Articles | Volume 14, issue 2
https://doi.org/10.5194/gmd-14-1101-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1101-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
24 Feb 2021
Development and technical paper |
| 24 Feb 2021
| 24 Feb 2021
A fully coupled Arctic sea-ice–ocean–atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description and preliminary results
Shihe Ren
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Qizhen Sun
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing, China
L. Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University,
Montreal, Canada
Bo Lin
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Xiaoping Mai
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Fu Zhao
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Ming Li
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Na Liu
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Zhikun Chen
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
Yunfei Zhang
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, China
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Revised manuscript not accepted
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The Cryosphere, 14, 3465–3478, https://doi.org/10.5194/tc-14-3465-2020, https://doi.org/10.5194/tc-14-3465-2020, 2020
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Damien Ringeisen, Martin Losch, L. Bruno Tremblay, and Nils Hutter
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The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
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Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
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Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)
SnowClim v1.0: high-resolution snow model and data for the western United States
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes
Explicitly modelling microtopography in permafrost landscapes in a land surface model (JULES vn5.4_microtopography)
Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)
Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)
NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture
The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation
SNICAR-ADv3: a community tool for modeling spectral snow albedo
STEMMUS-UEB v1.0.0: integrated modeling of snowpack and soil water and energy transfer with three complexity levels of soil physical processes
A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
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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.
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.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
EGUsphere, https://doi.org/10.5194/egusphere-2024-249, https://doi.org/10.5194/egusphere-2024-249, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge, even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties in representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
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.
Alessandro Maissen, Frank Techel, and Michele Volpi
EGUsphere, https://doi.org/10.5194/egusphere-2023-2948, https://doi.org/10.5194/egusphere-2023-2948, 2024
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By harnessing AI-models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, and thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
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
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
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We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
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In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Cited articles
Antonov, J. I., Locarnini, R., Boyer, T., Mishonov, A., Garcia, H., and
Levitus, S.: World Ocean Atlas 2005 Volume 2: Salinity, NOAA Atlas NESDIS,
62, 2006.
Bailey, D. A., Holland, M. M., DuVivier, A. K., Hunke, E. C., and Turner, A. K.: Impact of a New Sea Ice Thermodynamic Formulation in the CESM2 Sea Ice Component, J. Adv. Model. Earth Sy., 12, e2020MS002154,
https://doi.org/10.1029/2020MS002154, 2020.
Beszczynska, A.: Validation Data – WSC Moorings, available at: https://www.whoi.edu/page.do?pid=30914 (last access: 16 December 2018), 2011.
Brassington, G. B., Martin, M. J., Tolman, H. L., Akella, S., Balmeseda, M.,
Chambers, C. R. S., Chassignet, E., Cummings, J. A., Drillet, Y., Jansen, P.
A. E. M., Laloyaux, P., Lea, D., Mehra, A., Mirouze, I., Ritchie, H.,
Samson, G., Sandery, P. A., Smith, G. C., Suarez, M., and Todling, R.:
Progress and challenges in short- to medium-range coupled prediction,
J. Oper. Oceanogr., 8, s239–s258,
https://doi.org/10.1080/1755876X.2015.1049875, 2015.
Bromwich, D., Kuo, Y.-H., Serreze, M., Walsh, J., Bai, L.-S., Barlage, M.,
Hines, K., and Slater, A.: Arctic System Reanalysis: Call for Community
Involvement, Eos T. Am. Geophys. Un., 91, 13–14,
https://doi.org/10.1029/2010EO020001, 2010.
Bromwich, D. H., Otieno, F. O., Hines, K. M., Manning, K. W., and Shilo, E.: Comprehensive evaluation of polar weather research and forecasting model performance in the Antarctic, J. Geophys. Res.-Atmos.,
118, 274–292, https://doi.org/10.1029/2012JD018139, 2013.
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.: Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey, B. Am. Meteorol. Soc., 93, 1865–1877, https://doi.org/10.1175/BAMS-D-12-00018.1, 2012.
Cassano, J. J., DuVivier, A., Roberts, A., Hughes, M., Seefeldt, M., Brunke,
M., Craig, A., Fisel, B., Gutowski, W., Hamman, J., Higgins, M., Maslowski,
W., Nijssen, B., Osinski, R., and Zeng, X.: Development of the Regional
Arctic System Model (RASM): Near-Surface Atmospheric Climate Sensitivity,
J. Climate, 30, 5729–5753, https://doi.org/10.1175/jcli-d-15-0775.1, 2017.
Chen, F., and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:caalsh>2.0.co; 2, 2001.
Chen, S., Campbell, T. J., Jin, H., Gaberšek, S., Hodur, R. M., and Martin, P.: Effect of Two-Way Air–Sea Coupling in High and Low Wind Speed Regimes, Mon. Weather Rev., 138, 3579–3602, https://doi.org/10.1175/2009mwr3119.1,
2010.
Christidis, Z.: Performance and Scaling of WRF on Three Different Parallel Supercomputers, in: High Performance Computing, 30th International Conference, ISC High Performance 2015, Frankfurt, Germany, 12–16 July 2015, Springer, Cham, 514–528, 2015.
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017.
Daru, V. and Tenaud, C.: High order one-step monotonicity-preserving schemes for unsteady compressible flow calculations, J. Comput. Phys., 193, 563–594, 2004.
Day, J. J., Hawkins, E., and Tietsche, S.: Will Arctic sea ice thickness initialization improve seasonal forecast skill?, Geophys. Res. Lett., 41, 7566–7575, https://doi.org/10.1002/2014GL061694, 2014.
DeLuca, C., Theurich, G., and Balaji, V.: The Earth System Modeling Framework, in: Earth System Modelling – Volume 3: Coupling Software and Strategies, edited by: Valcke, S., Redler, R., and Budich, R., Springer, Berlin, Heidelberg, 43–54, 2012.
Doney, S. C., Large, W. G., and Bryan, F. O.: Surface Ocean Fluxes and Water-Mass Transformation Rates in the Coupled NCAR Climate System Model, J. Climate, 11, 1420–1441, https://doi.org/10.1175/1520-0442(1998)011<1420:sofawm>2.0.co; 2, 1998.
Dupont, F., Higginson, S., Bourdallé-Badie, R., Lu, Y., Roy, F., Smith, G. C., Lemieux, J.-F., Garric, G., and Davidson, F.: A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic oceans, Geosci. Model Dev., 8, 1577–1594, https://doi.org/10.5194/gmd-8-1577-2015, 2015.
Eastwood, S., Larsen, K. R., Lavergne, T., Nielsen, E., and Tonboe, R.: Global sea ice concentration reprocessing Product User Manual, EUMETSAT OSISAF, 2011.
Faucher, M., Roy, F., Desjardins, S., Fogarty, C., Pellerin, P., Ritchie, H., and Denis, B.: Operational coupled atmosphere – ocean – ice forecast system for the Gulf of St. Lawrence, Canada, 9th EMS Annual Meeting, Toulouse, France, 28 September–2 October 2009, EMS2009-274, available at: https://ui.adsabs.harvard.edu/abs/2009ems..confE.274F/abstract (last access: 10 October 2018), 2009.
Francis, J. A.: The where and when of wetter and drier: disappearing Arctic sea ice plays a role, Environ. Res. Lett., 8, 041002,
https://doi.org/10.1088/1748-9326/8/4/041002, 2013.
Grell, G. A. and Dévényi, D.: A generalized approach to
parameterizing convection combining ensemble and data assimilation
techniques, Geophys. Res. Lett., 29, 38-31–38-34,
https://doi.org/10.1029/2002gl015311, 2002.
Harada, Y., Kamahori, H., Kobayashi, C., Endo, H., Kobayashi, S., Ota, Y.,
Onoda, H., Onogi, K., Miyaoka, K., and Takahashi, K.: The JRA-55 Reanalysis:
Representation of Atmospheric Circulation and Climate Variability, J.
Meteorol. Soc. Jpn., 94, 269–302, https://doi.org/10.2151/jmsj.2016-015, 2016.
Hibler, W. D.: Modeling a Variable Thickness Sea Ice Cover, Mon. Weather Rev., 108, 1943–1973, https://doi.org/10.1175/1520-0493(1980)108<1943:MAVTSI>2.0.CO; 2, 1980.
Hines, K. M. and Bromwich, D. H.: Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet
Meteorology, Mon. Weather Rev., 136, 1971–1989, https://doi.org/10.1175/2007mwr2112.1,
2008.
Hines, K. M., Bromwich, D. H., Bai, L.-S., Barlage, M., and Slater, A. G.: Development and Testing of Polar WRF. Part III: Arctic Land, J. Climate, 24, 26–48, https://doi.org/10.1175/2010JCLI3460.1, 2011.
Jackett, D. R. and McDougall, T. J.: Minimal Adjustment of Hydrographic Profiles to Achieve Static Stability, J. Atmos. Ocean. Techn., 12, 381–389, https://doi.org/10.1175/1520-0426(1995)012<0381:MAOHPT>2.0.CO; 2, 1995.
Janjić, Z.: Nonsingular Implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model (NCEP Office Note No. 437), NCEP, Camp Springs, Md, 2002.
Jung, T., Gordon, N. D., Bauer, P., Bromwich, D. H., Chevallier, M., Day, J. J., Dawson, J., Doblas-Reyes, F., Fairall, C., Goessling, H. F., Holland, M., Inoue, J., Iversen, T., Klebe, S., Lemke, P., Losch, M., Makshtas, A., Mills, B., Nurmi, P., Perovich, D., Reid, P., Renfrew, I. A., Smith, G., Svensson, G., Tolstykh, M., and Yang, Q.: Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales, B. Am.
Meteorol. Soc., 97, 1631–1647, https://doi.org/10.1175/bams-d-14-00246.1, 2016.
Kaleschke, L., Tian-Kunze, X., Maaß, N., Mäkynen, M., and Drusch, M.: Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophys. Res. Lett., 39, L05501,
https://doi.org/10.1029/2012GL050916, 2012.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics,
J. Meteorol. Soc. Jpn., 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Large, W. G., Mcwilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, 1994.
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen, R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737, https://doi.org/10.1002/grl.50193, 2013.
Liang, X. and Losch, M.: On the Effects of Increased Vertical Mixing on the Arctic Ocean and Sea Ice, J. Geophys. Res.-Oceans, 123, 9266–9282, https://doi.org/10.1029/2018jc014303, 2018.
Liang, X., Losch, M., Nerger, L., Mu, L., Yang, Q., and Liu, C.: Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice-Ocean Data Assimilation System, J. Geophys. Res.-Oceans, 124, 4727–4743, https://doi.org/10.1029/2019jc015073, 2019.
Liang, X., Zhao, F., Li, C., Zhang, L., and Li, B.: Evaluation of ArcIOPS sea ice forecasting products during the ninth CHINARE-Arctic in summer 2018, Adv. Polar Sci., 31, 19–30, 2020.
Liu, L., Yang, G., Wang, B., Zhang, C., Li, R., Zhang, Z., Ji, Y., and Wang, L.: C-Coupler1: a Chinese community coupler for Earth system modeling, Geosci. Model Dev., 7, 2281–2302, https://doi.org/10.5194/gmd-7-2281-2014, 2014.
Liu, L., Zhang, C., Li, R., Wang, B., and Yang, G.: C-Coupler2: a flexible and user-friendly community coupler for model coupling and nesting, Geosci. Model Dev., 11, 3557–3586, https://doi.org/10.5194/gmd-11-3557-2018, 2018.
Locarnini, R. A., Mishonov, A., Antonov, J., Boyer, T., Garcia, H., and Levitus, S.: World Ocean Atlas 2005 Volume 1: Temperature NOAA Atlas NESDIS, 61, 2006.
Losch, M., Menemenlis, D., Campin, J.-M., Heimbach, P., and Hill, C.: On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations, Ocean Model., 33, 129–144, https://doi.org/10.1016/j.ocemod.2009.12.008, 2010.
Lynch, A. H., Chapman, W. L., Walsh, J. E., and Weller, G.: Development of a Regional Climate Model of the Western Arctic, J. Climate, 8, 1555–1570, https://doi.org/10.1175/1520-0442(1995)008<1555:doarcm>2.0.co;2, 1995.
Marshall, J., Hill, C., Perelman, L., and Adcroft, A.: Hydrostatic, quasi-hydrostatic, and nonhydrostatic ocean modeling, J. Geophys. Res.-Oceans, 102, 5733–5752, https://doi.org/10.1029/96jc02776, 1997.
Martin, M., Dash, P., Ignatov, A., Banzon, V., Beggs, H., Brasnett, B., Cayula, J.-F., Cummings, J., Donlon, C., Gentemann, C., Grumbine, R., Ishizaki, S., Maturi, E., Reynolds, R. W., and Roberts-Jones, J.: Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE), Deep-Sea
Res. Pt. II, 77–80, 21–30,
https://doi.org/10.1016/j.dsr2.2012.04.013, 2012.
Maslowski, W., Clement Kinney, J., Higgins, M., and Roberts, A.: The Future of Arctic Sea Ice, Annu. Rev. Earth Pl. Sc., 40, 625–654, https://doi.org/10.1146/annurev-earth-042711-105345, 2012.
Melling, H. and Riedel, D. A.: The underside topography of sea ice over the continental shelf of the Beaufort Sea in the winter of 1990, J. Geophys. Res.-Oceans, 100, 13641–13653, https://doi.org/10.1029/95JC00309, 1995.
Menemenlis, D., Campin, J. M., Heimbach, P., Hill, C., Lee, T., Nguyen, A., Schodlok, M., and Zhang, H.: ECCO2: High resolution global ocean and sea ice data synthesis, Ocean Q. Newsl., 31, 13–21, 2008.
Neelin, J. D., Latif, M., and Jin, F.: Dynamics of Coupled Ocean-Atmosphere Models: The Tropical Problem, Annu. Rev. Fluid Mech., 26, 617–659, https://doi.org/10.1146/annurev.fl.26.010194.003153, 1994.
Nguyen, A. T., Menemenlis, D., and Kwok, R.: Arctic ice-ocean simulation with optimized model parameters: Approach and assessment, J. Geophys. Res.-Oceans, 116, C04025, https://doi.org/10.1029/2010JC006573, 2011.
Notz, D. and Bitz, C. M.: Sea ice in Earth system models, in: Sea Ice, edited by: Thomas, D. N., Wiley, US, 304–325, https://doi.org/10.1002/9781118778371.ch12, 2017.
Pellerin, P., Ritchie, H., Saucier, F. J., Roy, F., Desjardins, S., Valin, M., and Lee, V.: Impact of a Two-Way Coupling between an Atmospheric and an Ocean-Ice Model over the Gulf of St. Lawrence, Mon. Weather Rev., 132, 1379–1398, https://doi.org/10.1175/1520-0493(2004)132<1379:ioatcb>2.0.co;2, 2004.
Posey, P. G., Metzger, E. J., Wallcraft, A. J., Hebert, D. A., Allard, R. A., Smedstad, O. M., Phelps, M. W., Fetterer, F., Stewart, J. S., Meier, W. N., and Helfrich, S. R.: Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems, The Cryosphere, 9, 1735–1745, https://doi.org/10.5194/tc-9-1735-2015, 2015.
Proshutinsky, A., Yang, J., Krishfield, R., Gerdes, R., Karcher, M., Kauker, F., Koeberle, C., Hakkinen, S., Hibler, W., Holland, D., Maqueda, M., Holloway, G., Hunke, E., Maslowski, W., Steele, M., and Zhang, J.: Arctic ocean study: Synthesis of model results and observations, Eos T. Am. Geophys. Un., 86, 368–371, https://doi.org/10.1029/2005EO400003, 2005.
Ren, S., Yu, H., Liang, X., and Mai, X.: A fully coupled Arctic sea ice-ocean-atmosphere model (ArcIOAM) based on C-Coupler2, (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.3742692, 2020.
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.: Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation, The Cryosphere, 8, 1607–1622, https://doi.org/10.5194/tc-8-1607-2014, 2014.
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017.
Rinke, A., Lynch, A. H., and Dethloff, K.: Intercomparison of Arctic regional climate simulations: Case studies of January and June 1990, J. Geophys. Res.-Atmos., 105, 29669–29683,
https://doi.org/10.1029/2000jd900325, 2000.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-y., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Delst, P. V., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Dool, H. v. d., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou,
C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge,
G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B.
Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010bams3001.1, 2010.
Schrum, C., Hübner, U., Jacob, D., and Podzun, R.: A coupled
atmosphere/ice/ocean model for the North Sea and the Baltic Sea, Clim.
Dynam., 21, 131–151, https://doi.org/10.1007/s00382-003-0322-8, 2003.
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate, J. Phys. Oceanogr., 6, 379–389,
https://doi.org/10.1175/1520-0485(1976)006<0379:amfttg>2.0.co;2, 1976.
Simmonds, I. and Rudeva, I.: The great Arctic cyclone of August 2012, Geophys. Res. Lett., 39, L23709, https://doi.org/10.1029/2012gl054259, 2012.
Skachko, S., Buehner, M., Laroche, S., Lapalme, E., Smith, G., Roy, F., Surcel-Colan, D., Bélanger, J.-M., and Garand, L.: Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1), Geosci. Model Dev., 12, 5097–5112, https://doi.org/10.5194/gmd-12-5097-2019, 2019.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., and Duda, M. G.: A Description of the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008.
Smith, G. C., Roy, F., Mann, P., Dupont, F., Brasnett, B., Lemieux, J.-F.,
Laroche, S., and Bélair, S.: A new atmospheric dataset for forcing
ice–ocean models: Evaluation of reforecasts using the Canadian global
deterministic prediction system, Q. J. Roy.
Meteor. Soc., 140, 881–894, https://doi.org/10.1002/qj.2194, 2014.
Smith, G. C., Roy, F., Reszka, M., Surcel Colan, D., He, Z., Deacu, D.,
Belanger, J.-M., Skachko, S., Liu, Y., Dupont, F., Lemieux, J.-F., Beaudoin,
C., Tranchant, B., Drévillon, M., Garric, G., Testut, C.-E., Lellouche,
J.-M., Pellerin, P., Ritchie, H., Lu, Y., Davidson, F., Buehner, M., Caya,
A., and Lajoie, M.: Sea ice forecast verification in the Canadian Global Ice
Ocean Prediction System, Q. J. Roy. Meteor. Soc., 142, 659–671, https://doi.org/10.1002/qj.2555, 2016.
Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu, D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Weather Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018.
Sun, R., Subramanian, A. C., Miller, A. J., Mazloff, M. R., Hoteit, I., and Cornuelle, B. D.: SKRIPS v1.0: a regional coupled ocean–atmosphere modeling framework (MITgcm–WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea, Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, 2019.
Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N., Drusch, M., and Krumpen, T.: SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 8, 997–1018, https://doi.org/10.5194/tc-8-997-2014, 2014.
U.S. National Ice Center and National Snow and Ice Data Center: compiled by Fetterer, F., Savoie, M., Helfrich, S., and Clemente-Colón, P., updated daily, Multisensor Analyzed Sea Ice Extent – Northern Hemisphere (MASIE-NH), Version 1. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center, https://doi.org/10.7265/N5GT5K3K, 2010.
Van Pham, T., Brauch, J., Dieterich, C., Frueh, B., and Ahrens, B.: New coupled atmosphere-ocean-ice system COSMO-CLM/NEMO: assessing air
temperature sensitivity over the North and Baltic Seas, Oceanologia, 56,
167–189, https://doi.org/10.5697/oc.56-2.167, 2014.
Walters, D. N., Best, M. J., Bushell, A. C., Copsey, D., Edwards, J. M., Falloon, P. D., Harris, C. M., Lock, A. P., Manners, J. C., Morcrette, C. J., Roberts, M. J., Stratton, R. A., Webster, S., Wilkinson, J. M., Willett, M. R., Boutle, I. A., Earnshaw, P. D., Hill, P. G., MacLachlan, C., Martin, G. M., Moufouma-Okia, W., Palmer, M. D., Petch, J. C., Rooney, G. G., Scaife, A. A., and Williams, K. D.: The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations, Geosci. Model Dev., 4, 919–941, https://doi.org/10.5194/gmd-4-919-2011, 2011.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert,
D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P.,
Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S.
F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey,
D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A.,
Woollings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0
and 3.1 (GC3.0 and GC3.1) Configurations, J. Adv. Model.
Earth Sy., 10, 357–380, https://doi.org/10.1002/2017ms001115, 2018.
Wingham, D. J., Francis, C. R., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., de Chateau-Thierry, P., Laxon, S. W., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D. W.: CryoSat: A mission to determine the fluctuations in Earth's land and marine ice fields, Adv. Space Res., 37, 841–871,
https://doi.org/10.1016/j.asr.2005.07.027, 2006.
Yang, C.-Y., Liu, J., and Xu, S.: Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations, J. Adv. Model. Earth Sy., 12, e2019MS001938, https://doi.org/10.1029/2019MS001938, 2020.
Yang, Y., Qiao, F., Zhao, W., Y., T., and Yuan, Y.: MASNUM ocean wave
numerical model in spherical coordinates and its application, Acta Oceanol.
Sin., 27, 1–7, 2005.
Zhang, J. and Hibler III, W. D.: On an efficient numerical method for modeling sea ice dynamics, J. Geophys. Res.-Oceans, 102,
8691–8702, https://doi.org/10.1029/96JC03744, 1997.
Zhang, J. and Rothrock, D. A.: Modeling Global Sea Ice with a Thickness and Enthalpy Distribution Model in Generalized Curvilinear Coordinates, Mon. Weather Rev., 131, 845–861, https://doi.org/10.1175/1520-0493(2003)131<0845:mgsiwa>2.0.co;2, 2003.
Zhao, B., Qiao, F., Cavaleri, L., Wang, G., Bertotti, L., and Liu, L.: Sensitivity of typhoon modeling to surface waves and rainfall, J. Geophys. Res.-Oceans, 122, 1702–1723, https://doi.org/10.1002/2016jc012262, 2017.
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.
Sea ice plays a crucial role in global energy and water budgets. To get a better simulation of...
