Articles | Volume 13, issue 11
Geosci. Model Dev., 13, 5645–5662, 2020
https://doi.org/10.5194/gmd-13-5645-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 5645–5662, 2020
https://doi.org/10.5194/gmd-13-5645-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper 18 Nov 2020
Model description paper | 18 Nov 2020
COSIPY v1.3 – an open-source coupled snowpack and ice surface energy and mass balance model
Tobias Sauter et al.
Related authors
Tobias Sauter
Hydrol. Earth Syst. Sci., 24, 2003–2016, https://doi.org/10.5194/hess-24-2003-2020, https://doi.org/10.5194/hess-24-2003-2020, 2020
Short summary
Short summary
Patagonia is thought to be one of the wettest – if not the wettest – places on Earth. The plausibility of these numbers has never been carefully scrutinized, despite the significance of this topic to our understanding of observed environmental changes, such as glacier recession. The revised precipitation values are significantly smaller than the previously reported values, thus opening up a new perspective on the Patagonian glaciers' response to climate change.
Tobias Sauter and Stephan Peter Galos
The Cryosphere, 10, 2887–2905, https://doi.org/10.5194/tc-10-2887-2016, https://doi.org/10.5194/tc-10-2887-2016, 2016
Short summary
Short summary
The paper deals with the micrometeorological conditions on mountain glaciers. We use idealized large-eddy simulations to study the heat transport associated with the local wind systems and its impact on the energy exchange between atmosphere and glaciers. Our results demonstrate how the sensible heat flux variablility on glaciers is related to topographic effects and that the energy surplus is strong enough to significantly increase the local glacier melting rates.
T. Sauter, M. Möller, R. Finkelnburg, M. Grabiec, D. Scherer, and C. Schneider
The Cryosphere, 7, 1287–1301, https://doi.org/10.5194/tc-7-1287-2013, https://doi.org/10.5194/tc-7-1287-2013, 2013
Guisella Gacitúa, Christoph Schneider, Jorge Arigony, Inti González, Ricardo Jaña, and Gino Casassa
Earth Syst. Sci. Data, 13, 231–236, https://doi.org/10.5194/essd-13-231-2021, https://doi.org/10.5194/essd-13-231-2021, 2021
Short summary
Short summary
We performed the first successful ice thickness measurements using terrestrial ground-penetrating radar in the ablation area of Schiaparelli Glacier (Cordillera Darwin, Tierra del Fuego, Chile). Data are fundamental to understand glaciers dynamics, constrain ice dynamical modelling, and predict glacier evolution. Results show a valley-shaped bedrock below current sea level; thus further retreat of Schiaparelli Glacier will probably lead to an enlarged and strongly over-deepened proglacial lake.
Mohamed H. Salim, Sebastian Schubert, Jaroslav Resler, Pavel Krč, Björn Maronga, Farah Kanani-Sühring, Matthias Sühring, and Christoph Schneider
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-94, https://doi.org/10.5194/gmd-2020-94, 2020
Preprint under review for GMD
Short summary
Short summary
Radiative transfer processes are the main energy transport mechanism in urban area which influence the surface energy budget and drive local convection. We show here the importance of each process to help modellers decide on how much detail they should include in their models to parameterise radiative transfer in urban areas. We showed how the flow field may change in response to these processes and the essential processes needed to assure acceptable quality of the numerical simulations.
Tobias Sauter
Hydrol. Earth Syst. Sci., 24, 2003–2016, https://doi.org/10.5194/hess-24-2003-2020, https://doi.org/10.5194/hess-24-2003-2020, 2020
Short summary
Short summary
Patagonia is thought to be one of the wettest – if not the wettest – places on Earth. The plausibility of these numbers has never been carefully scrutinized, despite the significance of this topic to our understanding of observed environmental changes, such as glacier recession. The revised precipitation values are significantly smaller than the previously reported values, thus opening up a new perspective on the Patagonian glaciers' response to climate change.
Kirstin Hoffmann, Francisco Fernandoy, Hanno Meyer, Elizabeth R. Thomas, Marcelo Aliaga, Dieter Tetzner, Johannes Freitag, Thomas Opel, Jorge Arigony-Neto, Christian Florian Göbel, Ricardo Jaña, Delia Rodríguez Oroz, Rebecca Tuckwell, Emily Ludlow, Joseph R. McConnell, and Christoph Schneider
The Cryosphere, 14, 881–904, https://doi.org/10.5194/tc-14-881-2020, https://doi.org/10.5194/tc-14-881-2020, 2020
Rebecca Möller, Marco Möller, Peter A. Kukla, and Christoph Schneider
Earth Syst. Sci. Data, 10, 53–60, https://doi.org/10.5194/essd-10-53-2018, https://doi.org/10.5194/essd-10-53-2018, 2018
Short summary
Short summary
Deposits of volcanic tephra alter the energy balance at the surface of a glacier. The effects reach from intensified melt to complete insulation, mainly depending on tephra thickness. Data from a field experiment on Iceland reveal an additional minor dependency on tephra type and suggest a substantially different behavior of tephra-covered snowpacks than of tephra-covered glacier ice. The related 50-day dataset of hourly records can readily be used for model calibration and validation purposes.
Tobias Sauter and Stephan Peter Galos
The Cryosphere, 10, 2887–2905, https://doi.org/10.5194/tc-10-2887-2016, https://doi.org/10.5194/tc-10-2887-2016, 2016
Short summary
Short summary
The paper deals with the micrometeorological conditions on mountain glaciers. We use idealized large-eddy simulations to study the heat transport associated with the local wind systems and its impact on the energy exchange between atmosphere and glaciers. Our results demonstrate how the sensible heat flux variablility on glaciers is related to topographic effects and that the energy surplus is strong enough to significantly increase the local glacier melting rates.
J. Kropáček, N. Neckel, B. Tyrna, N. Holzer, A. Hovden, N. Gourmelen, C. Schneider, M. Buchroithner, and V. Hochschild
Nat. Hazards Earth Syst. Sci., 15, 2425–2437, https://doi.org/10.5194/nhess-15-2425-2015, https://doi.org/10.5194/nhess-15-2425-2015, 2015
Short summary
Short summary
The supraglacial lake basin was mapped by DGPS and the SFM approach from terrestrial photographs. The maximum filling capacity of the lake was estimated, with a maximum discharge of 77.8 m3/s, calculated using an empirical relation. The flooded area in the valley was delineated by employing a raster-based hydraulic model. A coincidence of the GLOF events with high values of cumulative above-zero temperature and precipitation calculated from the HAR data set was revealed.
T. Sauter, M. Möller, R. Finkelnburg, M. Grabiec, D. Scherer, and C. Schneider
The Cryosphere, 7, 1287–1301, https://doi.org/10.5194/tc-7-1287-2013, https://doi.org/10.5194/tc-7-1287-2013, 2013
Related subject area
Cryosphere
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)
PERICLIMv1.0: a model deriving palaeo-air temperatures from thaw depth in past permafrost regions
Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau
CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework
A fully coupled Arctic sea-ice–ocean–atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description and preliminary results
The Framework For Ice Sheet–Ocean Coupling (FISOC) V1.1
Comparison of sea ice kinematics at different resolutions modeled with a grid hierarchy in the Community Earth System Model (version 1.2.1)
Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating snowpack model output for avalanche forecasting
Improvements in one-dimensional grounding-line parameterizations in an ice-sheet model with lateral variations (PSUICE3D v2.1)
Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adelie Land, East Antarctica
Implementation of the RCIP scheme and its performance for 1-D age computations in ice-sheet models
Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model
Simulating the Early Holocene demise of the Laurentide Ice Sheet with BISICLES (public trunk revision 3298)
Extended enthalpy formulations in the Ice-sheet and Sea-level System Model (ISSM) version 4.17: discontinuous conductivity and anisotropic streamline upwind Petrov–Galerkin (SUPG) method
The Community Firn Model (CFM) v1.0
Coupling framework (1.0) for the ice sheet model PISM (1.1.1) and the ocean model MOM5 (5.1.0) via the ice-shelf cavity module PICO
Description and validation of the ice-sheet model Yelmo (version 1.0)
Evaluating integrated surface/subsurface permafrost thermal hydrology models in ATS (v0.88) against observations from a polygonal tundra site
SICOPOLIS-AD v1: an open-source adjoint modeling framework for ice sheet simulation enabled by the algorithmic differentiation tool OpenAD
On the calculation of normalized viscous–plastic sea ice stresses
Modelling thermomechanical ice deformation using an implicit pseudo-transient method (FastICE v1.0) based on graphical processing units (GPUs)
Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model
A module to convert spectral to narrowband snow albedo for use in climate models: SNOWBAL v1.2
On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model
Scientific workflows applied to the coupling of a continuum (Elmer v8.3) and a discrete element (HiDEM v1.0) ice dynamic model
A rapidly converging initialisation method to simulate the present-day Greenland ice sheet using the GRISLI ice sheet model (version 1.3)
Assessment of sub-shelf melting parameterisations using the ocean–ice-sheet coupled model NEMO(v3.6)–Elmer/Ice(v8.3)
CSIB v1 (Canadian Sea-ice Biogeochemistry): a sea-ice biogeochemical model for the NEMO community ocean modelling framework
LIVVkit 2.1: automated and extensible ice sheet model validation
ATAT 1.1, the Automated Timing Accordance Tool for comparing ice-sheet model output with geochronological data
The Open Global Glacier Model (OGGM) v1.1
Description and evaluation of the Community Ice Sheet Model (CISM) v2.1
Implementation and performance of adaptive mesh refinement in the Ice Sheet System Model (ISSM v4.14)
A continuum model (PSUMEL1) of ice mélange and its role during retreat of the Antarctic Ice Sheet
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
The GRISLI ice sheet model (version 2.0): calibration and validation for multi-millennial changes of the Antarctic ice sheet
CVPM 1.1: a flexible heat-transfer modeling system for permafrost
Dynamically coupling full Stokes and shallow shelf approximation for marine ice sheet flow using Elmer/Ice (v8.3)
The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis
MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids
BrAHMs V1.0: a fast, physically based subglacial hydrology model for continental-scale application
SHAKTI: Subglacial Hydrology and Kinetic, Transient Interactions v1.0
SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)
PIC v1.3: comprehensive R package for computing permafrost indices with daily weather observations and atmospheric forcing over the Qinghai–Tibet Plateau
Numerical experiments on vapor diffusion in polar snow and firn and its impact on isotopes using the multi-layer energy balance model Crocus in SURFEX v8.0
Implementation of higher-order vertical finite elements in ISSM v4.13 for improved ice sheet flow modeling over paleoclimate timescales
Intercomparison of Antarctic ice-shelf, ocean, and sea-ice interactions simulated by MetROMS-iceshelf and FESOM 1.4
The sea ice model component of HadGEM3-GC3.1
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.
Tomáš Uxa, Marek Křížek, and Filip Hrbáček
Geosci. Model Dev., 14, 1865–1884, https://doi.org/10.5194/gmd-14-1865-2021, https://doi.org/10.5194/gmd-14-1865-2021, 2021
Short summary
Short summary
We present a simple model that derives palaeo-air temperature characteristics related to the palaeo-active-layer thickness, which can be recognized using many relict periglacial features found in past permafrost regions. Its evaluation against modern temperature records and an experimental palaeo-air temperature reconstruction showed relatively high model accuracy, which suggests that it could become a useful tool for reconstructing Quaternary palaeo-environments.
Xiangfei Li, Tonghua Wu, Xiaodong Wu, Jie Chen, Xiaofan Zhu, Guojie Hu, Ren Li, Yongping Qiao, Cheng Yang, Junming Hao, Jie Ni, and Wensi Ma
Geosci. Model Dev., 14, 1753–1771, https://doi.org/10.5194/gmd-14-1753-2021, https://doi.org/10.5194/gmd-14-1753-2021, 2021
Short summary
Short summary
In this study, an ensemble simulation of 55296 scheme combinations for at a typical permafrost site on the Qinghai–Tibet Plateau (QTP) was conducted. The general performance of the Noah-MP model for snow cover events (SCEs), soil temperature (ST) and soil liquid water content (SLW) was assessed, and the sensitivities of parameterization schemes at different depths were investigated. We show that Noah-MP tends to overestimate SCEs and underestimate ST and topsoil SLW on the QTP.
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
Short summary
Short summary
In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Shihe Ren, Xi Liang, Qizhen Sun, Hao Yu, L. Bruno Tremblay, Bo Lin, Xiaoping Mai, Fu Zhao, Ming Li, Na Liu, Zhikun Chen, and Yunfei Zhang
Geosci. Model Dev., 14, 1101–1124, https://doi.org/10.5194/gmd-14-1101-2021, https://doi.org/10.5194/gmd-14-1101-2021, 2021
Short summary
Short summary
Sea ice plays a crucial role in global energy and water budgets. To get a better simulation of sea ice, we coupled a sea ice model with an atmospheric and ocean model to form a fully coupled system. The sea ice simulation results of this coupled system demonstrated that a two-way coupled model has better performance in terms of sea ice, especially in summer. This indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.
Rupert Gladstone, Benjamin Galton-Fenzi, David Gwyther, Qin Zhou, Tore Hattermann, Chen Zhao, Lenneke Jong, Yuwei Xia, Xiaoran Guo, Konstantinos Petrakopoulos, Thomas Zwinger, Daniel Shapero, and John Moore
Geosci. Model Dev., 14, 889–905, https://doi.org/10.5194/gmd-14-889-2021, https://doi.org/10.5194/gmd-14-889-2021, 2021
Short summary
Short summary
Retreat of the Antarctic ice sheet, and hence its contribution to sea level rise, is highly sensitive to melting of its floating ice shelves. This melt is caused by warm ocean currents coming into contact with the ice. Computer models used for future ice sheet projections are not able to realistically evolve these melt rates. We describe a new coupling framework to enable ice sheet and ocean computer models to interact, allowing projection of the evolution of melt and its impact on sea level.
Shiming Xu, Jialiang Ma, Lu Zhou, Yan Zhang, Jiping Liu, and Bin Wang
Geosci. Model Dev., 14, 603–628, https://doi.org/10.5194/gmd-14-603-2021, https://doi.org/10.5194/gmd-14-603-2021, 2021
Short summary
Short summary
A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version 1.2.1). The resolution range includes 0.45, 0.15, and 0.05°. Based on atmospherically forced sea ice experiments, the model simulates reasonable sea ice kinematics and scaling properties. Landfast ice thickness can also be systematically shifted due to non-convergent solutions to an
elastic–viscous–plastic (EVP) model. This work is a framework for multi-scale modeling of the ocean and sea ice with CESM.
Florian Herla, Simon Horton, Patrick Mair, and Pascal Haegeli
Geosci. Model Dev., 14, 239–258, https://doi.org/10.5194/gmd-14-239-2021, https://doi.org/10.5194/gmd-14-239-2021, 2021
Short summary
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.
David Pollard and Robert M. DeConto
Geosci. Model Dev., 13, 6481–6500, https://doi.org/10.5194/gmd-13-6481-2020, https://doi.org/10.5194/gmd-13-6481-2020, 2020
Short summary
Short summary
Buttressing by floating ice shelves at ice-sheet grounding lines is an
important process that affects ice retreat and whether structural failure
occurs in deep bathymetry. Here, we use a simple algorithm to better
represent 2-D grounding-line curvature in an ice-sheet model. Along with other
enhancements, this improves the performance in idealized-fjord intercomparisons
and enables better diagnosis of potential structural failure at future
retreating Antarctic grounding lines.
Charles Amory, Christoph Kittel, Louis Le Toumelin, Cécile Agosta, Alison Delhasse, Vincent Favier, and Xavier Fettweis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-368, https://doi.org/10.5194/gmd-2020-368, 2020
Revised manuscript accepted for GMD
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 Adelie Land in East Antarctica. The model is extensively described and evaluated against a multi-year drifting-snow dataset and SMB estimates available in the area. The model sensitivity to input parameters and improvements over a previously published version are also assessed.
Fuyuki Saito, Takashi Obase, and Ayako Abe-Ouchi
Geosci. Model Dev., 13, 5875–5896, https://doi.org/10.5194/gmd-13-5875-2020, https://doi.org/10.5194/gmd-13-5875-2020, 2020
Short summary
Short summary
The present study introduces the rational function-based constrained interpolation profile (RCIP) method for use in 1 d dating computations in ice sheets and demonstrates the performance of the scheme. Comparisons are examined among the RCIP schemes and the first- and second-order upwind schemes. The results show that, in particular, the RCIP scheme preserves the pattern of input histories, in terms of the profile of internal annual layer thickness, better than the other schemes.
Alex West, Mat Collins, and Ed Blockley
Geosci. Model Dev., 13, 4845–4868, https://doi.org/10.5194/gmd-13-4845-2020, https://doi.org/10.5194/gmd-13-4845-2020, 2020
Short summary
Short summary
This study calculates sea ice energy fluxes from data produced by ice mass balance buoys (devices measuring ice elevation and temperature). It is shown how the resulting dataset can be used to evaluate a coupled climate model (HadGEM2-ES), with biases in the energy fluxes seen to be consistent with biases in the sea ice state and surface radiation. This method has potential to improve sea ice model evaluation, so as to better understand spread in model simulations of sea ice state.
Eduardo Moreno-Chamarro, Pablo Ortega, and François Massonnet
Geosci. Model Dev., 13, 4773–4787, https://doi.org/10.5194/gmd-13-4773-2020, https://doi.org/10.5194/gmd-13-4773-2020, 2020
Short summary
Short summary
Climate models need to capture sea ice complexity to represent it realistically. Here we assess how distributing sea ice in discrete thickness categories impacts how sea ice variability is simulated in the NEMO3.6–LIM3 model. Simulations and satellite observations are compared by using k-means clustering of sea ice concentration in winter and summer between 1979 and 2014 at both poles. Little improvements in the modeled sea ice lead us to recommend using the standard number of five categories.
Ilkka S. O. Matero, Lauren J. Gregoire, and Ruza F. Ivanovic
Geosci. Model Dev., 13, 4555–4577, https://doi.org/10.5194/gmd-13-4555-2020, https://doi.org/10.5194/gmd-13-4555-2020, 2020
Short summary
Short summary
The Northern Hemisphere cooled by several degrees for a century 8000 years ago due to the collapse of an ice sheet in North America that released large amounts of meltwater into the North Atlantic and slowed down its circulation. We numerically model the ice sheet to understand its evolution during this event. Our results match data thanks to good ice dynamics but depend mostly on surface melt and snowfall. Further work will help us understand how past and future ice melt affects climate.
Martin Rückamp, Angelika Humbert, Thomas Kleiner, Mathieu Morlighem, and Helene Seroussi
Geosci. Model Dev., 13, 4491–4501, https://doi.org/10.5194/gmd-13-4491-2020, https://doi.org/10.5194/gmd-13-4491-2020, 2020
Short summary
Short summary
We present enthalpy formulations within the Ice-Sheet and Sea-Level System model that show better performance than earlier implementations. A first experiment indicates that the treatment of discontinuous conductivities of the solid–fluid system with a geometric mean produce accurate results when applied to coarse vertical resolutions. In a second experiment, we propose a novel stabilization formulation that avoids the problem of thin elements. This method provides accurate and stable results.
C. Max Stevens, Vincent Verjans, Jessica M. D. Lundin, Emma C. Kahle, Annika N. Horlings, Brita I. Horlings, and Edwin D. Waddington
Geosci. Model Dev., 13, 4355–4377, https://doi.org/10.5194/gmd-13-4355-2020, https://doi.org/10.5194/gmd-13-4355-2020, 2020
Short summary
Short summary
Understanding processes in snow (firn), including compaction and airflow, is important for calculating how much mass the ice sheets are losing and for interpreting climate records from ice cores. We have developed the open-source Community Firn Model to simulate these processes. We used it to compare 13 different firn compaction equations and found that they do not agree within 10 %. We also show that including firn compaction in a firn-air model improves the match with data from ice cores.
Moritz Kreuzer, Ronja Reese, Willem Nicholas Huiskamp, Stefan Petri, Torsten Albrecht, Georg Feulner, and Ricarda Winkelmann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-230, https://doi.org/10.5194/gmd-2020-230, 2020
Revised manuscript accepted for GMD
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 to simulate ice-ocean interactions at time scales from centuries to millennia. Since ice-shelf cavities cannot be resolved in the ocean model at coarse resolution, we bridge the gap using an sub-shelf cavity module. The manuscript describes the coupling procedure between the models, shows its computational efficiency, as well as that mass and energy are conserved.
Alexander Robinson, Jorge Alvarez-Solas, Marisa Montoya, Heiko Goelzer, Ralf Greve, and Catherine Ritz
Geosci. Model Dev., 13, 2805–2823, https://doi.org/10.5194/gmd-13-2805-2020, https://doi.org/10.5194/gmd-13-2805-2020, 2020
Short summary
Short summary
Here we describe Yelmo v1.0, an intuitive and state-of-the-art hybrid ice sheet model. The model design and physics are described, and benchmark simulations are provided to validate its performance. Yelmo is a versatile ice sheet model that can be applied to a wide variety of problems.
Ahmad Jan, Ethan T. Coon, and Scott L. Painter
Geosci. Model Dev., 13, 2259–2276, https://doi.org/10.5194/gmd-13-2259-2020, https://doi.org/10.5194/gmd-13-2259-2020, 2020
Short summary
Short summary
Computer simulations are important tools for understanding the response of Arctic permafrost to a warming climate. To build confidence in an emerging class of permafrost simulators, we evaluated the Advanced Terrestrial Simulator against field observations from a frozen tundra site near Utqiaġvik (formerly Barrow), Alaska. The 3-year simulations agree well with observations of snow depth, summer water table, soil temperature at multiple locations, and spatially averaged evaporation.
Liz C. Logan, Sri Hari Krishna Narayanan, Ralf Greve, and Patrick Heimbach
Geosci. Model Dev., 13, 1845–1864, https://doi.org/10.5194/gmd-13-1845-2020, https://doi.org/10.5194/gmd-13-1845-2020, 2020
Short summary
Short summary
A new capability has been developed for the ice sheet model SICOPOLIS (SImulation COde for POLythermal Ice Sheets) that enables the generation of derivative code, such as tangent linear or adjoint models, by means of algorithmic differentiation. It relies on the source transformation algorithmic (AD) differentiation tool OpenAD. The reverse mode of AD provides the adjoint model, SICOPOLIS-AD, which may be applied for comprehensive sensitivity analyses as well as gradient-based optimization.
Jean-François Lemieux and Frédéric Dupont
Geosci. Model Dev., 13, 1763–1769, https://doi.org/10.5194/gmd-13-1763-2020, https://doi.org/10.5194/gmd-13-1763-2020, 2020
Short summary
Short summary
Sea ice dynamics plays an important role in shaping the sea cover in polar regions. Winds and ocean currents exert large stresses on the sea ice cover. This can lead to the formation of long cracks and ridges, which strongly impact the exchange of heat, momentum and moisture between the atmosphere and the ocean. It is therefore crucial for a sea ice model to be able to represent these features. This article describes how internal sea ice stresses should be diagnosed from model simulations.
Ludovic Räss, Aleksandar Licul, Frédéric Herman, Yury Y. Podladchikov, and Jenny Suckale
Geosci. Model Dev., 13, 955–976, https://doi.org/10.5194/gmd-13-955-2020, https://doi.org/10.5194/gmd-13-955-2020, 2020
Short summary
Short summary
Accurate predictions of future sea level rise require numerical models that predict rapidly deforming ice. Localised ice deformation can be captured numerically only with high temporal and spatial resolution. This paper’s goal is to propose a parallel FastICE solver for modelling ice deformation. Our model is particularly useful for improving our process-based understanding of localised ice deformation. Our solver reaches a parallel efficiency of 99 % on GPU-based supercomputers.
Nander Wever, Leonard Rossmann, Nina Maaß, Katherine C. Leonard, Lars Kaleschke, Marcel Nicolaus, and Michael Lehning
Geosci. Model Dev., 13, 99–119, https://doi.org/10.5194/gmd-13-99-2020, https://doi.org/10.5194/gmd-13-99-2020, 2020
Short summary
Short summary
Sea ice is an important component of the global climate system. The presence of a snow layer covering sea ice can impact ice mass and energy budgets. The detailed, physics-based, multi-layer snow model SNOWPACK was modified to simulate the snow–sea-ice system, providing simulations of the snow microstructure, water percolation and flooding, and superimposed ice formation. The model is applied to in situ measurements from snow and ice mass-balance buoys installed in the Antarctic Weddell Sea.
Christiaan T. van Dalum, Willem Jan van de Berg, Quentin Libois, Ghislain Picard, and Michiel R. van den Broeke
Geosci. Model Dev., 12, 5157–5175, https://doi.org/10.5194/gmd-12-5157-2019, https://doi.org/10.5194/gmd-12-5157-2019, 2019
Short summary
Short summary
Climate models are often limited to relatively simple snow albedo schemes. Therefore, we have developed the SNOWBAL module to couple a climate model with a physically based wavelength dependent snow albedo model. Using SNOWBAL v1.2 to couple the snow albedo model TARTES with the regional climate model RACMO2 indicates a potential performance gain for the Greenland ice sheet.
François Massonnet, Antoine Barthélemy, Koffi Worou, Thierry Fichefet, Martin Vancoppenolle, Clément Rousset, and Eduardo Moreno-Chamarro
Geosci. Model Dev., 12, 3745–3758, https://doi.org/10.5194/gmd-12-3745-2019, https://doi.org/10.5194/gmd-12-3745-2019, 2019
Short summary
Short summary
Sea ice thickness varies considerably on spatial scales of several meters. However, contemporary climate models cannot resolve such scales yet. This is why sea ice models used in climate models include an ice thickness distribution (ITD) to account for this unresolved variability. Here, we explore with the ocean–sea ice model NEMO3.6-LIM3 the sensitivity of simulated mean Arctic and Antarctic sea ice states to the way the ITD is discretized.
Shahbaz Memon, Dorothée Vallot, Thomas Zwinger, Jan Åström, Helmut Neukirchen, Morris Riedel, and Matthias Book
Geosci. Model Dev., 12, 3001–3015, https://doi.org/10.5194/gmd-12-3001-2019, https://doi.org/10.5194/gmd-12-3001-2019, 2019
Short summary
Short summary
Scientific workflows enable complex scientific computational scenarios, which include data intensive scenarios, parametric executions, and interactive simulations. In this article, we applied the UNICORE workflow management system to automate a formerly hard-coded coupling of a glacier flow model and a calving model, which contain many tasks and dependencies, ranging from pre-processing and data management to repetitive executions on heterogeneous high-performance computing (HPC) resources.
Sébastien Le clec'h, Aurélien Quiquet, Sylvie Charbit, Christophe Dumas, Masa Kageyama, and Catherine Ritz
Geosci. Model Dev., 12, 2481–2499, https://doi.org/10.5194/gmd-12-2481-2019, https://doi.org/10.5194/gmd-12-2481-2019, 2019
Short summary
Short summary
To provide reliable projections of the ice-sheet contribution to future sea-level rise, ice sheet models must be able to simulate the observed ice sheet present-day state. Using a low computational iterative minimisation procedure, based on the adjustment of the basal drag coefficient, we rapidly minimise the errors between the simulated and the observed Greenland ice thickness and ice velocity, and we succeed in stabilising the simulated Greenland ice sheet state under present-day conditions.
Lionel Favier, Nicolas C. Jourdain, Adrian Jenkins, Nacho Merino, Gaël Durand, Olivier Gagliardini, Fabien Gillet-Chaulet, and Pierre Mathiot
Geosci. Model Dev., 12, 2255–2283, https://doi.org/10.5194/gmd-12-2255-2019, https://doi.org/10.5194/gmd-12-2255-2019, 2019
Short summary
Short summary
The melting at the base of floating ice shelves is the main driver of the Antarctic ice sheet current retreat. Here, we use an ideal set-up to assess a wide range of melting parameterisations depending on oceanic properties with regard to a new ocean–ice-sheet coupled model, published here for the first time. A parameterisation that depends quadratically on thermal forcing in both a local and a non-local way yields the best results and needs to be further assessed with more realistic set-ups.
Hakase Hayashida, James R. Christian, Amber M. Holdsworth, Xianmin Hu, Adam H. Monahan, Eric Mortenson, Paul G. Myers, Olivier G. J. Riche, Tessa Sou, and Nadja S. Steiner
Geosci. Model Dev., 12, 1965–1990, https://doi.org/10.5194/gmd-12-1965-2019, https://doi.org/10.5194/gmd-12-1965-2019, 2019
Short summary
Short summary
Ice algae, the primary producer in sea ice, play a fundamental role in shaping marine ecosystems and biogeochemical cycling of key elements in polar regions. In this study, we developed a process-based numerical model component representing sea-ice biogeochemistry for a sea ice–ocean coupled general circulation model. The model developed can be used to simulate the projected changes in sea-ice ecosystems and biogeochemistry in response to on-going rapid decline of the Arctic.
Katherine J. Evans, Joseph H. Kennedy, Dan Lu, Mary M. Forrester, Stephen Price, Jeremy Fyke, Andrew R. Bennett, Matthew J. Hoffman, Irina Tezaur, Charles S. Zender, and Miren Vizcaíno
Geosci. Model Dev., 12, 1067–1086, https://doi.org/10.5194/gmd-12-1067-2019, https://doi.org/10.5194/gmd-12-1067-2019, 2019
Short summary
Short summary
A robust validation of ice sheet models is presented using LIVVkit, version 2.1. It targets ice sheet and coupled Earth system models, and handles datasets and operations that require high-performance computing and storage. We apply LIVVkit to a Greenland ice sheet simulation to show the degree to which it captures the surface mass balance. LIVVkit identifies a positive bias due to insufficient melting compared to observations that is focused largely around Greenland's southwest region.
Jeremy C. Ely, Chris D. Clark, David Small, and Richard C. A. Hindmarsh
Geosci. Model Dev., 12, 933–953, https://doi.org/10.5194/gmd-12-933-2019, https://doi.org/10.5194/gmd-12-933-2019, 2019
Short summary
Short summary
During the last 2.6 million years, the Earth's climate has cycled between cold glacials and warm interglacials, causing the growth and retreat of ice sheets. These ice sheets can be independently reconstructed using numerical models or from dated evidence that they leave behind (e.g. sediments, boulders). Here, we present a tool for comparing numerical model simulations with dated ice-sheet material. We demonstrate the utility of this tool by applying it to the last British–Irish ice sheet.
Fabien Maussion, Anton Butenko, Nicolas Champollion, Matthias Dusch, Julia Eis, Kévin Fourteau, Philipp Gregor, Alexander H. Jarosch, Johannes Landmann, Felix Oesterle, Beatriz Recinos, Timo Rothenpieler, Anouk Vlug, Christian T. Wild, and Ben Marzeion
Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, https://doi.org/10.5194/gmd-12-909-2019, 2019
Short summary
Short summary
Mountain glaciers are one of the few remaining subsystems of the global climate system for which no globally applicable community-driven model exists. Here we present the Open Global Glacier Model (OGGM; www.oggm.org), developed to provide a modular and open-source numerical model framework for simulating past and future change of any glacier in the world.
William H. Lipscomb, Stephen F. Price, Matthew J. Hoffman, Gunter R. Leguy, Andrew R. Bennett, Sarah L. Bradley, Katherine J. Evans, Jeremy G. Fyke, Joseph H. Kennedy, Mauro Perego, Douglas M. Ranken, William J. Sacks, Andrew G. Salinger, Lauren J. Vargo, and Patrick H. Worley
Geosci. Model Dev., 12, 387–424, https://doi.org/10.5194/gmd-12-387-2019, https://doi.org/10.5194/gmd-12-387-2019, 2019
Short summary
Short summary
This paper describes the Community Ice Sheet Model (CISM) version 2.1. CISM solves equations for ice flow, heat conduction, surface melting, and other processes such as basal sliding and iceberg calving. It can be used for ice-sheet-only simulations or as the ice sheet component of the Community Earth System Model. Model solutions have been verified for standard test problems. CISM can efficiently simulate the whole Greenland ice sheet, with results that are broadly consistent with observations.
Thiago Dias dos Santos, Mathieu Morlighem, Hélène Seroussi, Philippe Remy Bernard Devloo, and Jefferson Cardia Simões
Geosci. Model Dev., 12, 215–232, https://doi.org/10.5194/gmd-12-215-2019, https://doi.org/10.5194/gmd-12-215-2019, 2019
Short summary
Short summary
The reduction of numerical errors in ice sheet modeling increases the results' accuracy reliability. We improve numerical accuracy by better capturing grounding line dynamics, while maintaining a low computational cost. We implement an adaptive mesh refinement (AMR) technique in the Ice Sheet System Model and compare AMR simulations with uniformly refined meshes. Our results show that the computational time with AMR is significantly shorter than for uniformly refined meshes for a given accuracy.
David Pollard, Robert M. DeConto, and Richard B. Alley
Geosci. Model Dev., 11, 5149–5172, https://doi.org/10.5194/gmd-11-5149-2018, https://doi.org/10.5194/gmd-11-5149-2018, 2018
Short summary
Short summary
Around the margins of ice sheets in contact with the ocean, calving of icebergs can generate large amounts of floating ice debris called "mélange". In major Greenland fjords, mélange significantly slows down ice flow from upstream. Our study applies numerical models to past and possible future episodes of rapid Antarctic Ice Sheet retreat. We find that, due to larger spatial scales, Antarctic mélange does not significantly impede flow or slow ice retreat and associated sea level rise.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
Short summary
Short summary
This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Aurélien Quiquet, Christophe Dumas, Catherine Ritz, Vincent Peyaud, and Didier M. Roche
Geosci. Model Dev., 11, 5003–5025, https://doi.org/10.5194/gmd-11-5003-2018, https://doi.org/10.5194/gmd-11-5003-2018, 2018
Short summary
Short summary
This paper presents the GRISLI (Grenoble ice sheet and land ice) model in its newest revision. We present the recent model improvements from its original version (Ritz et al., 2001), together with a discussion of the model performance in reproducing the present-day Antarctic ice sheet geometry and the grounding line advances and retreats during the last 400 000 years. We show that GRISLI is a computationally cheap model, able to reproduce the large-scale behaviour of ice sheets.
Gary D. Clow
Geosci. Model Dev., 11, 4889–4908, https://doi.org/10.5194/gmd-11-4889-2018, https://doi.org/10.5194/gmd-11-4889-2018, 2018
Short summary
Short summary
CVPM is a modular heat-transfer modeling system designed for scientific and engineering studies in permafrost terrain, and as an educational tool. CVPM implements the heat-transfer equations in both Cartesian and cylindrical coordinates. To accommodate a diversity of geologic settings, a variety of materials can be specified within the model domain. CVPM can be used over a broad range of depth, temperature, porosity, water saturation, and solute conditions on either Earth or Mars.
Eef C. H. van Dongen, Nina Kirchner, Martin B. van Gijzen, Roderik S. W. van de Wal, Thomas Zwinger, Gong Cheng, Per Lötstedt, and Lina von Sydow
Geosci. Model Dev., 11, 4563–4576, https://doi.org/10.5194/gmd-11-4563-2018, https://doi.org/10.5194/gmd-11-4563-2018, 2018
Short summary
Short summary
Ice flow forced by gravity is governed by the full Stokes (FS) equations, which are computationally expensive to solve. Therefore, approximations to the FS equations are used, especially when modeling an ice sheet on long time spans. Here, we report a combination of an approximation with the FS equations that allows simulating the dynamics of ice sheets over long time spans without introducing artifacts caused by application of approximations in parts of the domain where they are not valid.
Alek A. Petty, Melinda Webster, Linette Boisvert, and Thorsten Markus
Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, https://doi.org/10.5194/gmd-11-4577-2018, 2018
Matthew J. Hoffman, Mauro Perego, Stephen F. Price, William H. Lipscomb, Tong Zhang, Douglas Jacobsen, Irina Tezaur, Andrew G. Salinger, Raymond Tuminaro, and Luca Bertagna
Geosci. Model Dev., 11, 3747–3780, https://doi.org/10.5194/gmd-11-3747-2018, https://doi.org/10.5194/gmd-11-3747-2018, 2018
Short summary
Short summary
MPAS-Albany Land Ice (MALI) is a new variable-resolution land ice model that uses unstructured grids on a plane or sphere. MALI is built for Earth system modeling on high-performance computing platforms using existing software libraries. MALI simulates the evolution of ice thickness, velocity, and temperature, and it includes schemes for simulating iceberg calving and the flow of water beneath ice sheets and its effect on ice sliding. The model is demonstrated for the Antarctic ice sheet.
Mark Kavanagh and Lev Tarasov
Geosci. Model Dev., 11, 3497–3513, https://doi.org/10.5194/gmd-11-3497-2018, https://doi.org/10.5194/gmd-11-3497-2018, 2018
Short summary
Short summary
We present and validate BrAHMs (BAsal Hydrology Model): a new
physically based basal hydrology model, which captures the two main
types of subglacial drainage systems (high-pressure distributed systems and
low-pressure channelized systems). BrAHMs is designed for continental
glacial cycle scale contexts, for which computational speed is
essential. This speed is accomplished, in part, by numerical methods
novel to basal hydrology contexts.
Aleah Sommers, Harihar Rajaram, and Mathieu Morlighem
Geosci. Model Dev., 11, 2955–2974, https://doi.org/10.5194/gmd-11-2955-2018, https://doi.org/10.5194/gmd-11-2955-2018, 2018
Short summary
Short summary
Meltwater drainage beneath glaciers and ice sheets influences how fast they move and is complicated and constantly changing. Most models distinguish between
fastand
slowdrainage with different equations for each system. The SHAKTI model allows for the ice–water drainage arrangement to transition naturally between different types of flow. This model can be used to understand how drainage affects glacier speeds and the associated ice loss to further inform predictions of sea level rise.
Ghislain Picard, Melody Sandells, and Henning Löwe
Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, https://doi.org/10.5194/gmd-11-2763-2018, 2018
Short summary
Short summary
The Snow Microwave Radiative Transfer (SMRT) is a novel model developed to calculate how microwaves are scattered and emitted by snow. The model is built from separate, interconnecting modules to make it easy to compare different aspects of the theory. SMRT is the first model to allow a choice of how to represent the microstructure of the snow, which is extremely important, and has been used to unite multiple previous studies. This model will ultimately be used to observe snow from space.
Lihui Luo, Zhongqiong Zhang, Wei Ma, Shuhua Yi, and Yanli Zhuang
Geosci. Model Dev., 11, 2475–2491, https://doi.org/10.5194/gmd-11-2475-2018, https://doi.org/10.5194/gmd-11-2475-2018, 2018
Short summary
Short summary
Based on the current situation of permafrost modeling in the Qinghai–Tibet Plateau (QTP), a software PIC was developed to evaluate the temporal–spatial change trends of permafrost, which allows us to automatically compute permafrost indices with daily weather and atmospheric forcing datasets. The main features include computing, visualization, and statistics. The software will serve engineering applications and can be used to assess the impact of climate change on permafrost over the QTP.
Alexandra Touzeau, Amaëlle Landais, Samuel Morin, Laurent Arnaud, and Ghislain Picard
Geosci. Model Dev., 11, 2393–2418, https://doi.org/10.5194/gmd-11-2393-2018, https://doi.org/10.5194/gmd-11-2393-2018, 2018
Short summary
Short summary
We introduced a new module of water vapor diffusion into the snowpack model Crocus. Vapor transport locally modifies the density of snow layers, possibly influencing compaction. It also affects the original isotopic signature of snow layers. We also introduced water isotopes (𝛿18O) in the model. Over 10 years, the modeled attenuation of isotopic variations due to vapor diffusion is 7–18 % lower than the observations. Thus, other processes are required to explain the total attenuation.
Joshua K. Cuzzone, Mathieu Morlighem, Eric Larour, Nicole Schlegel, and Helene Seroussi
Geosci. Model Dev., 11, 1683–1694, https://doi.org/10.5194/gmd-11-1683-2018, https://doi.org/10.5194/gmd-11-1683-2018, 2018
Short summary
Short summary
This paper details the implementation of higher-order vertical finite elements in the Ice Sheet System Model (ISSM). When using higher-order vertical finite elements, fewer vertical layers are needed to accurately capture the thermal structure in an ice sheet versus a conventional linear vertical interpolation, therefore greatly improving model runtime speeds, particularly in higher-order stress balance ice sheet models. The implications for paleoclimate ice sheet simulations are discussed.
Kaitlin A. Naughten, Katrin J. Meissner, Benjamin K. Galton-Fenzi, Matthew H. England, Ralph Timmermann, Hartmut H. Hellmer, Tore Hattermann, and Jens B. Debernard
Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, https://doi.org/10.5194/gmd-11-1257-2018, 2018
Short summary
Short summary
MetROMS and FESOM are two ocean/sea-ice models which resolve Antarctic ice-shelf cavities and consider thermodynamics at the ice-shelf base. We simulate the period 1992–2016 with both models, and with two options for resolution in FESOM, and compare output from the three simulations. Ice-shelf melt rates, sub-ice-shelf circulation, continental shelf water masses, and sea-ice processes are compared and evaluated against available observations.
Jeff K. Ridley, Edward W. Blockley, Ann B. Keen, Jamie G. L. Rae, Alex E. West, and David Schroeder
Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, https://doi.org/10.5194/gmd-11-713-2018, 2018
Short summary
Short summary
The sea ice component of the Met Office coupled climate model, HadGEM3-GC3.1, is presented and evaluated. We determine that the mean state of the sea ice is well reproduced for the Arctic; however, a warm sea surface temperature bias over the Southern Ocean results in a low Antarctic sea ice cover.
Cited articles
Anderson, E. A.: Development and testing of snow pack energy balance equations,
Water Resour. Res., 4, 19–37, https://doi.org/10.1029/WR004i001p00019, 1968. a
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swis
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, b, c, d
Bintanja, R. and Van Den Broeke, M. R.: The Surface Energy Balance of
Antarctic Snow and Blue Ice, J. Appl. Meteorol., 34,
902–926, https://doi.org/10.1175/1520-0450(1995)034<0902:TSEBOA>2.0.CO;2, 1995. a
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux-profile
relationships in the atmospheric surface layer, J. Atmos. Sci., 28, 181–189,
https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2,
1971. a
Cogley, J. G., Hock, R., Rasmussen, L. A., Arendt, A., Bauder, A.,
Braithwaite, R. J., Jansson, P., Kaser, G., Möller, M., Nicholson, L., and
Zemp, M.: Glossary of glacier mass balance and related terms, International
Association of Cryospheric Sciences, IHP-VII Technical Documents in Hydrology No. 86,
IACS Contribution No. 2,
UNESCO, Paris,
https://doi.org/10.5167/uzh-53475, 2011. a
Coléou, C. and Lesaffre, B.: Irreducible water saturation in snow:
experimental results in a cold laboratory, Ann. Glaciol., 26, 64–68,
https://doi.org/10.3189/1998AoG26-1-64-68, 1998. a
Conway, J. and Cullen, N.: Constraining turbulent heat flux parameterization
over a temperate maritime glacier in New Zealand, Ann. Glaciol.,
54, 41–51, https://doi.org/10.3189/2013AoG63A604, 2013. a, b
Dask Development Team: Dask: Library for dynamic task scheduling, DASK,
available at: https://dask.org (last access: 20 June 2020), 2016. a
Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay.
Meteorol., 7, 363–372, https://doi.org/10.1007/BF00240838, 1974. a
Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of 1701
snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013. a, b
Ferziger, J. H. and Perić, M.: Computational Methods for Fluid Dynamics,
Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-56026-2, 2002. a
Fletcher, R.: Practical Methods of Optimization, John Wiley & Sons,
Chichester, West Sussex England, https://doi.org/10.1002/9781118723203, 2000. a
Foken, T.: Micrometeorology, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-74666-9, 2008. a, b, c, d
Galos, S. P., Klug, C., Maussion, F., Covi, F., Nicholson, L., Rieg, L., Gurgiser, W., Mölg, T.,
and Kaser, G.: Reanalysis of a 10-year record (2004–2013) of seasonal mass balances at
Langenferner/Vedretta Lunga, Ortler Alps, Italy, The Cryosphere, 11, 1417–1439, https://doi.org/10.5194/tc-11-1417-2017, 2017. a
Gurgiser, W., Marzeion, B., Nicholson, L., Ortner, M., and Kaser, G.: Modeling energy and mass balance of Shallap Glacier, Peru, The Cryosphere, 7, 1787–1802, https://doi.org/10.5194/tc-7-1787-2013, 2013. a
Hantel, M., Ehrendorfer, M., and Haslinger, A.: Climate sensitivity of snow
cover duration in Austria, Int. J. Climatol., 20, 615–640,
https://doi.org/10.1002/(SICI)1097-0088(200005)20:6<615::AID-JOC489>3.0.CO;2-0,
2000. a
Hersbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF
Newsletter, Tech. rep., European Centre for Medium-Range Weather Forecasts, available at: https://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production (last access: 11 November 2020),
vol. 147, 2016. a
Hock, R. and Holmgren, B.: A distributed surface energy-balance model for
complex topography and its application to Storglaciären, Sweden, J. Glaciol., 51, 25–36, https://doi.org/10.3189/172756505781829566, 2005. a, b
Hoyer, S. and Hamman, J. J.: xarray: N-D labeled Arrays and Datasets in
Python, Journal of Open Research Software, 5, p. 10, https://doi.org/10.5334/jors.148, 2017. a
Huintjes, E.: Energy and mass balance modelling for glaciers on the Tibetan
Plateau – Extension, validation and application of a coupled snow and
energy balance model, Ph.D. thesis, Rheinisch-Westfälischen Technischen
Hochschule Aachen, Aachen, 2014. a
Huintjes, E., Neckel, N., Hochschild, V., and Schneider, C.: Surface energy and
mass balance at Purogangri ice cap, central Tibetan Plateau,
2001–2011, J. Glaciol., 61, 1048–1060,
https://doi.org/10.3189/2015JoG15J056, 2015a. a
Huintjes, E., Sauter, T., Schröter, B., Maussion, F., Yang, W., Kropáček,
J., Buchroithner, M., Scherer, D., Kang, S., and Schneider, C.: Evaluation of
a Coupled Snow and Energy Balance Model for Zhadang Glacier,
Tibetan Plateau, Using Glaciological Measurements and
Time-Lapse Photography, Arctic, Antarctic, and Alpine Research, 47,
573–590, https://doi.org/10.1657/AAAR0014-073, 2015b. a, b, c, d
Klok, E. and Oerlemans, J.: Model study of the spatial distribution of the
energy and mass balance of Morteratschgletscher, Switzerland, J. Glaciol., 48, 505–518, https://doi.org/10.3189/172756502781831133, 2002. a, b, c, d
Kraus, H.: An energy balance model for ablation in mountainous areas, Proceedings of the Moscow Symposium, August 1971; Actes du Colloque de Moscou, août 1971, IAHS-AISH Publ. No. 104, 1975. a
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a, b, c
Kuhn, M.: On the Computation of Heat Transfer Coefficients from
Energy-Balance Gradients on a Glacier, J. Glaciol., 22,
263–272, https://doi.org/10.3189/S0022143000014258, 1979. a
Kuhn, M.: Micro-Meteorological Conditions for Snow Melt, J. Glaciol., 33, 24–26, https://doi.org/10.3189/S002214300000530X, 1987. a
Machguth, H., Paul, F., Hoelzle, M., and Haeberli, W.: Distributed glacier
mass-balance modelling as an important component of modern multi-level
glacier monitoring, Ann. Glaciol., 43, 335–343,
https://doi.org/10.3189/172756406781812285, 2006. a
Machguth, H., Paul, F., Kotlarski, S., and Hoelzle, M.: Calculating distributed
glacier mass balance for the Swiss Alps from regional climate model
output: A methodical description and interpretation of the results, J.
Geophys. Res., 114, D19106, https://doi.org/10.1029/2009JD011775, 2009. a
Male, D. H. and Granger, R. J.: Snow surface energy exchange, Water Resour.
Res., 17, 609–627, https://doi.org/10.1029/WR017i003p00609, 1981. a
Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and Finkelnburg,
R.: Precipitation Seasonality and Variability over the Tibetan
Plateau as Resolved by the High Asia Reanalysis, J.
Climate, 27, 1910–1927, https://doi.org/10.1175/JCLI-D-13-00282.1, 2014. a
Maussion, F., Gurgiser, W., Großhauser, M., Kaser, G., and Marzeion, B.: ENSO influence on surface energy and mass balance at Shallap Glacier, Cordillera Blanca, Peru, The Cryosphere, 9, 1663–1683, https://doi.org/10.5194/tc-9-1663-2015, 2015. a
Ménard, C. B. and Essery, R.: ESM-SnowMIP meteorological and evaluation
datasets at ten reference sites (in situ and bias corrected reanalysis data), dataset, PANGAEA, https://doi.org/10.1594/PANGAEA.897575,
2019. a, b
Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N.: Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, 2019. a
Michlmayr, G., Lehning, M., Koboltschnig, G., Holzmann, H., Zappa, M., Mott,
R., and Schöner, W.: Application of the Alpine 3D model for glacier mass balance and glacier runoff studies at Goldbergkees, Austria, Hydrol. Process, 22, 3941–3949, https://doi.org/10.1002/hyp.7102, 2008. a
Microsoft: Bing Maps, available at: https://www.bing.com/maps/, last access: 10 November 2020. a
Mölg, T. and Hardy, D. R.: Ablation and associated energy balance of a
horizontal glacier surface on Kilimanjaro, J. Geophys. Res.,
109, D16104, https://doi.org/10.1029/2003JD004338,2004. a
Mölg, T., Cullen, N. J., Hardy, D. R., Kaser, G., and Klok, L.: Mass balance
of a slope glacier on Kilimanjaro and its sensitivity to climate,
Int. J. Climatol., 28, 881–892, https://doi.org/10.1002/joc.1589, 2008. a
Mölg, T., Cullen, N. J., Hardy, D. R., Winkler, M., and Kaser, G.: Quantifying
Climate Change in the Tropical Midtroposphere over East Africa
from Glacier Shrinkage on Kilimanjaro, J. Climate, 22,
4162–4181, https://doi.org/10.1175/2009JCLI2954.1, 2009. a, b
Mölg, T., Maussion, F., Yang, W., and Scherer, D.: The footprint of Asian monsoon dynamics in the mass and energy balance of a Tibetan glacier, The Cryosphere, 6, 1445–1461, https://doi.org/10.5194/tc-6-1445-2012, 2012. a, b
Mölg, T., Maussion, F., and Scherer, D.: Mid-latitude westerlies as a driver
of glacier variability in monsoonal High Asia, Nat. Clim. Change, 4, 68–73, https://doi.org/10.1038/nclimate2055, 2014. a
Morris, E.: Turbulent transfer over snow and ice, J. Hydrol., 105,
205–223, https://doi.org/10.1016/0022-1694(89)90105-4, 1989. a
Morris, E. M.: Physics-Based Models of Snow, in: Recent Advances in the
Modeling of Hydrologic Systems, edited by: Bowles, D. S. and
O’Connell, P. E., Springer Netherlands, Dordrecht, 85–112
https://doi.org/10.1007/978-94-011-3480-4_5,
1991. a
Munro, D. S.: Surface Roughness and Bulk Heat Transfer on a Glacier: Comparison with Eddy Correlation, J. Glaciol., 35, 343–348,
https://doi.org/10.3189/S0022143000009266, 1989. a, b, c
Munro, D. S.: A surface energy exchange model of glacier melt and net mass
balance, Int. J. Climatol., 11, 689–700,
https://doi.org/10.1002/joc.3370110610, 1991. a
Nicholson, L. I., Prinz, R., Mölg, T., and Kaser, G.: Micrometeorological conditions and surface mass and energy fluxes on Lewis Glacier, Mt Kenya, in relation to other tropical glaciers, The Cryosphere, 7, 1205–1225, https://doi.org/10.5194/tc-7-1205-2013, 2013. a
Obleitner, F. and Lehning, M.: Measurement and simulation of snow and
superimposed ice at the Kongsvegen glacier, Svalbard (Spitzbergen),
J. Geophys. Res.-Atmos., 109, D04106,
https://doi.org/10.1029/2003JD003945, 2004. a
Oerlemans, J.: Glaciers and climate change, A.A. Balkema Publishers, Lisse;
Exton, (PA), 2001. a
Oerlemans, J. and Knap, W. H.: A 1 year record of global radiation and albedo
in the ablation zone of Morteratschgletscher, Switzerland, J. Glaciol., 44, 231–238, https://doi.org/10.1017/S0022143000002574, 1998. a
Østby, T. I., Schuler, T. V., Hagen, J. O., Hock, R., Kohler, J., and Reijmer, C. H.: Diagnosing the decline in climatic mass balance of glaciers in Svalbard over 1957–2014, The Cryosphere, 11, 191–215, https://doi.org/10.5194/tc-11-191-2017, 2017. a
Qu, B., Ming, J., Kang, S.-C., Zhang, G.-S., Li, Y.-W., Li, C.-D., Zhao, S.-Y., Ji, Z.-M., and Cao, J.-J.: The decreasing albedo of the Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities, Atmos. Chem. Phys., 14, 11117–11128, https://doi.org/10.5194/acp-14-11117-2014, 2014. a
Radić, V. and Hock, R.: Modeling future glacier mass balance and volume
changes using ERA-40 reanalysis and climate models: A sensitivity study
at Storglaciären, Sweden, J. Geophys. Res.-Earth, 111, F03003, https://doi.org/10.1029/2005JF000440, 2006. a
Radić, V., Menounos, B., Shea, J., Fitzpatrick, N., Tessema, M. A., and Déry, S. J.: Evaluation of different methods to model near-surface turbulent fluxes for a mountain glacier in the Cariboo Mountains, BC, Canada, The Cryosphere, 11, 2897–2918, https://doi.org/10.5194/tc-11-2897-2017, 2017. a
Reijmer, C. H. and Hock, R.: Internal accumulation on Storglaciären,
Sweden, in a multi-layer snow model coupled to a distributed energy-and
mass-balance model, J. Glaciol., 54, 61–72,
https://doi.org/10.3189/002214308784409161, 2008. a
Rye, C. J., Willis, I. C., Arnold, N. S., and Kohler, J.: On the need for
automated multiobjective optimization and uncertainty estimation of glacier
mass balance models, J. Geophys. Res.-Earth, 117,
F02005, https://doi.org/10.1029/2011JF002184, 2012. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model (Version v1.3), Zenodo, https://doi.org/10.5281/zenodo.3902191, 2020a. a, b
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model, GitHub repository, https://github.com/cryotools/cosipy, last access: 20 June 2020b. a
Sauter, T. and Arndt, A.: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model – Read the Docs documentation, available at: https://cosipy.readthedocs.io/en/latest/index.html, last access: 20 June 2020c. a
Sauter, T. and Arndt, A.: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model – Slack comunity platform for user communication, available at: https://cosipy.slack.com, last access: 20 June 2020d. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – General DOI pointing to the newest release, Zenodo, https://doi.org/10.5281/zenodo.2579668, 2020e. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – Travis CI repository, available at: https://travis-ci.org/cryotools/cosipy, last access: 20 June 2020f. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – CodeCov repository, available at: https://codecov.io/github/cryotools/cosipy/, last access: 20 June 2020g. a
Sauter, T. and Galos, S. P.: Effects of local advection on the spatial sensible heat flux variation on a mountain glacier, The Cryosphere, 10, 2887–2905, https://doi.org/10.5194/tc-10-2887-2016, 2016. a
Sauter, T. and Obleitner, F.: Assessing the uncertainty of glacier mass-balance simulations in the European Arctic based on variance decomposition, Geosci. Model Dev., 8, 3911–3928, https://doi.org/10.5194/gmd-8-3911-2015, 2015. a
Sauter, T., Möller, M., Finkelnburg, R., Grabiec, M., Scherer, D., and Schneider, C.: Snowdrift modelling for the Vestfonna ice cap, north-eastern Svalbard, The Cryosphere, 7, 1287–1301, https://doi.org/10.5194/tc-7-1287-2013, 2013. a
Schuler, T. V., Hock, R., Jackson, M., Elvehøy, H., Braun, M., Brown, I., and
Hagen, J.-O.: Distributed mass-balance and climate sensitivity modelling of
Engabreen, Norway, Ann. Glaciol., 42, 395–401,
https://doi.org/10.3189/172756405781812998,
2005. a
Sicart, J. E., Hock, R., Ribstein, P., Litt, M., and Ramirez, E.: Analysis of
seasonal variations in mass balance and meltwater discharge of the tropical
Zongo Glacier by application of a distributed energy balance model,
J. Geophys. Res., 116, D13105, https://doi.org/10.1029/2010JD015105, 2011. a, b
Siemer, A. H.: Ein eindimensionales Energie-Massenbilanzmodell einer
Schneedecke unter Berücksichtigung der Flüssigwassertransmission,
Berichte des Institutes für Meteorologie und Klimatologie der
Universität Hannover, 34, Universität Hannover, Hannover, 1988. a
Smeets, C. J. P. P. and van den Broeke, M. R.: Temporal and Spatial
Variations of the Aerodynamic Roughness Length in the Ablation
Zone of the Greenland Ice Sheet, Bound.-Lay. Meteorol., 128,
315–338, https://doi.org/10.1007/s10546-008-9291-0, 2008. a
Van Den Broeke, M., Reijmer, C., Van As, D., and Boot, W.: Daily cycle of the
surface energy balance in Antarctica and the influence of clouds,
International J. Climatol., 26, 1587–1605, https://doi.org/10.1002/joc.1323, 2006.
a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A
Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
van Pelt, W. J. J., Oerlemans, J., Reijmer, C. H., Pohjola, V. A., Pettersson, R., and van Angelen, J. H.: Simulating melt, runoff and refreezing on Nordenskiöldbreen, Svalbard, using a coupled snow and energy balance model, The Cryosphere, 6, 641–659, https://doi.org/10.5194/tc-6-641-2012, 2012. a, b
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, b, c
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and Contributors, S.: SciPy
1.0–Fundamental Algorithms for Scientific Computing in Python, Nature Methods 17, 261,
arXiv [preprint], arXiv:1907.10121, 2019. a
Wagnon, P., Ribstein, P., Kaser, G., and Berton, P.: Energy balance and runoff
seasonality of a Bolivian glacier, Global Planet. Change, 22, 49–58,
https://doi.org/10.1016/S0921-8181(99)00025-9, 1999. a
Weidemann, S. S., Sauter, T., Malz, P., Jaña, R., Arigony-Neto, J., Casassa,
G., and Schneider, C.: Glacier Mass Changes of Lake-Terminating
Grey and Tyndall Glaciers at the Southern Patagonia Icefield
Derived From Geodetic Observations and Energy and Mass Balance
Modeling, Front. Earth Sci., 6, 81, https://doi.org/10.3389/feart.2018.00081,
2018. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a
Whitaker, J., Khrulev, C., Huard, D., Paulik, C., Hoyer, S., and Kinoshita, B. P.: Unidata/netcdf4-python: version 1.5.1.2 release (Version v1.5.1.2rel), Zenodo, https://doi.org/10.5281/zenodo.2669496, 2019. a
Wohlfahrt, G., Hammerle, A., Niedrist, G., Scholz, K., Tomelleri, E., and Zhao,
P.: On the energy balance closure and net radiation in complex terrain,
Agr. Forest Meteorol., 226–227, 37–49,
https://doi.org/10.1016/j.agrformet.2016.05.012, 2016. a, b
Short summary
Glacial changes play a key role from a socioeconomic, political, and scientific point of view. Here, we present the open-source coupled snowpack and ice surface energy and mass balance model, which provides a lean, flexible, and user-friendly framework for modeling distributed snow and glacier mass changes. The model provides a suitable platform for sensitivity, detection, and attribution analyses for glacier changes and a tool for quantifying inherent uncertainties.
Glacial changes play a key role from a socioeconomic, political, and scientific point of view....
