Articles | Volume 6, issue 4
https://doi.org/10.5194/gmd-6-1061-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-6-1061-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
26 Jul 2013
Model description paper |
| 26 Jul 2013
Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model
G. Picard
CNRS, LGGE UMR5183, 38041 Grenoble, France
Univ. Grenoble Alpes, LGGE (UMR5183), 38041 Grenoble, France
L. Brucker
NASA Goddard Space Flight Center, Cryospheric Sciences Lab., code 615 Greenbelt, MD, 20771 USA
Goddard Earth Sciences Technology and Research Studies and Investigations, Universities Space Research Association, Greenbelt, MD, 20771 USA
A. Roy
Centre d'applications et de recherches en télédétection (CARTEL), Université de Sherbrooke, 2500 Bd Université, Sherbrooke, QC J1K 2R1 Canada
F. Dupont
CNRS, LGGE UMR5183, 38041 Grenoble, France
Univ. Grenoble Alpes, LGGE (UMR5183), 38041 Grenoble, France
Centre d'applications et de recherches en télédétection (CARTEL), Université de Sherbrooke, 2500 Bd Université, Sherbrooke, QC J1K 2R1 Canada
M. Fily
CNRS, LGGE UMR5183, 38041 Grenoble, France
Univ. Grenoble Alpes, LGGE (UMR5183), 38041 Grenoble, France
A. Royer
Centre d'applications et de recherches en télédétection (CARTEL), Université de Sherbrooke, 2500 Bd Université, Sherbrooke, QC J1K 2R1 Canada
C. Harlow
Met Office, EX1 3PB Exeter, UK
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L. Brucker, E. P. Dinnat, and L. S. Koenig
The Cryosphere, 8, 905–913, https://doi.org/10.5194/tc-8-905-2014, https://doi.org/10.5194/tc-8-905-2014, 2014
C. M. Carmagnola, S. Morin, M. Lafaysse, F. Domine, B. Lesaffre, Y. Lejeune, G. Picard, and L. Arnaud
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N. Champollion, G. Picard, L. Arnaud, E. Lefebvre, and M. Fily
The Cryosphere, 7, 1247–1262, https://doi.org/10.5194/tc-7-1247-2013, https://doi.org/10.5194/tc-7-1247-2013, 2013
C. M. Carmagnola, F. Domine, M. Dumont, P. Wright, B. Strellis, M. Bergin, J. Dibb, G. Picard, Q. Libois, L. Arnaud, and S. Morin
The Cryosphere, 7, 1139–1160, https://doi.org/10.5194/tc-7-1139-2013, https://doi.org/10.5194/tc-7-1139-2013, 2013
A. Roy, A. Royer, B. Montpetit, P. A. Bartlett, and A. Langlois
The Cryosphere, 7, 961–975, https://doi.org/10.5194/tc-7-961-2013, https://doi.org/10.5194/tc-7-961-2013, 2013
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openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Simon Horton, Florian Herla, and Pascal Haegeli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1609, https://doi.org/10.5194/egusphere-2024-1609, 2024
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We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, location, and time. Tested in the Columbia Mountains during winter 2022–23, it accurately matched real forecast regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Ghislain Picard and Quentin Libois
EGUsphere, https://doi.org/10.5194/egusphere-2024-1176, https://doi.org/10.5194/egusphere-2024-1176, 2024
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TARTES is a radiative transfer model to compute the reflectivity in the solar domain (albedo), and the profiles of solar light and energy absorption in a multi-layered snowpack whose physical properties are prescribed by the user. It uniquely considers snow grain shape in a flexible way, allowing us to apply the most recent advances showing that snow does not behave as a collection of ice spheres, but instead as a random medium. TARTES is also simple but compares well with other complex models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Ian Fenty, Matthew Mazloff, Köhl Armin, and Dimitris Menemenlis
EGUsphere, https://doi.org/10.5194/egusphere-2024-727, https://doi.org/10.5194/egusphere-2024-727, 2024
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Global and basin-scale ocean reanalyses are becoming easily accessible. Yet, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluations. We conduct intercomparison analyses of Massachusetts Institute of Technology general circulation model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open ocean temporal variability and Antarctic continental shelves require improvements.
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
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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
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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
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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
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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
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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
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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
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-298, https://doi.org/10.5194/gmd-2022-298, 2023
Revised manuscript accepted for GMD
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better quality maps. The correction can then be extended backwards and forwards in time for periods when better quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the last 60 years at a resolution of one day and one kilometre. This is the first time that such a dataset has been produced.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
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The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
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Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
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We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
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The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
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We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Zhen Yin, Chen Zuo, Emma J. MacKie, and Jef Caers
Geosci. Model Dev., 15, 1477–1497, https://doi.org/10.5194/gmd-15-1477-2022, https://doi.org/10.5194/gmd-15-1477-2022, 2022
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We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
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In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
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We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Christopher Horvat and Lettie A. Roach
Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, https://doi.org/10.5194/gmd-15-803-2022, 2022
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Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from meters to kilometers. Variations in sea ice geometry are often forced by ocean waves, a process that is an important target of global climate models as it affects the rate of sea ice melting. Yet directly simulating these interactions is computationally expensive. We present a neural-network-based model of wave–ice fracture that allows models to incorporate their effect without added computational cost.
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, https://doi.org/10.5194/gmd-15-617-2022, 2022
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Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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
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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.
Cited articles
Abdalati, W. and Steffen, K.: Snowmelt on the Greenland Ice Sheet as Derived from Passive Microwave Satellite Data, J. Climate, 10, 165–175, 1997.
Abdalati, W. and Steffen, K.: Accumulation and hoar effects on microwave emission on the Greenland ice sheet dry snow zones, J. Glaciol., 44, 523–531, 1998.
Anderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., and Sorensen, D.: LAPACK Users' Guide, 3rd Edn., Soc. Industrial Appl. Math., Philadelphia, PA, 1999.
Arnaud, L., Picard, G., Champollion, N., Domine, F., Gallet, J., Lefebvre, E., Fily, M., and Barnola, J.: Measurement of vertical profiles of snow specific surface area with a 1 cm resolution using infrared reflectance: instrument description and validation, J. Glaciol., 57, 17–29, https://doi.org/10.3189/002214311795306664, 2011.
Arthern, R. J., Winebrenner, D. P., and Vaughan, D. G.: Antarctic snow accumulation mapped using polarization of 4.3-cm wavelength microwave emission, J. Geophys. Res., 111, 10 pp., https://doi.org/10.1029/2004JD005667, 2006.
Borghese, F., Denti, P., and Saija, R.: Scattering from model nonspherical particles theory and applications to environmental physics, Springer, Berlin, New York, available at: http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=255898, 2007.
Brandt, R. E. and Warren, S. G.: Solar-heating rates and temperature profiles in Antarctic snow and ice, J. Glaciol., 39, 99–110, 1993.
Brogioni, M., Macelloni, G., Palchetti, E., Paloscia, S., Pampaloni, P., Pettinato, S., Santi, E., Cagnati, A., and Crepaz, A.: Monitoring Snow Characteristics With Ground-Based Multifrequency Microwave Radiometry, IEEE T. Geosci. Remote, 47, 3643–3655, https://doi.org/10.1109/TGRS.2009.2030791, 2009.
Brucker, L. and Markus, T.: Arctic-Scale Assessment of Satellite Passive Microwave Derived Snow Depth on Sea Ice using Operation IceBridge Airborne Data, J. Geophys. Res. Oceans, 118, 14 pp., https://doi.org/10.1002/jgrc.20228, 2013.
Brucker, L., Picard, G., and Fily, M.: Snow grain size profiles deduced from microwave snow emissivities in Antarctica, J. Glaciol., 56, 514–526, https://doi.org/10.3189/002214310792447806, 2010.
Brucker, L., Picard, G., Arnaud, L., Barnola, J., Schneebeli, M., Brunjail, H., Lefebvre, E., and Fily, M.: Modeling time series of microwave brightness temperature at Dome C, Antarctica, using vertically resolved snow temperature and microstructure measurements, J. Glaciol., 57, 171–182, 2011a.
Brucker, L., Royer, A., Picard, G., Langlois, A., and Fily, M.: Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution emission model, Remote Sens. Environ., 115, 1966–1977, https://doi.org/10.1016/j.rse.2011.03.019, 2011b.
Butt, M. J. and Kelly, R. E. J.: Estimation of snow depth in the UK using the HUT snow emission model, Int. J. Remote Sens., 29, 4249–4267, https://doi.org/10.1080/01431160801891754, 2008.
Cavalieri, D., Markus, T., Ivanoff, A., Miller, J., Brucker, L., Sturm, M., Maslanik, J., Heinrichs, J., Gasiewski, A., Leuschen, C., Krabill, W., and Sonntag, J.: A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator, IEEE T. Geosci. Remote, 50, 3027–3040, https://doi.org/10.1109/TGRS.2011.2180535, 2012.
Champollion, N., Picard, G., Arnaud, L., Lefebvre, E., and Fily, M.: Hoar crystal development and disappearance at Dome C, Antarctica: observation by near-infrared photography and passive microwave satellite, The Cryosphere Discuss., 7, 175–217, https://doi.org/10.5194/tcd-7-175-2013, 2013.
Chandrasekhar, S.: Radiative transfer, New York: Dover, 1960.
Chang, W. and Tsang, L.: Conical electromagnetic waves diffraction from sastrugi type surfaces of layered snow dunes on Greenland ice sheets in passive microwave remote sensing, 122–125, IEEE, https://doi.org/10.1109/IGARSS.2011.6048913, 2011.
Chopra, K. L. and Reddy, G. B.: Optically selective coatings, Pramana, 27, 193–217, https://doi.org/10.1007/BF02846338, 1986.
Chuah, H. and Tan, H.: A Monte Carlo method for radar backscatter from a half-space random medium, IEEE T. Geosci. Remote, 27, 86–93, https://doi.org/10.1109/36.20278, 1989.
Colbeck, S. C.: The vapor diffusion coefficient for snow, Water Resourc. Res., 29, 109–116, https://doi.org/10.1029/92WR02301, 1993.
Derksen, C., Walker, A., and Goodison, B.: A comparison of 18 winter seasons of in situ and passive microwave-derived snow water equivalent estimates in Western Canada, Remote Sens. Environ., 88, 271–282, https://doi.org/10.1016/j.rse.2003.07.003, 2003.
Dierking, W., Linow, S., and Rack, W.: Toward a robust retrieval of snow accumulation over the Antarctic ice sheet using satellite radar, J. Geophys. Res., 117, D09110, https://doi.org/10.1029/2011JD017227, 2012.
Dobson, M., Ulaby, F., Hallikainen, M., and El-rayes, M.: Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models, IEEE T. Geosci. Remote, GE-23, 35–46, https://doi.org/10.1109/TGRS.1985.289498, 1985.
Dupont, F., Picard, G., Royer, A., Fily, M., Roy, A., and Champollion, N.: Modeling the microwave emission of ice; Applications to the Cap Prud\textquoterightHomme Blue Ice Area in Antarctica and to the Barnes Ice Cap superimposed ice in Arctic, IEEE Trans. Geo. Remote, in review, 2012.
Durand, M. and Margulis, S. A.: Correcting first-order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme, J. Geophys. Res., 112, 15 pp., https://doi.org/10.1029/2006JD008067, 2007.
Durand, M., Kim, E. J., and Margulis, S. A.: Quantifying Uncertainty in Modeling Snow Microwave Radiance for a Mountain Snowpack at the Point-Scale, Including Stratigraphic Effects, IEEE T. Geosci. Remote, 46, 1753–1767, https://doi.org/10.1109/TGRS.2008.916221, 2008.
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo evolution, J. Geophys. Res., 111, 12 pp., https://doi.org/10.1029/2005JD006834, 2006.
Fung, A. K.: Microwave scattering and emission models and their applications, Remote sensing library, Artech House, Boston, USA, 1994.
Grody, N.: Relationship between snow parameters and microwave satellite measurements: Theory compared with Advanced Microwave Sounding Unit observations from 23 to 150 GHz, J. Geophys. Res., 113, 17 pp., https://doi.org/10.1029/2007JD009685, 2008.
Grody, N. and Basist, A.: Global identification of snowcover using SSM/I measurements, IEEE T. Geosci. Remote, 34, 237–249, https://doi.org/10.1109/36.481908, 1996.
Gunn, G. E., Duguay, C. R., Derksen, C., Lemmetyinen, J., and Toose, P.: Evaluation of the HUT modified snow emission model over lake ice using airborne passive microwave measurements, Remote Sens. Environ., 115, 233–244, https://doi.org/10.1016/j.rse.2010.09.001, 2011.
Harlow, R. C. and Essery, R.: Tundra Snow Emissivities at MHS Frequencies: MEMLS Validation Using Airborne Microwave Data Measured During CLPX-II, IEEE T. Geosci. Remote, 50, 4262–4278, https://doi.org/10.1109/TGRS.2012.2193132, 2012.
Huang, C., Margulis, S. A., Durand, M. T., and Musselman, K. N.: Assessment of Snow Grain-Size Model and Stratigraphy Representation Impacts on Snow Radiance Assimilation: Forward Modeling Evaluation, IEEE T. Geosci. Remote, 50, 4551–4564, https://doi.org/10.1109/TGRS.2012.2192480, 2012.
Ishimaru, A.: Wave propagation and scattering in random media, IEEE Press; Oxford University Press, New York; Oxford; New York, 1997.
Ishimaru, A. and Kuga, Y.: Attenuation constant of a coherent field in a dense distribution of particles, J. Optical Soc. Am., 72, 1317, https://doi.org/10.1364/JOSA.72.001317, 1982.
Jiang, J. H. and Wu, D. L.: Ice and water permittivities for millimeter and sub-millimeter remote sensing applications, Atmos. Sci. Lett., 5, 146–151, https://doi.org/10.1002/asl.77, 2004.
Jin, Y. Q.: Electromagnetic scattering modelling for quantitative remote sensing, World Scientific, 1994.
Josberger, E. G. and Mognard, N. M.: A passive microwave snow depth algorithm with a proxy for snow metamorphism, Hydrol. Process., 16, 1557–1568, https://doi.org/10.1002/hyp.1020, 2002.
Kelly, R. E. J. and Chang, A. T. C.: Development of a passive microwave global snow depth retrieval algorithm for Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data, Radio Sci., 38, 41–1, https://doi.org/10.1029/2002RS002648, 2003.
Koenig, L. S., Steig, E. J., Winebrenner, D. P., and Shuman, C. A.: A link between microwave extinction length, firn thermal diffusivity, and accumulation rate in West Antarctica, J. Geophys. Res., 112, 10 pp., https://doi.org/10.1029/2006JF000716, 2007.
Kong, J. A., Shin, R., Shiue, J. C., and Tsang, L.: Theory and Experiment for Passive Microwave Remote Sensing of Snowpacks, J. Geophys. Res., 84, 5669–5673, https://doi.org/10.1029/JB084iB10p05669, 1979.
Kuipers Munneke, P., Picard, G., van den Broeke, M. R., Lenaerts, J. T. M., and van Meijgaard, E.: Insignificant change in Antarctic snowmelt volume since 1979, Geophys. Res. Lett., 39, 5 pp., https://doi.org/10.1029/2011GL050207, 2012.
Lacroix, P., Legresy, B., Remy, F., Blarel, F., Picard, G., and Brucker, L.: Rapid change of snow surface properties at Vostok, East Antarctica, revealed by altimetry and radiometry, Remote Sens. Environ., 113, 2633–2641, https://doi.org/10.1016/j.rse.2009.07.019, 2009.
Lemmetyinen, J., Pulliainen, J., Rees, A., Kontu, A., Qiu, Y., and Derksen, C.: Multiple-Layer Adaptation of HUT Snow Emission Model: Comparison With Experimental Data, IEEE T. Geosci. Remote, 48, 2781–2794, https://doi.org/10.1109/TGRS.2010.2041357, 2010.
Liang, D., Tse, K., Tan, Y., Tsang, L., and Ding, K. H.: Scattering and Emission in Snow Based on QCA/DMRT and Numerical Maxwell Model of 3-Dimentional Simulations (NMM3-D), IEEE MicroRad, In Proceedings of the IEEE 9th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2006), 28 February–3 March 2006, San Juan, Puerto Rico, 197–202, https://doi.org/10.1109/MICRAD.2006.1677088, 2006.
Liang, D., Xu, X., Tsang, L., Andreadis, K. M., and Josberger, E. G.: The Effects of Layers in Dry Snow on Its Passive Microwave Emissions Using Dense Media Radiative Transfer Theory Based on the Quasicrystalline Approximation (QCA/DMRT), IEEE T. Geosci. Remote, 46, 3663–3671, https://doi.org/10.1109/TGRS.2008.922143, 2008.
Liang, D., Xu, P., Tsang, L., Gui, Z., and Chen, K.: Electromagnetic scattering by rough surfaces with large heights and slopes with applications to microwave remote sensing of rough surface over layered media, Prog. Electromagn. Res., 95, 199–218, https://doi.org/10.2528/PIER09071413, 2009.
Löwe, H. and van Herwijnen, A.: A Poisson shot noise model for micro-penetration of snow, Cold Reg. Sci. Technol., 70, 62–70, https://doi.org/10.1016/j.coldregions.2011.09.001, 2012.
Macelloni, G., Paloscia, S., Pampaloni, P., and Tedesco, M.: Microwave emission from dry snow: a comparison of experimental and model results, IEEE T. Geosci. Remote, 39, 2649–2656, https://doi.org/10.1109/36.974999, 2001.
Mätzler, C.: Improved Born approximation for scattering of radiation in a granular medium, J. Appl. Phys., 83, 6111–6117, 1998.
Mätzler, C.: Relation between grain-size and correlation length of snow, J. Glaciol., 48, 461–466, https://doi.org/10.3189/172756502781831287, 2002.
Mätzler, C. and Wegmüller, U.: Dielectric properties of freshwater ice at microwave frequencies, J. Phys. D Appl. Phys., 20, 1623, https://doi.org/10.1088/0022-3727/20/12/013, 1987.
Mätzler, C. and Wiesmann, A.: Extension of the microwave emission model of layered snowpacks to coarse-grained snow, Remote Sens. Environ., 70, 317–325, 1999.
Mätzler, C., Aebischer, H., and Schanda, E.: Microwave dielectric properties of surface snow, IEEE J. Oceanic Eng., OE-9, 366–371, 1984.
Mätzler, C., Rosenkranz, P. W., Battaglia, A., and Wigneron, J. P.: Thermal microwave radiation – applications for remote sensing, no. 52 in IET, Electromagnetic Waves, London, UK, 2006.
Peake, W.: Interaction of electromagnetic waves with some natural surfaces, IRE Transactions on Antennas and Propagation, 7, 324–329, https://doi.org/10.1109/TAP.1959.1144736, 1959.
Picard, G. and Fily, M.: Surface melting observations in Antarctica by microwave radiometers: correcting 26-year time series from changes in acquisition hours, Remote Sens. Environ., 104, 325–336, 2006.
Picard, G., Le Toan, T., Quegan, S., Caraglio, Y., and Castel, T.: Radiative transfer modeling of cross-polarized backscatter from a pine forest using the discrete ordinate and eigenvalue method, IEEE T. Geosci. Remote, 42, 1720–1730, https://doi.org/10.1109/TGRS.2004.831229, 2004.
Picard, G., Brucker, L., Fily, M., Gallee, H., and Krinner, G.: Modeling timeseries of microwave brightness temperature in Antarctica, J. Glaciol., 55, 537–551, 2009.
Picard, G., Domine, F., Krinner, G., Arnaud, L., and Lefebvre, E.: Inhibition of the positive snow-albedo feedback by precipitation in interior Antarctica, Nature Climate Change, 2, 795–798, https://doi.org/10.1038/nclimate1590, 2012.
Pulliainen, J. T., Grandell, J., and Hallikainen, M. T.: HUT snow emission model and its applicability to snow water equivalent retrieval, IEEE T. Geosci. Remote., 37, 1378–1390, https://doi.org/10.1109/36.763302, 1999.
Rees, A., Lemmetyinen, J., Derksen, C., Pulliainen, J., and English, M.: Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra, Remote Sens. Environ., 114, 116–126, https://doi.org/10.1016/j.rse.2009.08.013, 2010.
Rosenfeld, S. and Grody, N.: Anomalous microwave spectra of snow cover observed from Special Sensor Microwave/Imager measurements, J. Geophys. Res., 105, 14913–14926, https://doi.org/10.1029/1999JD900486, 2000.
Rosenkranz, P. W.: Water vapor microwave continuum absorption: A comparison of measurements and models, Radio Sci., 33, 919–928, https://doi.org/10.1029/98RS01182, 1998.
Roy, A., Picard, G., Royer, A., Montpetit, B., Dupont, F., Langlois, A., Derksen, C., and Champollion, N.: Brightness temperature simulations of the Canadian seasonal snowpack driven by measurements of the snow specific surface area, , IEEE T. Geosci. Remote, 1–13, https://doi.org/10.1109/TGRS.2012.2235842, 2013.
Saunders, R., Matricardi, M., and Brunel, P.: An improved fast radiative transfer model for assimilation of satellite radiance observations, Q. J. Roy. Meteor. Soc., 125, 1407–1425, https://doi.org/10.1256/smsqj.55614, 1999.
Schneider, D. P.: Spatial and temporal variability of Antarctic ice sheet microwave brightness temperatures, Geophys. Res. Lett., 29, 1964, https://doi.org/10.1029/2002GL015490, 2002.
Schneider, D. P., Steig, E. J., and Comiso, J. C.: Recent climate variability in Antarctica from satellite-derived temperature data, J. Climate, 17, 1569–1583, 2004.
Shih, S., Ding, K., Kong, J. A., Yang, Y. E., Davis, R. E., Hardy, J. P., and Jordan, R.: Modeling of Millimeter Wave Backscatter of Time-Varying Snowcover, Prog. Electromagn. Res., 16, 305–330, https://doi.org/10.2528/PIER97012600, 1997.
Shuman, C. A., Alley, R. B., Anandakrishnan, S., and Stearns, C. R.: An empirical technique for estimating near-surface air temperature trends in central Greenland from SSM/I brightness temperatures, Remote Sens. Environ., 51, 245–252, 1995.
Stamnes, K., Tsay, S. C., Jayaweera, K., and Wiscombe, W.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502–2509, 1988.
Stogryn, A.: A Study of the Microwave Brightness Temperature of Snow from the Point of View of Strong Fluctuation Theory, IEEE T. Geosci. Remote, 24, 220–231, https://doi.org/10.1109/TGRS.1986.289641, 1986.
Surdyk, S. and Fily, M.: Results of a stratified snow emissivity model based on the wave approach: application to the Antarctic ice sheet, J. Geophys. Res., 100, 8837–8848, 1995.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J., Koskinen, J., and Bojkov, B.: Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Remote Sens. Environ., 115, 3517–3529, https://doi.org/10.1016/j.rse.2011.08.014, 2011.
Tedesco, M. and Kim, E. J.: Intercomparison of Electromagnetic Models for Passive Microwave Remote Sensing of Snow, IEEE T. Geosci. Remote, 44, 2654–2666, https://doi.org/10.1109/TGRS.2006.873182, 2006.
Tedesco, M., Kim, E., Cline, D., Graf, T., Koike, T., Hardy, J., Armstrong, R., and Brodzik, M.: The cold land processes experiment (CLPX-1): analysis and modelling of LSOS data (IOP3 period), 6, 3725–3728, IEEE, https://doi.org/10.1109/IGARSS.2004.1369931, 2004.
Tedesco, M., Abdalati, W., and Zwally, H. J.: Persistent surface snowmelt over Antarctica (1987-2006) from 19.35 GHz brightness temperatures, Geophys. Res. Lett., 34, 6 pp., https://doi.org/10.1029/2007GL031199, 2007.
Toure, A. M., Goita, K., Royer, A., Kim, E. J., Durand, M., Margulis, S. A., and Lu, H.: A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation, IEEE T. Geosci. Remote, 49, 2828–2837, https://doi.org/10.1109/TGRS.2011.2118761, 2011.
Tsang, L. and Kong, J. A.: Scattering of electromagnetic waves, 3, Advanced Topics, Wiley Interscience, 2001.
Tsang, L., Kong, J. A., and Shin, R. T.: Theory of Microwave Remote Sensing, Wiley-Interscience, New York, 1985.
Tsang, L., Chen, C. T., Chang, A. T. C., Guo, J., and Ding, K. H.: Dense Media Radiative Transfer Theory Based on Quasicrystalline Approximation with Application to Passive Microwave Remote Sensing of Snow, Radio Sci., 35, 741–49, 2000a.
Tsang, L., Kong, J. A., Ding, K. H., and Ao, C.: Scattering of electromagnetic waves, 2, numerical solutions, Wiley Interscience, New York, 705 pp., http://www.amazon.com/Scattering-Electromagnetic-Waves-Numerical-Simulations/dp/0471388009/ref=pd_sim_b_1, 2000b.
Tsang, L., Pan, J., Liang, D., Li, Z. X., Cline, D., and Tan, Y. H.: Modeling active microwave remote sensing of snow using dense media radiative transfer(DMRT) theory with multiple scattering effects, IEEE T. Geosci. Remote, 45, 990–1004, 2007.
Tsang, L., Xu, P., and Chen, K. S.: Third and fourth stokes parameters in polarimetric passive microwave remote sensing of rough surfaces over layered media, Microw. Opt. Techn. Lett., 50, 3063–3069, 2008.
Twomey, S. and Bohren, C. F.: Simple Approximations for Calculations of Absorption in Clouds, J. Atmos. Sci., 37, 2086–2095, https://doi.org/10.1175/1520-0469(1980)037<2086:SAFCOA>2.0.CO;2, 1980.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing, 3, Artech house, Norwood, MA, 1986.
Vaughan, D. G., Bamber, J. L., Giovinetto, M., Russell, J., and Cooper, A. P. R.: Reassessment of net surface mass balance in Antarctica, J. Climate, 12, 933–946, 1999.
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.
Warren, S. G.: Optical Properties of Snow (Paper 1R1505), Rev. Geophys. Space Phys., 20, 67–89, 1982.
Watanabe, O.: Distribution of surface features of snow cover in Mizuho Plateau, Tech. rep., 1978.
West, R., Tsang, L., and Winebrenner, D. P.: Dense Medium Radiative Transfer theory for two scattering layers with a Rayleigh distribution of particle sizes, IEEE T. Geosci. Remote, 31, 436–437, 1993.
West, R. D., Winebrenner, D. P., Tsang, L., and Rott, H.: Microwave emission from density-stratified Antarctic firn at 6 cm wavelength, J. Glaciol., 42, 63–76, 1996.
Wiesmann, A. and Mätzler, C.: Microwave emission model of layered snowpacks, Remote Sens. Environ., 70, 307–316, 1999.
Wiesmann, A., Mätzler, C., and Weise, T.: Radiometric and structural mesurements of snow samples, Radio Sci., 33, 273–289, 1998.
Winebrenner, D. P., Arthern, R. J., and Shuman, C. A.: Mapping Greenland accumulation rates using observations of thermal emission at 4.5-cm wavelength, J. Geophys. Res., 106, 33919–33934, https://doi.org/10.1029/2001JD900235, 2001.
Zwally, H. J.: Microwave emissivity and accumulation rate of polar firn, J. Glaciol., 18, 195–214, 1977.
