Articles | Volume 7, issue 6
https://doi.org/10.5194/gmd-7-2831-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-2831-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
03 Dec 2014
Model description paper |
| 03 Dec 2014
GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects
S. Endrizzi
CORRESPONDING AUTHOR
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
S. Gruber
Carleton University, Department of Geography and Environmental Studies, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
M. Dall'Amico
Mountaineering GmbH, Siemensstrasse 19, 39100 Bozen, Italy
Dipartimento di Ingegneria Civile, Ambientale e Meccanica e CUDAM, Università di Trento, Via Mesiano 77, 38123 Trento, Italy
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Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
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Niccolò Tubini and Riccardo Rigon
Geosci. Model Dev., 15, 75–104, https://doi.org/10.5194/gmd-15-75-2022, https://doi.org/10.5194/gmd-15-75-2022, 2022
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Niccolò Tubini, Stephan Gruber, and Riccardo Rigon
The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, https://doi.org/10.5194/tc-15-2541-2021, 2021
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John Mohd Wani, Renoj J. Thayyen, Chandra Shekhar Prasad Ojha, and Stephan Gruber
The Cryosphere, 15, 2273–2293, https://doi.org/10.5194/tc-15-2273-2021, https://doi.org/10.5194/tc-15-2273-2021, 2021
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We study the surface energy balance from a cold-arid permafrost environment in the Indian Himalayan region. The GEOtop model was used for the modelling of surface energy balance. Our results show that the variability in the turbulent heat fluxes is similar to that reported from the seasonally frozen ground and permafrost regions of the Tibetan Plateau. Further, the low relative humidity could be playing a critical role in the surface energy balance and the permafrost processes.
Rupesh Subedi, Steven V. Kokelj, and Stephan Gruber
The Cryosphere, 14, 4341–4364, https://doi.org/10.5194/tc-14-4341-2020, https://doi.org/10.5194/tc-14-4341-2020, 2020
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Permafrost beneath tundra near Lac de Gras (Northwest Territories, Canada) contains more ice and less organic carbon than shown in global compilations. Excess-ice content of 20–60 %, likely remnant Laurentide basal ice, is found in upland till. This study is based on 24 boreholes up to 10 m deep. Findings highlight geology and glacial legacy as determinants of a mosaic of permafrost characteristics with potential for thaw subsidence up to several metres in some locations.
Bin Cao, Stephan Gruber, Donghai Zheng, and Xin Li
The Cryosphere, 14, 2581–2595, https://doi.org/10.5194/tc-14-2581-2020, https://doi.org/10.5194/tc-14-2581-2020, 2020
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This study reports that ERA5-Land (ERA5L) soil temperature bias in permafrost regions correlates with the bias in air temperature and with maximum snow height. While global reanalyses are important drivers for permafrost study, ERA5L soil data are not well suited for directly informing permafrost research decision making due to their warm bias in winter. To address this, future soil temperature products in reanalyses will require permafrost-specific alterations to their land surface models.
Stephan Gruber
The Cryosphere, 14, 1437–1447, https://doi.org/10.5194/tc-14-1437-2020, https://doi.org/10.5194/tc-14-1437-2020, 2020
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A simple method to record heave and subsidence of the land surface at specific field locations is described. Hourly observations from three sites, over two winters and one summer, are analyzed and discussed. The data are rich in features that point to the influence of freezing and thawing and of wetting and drying of the soil. This type of observation may offer new insight into the processes of heat and mass transfer in soil and help to monitor climate change impacts.
Bin Cao, Xiaojing Quan, Nicholas Brown, Emilie Stewart-Jones, and Stephan Gruber
Geosci. Model Dev., 12, 4661–4679, https://doi.org/10.5194/gmd-12-4661-2019, https://doi.org/10.5194/gmd-12-4661-2019, 2019
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GlobSim is a tool for simulating land-surface processes and phenomena at point locations globally, even where no site-specific meteorological observations exist. This is important because simulation can add insight to the analysis of observations or help in anticipating climate-change impacts and because site-specific simulation can help in model evaluation.
Joe R. Melton, Diana L. Verseghy, Reinel Sospedra-Alfonso, and Stephan Gruber
Geosci. Model Dev., 12, 4443–4467, https://doi.org/10.5194/gmd-12-4443-2019, https://doi.org/10.5194/gmd-12-4443-2019, 2019
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Soils in cold regions store large amounts of carbon that could be released to the atmosphere if the soils thaw. To best simulate these soils, we explored different configurations and parameterizations of the CLASS-CTEM model and compared to observations. The revised model with a deeper soil column, new soil depth dataset, and inclusion of moss simulated greatly improved annual thaw depths and ground temperatures. We estimate subgrid-scale features limit further improvements against observations.
Samuel Weber, Jan Beutel, Reto Da Forno, Alain Geiger, Stephan Gruber, Tonio Gsell, Andreas Hasler, Matthias Keller, Roman Lim, Philippe Limpach, Matthias Meyer, Igor Talzi, Lothar Thiele, Christian Tschudin, Andreas Vieli, Daniel Vonder Mühll, and Mustafa Yücel
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In this paper, we describe a unique 10-year or more data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500 m a.s.l. By documenting and sharing these data in this form, we contribute to facilitating future research based on them, e.g., in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models.
Bin Cao, Stephan Gruber, and Tingjun Zhang
Geosci. Model Dev., 10, 2905–2923, https://doi.org/10.5194/gmd-10-2905-2017, https://doi.org/10.5194/gmd-10-2905-2017, 2017
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To derive the air temperature in mountain enviroments, we propose a new downscaling method with a spatially variable magnitude of surface effects. Our findings suggest that the difference between near-surface air temperature and upper-air temerpature is a good proxy of surface effects. It can be used to improve downscaling results, especially in valleys with strong surface effects and cold air pooling during winter.
Wuletawu Abera, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 21, 3145–3165, https://doi.org/10.5194/hess-21-3145-2017, https://doi.org/10.5194/hess-21-3145-2017, 2017
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This study documents a state-of-the-art estimation of the water budget (rainfall, evapotranspiration, discharge, and soil and groundwater storage) components for the Upper Blue Nile river. The budget uses various JGrass-NewAGE components, satellite data and all ground measurements available. The analysis shows that precipitation of the basin is 1360 ± 230 mm per year. Evapotranspiration accounts for 56 %, runoff is 33 %, and storage varies from minus 10 % to plus 17 % of the annual water budget.
Stephan Gruber, Renate Fleiner, Emilie Guegan, Prajjwal Panday, Marc-Olivier Schmid, Dorothea Stumm, Philippus Wester, Yinsheng Zhang, and Lin Zhao
The Cryosphere, 11, 81–99, https://doi.org/10.5194/tc-11-81-2017, https://doi.org/10.5194/tc-11-81-2017, 2017
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We review what can be inferred about permafrost in the mountains of the Hindu Kush Himalaya region. This is important because the area of permafrost exceeds that of glaciers in this region. Climate change will produce diverse permafrost-related impacts on vegetation, water quality, geohazards, and livelihoods. To mitigate this, a better understanding of high-elevation permafrost in subtropical latitudes as well as the pathways connecting environmental change and human livelihoods, is needed.
Riccardo Rigon, Marialaura Bancheri, and Timothy R. Green
Hydrol. Earth Syst. Sci., 20, 4929–4947, https://doi.org/10.5194/hess-20-4929-2016, https://doi.org/10.5194/hess-20-4929-2016, 2016
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The goal of the paper is to analyze the theory of water age inside a catchment while accounting for multiple outflows. It tries to propose the material under a new perspective where it lines up concepts, cleans the notation, discusses some classical results, and offers some examples that help to relate the modern achievements to the theory of the IUH, clarifying assets of both of them. In doing all of this, it also produces various new results, and some regarding solute transport.
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, https://doi.org/10.5194/hess-20-4641-2016, 2016
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Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.
V. Wirz, S. Gruber, R. S. Purves, J. Beutel, I. Gärtner-Roer, S. Gubler, and A. Vieli
Earth Surf. Dynam., 4, 103–123, https://doi.org/10.5194/esurf-4-103-2016, https://doi.org/10.5194/esurf-4-103-2016, 2016
M.-O. Schmid, P. Baral, S. Gruber, S. Shahi, T. Shrestha, D. Stumm, and P. Wester
The Cryosphere, 9, 2089–2099, https://doi.org/10.5194/tc-9-2089-2015, https://doi.org/10.5194/tc-9-2089-2015, 2015
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The extent and distribution of permafrost in the mountainous parts of the Hindu Kush Himalayan (HKH) region are largely unknown. This article provides a first-order assessment of the two available permafrost maps in the HKH region based on the mapping of rock glaciers in Google Earth. The Circum-Arctic Map of Permafrost and Ground Ice Conditions does not reproduce mapped conditions in the HKH region adequately, whereas the Global Permafrost Zonation Index does so with more success.
A. Hasler, M. Geertsema, V. Foord, S. Gruber, and J. Noetzli
The Cryosphere, 9, 1025–1038, https://doi.org/10.5194/tc-9-1025-2015, https://doi.org/10.5194/tc-9-1025-2015, 2015
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In this paper we describe surface and thermal offsets derived from distributed measurements at seven field sites in British Columbia. Key findings are i) a small variation of the surface offsets between surface types; ii) small thermal offsets at all sites; iii) a clear influence of the micro-topography due to snow cover effects; iv) a north--south difference of the surface offset of 4°C in vertical bedrock and of 1.5–-3°C on open gentle slopes; v) only small macroclimatic differences.
J. Fiddes, S. Endrizzi, and S. Gruber
The Cryosphere, 9, 411–426, https://doi.org/10.5194/tc-9-411-2015, https://doi.org/10.5194/tc-9-411-2015, 2015
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This paper demonstrates a new land surface modelling approach that uses globally available data sets to generate high-resolution simulation results of land surface processes. We successfully simulate a highly resolution-dependent variable, ground surface temperatures, over the entire Swiss Alps at high resolution. We use a large evaluation data set to test the model. We suggest that this scheme represents a useful step in application of numerical models over large areas in heterogeneous terrain.
V. Wirz, J. Beutel, S. Gruber, S. Gubler, and R. S. Purves
Nat. Hazards Earth Syst. Sci., 14, 2503–2520, https://doi.org/10.5194/nhess-14-2503-2014, https://doi.org/10.5194/nhess-14-2503-2014, 2014
G. Formetta, S. K. Kampf, O. David, and R. Rigon
Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, https://doi.org/10.5194/gmd-7-725-2014, 2014
J. Fiddes and S. Gruber
Geosci. Model Dev., 7, 387–405, https://doi.org/10.5194/gmd-7-387-2014, https://doi.org/10.5194/gmd-7-387-2014, 2014
S. Gubler, S. Endrizzi, S. Gruber, and R. S. Purves
Geosci. Model Dev., 6, 1319–1336, https://doi.org/10.5194/gmd-6-1319-2013, https://doi.org/10.5194/gmd-6-1319-2013, 2013
G. Formetta, R. Rigon, J. L. Chávez, and O. David
Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, https://doi.org/10.5194/gmd-6-915-2013, 2013
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-298, https://doi.org/10.5194/gmd-2022-298, 2023
Revised manuscript accepted for GMD
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better quality maps. The correction can then be extended backwards and forwards in time for periods when better quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the last 60 years at a resolution of one day and one kilometre. This is the first time that such a dataset has been produced.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
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The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
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Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
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We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
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The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
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We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Zhen Yin, Chen Zuo, Emma J. MacKie, and Jef Caers
Geosci. Model Dev., 15, 1477–1497, https://doi.org/10.5194/gmd-15-1477-2022, https://doi.org/10.5194/gmd-15-1477-2022, 2022
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We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
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In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
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We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Christopher Horvat and Lettie A. Roach
Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, https://doi.org/10.5194/gmd-15-803-2022, 2022
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Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from meters to kilometers. Variations in sea ice geometry are often forced by ocean waves, a process that is an important target of global climate models as it affects the rate of sea ice melting. Yet directly simulating these interactions is computationally expensive. We present a neural-network-based model of wave–ice fracture that allows models to incorporate their effect without added computational cost.
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, https://doi.org/10.5194/gmd-15-617-2022, 2022
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Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
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We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
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In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
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Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Jonas Van Breedam, Philippe Huybrechts, and Michel Crucifix
Geosci. Model Dev., 14, 6373–6401, https://doi.org/10.5194/gmd-14-6373-2021, https://doi.org/10.5194/gmd-14-6373-2021, 2021
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Ice sheets are an important component of the climate system and interact with the atmosphere through albedo variations and changes in the surface height. On very long timescales, it is impossible to directly couple ice sheet models with climate models and other techniques have to be used. Here we present a novel coupling method between ice sheets and the atmosphere by making use of an emulator to simulate ice sheet–climate interactions for several million years.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
Geosci. Model Dev., 14, 6331–6354, https://doi.org/10.5194/gmd-14-6331-2021, https://doi.org/10.5194/gmd-14-6331-2021, 2021
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This study introduces a new Sea Ice Evaluation Tool (SITool) to evaluate the model skills on the bipolar sea ice simulations by providing performance metrics and diagnostics. SITool is applied to evaluate the CMIP6 OMIP simulations. By changing the atmospheric forcing from CORE-II to JRA55-do data, many aspects of sea ice simulations are improved. SITool will be useful for helping teams managing various versions of a sea ice model or tracking the time evolution of model performance.
Conrad P. Koziol, Joe A. Todd, Daniel N. Goldberg, and James R. Maddison
Geosci. Model Dev., 14, 5843–5861, https://doi.org/10.5194/gmd-14-5843-2021, https://doi.org/10.5194/gmd-14-5843-2021, 2021
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Sea level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depends strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent or how it will affect forecasts. We provide a framework for quantifying how this
initialization uncertaintyaffects ice loss forecasts.
Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, and An T. Nguyen
Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, https://doi.org/10.5194/gmd-14-4909-2021, 2021
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High ice shelf melting in the Amundsen Sea has attracted many observational campaigns in the past decade. One method to combine observations with numerical models is the adjoint method. After 20 iterations, the cost function, defined as a sum of the weighted model–data difference, is reduced by 65 % by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. This study demonstrates adjoint-method optimization with explicit representation of ice shelf cavity circulation.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, https://doi.org/10.5194/gmd-14-4891-2021, 2021
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The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Daniel R. Shapero, Jessica A. Badgeley, Andrew O. Hoffman, and Ian R. Joughin
Geosci. Model Dev., 14, 4593–4616, https://doi.org/10.5194/gmd-14-4593-2021, https://doi.org/10.5194/gmd-14-4593-2021, 2021
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This paper describes a new software package called "icepack" for modeling the flow of ice sheets and glaciers. Glaciologists use tools like icepack to better understand how ice sheets flow, what role they have played in shaping Earth's climate, and how much sea level rise we can expect in the coming decades to centuries. The icepack package includes several innovations to help researchers describe and solve interesting glaciological problems and to experiment with the underlying model physics.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
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Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Moritz Kreuzer, Ronja Reese, Willem Nicholas Huiskamp, Stefan Petri, Torsten Albrecht, Georg Feulner, and Ricarda Winkelmann
Geosci. Model Dev., 14, 3697–3714, https://doi.org/10.5194/gmd-14-3697-2021, https://doi.org/10.5194/gmd-14-3697-2021, 2021
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We present the technical implementation of a coarse-resolution coupling between an ice sheet model and an ocean model that allows one to simulate ice–ocean interactions at timescales from centuries to millennia. As ice shelf cavities cannot be resolved in the ocean model at coarse resolution, we bridge the gap using an sub-shelf cavity module. It is shown that the framework is computationally efficient, conserves mass and energy, and can produce a stable coupled state under present-day forcing.
Charles Amory, Christoph Kittel, Louis Le Toumelin, Cécile Agosta, Alison Delhasse, Vincent Favier, and Xavier Fettweis
Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, https://doi.org/10.5194/gmd-14-3487-2021, 2021
Short summary
Short summary
This paper presents recent developments in the drifting-snow scheme of the regional climate model MAR and its application to simulate drifting snow and the surface mass balance of Adélie Land in East Antarctica. The model is extensively described and evaluated against a multi-year drifting-snow dataset and surface mass balance estimates available in the area. The model sensitivity to input parameters and improvements over a previously published version are also assessed.
Cited articles
Abbott, M., Bathurst, J., Cunge, J., O'Connell, P., and Rasmussen, J.: An introduction to the European Hydrological System – Systeme Hydrologique Europeen, "SHE", 1: History and philosophy of a physically-based, distributed modelling system, J. Hydrol., 87, 45–59, 1986.
Anderson, E. A.: A point energy and mass balance model of a snow cover, Tech. rep., Office of Hydrology, National Weather Service, Silver Spring, MA, USA, 1976.
Armijo, L.: Minimization of functions having Lipschitz continuous first partial derivatives, Pacific J. Math., 6, 1–3, 1966.
Atwater, M. and Brown, P. S.: Numerical computation of the latitudinal variations of solar radiation for an atmosphere of varying opacity, J. Appl. Meteorol., 13, 289–297, 1974.
Auer, A. H. J.: The rain versus snow threshold temperatures, Weatherwise, 27, p. 67, 1974.
Balland, V. and Arp, P.: Modeling soil thermal conductivities over a wide range of conditions, J. Environ. Eng. Sci., 4, 549–558, 2005.
Barnes, S. L.: A technique for maximizing details in numerical weather map analysis, J. Appl. Meteor., 3, 396–409, 1964.
Barnett, T., Adam, J., and Lettenmeier, D.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, 2005.
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for meteorological data, Geosci. Model Dev. Discuss., 7, 3595–3645, https://doi.org/10.5194/gmdd-7-3595-2014, 2014.
Bertoldi, G., Rigon, R., and Over, T. M.: Impact of watershed geomorphic characteristics on the energy and water budgets, J. Hydrometeorol., 7, 389–403, 2006.
Bertoldi, G., Notarnicola, C., Leitinger, G., Endrizzi, S., Zebisch, M., Della Chiesa, S., and Tappeiner, U.: Topographical and ecohydrological controls on land surface temperature in an alpine catchment, Ecohydrology, 3, 189–204, https://doi.org/10.1002/eco.129, 2010.
Bertoldi, G., Della Chiesa, S., Notarnicola, C., Pasolli, L., Niedrist, G., and Tappeiner, U.: Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling, J. Hydrol., 516, 145–257, 2014.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R .L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Brenner, J.: Spatial variability and temporal trends of evaporation and soil moisture in an innerapline dry catchment (Venosta Valley, South Tirol), Master's thesis, Institut of Earth and Environmental Sciences, University of Potsdam, Germany, 2014.
Brooks, R. H. and Corey, A. T.: Hydraulic properties of porous media, Hydrology paper, 3, Colorado State University, Fort Collins, 1964.
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13–22, 1992.
Brutsaert, W.: On a derivable formula for long-wave radiation from clear skies, Water Resour. Res., 11, 742–744, 1975a.
Brutsaert, W.: A theory for local evaporation (or heat transfer) from rough and smooth surfaces at ground level, Water Resour. Res., 11, 543–550, 1975b.
Buri, P.: Simulation of cold-firn-temperatures at an Alpine site using the model GEOtop, Master's thesis, University of Zurich, Department of Geography, 2013.
Calonne, N., Flin, F., Morin, S., Lesaffre, B., du Roscoat, S. R., and Geindreau, C.: Numerical and experimental investigations of the effective thermal conductivity of snow, Geophys. Res. Lett., 38, L23501, https://doi.org/10.1029/2011GL049234, 2011.
Colbeck, S. C.: A theory of water percolation in snow, J. Glaciol., 11, 369–385, 1972.
Cosenza, P., Guerin, R., and Tabbagh, A.: Relationship between thermal conductivity and water content of soils using numerical modelling, Eur. J. Soil Sci., 54, 581–587, 2003.
Crawford, T. M. and Duchon, C. E.: An improved parameterization for estimating effective atmospheric emissivity for use in calculating daytime downwelling longwave radiation, J. Appl. Meteorol., 38, 474–480, 1998.
Daanen, R., Misra, D., and Epstein, H.: Active-layer hydrology in nonsorted circle ecosystems of the Arctic tundra, Vadose Zone J., 6, 694–704, 2007.
Dall'Amico, M., Endrizzi, S., Gruber, S., and Rigon, R.: A robust and energy-conserving model of freezing variably-saturated soil, The Cryosphere, 5, 469–484, https://doi.org/10.5194/tc-5-469-2011, 2011.a.
Dall'Amico, M., Endrizzi, S., Gruber, S., and Rigon, R.: GEOtop Users Manual. Version 1.0, Technichal report, Mountain-eering Srl, Siemensstr. 19 Bolzano, Italy, 2011b.
De la Casiniere, A. C.: Heat exchange over a melting snow surface, J. Glaciol., 13, 55–72, 1974.
de Vries, D. A.: Thermal properties of soils, North Holland, Amsterdam, Netherlands, 1963.
Deardorff, J. W.: Efficient prediction of ground surface temperature and moisture with inclusion of a layer of vegetation, J. Geophys. Res., 83, 1889–1903, 1978.
Della Chiesa, S., Bertoldi, G., Niedrist, G., Obojes, N., Endrizzi, S., Albertson, J. D., Wohlfahrt, G., Hörtnagl, L., and Tappeiner, U.: Modelling changes in grassland hydrological cycling along an elevational gradient in the Alps, Ecohydrology, https://doi.org/10.1002/eco.1471, 2014.
Denby, B. and Greuell, W.: The use of bulk and profile methods for determining surface heat fluxes in the presence of glacier winds, J. Glaciol., 46, 445–452, 2000.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P. J.: Biosphere-Atmosphere T}ransfer Scheme (BATS) version 1e as coupled to the {NCAR Community Land Model, Tech. rep., National Center for Atmospheric Research, Boulder, CO, USA, 1993.
Dilley, A. C. and O'Brien, D. M.: Estimating downward clear sky long-wave irradiance at the surface from screen temperature and precipitable water, Q. J. R. Meteorol. Soc., 124a, 1391–1401, 1997.
El-Mikkawy, M. and Karawia, A.: Inversion of general tridiagonal matrices, Appl. Math. Lett., 19, 712–720, 2006.
Endrizzi, S.: Snow cover modelling at local and distributed scale over complex terrain, Ph.D. thesis, Institute of Civil and Environmental Engineering, University of Trento, Trento, available at: http://web.unitn.it/files/download/9673/endrizzi_tesi_def.pdf (last access: 17 November 2014), 2009.
Endrizzi, S. and Gruber, S.: Investigating the effects of lateral water flow on spatial patterns of ground temperature, depth of thaw and ice content, in: Proceedings of the 10th International Conference on Permafrost, Salekhard, Russia, 91–96, 2012.
Endrizzi, S. and Marsh, P.: Observations and modeling of turbulent fluxes during melt at the shrub-tundra transition zone 1: point scale variations, Hydrol. Res., 41, 471–491, 2010.
Endrizzi, S., Quinton, W. L., and Marsh, P.: Modelling the spatial pattern of ground thaw in a small basin in the arctic tundra, The Cryosphere Discuss., 5, 367–400, https://doi.org/10.5194/tcd-5-367-2011, 2011.
Erbs, D. G., Klein, S. A., and Duffie, J. A.: Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Sol. Energy, 28, 293–302, 1982.
Fiddes, J., Endrizzi, S., and Gruber, S.: Large area land surface simulations in heterogeneous terrain driven by global datasets: application to mountain permafrost, The Cryosphere Discuss., 7, 5853–5887, https://doi.org/10.5194/tcd-7-5853-2013, 2013.
Flerchinger, G. N., Xaio, W., Marks, D., Sauer, T. J., and Yu, Q.: Comparison of algorithms for incoming atmospheric long-wave radiation, Water Resour. Res., 45, W03423, https://doi.org/10.1029/2008WR007394, 2009.
Formetta, G., Mantilla, R., Franceschi, S., Antonello, A., and Rigon, R.: The JGrass-NewAge system for forecasting and managing the hydrological budgets at the basin scale: models of flow generation and propagation/routing, Geosci. Model Dev., 4, 943–955, https://doi.org/10.5194/gmd-4-943-2011, 2011.
Freeze, R. A. and Harlan, R. L.: Blueprint for a physically-based, digitally simulated hydrologic response model, J. Hydrol., 9, 237–258, 1969.
Garen, D. C. and Marks, D.: Spatially distributed energy balance snowmelt modelling in a mountainous river basin: estimation of meteorological inputs and verification of model results, J. Hydrol., 315, 126–153, 2005.
Garratt, J. R.: The Atmospheric Boundary Layer, Cambridge University Press, 1992.
Gottardi, G. and Venutelli, M.: A control-volume finite-element model for two-dimensional overland flow, Adv. Water Resour., 16, 277–284, 1993.
Gruber, S.: A mass-conserving fast algorithm to parameterize gravitational transport and deposition using digital elevation models, Water Resour. Res., 43, W06412, https://doi.org/10.1029/2006WR004868, 2007.
Gruber, S., Peter, M., Hoelzle, M., Woodhatch, I., and Haeberli, W.: Surface temperatures in steep alpine rock faces – a strategy for regional-scale measurement and modelling, in: Proceedings of the 8th International Conference on Permafrost, 2003.
Gruber, S., Hoelzle, M., and Haeberli, W.: Rock-wall temperatures in the Alps: modelling their topographic distribution and regional differences, Permafrost Periglac., 15, 299–307, 2004.
Gubler, S., Gruber, S., and Purves, R. S.: Uncertainties of parameterized surface downward clear-sky shortwave and all-sky longwave radiation, Atmos. Chem. Phys., 12, 5077–5098, https://doi.org/10.5194/acp-12-5077-2012, 2012.
Gubler, S., Endrizzi, S., Gruber, S., and Purves, R. S.: Sensitivities and uncertainties of modeled ground temperatures in mountain environments, Geosci. Model Dev., 6, 1319–1336, https://doi.org/10.5194/gmd-6-1319-2013, 2013.
Halberstam, I. and Schieldge, J. P.: Anomalous behavior of the atmospheric surface layer over a melting snowpack, J. Appl. Met., 20, 255–265, 1981.
Hansson, K., Simunek, J., Mizoguchi, M., Lundin, L., and van Genuchten, M.: Water flow and heat transport in frozen soil numerical solution and freeze-thaw applications, Vadose Zone J., 3, 693–704, 2004.
Helbig, N., Löwe, H., and Lehning, M.: Radiosity approach for the shortwave surface radiation balance in complex terrain, J. Atmos. Sci., 66, 2900–2912, 2009.
Hinzman, L. D., Goering, D. J., and Kane, D. L.: A distributed thermal model for calculating soil temperature profiles and depth of thaw in permafrost regions, J. Geophys. Res., 103, 975–991, 1998.
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, 2003.
Horton, P., Schaefli, B., Mezghani, A., Hingray, B., and Musy, A.: Assessment of climate-change impacts on alpine discharge regimes with climate model uncertainty, Hydrol. Process., 20, 2091–2109, 2006.
Houghton, H. G.: On the annual heat balance of the northern hemisphere, J. Meteorol., 11, 1–9, 1954.
Idso, S. B.: A set of equations for full spectrum and 8 to 14μm and 10.5 to 12.5 μm thermal radiation from cloudless skies, Water Resour. Res., 17, 295–304, 1981.
Iqbal, M.: An introduction to solar radiation, Academic Press, Toronto, 1983.
Ivanov, V. Y., Vivoni, E. R., Bras, R. L., and Entekhabi, D.: Catchment hydrologic response with a fully distributed triangulated irregular network model, Water Resour. Res., 40, 1–23, 2004.
Johansen, O.: Thermal conductivity of soils, Ph.D. thesis, Norwegian Technical University, Trondheim, 1975.
Jordan, R.: A one-dimensional temperature model for a snow cover: Technical documentation for SNTHERM 89, Tech. rep., US Army Cold Regions Research and Engineering Laboratory, Hanover, NH, USA, 1991.
Jordan, R., Andreas, E. L., and Makshtas, A. P.: Heat budget of snow-covered sea ice at North Pole 4, J. Geophys. Res., 104, 7785–7806, 1999.
Kelley, C.: Solving nonlinear equations with Newton's method, Society for Industrial Mathematics, 2003.
Kienzle, S. W.: A new temperature based method to separate rain and snow, Hydrol. Process., 22, 5067–5085, 2008.
Konzelmann, T., van de Wal, R. S. W., Greuell, W., Bintanja, R., Henneken, E. A. C., and Abe-Ouchi, A.: Parameterization of global and longwave incoming radiation for the Greenland Ice Sheet, Global Planet. Chang., 9, 143–164, 1994.
Koopmans, R. W. R. and Miller, R. D.: Soil freezing and soil water characteristic curves, Soil Sci. Soc. Am. J., 30, 680–685, 1966.
Kuchment, L. S., Gelfan, A. N., and Demidov, V. N.: A distributed model of runoff generation in the permafrost regions, J. Hydrol., 240, 1–22, 2000.
Kunstmann, H., Hingerl, L., Mauder, M., Wagner, S., and Rigon, R.: A combined water and energy flux observation and modelling study at the TERENO-preAlpine observatory, in Climate and Land-surface Changes in Hydrology, edited by: Kunstmann, H., Boegh, E., Blyth, E., Hannah, D. M., Hisdal, H., Su, B., and Yilmaz, K. K., Proceedings of H01, IAHS-IAPSO-IASPEI Assembly, Gothenburg, Sweden, July 2013, IAHS Publ. 359, 2013.
Kurylyk, B. and Watanabe, K.: The mathematical representation of freezing and thawing processes in variably-saturated, non-deformable soils, Adv. Water Resour., 60, 160–177, 2013.
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T., Stähli, M., and Zappa, M.: ALPINE3D: a detailed model of mountain surface processes and its application to snow hydrology, Hydrol. Process., 20, 2111–2128, 2006.
Lewis, C., Albertson, J., Zi, T., Xu, X., and Kiely, G.: How does afforestation affect the hydrology of a blanket peatland? A modelling study, Hydrol. Process., 27, 3577–3588, 2012.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, 1994.
Liston, G. E. and Elder, K.: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet), J. Hydrometeorol., 11, 217–234, 2006a.
Liston, G. E. and Elder, K.: A distributed snow-evolution modeling system (SnowModel), J. Hydrometeorol., 7, 1259–1276, 2006b.
Lunardini, V.: Heat transfer in cold climates, Van Nostrand Rheinhold, New York, 1981.
Marchenko, S., Romanovsky, V., and Tipenko, G.: Numerical modeling of spatial permafrost dynamics in Alaska, in: Proceedings of the Ninth International Conference on Permafrost, vol. 2, 1125–1130, 2008.
McDonald, J. E.: Direct absorption of solar radiation by atmospheric water vapor, J. Meteor., 17, 319–328, 1960.
McGurk, B., Azuma, D., and Kattelmann, R.: Density of new snow in the central Sierra Nevada, in: Proceedings 56th Western Snow Conference, 1988.
McKenzie, J., Voss, C., and Siegel, D.: Groundwater flow with energy transport and water–ice phase change: Numerical simulations, benchmarks, and application to freezing in peat bogs, Adv. Water Resour., 30, 966–983, 2007.
Meesters, A. G. C. A., Bink, N. J., Vugt, H. H., Cannemeijer, F., and Henneken, E. A. C.: Turbulence observations above a smooth melting surface on the Greenland ice sheet, Bound. Lay. Meteorol., 85, 81–110, 1997.
Meyers, T. and Dale, R.: Predicting daily insolation with hourly cloud height and coverage, J. Climate Appl. Meteorol., 22, 537–545, 1983.
Miller, R.: Phase equilibria and soil freezing, in: Permafrost: Proceedings of the Second International Conference. Washington DC: National Academy of Science-National Research Council, 287, 193–197, 1965.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst., 24, 163–187, 1954.
Morin, S., Lejeune, Y., Lesaffre, B., Panel, J.-M., Poncet, D., David, P., and Sudul, M.: An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models, Earth Syst. Sci. Data, 4, 13–21, https://doi.org/10.5194/essd-4-13-2012, 2012.
Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12, 513–522, 1976.
Niessner, H. and Reichert, K.: On computing the inverse of a sparse matrix, Int. J. Num. Methods Eng., 19, 1513–1526, 1983.
Oleson, K., Dai, Y., Bonan, G., Bosilovich, M., Dickinson, R., Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., Niu, G., Thornton, P., Vertenstein, M., Yang, Z., and Zeng, X.: Technical description of the Community Land Model (CLM), Tech. Rep. NCAR/TN-461+STR, National Center for Atmospheric Research, Boulder, CO, USA, 2004.
Painter, S. L.: Three-phase numerical model of water migration in partially frozen geological media: model formulation, validation, and applications, Comput. Geosci., 15, 69–85, 2011.
Panday, S. and Huyakorn, P.: A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow, Adv. Water Resour., 27, 361–-382, 2004.
Paniconi, C. and Putti, M.: A comparison of Picard and Newton iteration in the numerical solution of multidimensional variably saturated flow problems, Water Resour. Res., 30, 3357–3374, 1994.
Panoksky, H. A. and Dutton, J. A.: Atmospheric Turbulence Models and Methods for Engineering Applications, John Wiley and Sons, 1984.
PERMOS: Permafrost in Switzerland 2004/2005 and 2005/2006, in: Glaciological Report (Permafrost) No. 6/7 of the Cryospheric Commission of the Swiss Academy of Sciences, edited by: Nötzli, J. and Vonder Mühll, D., University of Zurich, 2009.
Pomeroy, J., Gray, D., and Landine, P.: The prairie blowing snow model: characteristics, validation, operation, J. Hydrol., 144, 165–192, 1993.
Prata, A. J.: A new long-wave formula for estimating downward clearsky radiation at the surface, Q. J. R. Meteorol. Soc., 122, 1127–1151, 1996.
Ray, C.: Some Numerical Experiments on the Variably-Saturated Flow Equation, in: Subsurface-Water Hydrology, edited by: Singh, V. and Kumar, B., vol. 16 of Water Science and Technology Library, Springer Netherlands, 49–63, 1996.
Rigon, R., Bertoldi, G., and Over, T. M.: GEOtop: a distributed hydrological model with coupled water and energy budgets, J. Hydrometeorol., 7, 371–388, 2006.
Ryan, B. C.: A mathematical model for diagnosis and prediction of surface winds in mountainous terrain, J. Appl. Meteor., 16, 571–584, 1977.
Satterlund, D. R.: An improved equation for estimating longwave radiation from the atmosphere, Water Resour. Res., 15, 1649–1650, 1979.
Shimizu, H.: Air permeability of deposited snow, vol. 22, Institute of Low Temperature Science, Sapporo, Japan, 1970.
Sicart, J. E., Pomeroy, J. W., Essery, R. L. H., and Bewley, D.: Incoming longwave radiation to melting snow: observations, sensitivity and estimation in northern environments, Hydrol. Process., 20, 3697–3708, 2006.
Simoni, S., Zanotti, F., Bertoldi, G., and Rigon, R.: Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS, Hydrol. Process., 22, 532–545, 2008.
Snyder, W. C., Wan, Z., Zhang, Y., and Feng, Y.-Z.: Classification-based emissivity for land surface temperature measurement from space, International J. Remote Sens., 19, 2753–2774, 1998.
Spaans, E. and Baker, J.: The soil freezing characteristic: its measurement and similarity to the soil moisture characteristic, Soil Sci. Soc. Am. J., 60, 13–19, 1996.
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal conductivity of seasonal snow, J. Glaciol., 43, 26–41, 1997.
Tarboton, D. G. and Luce, C. H.: Utah energy balance snow accumulation and melt model (UEB), Tech. rep., Utah Water Research Laboratory Utah State University and USDA Forest Service Intermountain Research Station, 1996.
Therrien, R. and Sudicky, E.: Three-dimensional analysis of variably-saturated flow and solute transport in discretely-fractured porous media, J. Contaminant Hydrol., 23, 1–44, 1996.
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of daily meteorological variables over large regions of complex terrain, J. Hydrol., 190, 215–251, 1997.
US Army Corps of Engineers: Snow hydrology, Summary report of the snow investigations, Tech. rep., US Army Corps of Engineers, North Pacific Division, Portland, OR, USA, 1956.
VanderKwaak, J. E. and Loague, K.: Hydrologic-Response simulations for the R-5 catchment with a comprehensive physics-based model, Water Resour. Res., 37, 999–1013, 2001.
Van Der Vorst, H. A.: BI-CGSTAB: A fast and smoothly converging variant of BI-CGSTAB for the solution of nonsymmetric linear systems, SIAM J. Sci. Stat. Comput., 13, 631–644, 1992.
Van Genuchten, M. T.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils., Soil Sci. Soc. Am. J., 44, 892–898, 1980.
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012.
Wagnon, P., Ribstein, P., Kaser, G., and Berton, P.: Energy balance and runoff seasonality of a Bolivian glacier, Global Planet. Chang., 22, 49–58, 1999.
Wettlaufer, J. S. and Worster, M. G.: Premelting dynamics, Ann. Rev. Fluid Mech., 38, 427–452, 2006.
Ye, Z. and Pielke, R. A.: Atmospheric parameterization of evaporation from non-plant-covered surfaces, J. Appl. Meteor., 32, 1248–1258, 1993.
Yen, Y.: Review of thermal properties of snow, ice and sea ice, Tech. Rep. 81–10, Cold Regions Research and Engineering Laboratory, Hanover, NH, USA, 1981.
Zanotti, F., Endrizzi, S., Bertoldi, G., and Rigon, R.: The GEOTOP snow module, Hydrol. Process., 18, 3667–3679, 2004.
Zehe, E., Maurer, T., Ihringer, J., and Plate, E.: Modeling water flow and mass transport in a loess catchment, Phys. Chem. Earth Pt B, 26, 487–507, 2001.
Zhang, Z., Kane, D. L., and Hinzman, L. D.: Development and application of a spatially-distributed Arctic hydrological and thermal process model (ARHYTHM), Hydrol. Process., 14, 1017–1044, 2000.
Short summary
GEOtop is a fine scale grid-based simulator that represents the heat and water budgets at and below the soil surface, reproduces the highly non-linear interactions between the water and energy balance during soil freezing and thawing and simulates snow cover. The core components of GEOtop 2.0. are described. Based on a synthetic simulation, it is shown that the interaction of processes represented in GEOtop 2.0. can result in phenomena that are relevant for applications involving frozen soils.
GEOtop is a fine scale grid-based simulator that represents the heat and water budgets at and...
