Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1155-2022
© Author(s) 2022. 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-15-1155-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Feb 2022
Model description paper |
| 08 Feb 2022
| 08 Feb 2022
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
Chao-Yuan Yang
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, and Southern
Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai,
Guangdong, China
Jiping Liu
CORRESPONDING AUTHOR
Department of Atmospheric and Environmental Sciences, University at
Albany, State University of New York, Albany, NY, USA
Dake Chen
School of Atmospheric Sciences, Sun Yat-sen University, and Southern
Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai,
Guangdong, China
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Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
Geosci. Model Dev., 18, 2945–2960, https://doi.org/10.5194/gmd-18-2945-2025, https://doi.org/10.5194/gmd-18-2945-2025, 2025
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Geosci. Model Dev., 18, 2545–2568, https://doi.org/10.5194/gmd-18-2545-2025, https://doi.org/10.5194/gmd-18-2545-2025, 2025
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Maxence Menthon, Pepijn Bakker, Aurélien Quiquet, Didier M. Roche, and Ronja Reese
EGUsphere, https://doi.org/10.5194/egusphere-2025-777, https://doi.org/10.5194/egusphere-2025-777, 2025
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The ice-ocean interaction is a large source of uncertainty in future projections of the Antarctic ice sheet. Here we implement a basal ice shelf melt module (PICO) in a ice sheet model (GRISLI) and test six simple statistical methods to calibrate this module. We show that calculating the mean absolute error of bins best fits the observational datasets under multiple conditions. We also assess the impact of the module implementation and calibration choice on future projections until 2300.
Gong Cheng, Mansa Krishna, and Mathieu Morlighem
EGUsphere, https://doi.org/10.5194/egusphere-2025-1188, https://doi.org/10.5194/egusphere-2025-1188, 2025
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Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE, an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
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We put global climate models to the test against NASA’s ICESat-2 satellite to see how well they simulate global sea ice cover. By adding fancy laser data from ICESat-2, we can better assess how well the models are performing compared to the standard assessments of sea ice area. Overall the models do a good job but there’s room for improvement, especially across the Southern Ocean. We should think a bit more about sea ice density if we want more reliable freeboard comparisons.
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Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
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We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
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We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Théo Brivoal, Virginie Guemas, Martin Vancoppenolle, Clément Rousset, and Bertrand Decharme
EGUsphere, https://doi.org/10.5194/egusphere-2024-3220, https://doi.org/10.5194/egusphere-2024-3220, 2025
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Snow in polar regions is key to sea ice formation and the Earth's climate, but current climate models simplify snow cover on sea ice. This study integrates an intermediate complexity snow-physics scheme into a sea-ice model designed for climate applications. We show that modelling the temporal changes in properties such as the density and thermal conductivity of the snow layers leads to a more accurate representation of heat transfer between the underlying sea ice and the atmosphere.
Samar Minallah, William Lipscomb, Gunter Leguy, and Harry Zekollari
EGUsphere, https://doi.org/10.5194/egusphere-2024-4152, https://doi.org/10.5194/egusphere-2024-4152, 2025
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We implemented a new modeling framework within an Earth system model to study the evolution of mountain glaciers under different climate scenarios and applied it to the European Alps. Alpine glaciers will lose a large volume fraction under current temperatures, with near complete ice loss under warmer scenarios. This is the first use of a 3D, higher-order ice flow model for regional-scale glacier simulations that will enable assessments of coupled land ice and Earth system processes.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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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, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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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 past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. 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
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 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 when 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.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 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, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
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.
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.
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.
Cited articles
Aagaard, K.: A synthesis of the Arctic Ocean circulation, Rapp. P.-V. Reun.-Cons. Int. Explor. Mer., 188, 11–22, 1989.
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.
Bateson, A. W., Feltham, D. L., Schröder, D., Hosekova, L., Ridley, J. K., and Aksenov, Y.: Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice, The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, 2020.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic sea ice
model for climate study, J. Geophys. Res.-Oceans, 104, 15669–15677, 1999.
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R.,
Smirnova, T. G., and Manikin, G. S.: A North American hourly assimilation and
model forecast cycle: the Rapid Refresh, Mon. Weather Rev.,
144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1, 2016.
Bennetts, L. G., O'Farrell, S., and Uotila, P.: Brief communication: Impacts of ocean-wave-induced breakup of Antarctic sea ice via thermodynamics in a stand-alone version of the CICE sea-ice model, The Cryosphere, 11, 1035–1040, https://doi.org/10.5194/tc-11-1035-2017, 2017.
Biswas, M. K., Zhang, J. A., Grell, E., Kalina, E., Newman, K., Bernardet,
L., Carson, L., Frimel, J., and Grell, G.: Evaluation of the Grell–Freitas
Convective Scheme in the Hurricane Weather Research and Forecasting (HWRF)
Model, Weather Forecast., 35, 1017–1033, 2020.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic sea ice model for climate study, J. Geophys. Res.-Oceans, 104, 15669–15677, https://doi.org/10.1029/1999JC900100, 1999.
Blanchard-Wrigglesworth, E. and Bushuk, M.: Robustness of Arctic sea-ice
predictability in GCMs, Clim. Dynam., 52, 5555–5566, 2018.
Blanchard-Wrigglesworth, E., Bitz, C., and Holland, M.: Influence of initial
conditions and climate forcing on predicting Arctic sea ice, Geophys.
Res. Lett., 38, L18503, https://doi.org/10.1029/2011GL048807, 2011.
Blanchard-Wrigglesworth, E., Cullather, R., Wang, W., Zhang, J., and Bitz,
C. M.: Model forecast skill and sensitivity to initial conditions in the
seasonal sea ice outlook, Geophys. Res. Lett., 42, 8042–8048,
https://doi.org/10.1002/2015GL065860, 2015.
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F.: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone, The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, 2020.
Briegleb, B. P. and Light, B.: A Delta-Eddington multiple scattering
parameterization for solar radiation in the sea ice component of the
Community Climate System Model, NCAR Tech. Note NCAR/TN-472+STR, National
Center for Atmospheric Research, 2007.
Bruyère, C. L., Done, J. M., Holland, G. J., and Fredrick, S.: Bias
corrections of global models for regional climate simulations of high-impact
weather, Clim. Dynam., 43, 1847–1856,
https://doi.org/10.1007/s00382-013-2011-6, 2014.
Carmack, E., Polyakov, I., Padman, L., Fer, I., Hunke, E., Hutchings, J.,
Jackson, J., Kelley, D., Kwok, R., Layton, C., Melling, H., Perovich, D.,
Persson, O., Ruddick, B., Timmermans, M.-L., Toole, J., Ross, T., Vavrus,
S., and Winsor, P.: Toward Quantifying the Increasing Role of Oceanic Heat
in Sea Ice Loss in the New Arctic, B. Am. Meteorol.
Soc., 96, 2079–2105,
https://doi.org/10.1175/BAMS-D-13-00177.1, 2015.
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H. J.: updated
yearly, Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS
Passive Microwave Data, Version 1. Boulder, Colorado USA, NASA National Snow
and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/8GQ8LZQVL0VL, 1996.
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, 2001.
Chevallier, M., Salas y Mélia, D., Voldoire, A., Déqué, M., and
Garric, G.: Seasonal forecasts of the pan-Arctic sea ice extent using a
GCM-based seasonal prediction system, J. Climate, 26,
6092–6104, 2013.
Colette, A., Vautard, R., and Vrac, M.: Regional climate downscaling with
prior statistical correction of the global climate forcing, Geophys. Res.
Lett., 39, L13707, https://doi.org/10.1029/2012GL052258, 2012.
Collins, W. D., Rasch, P. J., Boville, B. A., McCaa, J., Williamson, D. L., Kiehl, J. T., Briegleb, B. P., Bitz, C., Lin, S.-J., Zhang, M., and Dai, Y.: Description of the NCAR Community Atmosphere Model (3.0), No. NCAR/TN-464+STR, University Corporation for Atmospheric Research, https://doi.org/10.5065/D63N21CH, 2004.
Craig, T., Hunke, E., DuVivier, A., dabail10, Damsgaard, A., JFLemieux73, Blain, P., Turner, M., mhrib, Rasmussen, T., and Jeffery, N.: CICE-Consortium/CICE: CICE version 6.0.0 (CICE6.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.1900639, 2018.
Day, J. J., Tietsche, S., Collins, M., Goessling, H. F., Guemas, V., Guillory, A., Hurlin, W. J., Ishii, M., Keeley, S. P. E., Matei, D., Msadek, R., Sigmond, M., Tatebe, H., and Hawkins, E.: The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1, Geosci. Model Dev., 9, 2255–2270, https://doi.org/10.5194/gmd-9-2255-2016, 2016.
Ding, Y., Cheng, X., Liu, J., Hui, F., Wang, Z., and Chen, S.: Retrieval of
Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an
Ensemble-Based Deep Neural Network, Remote Sensing, 12, 2746, https://doi.org/10.3390/rs12172746, 2020.
DuVivier, A. K., Holland, M. M., Landrum, L., Singh, H. A., Bailey, D. A.,
and Maroon, E. A.: Impacts of sea ice mushy thermodynamics in the Antarctic
on the coupled Earth system, Geophys. Res. Lett., 48,
e2021GL094287, https://doi.org/10.1029/2021GL094287, 2021.
Fer, I.: Near-inertial mixing in the central Arctic Ocean, J. Phys.
Oceanogr., 44, 2031–2049, https://doi.org/10.1175/JPO-D-13-0133.1, 2014.
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.:
updated daily, Sea Ice Index, Version 3. Boulder, Colorado USA, NSIDC:
National Snow and Ice Data Center [data set], https://doi.org/10.7265/N5K072F8, 2017.
Freitas, S. R., Grell, G. A., Molod, A., Thompson, M. A., Putman, W. M.,
Santos e Silva, C. M., and Souza, E. P.: Assessing the Grell–Freitas
convection parameterization in the NASA GEOS modeling system, J. Adv. Model.
Earth Sy., 10, 1266–1289, https://doi.org/10.1029/2017MS001251, 2018.
Freitas, S. R., Grell, G. A., and Li, H.: The Grell–Freitas (GF) convection parameterization: recent developments, extensions, and applications, Geosci. Model Dev., 14, 5393–5411, https://doi.org/10.5194/gmd-14-5393-2021, 2021.
Germe, A., Chevallier, M., y Mélia, D. S., Sanchez-Gomez, E., and
Cassou, C.: Interannual predictability of Arctic sea ice in a global climate
model: Regional contrasts and temporal evolution, Clim. Dynam.,
43, 2519–2538, 2014.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J., Déqué, M., Doblas-Reyes, F. J., Fučkar, N. S., Germe, A., Hawkins, E., Keeley, S., Koenigk, T., Salas y Mélia, D., and Tietsche, S.: A review on Arctic sea-ice
predictability and prediction on seasonal to decadal time-scales, Q.
J. Roy. Meteorol. Soc., 142, 546–561, 2016.
Hecht, M. W., Wingate, B. A., and Kassis, P.: A better, more discriminating
test problem for ocean tracer transport, Ocean Model., 2, 1–15,
https://doi.org/10.1016/S1463-5003(00)00004-4, 2000.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Horvat, C. and Tziperman, E.: A prognostic model of the sea-ice floe size and thickness distribution, The Cryosphere, 9, 2119–2134, https://doi.org/10.5194/tc-9-2119-2015, 2015.
Horvat, C., Tziperman, E., and Campin, J.-M.: Interaction of sea ice floe
size, ocean eddies, and sea ice melting, Geophys. Res. Lett., 43,
8083–8090, https://doi.org/10.1002/2016GL069742, 2016.
Huang, Y., Chou, G., Xie, Y., and Soulard, N.: Radiative control of the
interannual variability of Arctic sea ice, Geophys. Res. Lett., 46,
9899–9908, https://doi.org/10.1029/2019GL084204, 2019.
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997.
IPCC: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B.,
Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp., 2007.
Itoh, M., Nishino, S., Kawaguchi, Y., and Kikuchi, T.: Barrow Canyon volume,
heat, and freshwater fluxes revealed by long-term mooring observations
between 2000 and 2008, J. Geophys. Res.-Oceans, 118, 4363–4379,
https://doi.org/10.1002/jgrc.20290, 2013.
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.
Kapsch, M., Graversen, R. G., Tjernström, M., and Bintanja, R.: The
Effect of Downwelling Longwave and Shortwave Radiation on Arctic Summer Sea
Ice, J. Climate, 29, 1143–1159, https://doi.org/10.1175/JCLI-D-15-0238.1, 2016.
Kay, J. E., L'Ecuyer, T., Gettelman, A., Stephens, G., and O'Dell, C.: The
contribution of cloud and radiation anomalies to the 2007 Arctic sea ice
extent minimum, Geophys. Res. Lett., 35, L08503, https://doi.org/10.1029/2008GL033451,
2008.
Keen, A., Blockley, E., Bailey, D. A., Boldingh Debernard, J., Bushuk, M., Delhaye, S., Docquier, D., Feltham, D., Massonnet, F., O'Farrell, S., Ponsoni, L., Rodriguez, J. M., Schroeder, D., Swart, N., Toyoda, T., Tsujino, H., Vancoppenolle, M., and Wyser, K.: An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models, The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, 2021.
Kirkman, C. H. IV, and Bitz, C. M.: The Effect of the Sea Ice Freshwater
Flux on Southern Ocean Temperatures in CCSM3: Deep-Ocean Warming and Delayed
Surface Warming, J. Climate, 24, 2224–2237, https://doi.org/10.1175/2010JCLI3625.1, 2011.
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage:
Losses and coupled variability (1958–2018), Environ. Res. Lett.,
13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018.
Laxon, S., 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.
Leonard, B. and Mokhtari, S.: ULTRA-SHARP Non oscillatory Convection Schemes
for High-Speed Steady Multidimensional Flow, Technical Report, NASA, 1990.
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.
Liu, J., Song, M., Horton, R., and Hu, Y.: Revisiting the potential of melt
pond fraction as a predictor for the seasonal Arctic sea ice minimum,
Environ. Res. Lett., 10, 054017,
https://doi.org/10.1088/1748-9326/10/5/054017, 2015.
Liu, J., Chen, Z., Hu, Y., Zhang, Y., Ding, Y., Cheng, X., Yang, Q., Nerger, L., Spreen, G., Horton, R., Inoue, J., Yang, C.-Y., Li, M., and Song, M.: Towards
reliable arctic sea ice prediction using multivariate data assimilation,
Sci. Bull., 64, 63–72, 2019.
Mallett, R. D. C., Stroeve, J. C., Cornish, S. B. Crawford, A. D., Lukovich,
J. V., Serreze, M. C., Barrett, A. P., Meier, W. N., Heorton, H. D. B. S.,
and Tsamados, M.: Record winter winds in 2020/21 drove exceptional Arctic
sea ice transport, Commun. Earth Environ., 2, 149, https://doi.org/10.1038/s43247-021-00221-8, 2021.
Maslanik, J. and Stroeve, J.: Near-Real-Time DMSP SSMIS Daily Polar Gridded
Sea Ice Concentrations, Version 1, Boulder, Colorado USA, NASA National Snow
and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/U8C09DWVX9LM, 1999.
McLaughlin, F. A., Carmack, E. C., Williams, W. J., Zimmerman, S., Shimada,
K., and Itoh, M.: Joint effects of boundary currents and thermohaline
intrusions on the warming of Atlantic water in the Canada Basin, 1993–2007,
J. Geophys. Res., 114, C00A12, https://doi.org/10.1029/2008JC005001, 2009.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
Msadek, R., Vecchi, G., Winton, M., and Gudgel, R.: Importance of initial
conditions in seasonal predictions of Arctic sea ice extent, Geophys.
Res. Lett., 41, 5208–5215, https://doi.org/10.1002/2014GL060799,
2014.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn., 87,
895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
Naughten, K. A., Galton-Fenzi, B. K., Meissner, K. J., England, M. H.,
Brassington, G. B., Colberg, F., Hattermann, T., and Debernard, J. B.:
Spurious sea ice formation caused by oscillatory ocean tracer advection
schemes, Ocean Model., 116, 108–117, 2017.
Naughten, K. A., Meissner, K. J., Galton-Fenzi, B. K., England, M. H., Timmermann, R., Hellmer, H. H., Hattermann, T., and Debernard, J. B.: Intercomparison of Antarctic ice-shelf, ocean, and sea-ice interactions simulated by MetROMS-iceshelf and FESOM 1.4, Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, 2018.
Nerger, L. and Hiller, W.: Software for Ensemble-based Data Assimilation
Systems – Implementation Strategies and Scalability, Comput.
Geosci., 55, 110–118, https://doi.org/10.1016/j.cageo.2012.03.026, 2013.
Nerger, L., Janjić, T., Schröter, J., and Hiller, W.: A unification
of ensemble square root Kalman filters, Mon. Weather Rev., 140,
2335–2345, https://doi.org/10.1175/MWR-D-11-00102.1, 2012.
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020.
Newton, R., Pfirman, S., Schlosser, P., Tremblay, B., Murray, M., and
Pomerance, R.: White Arctic vs. Blue Arctic: A case study of diverging
stakeholder responses to environmental change, Earth's Future, 4, 396–405,
https://doi.org/10.1002/2016EF000356, 2016.
Nicolaus, M. and Katlein, C.: Mapping radiation transfer through sea ice using a remotely operated vehicle (ROV), The Cryosphere, 7, 763–777, https://doi.org/10.5194/tc-7-763-2013, 2013.
Nicolaus M., Katlein, C., Maslanik, J., and Hendricks, S.: Changes in Arctic
sea ice result in increasing light transmittance and absorption, Geophys.
Res. Lett., 39, L24501, https://doi.org/10.1029/2012GL053738, 2012.
Notz, D., Jahn, A., Holland, M., Hunke, E., Massonnet, F., Stroeve, J., Tremblay, B., and Vancoppenolle, M.: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): understanding sea ice through climate-model simulations, Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, 2016.
Ogi, M., Yamazaki, K., and Wallace, J. M.: Influence of winter and summer
surface wind anomalies on summer Arctic sea ice extent, Geophys. Res. Lett.,
37, L07701, https://doi.org/10.1029/2009GL042356, 2010.
Olonscheck, D., Mauritsen, T., and Notz, D.: Arctic sea-ice variability is
primarily driven by atmospheric temperature fluctuations, Nat. Geosci., 12,
430–434, https://doi.org/10.1038/s41561-019-0363-1, 2019.
Padman, L. and Dillon, T. M.: Vertical heat fluxes through the Beaufort Sea
thermohaline staircase, J. Geophys. Res., 92, 10799–10806,
https://doi.org/10.1029/JC092iC10p10799, 1987.
Perovich, D., Richter-Menge, J., Jones, K., Light, B., Elder, B.,
Polashenski, C., Laroche, D., Markus, T., and Lindsay, R.: Arctic sea-ice
melt in 2008 and the role of solar heating, Ann. Glaciol., 52,
355–359, https://doi.org/10.3189/172756411795931714, 2011.
Perovich, D., Richter-Menge, J., Polashenski, C., Elder, B., Arbetter, T.,
and Brennick, O.: Sea ice mass balance observations from the North Pole
Environmental Observatory, Geophys. Res. Lett., 41, 2019–2025,
https://doi.org/10.1002/2014GL059356, 2014.
Peterson, K., Arribas, A., Hewitt, H., Keen, A., Lea, D., and McLaren, A.:
Assessing the forecast skill of Arctic sea ice extent in the GloSea4
seasonal prediction system, Clim. Dynam., 44, 147–162, 2015.
Pham, D. T.: Stochastic methods for sequential data assimilation in strongly
nonlinear systems, Mon. Weather Rev., 129, 1194–1207, 2001.
Rasch, P. J.: Conservative shape-preserving two-dimensional transport on a
spherical reduced grid, Mon. Weather Rev, 122, 1337–1350, 1994.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface
Temperature, J. Climate, 20, 5473–5496, 2007.
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.
Roach, L. A., Horvat, C., Dean, S. M., and Bitz, C. M.: An emergent sea ice
floe size distribution in a global coupled ocean–sea ice model, J.
Geophys. Res.-Oceans, 123, 4322–4337, https://doi.org/10.1029/2017JC013692, 2018.
Roach, L. A., Bitz, C. M., Horvat, C., and Dean, S. M.: Advances in modeling
interactions between sea ice and ocean surface waves, J. Adv.
Model. Earth Sy., 11, 4167–4181,
https://doi.org/10.1029/2019MS001836, 2019.
Rocheta, E., Evans, J. P., and Sharma, A.: Can Bias Correction of Regional
Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall
Variability?, J. Climate, 30, 9785–9806, 2017.
Rocheta, E., Evans, J. P., and Sharma, A.: Correcting lateral boundary biases
in regional climate modelling: the effect of the relaxation zone, Clim.
Dynam., 55, 2511–2521, https://doi.org/10.1007/s00382-020-05393-1, 2020.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP climate forecast system
version 2, J. Climate, 27, 2185–2208, 2014.
Schmidt, G. A., Bitz, C. M., Mikolajewicz, U., and Tremblay, L.-B.:
Ice–ocean boundary conditions for coupled models, Ocean Model., 7, 59–74,
2004.
Serreze, M. C. and Meier, W. N.: The Arctic's sea ice cover: trends,
variability, predictability, and comparisons to the Antarctic, Ann. N.Y.
Acad. Sci., 1436, 36–53, https://doi.org/10.1111/nyas.13856, 2019.
Shchepetkin, A. F. and McWilliams, J. C.: Quasi-monotone advection schemes based
on explicit locally adaptive dissipation, Mon. Weather Rev., 126,
1541–1580, 1998.
Shchepetkin, A. F. and McWilliams, J. C.: The Regional Ocean Modeling
System: A split-explicit, free-surface, topography following coordinates
ocean model, Ocean Model., 9, 347–404, 2005.
Shi, X., Notz, D., Liu, J., Yang, H., and Lohmann, G.: Sensitivity of Northern Hemisphere climate to ice–ocean interface heat flux parameterizations, Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, 2021.
Sigmond, M., Fyfe, J., Flato, G., Kharin, V., and Merryfield, W.: Seasonal
forecast skill of Arctic sea ice area in a dynamical forecast system,
Geophys. Res. Lett., 40, 529–534,
https://doi.org/10.1002/grl.50129, 2013.
Smolarkiewicz, P. K.: Multidimensional positive definite advection transport
algorithm: An overview, Int. J. Numer. Meth. Fl., 50, 1123–1144, 2006.
Song, Y. and Haidvogel, D. B.: A semi-implicit ocean circulation model using
a generalized topography-following coordinate system, J. Comp. Phys.,
115, 228–244, 1994.
Steele, M.: Sea ice melting and floe geometry in a simple ice-ocean model,
J. Geophys. Res.-Oceans, 97, 17729–17738, https://doi.org/10.1029/92JC01755, 1992.
Stroeve, J., Hamilton, L. C., Bitz, C. M., and Blanchard-Wrigglesworth, E.:
Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook
2008–2013, Geophys. Res. Lett., 41, 2411–2418,
https://doi.org/10.1002/2014GL059388, 2014.
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.
Tietsche, S., Day, J., Guemas, V., Hurlin, W., Keeley, S., Matei, D., Msadek, R., Collins, M., and Hawkins, E.: Seasonal to interannual Arctic sea ice predictability in current global
climate models, Geophys. Res. Lett., 41, 1035–1043,
https://doi.org/10.1002/2013GL058755, 2014.
Turner, A. K. and Hunke, E. C.: Impacts of a mushy-layer thermodynamic
approach in global sea-ice simulations using the CICE sea-ice model, J.
Geophys. Res.-Oceans, 120, 1253–1275, https://doi.org/10.1002/2014JC010358, 2015.
Turner, A. K., Hunke, E. C., and Bitz, C. M.: Two modes of sea-ice gravity
drainage: A parameterization for large-scale modeling, J. Geophys. Res.,
118, 2279–2294, https://doi.org/10.1002/jgrc.20171, 2013.
Turner, J. S.: Buoyancy Effects in Fluids, Cambridge University Press, 368
pp., 1973.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical turbulence models, J. Mar. Res., 61, 235–265, https://doi.org/10.1357/002224003322005087, 2003.
Van den Dool, H.: Empirical Methods in Short-Term Climate Prediction, Oxford
Univ. Press, Oxford, U. K., 2006.
Wang, W., Chen, M., and Kumar, A.: Seasonal prediction of Arctic sea ice
extent from a coupled dynamical forecast system, Mon. Weather Rev.,
141, 1375–1394, 2013.
Warner, J. C., Armstrong, B., He, R., and Zambon, J.: Development of a
coupled ocean–atmosphere–wave–sediment transport (COAWST) modeling
system, Ocean Model., 35, 230–244, 2010.
Woodgate, R. A., Aagaard, K., and Weingartner, T. J.: A year in the physical
oceanography of the Chukchi Sea: Moored measurements from autumn 1990–1991,
Deep-Sea Res. Pt. II, 52, 3116–3149,
https://doi.org/10.1016/j.dsr2.2005.10.016, 2005.
Wu, W., Lynch, A. H., and Rivers, A.: Estimating the Uncertainty in a
Regional Climate Model Related to Initial and Lateral Boundary Conditions,
J. Climate, 18, 917–933, 2005.
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, C.-Y., Liu, J., and Chen, D.: The model code of Coupled Arctic Prediction System version 1.0 (CAPS v1.0) for the permanent archive in the article “An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018”, Zenodo [code], https://doi.org/10.5281/zenodo.5842668, 2022a.
Yang, C.-Y., Liu, J., and Chen, D.: The prediction data analyzed in the article: “An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018”, Zenodo [data set], https://doi.org/10.5281/zenodo.5839510, 2022b.
Zampieri, L., Goessling, H. F., and Jung, T.: Bright prospects for Arctic
sea ice prediction on subseasonal time scales, Geophys. Res. Lett.,
45, 9731–9738, https://doi.org/10.1029/2018GL079394, 2018.
Zhang, J. and Rothrock, D.: Modeling global sea ice with a thickness and
enthalpy distribution model in generalized curvilinear coordinates, Mon.
Weather Rev., 131, 845–861, 2003.
Zhang, J., Lindsay, R., Steele, M., and Schweiger, A.: What drove the
dramatic retreat of arctic sea ice during summer 2007?, Geophys. Res. Lett.,
35, L11505, https://doi.org/10.1029/2008GL034005, 2008.
Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Sea ice floe size
distribution in the marginal ice zone: Theory and numerical experiments,
J. Geophys. Res-.Oceans, 120, 3484–3498, https://doi.org/10.1002/2015JC010770, 2015.
Zhang, J., Stern, H., Hwang, B., Schweiger, A., Steele, M., Stark, M., and
Graber, H. C.: Modeling the seasonal evolution of the Arctic sea ice floe
size distribution, Elementa, 4, 126,
https://doi.org/10.12952/journal.elementa.000126, 2016.
Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic...
