Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-239-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-239-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating snowpack model output for avalanche forecasting
Florian Herla
CORRESPONDING AUTHOR
Simon Fraser University, Burnaby, BC, Canada
Simon Horton
Simon Fraser University, Burnaby, BC, Canada
Avalanche Canada, Revelstoke, BC, Canada
Patrick Mair
Harvard University, Cambridge, MA, USA
Pascal Haegeli
Simon Fraser University, Burnaby, BC, Canada
Related authors
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair
EGUsphere, https://doi.org/10.5194/egusphere-2023-420, https://doi.org/10.5194/egusphere-2023-420, 2023
Short summary
Short summary
Snowpack simulations are increasingly employed by avalanche warning services to inform about critical avalanche layers buried in the snowpack. However, validity concerns limit their operational value. We present methods that enable meaningful comparisons between snowpack simulations and regional assessments of avalanche forecasters to quantify the performance of the Canadian weather and snowpack model chain to represent thin critical avalanche layers on a large scale and in real-time.
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
Short summary
Short summary
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.
John Sykes, Håvard Toft, Pascal Haegeli, and Grant Statham
Nat. Hazards Earth Syst. Sci., 24, 947–971, https://doi.org/10.5194/nhess-24-947-2024, https://doi.org/10.5194/nhess-24-947-2024, 2024
Short summary
Short summary
The research validates and optimizes an automated approach for creating classified snow avalanche terrain maps using open-source geospatial modeling tools. Validation is based on avalanche-expert-based maps for two study areas. Our results show that automated maps have an overall accuracy equivalent to the average accuracy of three human maps. Automated mapping requires a fraction of the time and cost of traditional methods and opens the door for large-scale mapping of mountainous terrain.
Abby Morgan, Pascal Haegeli, Henry Finn, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 23, 1719–1742, https://doi.org/10.5194/nhess-23-1719-2023, https://doi.org/10.5194/nhess-23-1719-2023, 2023
Short summary
Short summary
The avalanche danger scale is a critical component for communicating the severity of avalanche hazard conditions to the public. We examine how backcountry recreationists in North America understand and use the danger scale for planning trips into the backcountry. Our results provide an important user perspective on the strengths and weaknesses of the existing scale and highlight opportunities for future improvements.
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair
EGUsphere, https://doi.org/10.5194/egusphere-2023-420, https://doi.org/10.5194/egusphere-2023-420, 2023
Short summary
Short summary
Snowpack simulations are increasingly employed by avalanche warning services to inform about critical avalanche layers buried in the snowpack. However, validity concerns limit their operational value. We present methods that enable meaningful comparisons between snowpack simulations and regional assessments of avalanche forecasters to quantify the performance of the Canadian weather and snowpack model chain to represent thin critical avalanche layers on a large scale and in real-time.
John Sykes, Pascal Haegeli, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 22, 3247–3270, https://doi.org/10.5194/nhess-22-3247-2022, https://doi.org/10.5194/nhess-22-3247-2022, 2022
Short summary
Short summary
Automated snow avalanche terrain mapping provides an efficient method for large-scale assessment of avalanche hazards, which informs risk management decisions for transportation and recreation. This research reduces the cost of developing avalanche terrain maps by using satellite imagery and open-source software as well as improving performance in forested terrain. The research relies on local expertise to evaluate accuracy, so the methods are broadly applicable in mountainous regions worldwide.
Simon Horton and Pascal Haegeli
The Cryosphere, 16, 3393–3411, https://doi.org/10.5194/tc-16-3393-2022, https://doi.org/10.5194/tc-16-3393-2022, 2022
Short summary
Short summary
Snowpack models can help avalanche forecasters but are difficult to verify. We present a method for evaluating the accuracy of simulated snow profiles using readily available observations of snow depth. This method could be easily applied to understand the representativeness of available observations, the agreement between modelled and observed snow depths, and the implications for interpreting avalanche conditions.
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
Short summary
Short summary
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.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 22, 1973–2000, https://doi.org/10.5194/nhess-22-1973-2022, https://doi.org/10.5194/nhess-22-1973-2022, 2022
Short summary
Short summary
Avalanche bulletins include travel and terrain statements to provide recreationists with tangible guidance about how to apply the hazard information. We examined which bulletin users pay attention to these statements, what determines their usefulness, and how they could be improved. Our study shows that reducing jargon and adding simple explanations can significantly improve the usefulness of the statements for users with lower levels of avalanche awareness education who depend on this advice.
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 985–993, https://doi.org/10.5194/nhess-22-985-2022, https://doi.org/10.5194/nhess-22-985-2022, 2022
Short summary
Short summary
To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 21, 3219–3242, https://doi.org/10.5194/nhess-21-3219-2021, https://doi.org/10.5194/nhess-21-3219-2021, 2021
Short summary
Short summary
Avalanche warning services publish condition reports to help backcountry recreationists make informed decisions about when and where to travel in avalanche terrain. We tested how different graphic representations of terrain information can affect users’ ability to interpret and apply the provided information. Our study shows that a combined presentation of aspect and elevation information is the most effective. These results can be used to improve avalanche risk communication products.
Pascal Haegeli, Bret Shandro, and Patrick Mair
The Cryosphere, 15, 1567–1586, https://doi.org/10.5194/tc-15-1567-2021, https://doi.org/10.5194/tc-15-1567-2021, 2021
Short summary
Short summary
Numerous large-scale atmosphere–ocean oscillations including the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, the Pacific North American Teleconnection Pattern, and the Arctic Oscillation are known to substantially affect winter weather patterns in western Canada. Using avalanche problem information from public avalanche bulletins, this study presents a new approach for examining the effect of these atmospheric oscillations on the nature of avalanche hazard in western Canada.
Simon Horton, Moses Towell, and Pascal Haegeli
Nat. Hazards Earth Syst. Sci., 20, 3551–3576, https://doi.org/10.5194/nhess-20-3551-2020, https://doi.org/10.5194/nhess-20-3551-2020, 2020
Short summary
Short summary
We investigate patterns in how avalanche forecasters characterize snow avalanche hazard with avalanche problem types. Decision tree analysis was used to investigate both physical influences based on weather and on snowpack variables and operational practices. The results highlight challenges with developing decision aids based on previous hazard assessments.
Simon Horton, Stan Nowak, and Pascal Haegeli
Nat. Hazards Earth Syst. Sci., 20, 1557–1572, https://doi.org/10.5194/nhess-20-1557-2020, https://doi.org/10.5194/nhess-20-1557-2020, 2020
Short summary
Short summary
Numeric snowpack models currently offer limited value to operational avalanche forecasters. To improve the relevance and interpretability of model data, we introduce and discuss visualization principles that map model data into visual representations that can inform avalanche hazard assessments.
Reto Sterchi, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 19, 2011–2026, https://doi.org/10.5194/nhess-19-2011-2019, https://doi.org/10.5194/nhess-19-2011-2019, 2019
Short summary
Short summary
Mechanized skiing operations use an established process to select skiing terrain with a low risk level. However, the relationship between appropriate skiing terrain and avalanche conditions has only received limited research attention. Our study examines this relationship numerically for the first time and shows the effects of avalanche hazard, previous skiing, and previous acceptability on different types of skiing terrain and offers the foundation to develop evidence-based decision tools.
Reto Sterchi and Pascal Haegeli
Nat. Hazards Earth Syst. Sci., 19, 269–285, https://doi.org/10.5194/nhess-19-269-2019, https://doi.org/10.5194/nhess-19-269-2019, 2019
Short summary
Short summary
We used a revealed preference approach and identified patterns in risk management decisions of mechanized skiing operations. Our results show that terrain choices of experienced guides depend on a much broader set of factors beyond just the avalanche hazard, including skiing experience or accessibility due to weather. The identified high-resolution ski run hierarchies provide new opportunities for examining professional avalanche risk management practices and developing meaningful decision aids.
Bret Shandro and Pascal Haegeli
Nat. Hazards Earth Syst. Sci., 18, 1141–1158, https://doi.org/10.5194/nhess-18-1141-2018, https://doi.org/10.5194/nhess-18-1141-2018, 2018
Short summary
Short summary
While the concept of snow and avalanche climates is widely used to describe the general nature of avalanche hazard, no research has described the hazard character of avalanche climates in detail. We use Canadian avalanche bulletin data that use the conceptual model of avalanche hazard from 2009/2010 to 2016/2017 to identify common hazard situations and calculate their seasonal prevalence. Our results provide detailed insights into the nature and variability of avalanche hazard in western Canada.
S. Horton, M. Schirmer, and B. Jamieson
The Cryosphere, 9, 1523–1533, https://doi.org/10.5194/tc-9-1523-2015, https://doi.org/10.5194/tc-9-1523-2015, 2015
Short summary
Short summary
We investigate how various meteorological and terrain factors affect surface hoar formation in complex terrain. We modelled the distribution of three surface hoar layers with a coupled NWP - snow cover model, and verified the model with field studies. The layers developed in regions and elevation bands with warm moist air, light winds, and cold snow surfaces. Possible avalanche forecasting applications are discussed.
Related subject area
Cryosphere
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)
A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)
AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)
SnowClim v1.0: high-resolution snow model and data for the western United States
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes
Explicitly modelling microtopography in permafrost landscapes in a land surface model (JULES vn5.4_microtopography)
Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)
Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)
NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture
The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation
SNICAR-ADv3: a community tool for modeling spectral snow albedo
STEMMUS-UEB v1.0.0: integrated modeling of snowpack and soil water and energy transfer with three complexity levels of soil physical processes
A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0
Ice Algae Model Intercomparison Project phase 2 (IAMIP2)
A Gaussian process emulator for simulating ice sheet–climate interactions on a multi-million-year timescale: CLISEMv1.0
SITool (v1.0) – a new evaluation tool for large-scale sea ice simulations: application to CMIP6 OMIP
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models
Development of adjoint-based ocean state estimation for the Amundsen and Bellingshausen seas and ice shelf cavities using MITgcm–ECCO (66j)
Sensitivity of Northern Hemisphere climate to ice–ocean interface heat flux parameterizations
icepack: a new glacier flow modeling package in Python, version 1.0
Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3
Coupling framework (1.0) for the PISM (1.1.4) ice sheet model and the MOM5 (5.1.0) ocean model via the PICO ice shelf cavity model in an Antarctic domain
Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica
Assessment of numerical schemes for transient, finite-element ice flow models using ISSM v4.18
The Utrecht Finite Volume Ice-Sheet Model: UFEMISM (version 1.0)
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
Short summary
Short summary
In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
Short summary
Short summary
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
Short summary
Short summary
The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
Short summary
Short summary
We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
Short summary
Short summary
Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
Short summary
Short summary
Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Thiago Dias dos Santos, Mathieu Morlighem, and Hélène Seroussi
Geosci. Model Dev., 14, 2545–2573, https://doi.org/10.5194/gmd-14-2545-2021, https://doi.org/10.5194/gmd-14-2545-2021, 2021
Short summary
Short summary
Numerical models are routinely used to understand the past and future behavior of ice sheets in response to climate evolution. As is always the case with numerical modeling, one needs to minimize biases and numerical artifacts due to the choice of numerical scheme employed in such models. Here, we assess different numerical schemes in time-dependent simulations of ice sheets. We also introduce a new parameterization for the driving stress, the force that drives the ice sheet flow.
Constantijn J. Berends, Heiko Goelzer, and Roderik S. W. van de Wal
Geosci. Model Dev., 14, 2443–2470, https://doi.org/10.5194/gmd-14-2443-2021, https://doi.org/10.5194/gmd-14-2443-2021, 2021
Short summary
Short summary
The largest uncertainty in projections of sea-level rise comes from ice-sheet retreat. To better understand how these ice sheets respond to the changing climate, ice-sheet models are used, which must be able to reproduce both their present and past evolution. We have created a model that is fast enough to simulate an ice sheet at a high resolution over the course of an entire 120 000-year glacial cycle. This allows us to study processes that cannot be captured by lower-resolution models.
Cited articles
Bartelt, P., Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.:
A physical SNOWPACK model for the Swiss avalanche warning: Part I: Numerical
model, Cold Reg. Sci. Technol., 35, 123–145,
https://doi.org/10.1016/s0165-232x(02)00074-5, 2002. a
Bellaire, S. and Jamieson, J. B.: On estimating avalanche danger from
simulated snow profiles, in: Proceedings of the International Snow Science
Workshop, Grenoble–Chamonix Mont-Blanc, 7–11, 2013a. a
Bellaire, S. and Jamieson, J. B.: Forecasting the formation of critical snow
layers using a coupled snow cover and weather model, Cold Reg. Sci.
Technol., 94, 37–44, https://doi.org/10.1016/j.coldregions.2013.06.007,
2013b. a
Bellaire, S., Jamieson, J. B., and Fierz, C.: Forcing the snow-cover model SNOWPACK with forecasted weather data, The Cryosphere, 5, 1115–1125, https://doi.org/10.5194/tc-5-1115-2011, 2011. a
Berndt, D. J. and Clifford, J.: Using dynamic time warping to find patterns in
time series, in: KDD workshop, Seattle, WA, 10, 359–370, 1994. a
Brun, E., Martin, E., Simon, V., Gendre, C., and Coléou, C.: An energy
and mass model of snow cover suitable for operational avalanche forecasting,
J. Glaciol., 35, 333–342, https://doi.org/10.1017/S0022143000009254, 1989. a
Campbell, C., Conger, S., Gould, B., Haegeli, P., Jamieson, J. B., and Statham,
G.: Technical Aspects of Snow Avalanche Risk Management–Resources and
Guidelines for Avalanche Practitioners in Canada, Revelstoke, BC, Canada,
2016. a
Fierz, C.: Field observation and modelling of weak-layer evolution, Ann.
Glaciol., 26, 7–13, https://doi.org/10.3189/1998AoG26-1-7-13, 1998. a
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung,
D. M., Nishimura, K., Satyawali, P., and Sokratov, S. A.: The International
Classification for Seasonal Snow on the Ground, 5, UNESCO/IHP,
available at: https://unesdoc.unesco.org/ark:/48223/pf000018646 (last access: 7 January 2021), 2009. a, b
Fu, A. W.-C., Keogh, E. J., Lau, L. Y. H., Ratanamahatana, C. A., and Wong, R.
C.-W. W.: Scaling and time warping in time series querying, VLDB J., 17,
899–921, https://doi.org/10.1007/s00778-006-0040-z, 2007. a
Giorgino, T.: Computing and Visualizing Dynamic Time Warping Alignments in
R: The dtw Package, J. Stat. Softw., 31, 7, https://doi.org/10.18637/jss.v031.i07,
2009. a, b, c, d
Hagenmuller, P. and Pilloix, T.: A New Method for Comparing and Matching Snow
Profiles, Application for Profiles Measured by Penetrometers, Front. Earth
Sci., 4, 52, https://doi.org/10.3389/feart.2016.00052, 2016. a, b, c
Hagenmuller, P., van Herwijnen, A., Pielmeier, C., and Marshall, H.-P.:
Evaluation of the snow penetrometer Avatech SP2, Cold Reg. Sci. Technol.,
149, 83–94, https://doi.org/10.1016/j.coldregions.2018.02.006, 2018a. a
Hagenmuller, P., Viallon, L., Bouchayer, C., Teich, M., Lafaysse, M., and
Vionnet, V.: Quantitative Comparison of Snow Profiles, in: Proceedings of
the 2018 international snow science workshop, Innsbruck, AUT, 876–879,
available at: https://arc.lib.montana.edu/snow-science/item/2668 (last access: 7 January 2021),
2018b. a
Herla, F., Horton, S., Mair, P., and Haegeli, P.: Snow profile alignment and similarity assessment – Data and Code,
Open Science Framework (OSF), https://doi.org/10.17605/OSF.IO/9V8AD, 2020. a
Horton, S. and Jamieson, J. B.: Modelling hazardous surface hoar layers across
western Canada with a coupled weather and snow cover model, Cold Reg. Sci.
Technol., 128, 22–31, https://doi.org/10.1016/j.coldregions.2016.05.002, 2016. a
Horton, S., Bellaire, S., and Jamieson, J. B.: Modelling the formation of
surface hoar layers and tracking post-burial changes for avalanche
forecasting, Cold Reg. Sci. Technol., 97, 81–89,
https://doi.org/10.1016/j.coldregions.2013.06.012, 2014. a
Horton, S., Schirmer, M., and Jamieson, B.: Meteorological, elevation, and slope effects on surface hoar formation, The Cryosphere, 9, 1523–1533, https://doi.org/10.5194/tc-9-1523-2015, 2015. a
James, G., Witten, D., Hastie, T., and Tibshirani, R.: Statistical Learning, Springer Texts in Statistics, Springer New York, NY,
103, https://doi.org/10.1007/978-1-4614-7138-7, 2013. a, b
Jamieson, J. B.: Formation of refrozen snowpack layers and their role in slab
avalanche release, Rev. Geophys., 44, RG2001, https://doi.org/10.1029/2005RG000176,
2006. a, b
Keogh, E. J. and Ratanamahatana, C. A.: Exact indexing of dynamic time
warping, Knowl. Inf. Syst., 7, 358–386, https://doi.org/10.1007/s10115-004-0154-9,
2005. a, b
LaChapelle, E. R.: Avalanche Forecasting – A Modern Synthesis, in:
International Association of Scientific Hydrology, Publication, 69,
410–417, 1966. a
LaChapelle, E. R.: The fundamental processes in conventional avalanche
forecasting, J. Glaciol., 26, 75–84, https://doi.org/10.3189/s0022143000010601,
1980. a
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and
Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a
new network of weather and snow stations, Cold Reg. Sci. Technol., 30,
145–157, https://doi.org/10.1016/S0165-232X(99)00022-1, 1999. a
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK model
for the Swiss avalanche warning Part III: Meteorological forcing, thin layer
formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184,
https://doi.org/10.1016/S0165-232X(02)00072-1, 2002a. a
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A physical
SNOWPACK model for the Swiss avalanche warning Part II. Snow microstructure,
Cold Reg. Sci. Technol., 35, 147–167, https://doi.org/10.1016/S0165-232X(02)00073-3,
2002b. a
Lehning, M., Fierz, C., Brown, B., and Jamieson, J. B.: Modeling snow
instability with the snow-cover model SNOWPACK, Ann. Glaciol., 38, 331–338,
https://doi.org/10.3189/172756404781815220, 2004. a
Mair, P.: Modern Psychometrics with R, Use R!, Springer International
Publishing, Cham, ISBN: 978-3-319-93175-3, https://doi.org/10.1007/978-3-319-93177-7, 2018. a
McClung, D. M.: The Elements of Applied Avalanche Forecasting, Part II: The
Physical Issues and the Rules of Applied Avalanche Forecasting, Nat.
Hazards, 26, 131–146, https://doi.org/10.1023/a:1015604600361, 2002. a
McClung, D. M. and Schaerer, P.: The avalanche handbook, 3rd Edn., Mountaineers Books, Seattle, WA
ISBN: 978-0-89886-809-8,
2006. a
Monti, F., Schweizer, J., and Fierz, C.: Hardness estimation and weak layer
detection in simulated snow stratigraphy, Cold Reg. Sci. Technol., 103,
82–90, https://doi.org/10.1016/j.coldregions.2014.03.009, 2014a. a
Monti, F., Schweizer, J., Gaume, J., and Fierz, C.: Deriving snow stability
information from simulated snow cover stratigraphy, in: Proceedings of the
2014 international snow science workshop, Banff, AB, 465–469,
2014b. a
Morin, S., Fierz, C., Horton, S., Bavay, M., Dumont, M., Hagenmuller, P.,
Lafaysse, M., Mitterer, C., Monti, F., Olefs, M., Snook, J. S., Techel, F.,
Van Herwijnen, A., and Vionnet, V.: Application of physical snowpack
models in support of operational avalanche hazard forecasting: A status
report on current implementations and prospects for the future, Cold Reg.
Sci. Technol., 170, 1098–1107, https://doi.org/10.1016/J.COLDREGIONS.2019.102910,
2020. a
Petitjean, F., Ketterlin, A., and Gançarski, P.: A global averaging
method for dynamic time warping, with applications to clustering, Pattern
Recogn., 44, 678–693, https://doi.org/10.1016/j.patcog.2010.09.013, 2011. a, b, c
R Core Team: R: A Language and Environment for Statistical Computing,
available at: https://www.r-project.org/ (last access: 7 January 2021), 2020. a
Sakoe, H.: Dynamic-programming approach to continuous speech recognition, in:
1971 Proc. the International Congress of Acoustics, Budapest, 1971. a
Sakoe, H. and Chiba, S.: A similarity evaluation of speech patterns by dynamic
programming, in: Nat. Meeting of Institute of Electronic Communications
Engineers of Japan, p. 136, 1970. a
Sarda-Espinosa, A.: dtwclust: Time Series Clustering Along with
Optimizations for the Dynamic Time Warping Distance,
available at: https://cran.r-project.org/package=dtwclust (last access: 7 January 2021), 2019. a
Schaller, C. F., Freitag, J., Kipfstuhl, S., Laepple, T., Steen-Larsen, H. C., and Eisen, O.: A representative density profile of the North Greenland snowpack, The Cryosphere, 10, 1991–2002, https://doi.org/10.5194/tc-10-1991-2016, 2016. a
Schirmer, M., Lehning, M., and Schweizer, J.: Statistical forecasting of
regional avalanche danger using simulated snow-cover data, J. Glaciol., 55,
761–768, 2009. a
Schirmer, M., Schweizer, J., and Lehning, M.: Statistical evaluation of local
to regional snowpack stability using simulated snow-cover data, Cold Reg.
Sci. Technol., 64, 110–118, https://doi.org/10.1016/j.coldregions.2010.04.012, 2010. a
Schweizer, J. and Jamieson, J. B.: Snow cover properties for skier triggering
of avalanches, Cold Reg. Sci. Technol., 33, 207–221,
https://doi.org/10.1016/S0165-232X(01)00039-8, 2001. a
Schweizer, J. and Jamieson, J. B.: A threshold sum approach to stability
evaluation of manual snow profiles, Cold Reg. Sci. Technol., 47, 50–59,
https://doi.org/10.1016/j.coldregions.2006.08.011, 2007. a, b
Schweizer, J., Bellaire, S., Fierz, C., Lehning, M., and Pielmeier, C.:
Evaluating and improving the stability predictions of the snow cover model
SNOWPACK, Cold Reg. Sci. Technol., 46, 52–59,
https://doi.org/10.1016/j.coldregions.2006.05.007, 2006. a
Schweizer, J., Kronholm, K., Jamieson, J. B., and Birkeland, K. W.: Review of
spatial variability of snowpack properties and its importance for avalanche
formation, Cold Reg. Sci. Technol., 51, 253–272,
https://doi.org/10.1016/j.coldregions.2007.04.009, 2007. a, b
Statham, G., Haegeli, P., Greene, E., Birkeland, K. W., Israelson, C., Tremper,
B., Stethem, C., McMahon, B., White, B., and Kelly, J.: A conceptual model
of avalanche hazard, Nat. Hazards, 90, 663–691,
https://doi.org/10.1007/s11069-017-3070-5, 2018. a
Storm, I.: Public Avalanche Forecast Challenges: Canada's Large Data-Sparse
Regions, in: Proceedings, 2012 International Snow Science Workshop,
Anchorage, Alaska, 908–912, 2012. a
Teich, M., Giunta, A. D., Hagenmuller, P., Bebi, P., Schneebeli, M., and
Jenkins, M. J.: Effects of bark beetle attacks on forest snowpack and
avalanche formation – Implications for protection forest management, Forest
Ecol. Manage., 438, 186–203, https://doi.org/10.1016/j.foreco.2019.01.052, 2019. a
Tormene, P., Giorgino, T., Quaglini, S., and Stefanelli, M.: Matching
incomplete time series with dynamic time warping: an algorithm and an
application to post-stroke rehabilitation, Artif. Intell. Med., 45, 11–34,
https://doi.org/10.1016/j.artmed.2008.11.007, 2009.
a, b
Van Peursem, K., Hendrikx, J., Birkeland, K. W., Miller, D., and Gibson, C.:
Validation of a coupled weather and snowpack model across western montana,
in: Proceedings of the 2016 international snow science workshop,
Breckenridge, Montana, Breckenridge, CO, 2016. a
Viallon-Galinier, L., Hagenmuller, P., and Lafaysse, M.: Forcing and
evaluating detailed snow cover models with stratigraphy observations, Cold
Reg. Sci. Technol., 180, 103163, https://doi.org/10.1016/j.coldregions.2020.103163,
2020. a
Vick, S. G.: Degrees of belief: Subjective probability and engineering
judgment, 472 pp.,
ISBN: 978-0784405987, ASCE Publications, Reston, VA, USA,
2002. a
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a
Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Quéno, L., Seity,
Y., and Bazile, E.: Numerical Weather Forecasts at Kilometer Scale in the
French Alps: Evaluation and Application for Snowpack Modeling, J.
Hydrometeorol., 17, 2591–2614, https://doi.org/10.1175/jhm-d-15-0241.1, 2016. a
Vionnet, V., Guyomarc'h, G., Lafaysse, M., Naaim-Bouvet, F., Giraud, G., and
Deliot, Y.: Operational implementation and evaluation of a blowing snow
scheme for avalanche hazard forecasting, Cold Reg. Sci. Technol., 147,
1–10, https://doi.org/10.1016/j.coldregions.2017.12.006, 2018. a
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., and Keogh,
E. J.: Experimental comparison of representation methods and distance
measures for time series data, Data Min. Knowl. Disc., 26, 275–309,
https://doi.org/10.1007/s10618-012-0250-5, 2013. a, b
Short summary
The adoption of snowpack models in support of avalanche forecasting has been limited. To promote their operational application, we present a numerical method for processing multivariate snow stratigraphy profiles of mixed data types. Our algorithm enables applications like dynamical grouping and summarizing of model simulations, model evaluation, and data assimilation. By emulating the human analysis process, our approach will allow forecasters to familiarly interact with snowpack simulations.
The adoption of snowpack models in support of avalanche forecasting has been limited. To promote...