulin, C. Schär, C. Teichmann, R. Vautard, K. Warrach-Sagi, and V. Wulfmeyer
Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, https://doi.org/10.5194/gmd-7-1297-2014, 2014
A. F. Lutz, W. W. Immerzeel, A. Gobiet, F. Pellicciotti, and M. F. P. Bierkens
Hydrol. Earth Syst. Sci., 17, 3661–3677, https://doi.org/10.5194/hess-17-3661-2013, https://doi.org/10.5194/hess-17-3661-2013, 2013
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Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network
Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014
Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada
Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Observed snow depth trends in the European Alps: 1971 to 2019
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)
Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
Evaluation of long-term Northern Hemisphere snow water equivalent products
Towards a webcam-based snow cover monitoring network: methodology and evaluation
Simulated single-layer forest canopies delay Northern Hemisphere snowmelt
Converting snow depth to snow water equivalent using climatological variables
Avalanches and micrometeorology driving mass and energy balance of the lowest perennial ice field of the Alps: a case study
The optical characteristics and sources of chromophoric dissolved organic matter (CDOM) in seasonal snow of northwestern China
Brief Communication: Early season snowpack loss and implications for oversnow vehicle recreation travel planning
Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
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Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão
The Cryosphere, 18, 5139–5152, https://doi.org/10.5194/tc-18-5139-2024, https://doi.org/10.5194/tc-18-5139-2024, 2024
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Snow distribution characterization is essential for accurate snow water estimation for water resource prediction from existing in situ observations and remote-sensing data at a finite spatial resolution. Four different observed snow distribution datasets were analyzed for Gaussianity. We found that non-Gaussianity of snow distribution is a signature of the wind redistribution effect. Generally, seasonal snowpack can be approximated well by a Gaussian distribution for a fully snow-covered area.
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, https://doi.org/10.5194/tc-18-4955-2024, 2024
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We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
Shirui Yan, Yang Chen, Yaliang Hou, Kexin Liu, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, Wei Pu, and Xin Wang
The Cryosphere, 18, 4089–4109, https://doi.org/10.5194/tc-18-4089-2024, https://doi.org/10.5194/tc-18-4089-2024, 2024
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The snow cover over the Tibetan Plateau (TP) plays a role in climate and hydrological systems, yet there are uncertainties in snow cover fraction (SCF) estimations within reanalysis datasets. This study utilized the Snow Property Inversion from Remote Sensing (SPIReS) SCF data to assess the accuracy of eight widely used reanalysis SCF datasets over the TP. Factors contributing to uncertainties were analyzed, and a combined averaging method was employed to provide optimized SCF simulations.
Gareth J. Marshall
EGUsphere, https://doi.org/10.5194/egusphere-2024-1892, https://doi.org/10.5194/egusphere-2024-1892, 2024
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Eurasian autumn snow cover (SC) can influence Northern Hemisphere weather in the following winter by affecting the Arctic Oscillation (AO) mode of atmospheric variability. We find that the relationship between the rate of October snow advance and the AO is predominantly of opposite sign between east and west Eurasia. Periods when the SC advance is strongly related to the AO, and thus might be used for weather prediction, occur when the sign of the relationship is reversed in one of the regions.
Vincenzo Capozzi, Francesco Serrapica, Armando Rocco, Clizia Annella, and Giorgio Budillon
EGUsphere, https://doi.org/10.5194/egusphere-2024-1056, https://doi.org/10.5194/egusphere-2024-1056, 2024
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This study offers a “journey through time” to discover historical information about snow precipitation in the Italian Apennines. In this area, in the second half of past century, a gradual decline in snow persistence on the ground as well as in the frequency of occurrence of snowfall events has been observed, especially in sites located above 1000 m a.s.l.. The old data rescued in this study strongly enhances our knowledge about past snowfall variability and climate in the Mediterranean area.
Benjamin Poschlod and Anne Sophie Daloz
The Cryosphere, 18, 1959–1981, https://doi.org/10.5194/tc-18-1959-2024, https://doi.org/10.5194/tc-18-1959-2024, 2024
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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.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
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We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
EGUsphere, https://doi.org/10.5194/egusphere-2023-3014, https://doi.org/10.5194/egusphere-2023-3014, 2024
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We evaluate and rank 23 products that estimate historical snow amounts. The evaluation uses new a set of ground measurements with improved spatial coverage enabling evaluation across both mountain and non-mountain regions. Performance measures vary tremendously across the products: while most perform reasonably in non-mountain regions, accurate representation of snow amounts in mountain regions and of historical trends is much more variable.
Yiwen Fang, Yufei Liu, Dongyue Li, Haorui Sun, and Steven A. Margulis
The Cryosphere, 17, 5175–5195, https://doi.org/10.5194/tc-17-5175-2023, https://doi.org/10.5194/tc-17-5175-2023, 2023
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Using newly developed snow reanalysis datasets as references, snow water storage is at high uncertainty among commonly used global products in the Andes and low-resolution products in the western United States, where snow is the key element of water resources. In addition to precipitation, elevation differences and model mechanism variances drive snow uncertainty. This work provides insights for research applying these products and generating future products in areas with limited in situ data.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
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We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Diego Monteiro and Samuel Morin
The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, https://doi.org/10.5194/tc-17-3617-2023, 2023
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Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
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Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Xuemei Li, Xinyu Liu, Kaixin Zhao, Xu Zhang, and Lanhai Li
The Cryosphere, 17, 2437–2453, https://doi.org/10.5194/tc-17-2437-2023, https://doi.org/10.5194/tc-17-2437-2023, 2023
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Quantifying change in the potential snowfall phenology (PSP) is an important area of research for understanding regional climate change past, present, and future. However, few studies have focused on the PSP and its change in alpine mountainous regions. We proposed three innovative indicators to characterize the PSP and its spatial–temporal variation. Our study provides a novel approach to understanding PSP in alpine mountainous regions and can be easily extended to other snow-dominated regions.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
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Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Zachary S. Miller, Erich H. Peitzsch, Eric A. Sproles, Karl W. Birkeland, and Ross T. Palomaki
The Cryosphere, 16, 4907–4930, https://doi.org/10.5194/tc-16-4907-2022, https://doi.org/10.5194/tc-16-4907-2022, 2022
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Snow depth varies across steep, complex mountain landscapes due to interactions between dynamic natural processes. Our study of a winter time series of high-resolution snow depth maps found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability at a couloir study site in the Bridger Range of Montana, USA. The results of this research have the potential to reduce uncertainty associated with snowpack and snow water resource analysis.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
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Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
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The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
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We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
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Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Yufei Liu, Yiwen Fang, and Steven A. Margulis
The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, https://doi.org/10.5194/tc-15-5261-2021, 2021
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We examined the spatiotemporal distribution of stored water in the seasonal snowpack over High Mountain Asia, based on a new snow reanalysis dataset. The dataset was derived utilizing satellite-observed snow information, which spans across 18 water years, at a high spatial (~ 500 m) and temporal (daily) resolution. Snow mass and snow storage distribution over space and time are analyzed in this paper, which brings new insights into understanding the snowpack variability over this region.
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, https://doi.org/10.5194/tc-15-4625-2021, 2021
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We investigated the impacts of local-scale variations by analysing snow climate indicators derived from parallel snow measurements. We found the largest relative inter-pair differences for all indicators in spring and the smallest in winter. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and provide, in combination with break-detection methods, the groundwork in view of any homogenization efforts regarding snow time series.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
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The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
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High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
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This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu
The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, https://doi.org/10.5194/tc-14-1763-2020, 2020
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There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
Colleen Mortimer, Lawrence Mudryk, Chris Derksen, Kari Luojus, Ross Brown, Richard Kelly, and Marco Tedesco
The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, https://doi.org/10.5194/tc-14-1579-2020, 2020
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Existing stand-alone passive microwave SWE products have markedly different climatological SWE patterns compared to reanalysis-based datasets. The AMSR-E SWE has low spatial and temporal correlations with the four reanalysis-based products evaluated and GlobSnow and perform poorly in comparisons with snow transect data from Finland, Russia, and Canada. There is better agreement with in situ data when multiple SWE products, excluding the stand-alone passive microwave SWE products, are combined.
Céline Portenier, Fabia Hüsler, Stefan Härer, and Stefan Wunderle
The Cryosphere, 14, 1409–1423, https://doi.org/10.5194/tc-14-1409-2020, https://doi.org/10.5194/tc-14-1409-2020, 2020
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We present a method to derive snow cover maps from freely available webcam images in the Swiss Alps. With marginal manual user input, we can transform a webcam image into a georeferenced map and therewith perform snow cover analyses with a high spatiotemporal resolution over a large area. Our evaluation has shown that webcams could not only serve as a reference for improved validation of satellite-based approaches, but also complement satellite-based snow cover retrieval.
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere, 13, 3077–3091, https://doi.org/10.5194/tc-13-3077-2019, https://doi.org/10.5194/tc-13-3077-2019, 2019
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Vegetation is often represented by a single layer in global land models. Studies have found deficient simulation of thermal radiation beneath forest canopies when represented by single-layer vegetation. This study corrects thermal radiation in forests for a global land model using single-layer vegetation in order to assess the effect of deficient thermal radiation on snow cover and snowmelt. Results indicate that single-layer vegetation causes snow in forests to be too cold and melt too late.
David F. Hill, Elizabeth A. Burakowski, Ryan L. Crumley, Julia Keon, J. Michelle Hu, Anthony A. Arendt, Katreen Wikstrom Jones, and Gabriel J. Wolken
The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, https://doi.org/10.5194/tc-13-1767-2019, 2019
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We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean winter precipitation and mean temperature difference (difference between hottest month and coldest month). The model is simple by design so that it can be applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019, https://doi.org/10.5194/tc-13-1247-2019, 2019
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The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes, or by suppressed snow ablation driven by micrometeorological effects lowering net radiation and turbulent heat exchange. In this study we discuss the relative contribution of snow accumulation (avalanches) versus micrometeorology (katabatic flow) on the mass balance of the lowest perennial ice field of the Alps, the Ice Chapel.
Yue Zhou, Hui Wen, Jun Liu, Wei Pu, Qingcai Chen, and Xin Wang
The Cryosphere, 13, 157–175, https://doi.org/10.5194/tc-13-157-2019, https://doi.org/10.5194/tc-13-157-2019, 2019
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We first investigated the optical characteristics and potential sources of chromophoric dissolved organic matter (CDOM) in seasonal snow over northwestern China. The abundance of CDOM showed regional variation. At some sites strongly influenced by local soil, the absorption of CDOM cannot be neglected compared to black carbon. We found two humic-like and one protein-like fluorophores in snow. The major sources of snow CDOM were soil, biomass burning, and anthropogenic pollution.
Benjamin J. Hatchett and Hilary G. Eisen
The Cryosphere, 13, 21–28, https://doi.org/10.5194/tc-13-21-2019, https://doi.org/10.5194/tc-13-21-2019, 2019
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We examine the timing of early season snowpack relevant to oversnow vehicle (OSV) recreation over the past 3 decades in the Lake Tahoe region (USA). Data from two independent data sources suggest that the timing of achieving sufficient snowpack has shifted later by 2 weeks. Increasing rainfall and more dry days play a role in the later onset. Adaptation strategies are provided for winter travel management planning to address negative impacts of loss of early season snowpack for OSV usage.
Deborah Verfaillie, Matthieu Lafaysse, Michel Déqué, Nicolas Eckert, Yves Lejeune, and Samuel Morin
The Cryosphere, 12, 1249–1271, https://doi.org/10.5194/tc-12-1249-2018, https://doi.org/10.5194/tc-12-1249-2018, 2018
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This article addresses local changes of seasonal snow and its meteorological drivers, at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps, for the period 1960–2100. We use an ensemble of adjusted RCM outputs consistent with IPCC AR5 GCM outputs (RCPs 2.6, 4.5 and 8.5) and the snowpack model Crocus. Beyond scenario-based approach, global temperature levels on the order of 1.5 °C and 2 °C above preindustrial levels correspond to 25 and 32% reduction of mean snow depth.
Cited articles
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Buchmann, M., Coll, J., Aschauer, J., Begert, M., Brönnimann, S., Chimani, B., Resch, G., Schöner, W., and Marty, C.: Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods, The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, 2022. a
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Short summary
Snow depth plays an important role in water resources, mountain tourism, and hazard management across the European Alps. Our study uses station-based historical observations to quantify how changes in temperature and precipitation affect average seasonal snow depth. We find that the relationship between these variables has been surprisingly robust over the last 120 years. This allows us to more accurately estimate how future climate will affect seasonal snow depth in different elevation zones.
Snow depth plays an important role in water resources, mountain tourism, and hazard management...
