Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-519-2023
© Author(s) 2023. 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-17-519-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Feb 2023
Research article |
| 07 Feb 2023
| 07 Feb 2023
Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models
Edoardo Raparelli
CORRESPONDING AUTHOR
Dept. Information Engineering, Electronics and Telecommunications, Sapienza Università di Roma, Rome, Italy
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Paolo Tuccella
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Dept. Physical and Chemical Sciences, Università degli Studi dell'Aquila, L'Aquila, Italy
Italian Glaciological Committee, Turin, Italy
Valentina Colaiuda
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Dept. Physical and Chemical Sciences, Università degli Studi dell'Aquila, L'Aquila, Italy
Frank S. Marzano
Dept. Information Engineering, Electronics and Telecommunications, Sapienza Università di Roma, Rome, Italy
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
deceased
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Ulas Im, Jesper Heile Christensen, Camilla Geels, Kaj Mantzius Hansen, Jørgen Brandt, Efisio Solazzo, Ummugulsum Alyuz, Alessandra Balzarini, Rocio Baro, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Augustin Colette, Gabriele Curci, Aidan Farrow, Johannes Flemming, Andrea Fraser, Pedro Jimenez-Guerrero, Nutthida Kitwiroon, Peng Liu, Uarporn Nopmongcol, Laura Palacios-Peña, Guido Pirovano, Luca Pozzoli, Marje Prank, Rebecca Rose, Ranjeet Sokhi, Paolo Tuccella, Alper Unal, Marta G. Vivanco, Greg Yarwood, Christian Hogrefe, and Stefano Galmarini
Atmos. Chem. Phys., 18, 8929–8952, https://doi.org/10.5194/acp-18-8929-2018, https://doi.org/10.5194/acp-18-8929-2018, 2018
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Ulas Im, Jørgen Brandt, Camilla Geels, Kaj Mantzius Hansen, Jesper Heile Christensen, Mikael Skou Andersen, Efisio Solazzo, Ioannis Kioutsioukis, Ummugulsum Alyuz, Alessandra Balzarini, Rocio Baro, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Augustin Colette, Gabriele Curci, Aidan Farrow, Johannes Flemming, Andrea Fraser, Pedro Jimenez-Guerrero, Nutthida Kitwiroon, Ciao-Kai Liang, Uarporn Nopmongcol, Guido Pirovano, Luca Pozzoli, Marje Prank, Rebecca Rose, Ranjeet Sokhi, Paolo Tuccella, Alper Unal, Marta Garcia Vivanco, Jason West, Greg Yarwood, Christian Hogrefe, and Stefano Galmarini
Atmos. Chem. Phys., 18, 5967–5989, https://doi.org/10.5194/acp-18-5967-2018, https://doi.org/10.5194/acp-18-5967-2018, 2018
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The impacts of air pollution on human health and their costs in Europe and the United States for the year 2010 ared modeled by a multi-model ensemble. In Europe, the number of premature deaths is calculated to be 414 000, while in the US it is estimated to be 160 000. Health impacts estimated by individual models can vary up to a factor of 3. Results show that the domestic emissions have the largest impact on premature deaths, compared to foreign sources.
Daniele Visioni, Giovanni Pitari, Paolo Tuccella, and Gabriele Curci
Atmos. Chem. Phys., 18, 2787–2808, https://doi.org/10.5194/acp-18-2787-2018, https://doi.org/10.5194/acp-18-2787-2018, 2018
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Sulfate geoengineering is a proposed technique that would mimic explosive volcanic eruptions by injecting sulfur dioxide (SO2) into the stratosphere to counteract global warming produced by greenhouse gases by reflecting part of the incoming solar radiation. In this study we use two models to simulate how the injected aerosols would react to dynamical changes in the stratosphere (due to the quasi-biennial oscillation - QBO) and how this would affect the deposition of sulfate at the surface.
Efisio Solazzo, Roberto Bianconi, Christian Hogrefe, Gabriele Curci, Paolo Tuccella, Ummugulsum Alyuz, Alessandra Balzarini, Rocío Baró, Roberto Bellasio, Johannes Bieser, Jørgen Brandt, Jesper H. Christensen, Augistin Colette, Xavier Francis, Andrea Fraser, Marta Garcia Vivanco, Pedro Jiménez-Guerrero, Ulas Im, Astrid Manders, Uarporn Nopmongcol, Nutthida Kitwiroon, Guido Pirovano, Luca Pozzoli, Marje Prank, Ranjeet S. Sokhi, Alper Unal, Greg Yarwood, and Stefano Galmarini
Atmos. Chem. Phys., 17, 3001–3054, https://doi.org/10.5194/acp-17-3001-2017, https://doi.org/10.5194/acp-17-3001-2017, 2017
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As part of the third phase of AQMEII, this study uses timescale analysis to apportion error to the responsible processes, detect causes of model error, and identify the processes and scales that require dedicated investigations. The analysis tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of model biases for ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature over Europe and North America.
I. Maiello, R. Ferretti, S. Gentile, M. Montopoli, E. Picciotti, F. S. Marzano, and C. Faccani
Atmos. Meas. Tech., 7, 2919–2935, https://doi.org/10.5194/amt-7-2919-2014, https://doi.org/10.5194/amt-7-2919-2014, 2014
M. Montopoli, G. Vulpiani, D. Cimini, E. Picciotti, and F. S. Marzano
Atmos. Meas. Tech., 7, 537–552, https://doi.org/10.5194/amt-7-537-2014, https://doi.org/10.5194/amt-7-537-2014, 2014
E. Picciotti, F. S. Marzano, E. N. Anagnostou, J. Kalogiros, Y. Fessas, A. Volpi, V. Cazac, R. Pace, G. Cinque, L. Bernardini, K. De Sanctis, S. Di Fabio, M. Montopoli, M. N. Anagnostou, A. Telleschi, E. Dimitriou, and J. Stella
Nat. Hazards Earth Syst. Sci., 13, 1229–1241, https://doi.org/10.5194/nhess-13-1229-2013, https://doi.org/10.5194/nhess-13-1229-2013, 2013
Related subject area
Discipline: Snow | Subject: Numerical Modelling
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Clémence Herny, Pascal Hagenmuller, Guillaume Chambon, Isabel Peinke, and Jacques Roulle
The Cryosphere, 18, 3787–3805, https://doi.org/10.5194/tc-18-3787-2024, https://doi.org/10.5194/tc-18-3787-2024, 2024
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This paper presents the evaluation of a numerical discrete element method (DEM) by simulating cone penetration tests in different snow samples. The DEM model demonstrated a good ability to reproduce the measured mechanical behaviour of the snow, namely the force evolution on the cone and the grain displacement field. Systematic sensitivity tests showed that the mechanical response depends not only on the microstructure of the sample but also on the mechanical parameters of grain contacts.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
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Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
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Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Michael A. Rawlins and Ambarish V. Karmalkar
The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, https://doi.org/10.5194/tc-18-1033-2024, 2024
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Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, Nina Raoult, and Xavier Fettweis
EGUsphere, https://doi.org/10.5194/egusphere-2024-285, https://doi.org/10.5194/egusphere-2024-285, 2024
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The evolution of the Greenland ice sheet is highly dependent on surface melting and therefore on the processes operating at the snow-atmosphere interface and within the snow cover. Here we present new developments to apply a snow model to the Greenland ice sheet. The performance of this model is analysed in terms of its ability to simulate ablation processes. Our analysis shows that the model performs well when compared with the MAR regional polar atmospheric model.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
EGUsphere, https://doi.org/10.5194/egusphere-2023-2926, https://doi.org/10.5194/egusphere-2023-2926, 2024
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Computer models, like those used in climate change studies, are written by modelers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modeling as an example, we examine the process behind the decisions to understand what motivates or limits modelers in their decision-making. We found that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency into our research practice.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2023-2781, https://doi.org/10.5194/egusphere-2023-2781, 2023
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As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time, because different processes prevail at different locations in the forest.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
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We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
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
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Konstantin Schürholt, Julia Kowalski, and Henning Löwe
The Cryosphere, 16, 903–923, https://doi.org/10.5194/tc-16-903-2022, https://doi.org/10.5194/tc-16-903-2022, 2022
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This companion paper deals with numerical particularities of partial differential equations underlying 1D snow models. In this first part we neglect mechanical settling and demonstrate that the nonlinear coupling between diffusive transport (heat and vapor), phase changes and ice mass conservation contains a wave instability that may be relevant for weak layer formation. Numerical requirements are discussed in view of the underlying homogenization scheme.
Anna Simson, Henning Löwe, and Julia Kowalski
The Cryosphere, 15, 5423–5445, https://doi.org/10.5194/tc-15-5423-2021, https://doi.org/10.5194/tc-15-5423-2021, 2021
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This companion paper deals with numerical particularities of partial differential equations underlying one-dimensional snow models. In this second part we include mechanical settling and develop a new hybrid (Eulerian–Lagrangian) method for solving the advection-dominated ice mass conservation on a moving mesh alongside Eulerian diffusion (heat and vapor) and phase changes. The scheme facilitates a modular and extendable solver strategy while retaining controls on numerical accuracy.
Thomas Brall, Vladimir Mares, Rolf Bütikofer, and Werner Rühm
The Cryosphere, 15, 4769–4780, https://doi.org/10.5194/tc-15-4769-2021, https://doi.org/10.5194/tc-15-4769-2021, 2021
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Neutrons from secondary cosmic rays, measured at 2660 m a.s.l. at Zugspitze, Germany, are highly affected by the environment, in particular by snow, soil moisture, and mountain shielding. To quantify these effects, computer simulations were carried out, including a sensitivity analysis on snow depth and soil moisture. This provides a possibility for snow depth estimation based on the measured number of secondary neutrons. This method was applied at Zugspitze in 2018.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
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The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
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.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
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Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
David M. W. Pritchard, Nathan Forsythe, Greg O'Donnell, Hayley J. Fowler, and Nick Rutter
The Cryosphere, 14, 1225–1244, https://doi.org/10.5194/tc-14-1225-2020, https://doi.org/10.5194/tc-14-1225-2020, 2020
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This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
Grégoire Bobillier, Bastian Bergfeld, Achille Capelli, Jürg Dual, Johan Gaume, Alec van Herwijnen, and Jürg Schweizer
The Cryosphere, 14, 39–49, https://doi.org/10.5194/tc-14-39-2020, https://doi.org/10.5194/tc-14-39-2020, 2020
Michael A. Rawlins, Lei Cai, Svetlana L. Stuefer, and Dmitry Nicolsky
The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, https://doi.org/10.5194/tc-13-3337-2019, 2019
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We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
Pierre Spandre, Hugues François, Deborah Verfaillie, Marc Pons, Matthieu Vernay, Matthieu Lafaysse, Emmanuelle George, and Samuel Morin
The Cryosphere, 13, 1325–1347, https://doi.org/10.5194/tc-13-1325-2019, https://doi.org/10.5194/tc-13-1325-2019, 2019
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This study investigates the snow reliability of 175 ski resorts in the Pyrenees (France, Spain and Andorra) and the French Alps under past and future conditions (1950–2100) using state-of-the-art climate projections and snowpack modelling accounting for snow management, i.e. grooming and snowmaking. The snow reliability of ski resorts shows strong elevation and regional differences, and our study quantifies changes in snow reliability induced by snowmaking under various climate scenarios.
Zhengshi Wang and Shuming Jia
The Cryosphere, 12, 3841–3851, https://doi.org/10.5194/tc-12-3841-2018, https://doi.org/10.5194/tc-12-3841-2018, 2018
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Drifting snowstorms that are hundreds of meters in depth are reproduced using a large-eddy simulation model combined with a Lagrangian particle tracking method, which also exhibits obvious spatial structures following large-scale turbulent vortexes. The horizontal snow transport flux at high altitude, previously not observed, actually occupies a significant proportion of the total flux. Thus, previous models may largely underestimate the total mass flux and consequently snow sublimation.
Franziska Gerber, Nikola Besic, Varun Sharma, Rebecca Mott, Megan Daniels, Marco Gabella, Alexis Berne, Urs Germann, and Michael Lehning
The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, https://doi.org/10.5194/tc-12-3137-2018, 2018
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A comparison of winter precipitation variability in operational radar measurements and high-resolution simulations reveals that large-scale variability is well captured by the model, depending on the event. Precipitation variability is driven by topography and wind. A good portion of small-scale variability is captured at the highest resolution. This is essential to address small-scale precipitation processes forming the alpine snow seasonal snow cover – an important source of water.
Edward H. Bair, Andre Abreu Calfa, Karl Rittger, and Jeff Dozier
The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, https://doi.org/10.5194/tc-12-1579-2018, 2018
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In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
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Short summary
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced...
