Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8613-2024
© Author(s) 2024. 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-17-8613-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
05 Dec 2024
Model evaluation paper |
| 05 Dec 2024
| 05 Dec 2024
Evaluation of MITgcm-based ocean reanalyses for the Southern Ocean
Yoshihiro Nakayama
CORRESPONDING AUTHOR
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan
Alena Malyarenko
Te Kura Aronukurangi | School of Earth and Environment, Te Whare Wānanga o Waitaha | University of Canterbury, Ōtautahi / Christchurch, Aotearoa / New Zealand
Te Puna Pātiotio | Antarctic Research Centre, Te Herenga Waka | Victoria University of Wellington, Te Whanganui-a-Tara / Wellington, Aotearoa / New Zealand
Hong Zhang
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Matthis Auger
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Australian Centre for Excellence in Antarctic Science, University of Tasmania, Hobart, Australia
Yafei Nie
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Ian Fenty
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Matthew Mazloff
Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Armin Köhl
Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
Dimitris Menemenlis
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
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Ocean Sci., 18, 1339–1359, https://doi.org/10.5194/os-18-1339-2022, https://doi.org/10.5194/os-18-1339-2022, 2022
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David S. Trossman, Caitlin B. Whalen, Thomas W. N. Haine, Amy F. Waterhouse, An T. Nguyen, Arash Bigdeli, Matthew Mazloff, and Patrick Heimbach
Ocean Sci., 18, 729–759, https://doi.org/10.5194/os-18-729-2022, https://doi.org/10.5194/os-18-729-2022, 2022
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Olivier Sulpis, Matthew P. Humphreys, Monica M. Wilhelmus, Dustin Carroll, William M. Berelson, Dimitris Menemenlis, Jack J. Middelburg, and Jess F. Adkins
Geosci. Model Dev., 15, 2105–2131, https://doi.org/10.5194/gmd-15-2105-2022, https://doi.org/10.5194/gmd-15-2105-2022, 2022
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Marion Kersalé, Denis L. Volkov, Kandaga Pujiana, and Hong Zhang
Ocean Sci., 18, 193–212, https://doi.org/10.5194/os-18-193-2022, https://doi.org/10.5194/os-18-193-2022, 2022
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Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, and An T. Nguyen
Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, https://doi.org/10.5194/gmd-14-4909-2021, 2021
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Yang Feng, Dimitris Menemenlis, Huijie Xue, Hong Zhang, Dustin Carroll, Yan Du, and Hui Wu
Geosci. Model Dev., 14, 1801–1819, https://doi.org/10.5194/gmd-14-1801-2021, https://doi.org/10.5194/gmd-14-1801-2021, 2021
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Simulation of coastal plume regions was improved in global ECCOv4 with a series of sensitivity tests. We find modeled SSS is closer to SMAP when using daily point-source runoff as well as increasing the resolution from coarse to intermediate. The plume characteristics, freshwater transport, and critical water properties are modified greatly. But this may not happen with a further increase to high resolution. The study will advance the seamless modeling of land–ocean–atmosphere feedback in ESMs.
Junjie Liu, Latha Baskaran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nicholas C. Parazoo, Tomohiro Oda, Dustin Carroll, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney, and Steven Wofsy
Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, https://doi.org/10.5194/essd-13-299-2021, 2021
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On average, the terrestrial biosphere carbon sink is equivalent to ~ 20 % of fossil fuel emissions. Understanding where and why the terrestrial biosphere absorbs carbon from the atmosphere is pivotal to any mitigation policy. Here we present a regionally resolved satellite-constrained net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: CMS-Flux NBE 2020. The dataset provides a unique perspective on monitoring regional contributions to the CO2 growth rate.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
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The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Yoshihiro Nakayama, Ralph Timmermann, and Hartmut H. Hellmer
The Cryosphere, 14, 2205–2216, https://doi.org/10.5194/tc-14-2205-2020, https://doi.org/10.5194/tc-14-2205-2020, 2020
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Previous studies have shown accelerations of West Antarctic glaciers, implying that basal melt rates of these glaciers were small and increased in the middle of the 20th century. We conduct coupled sea ice–ice shelf–ocean simulations with different levels of ice shelf melting from West Antarctic glaciers. This study reveals how far and how quickly glacial meltwater from ice shelves in the Amundsen and Bellingshausen seas propagates downstream into the Ross Sea and along the East Antarctic coast.
Rui Sun, Aneesh C. Subramanian, Arthur J. Miller, Matthew R. Mazloff, Ibrahim Hoteit, and Bruce D. Cornuelle
Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, https://doi.org/10.5194/gmd-12-4221-2019, 2019
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A new regional coupled ocean–atmosphere model, SKRIPS, is developed and presented. The oceanic component is the MITgcm and the atmospheric component is the WRF model. The coupler is implemented using ESMF according to NUOPC protocols. SKRIPS is demonstrated by simulating a series of extreme heat events occurring in the Red Sea region. We show that SKRIPS is capable of performing coupled ocean–atmosphere simulations. In addition, the scalability test shows SKRIPS is computationally efficient.
Nikolay V. Koldunov, Armin Köhl, Nuno Serra, and Detlef Stammer
The Cryosphere, 11, 2265–2281, https://doi.org/10.5194/tc-11-2265-2017, https://doi.org/10.5194/tc-11-2265-2017, 2017
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The paper describes one of the first attempts to use the so-called adjoint data assimilation method to bring Arctic Ocean model simulations closer to observation, especially in terms of the sea ice. It is shown that after assimilation the model bias in simulating the Arctic sea ice is considerably reduced. There is also additional improvement in the sea ice thickens representation that is not assimilated directly.
Christopher N. Williams, Stephen L. Cornford, Thomas M. Jordan, Julian A. Dowdeswell, Martin J. Siegert, Christopher D. Clark, Darrel A. Swift, Andrew Sole, Ian Fenty, and Jonathan L. Bamber
The Cryosphere, 11, 363–380, https://doi.org/10.5194/tc-11-363-2017, https://doi.org/10.5194/tc-11-363-2017, 2017
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Knowledge of ice sheet bed topography and surrounding sea floor bathymetry is integral to the understanding of ice sheet processes. Existing elevation data products for Greenland underestimate fjord bathymetry due to sparse data availability. We present a new method to create physically based synthetic fjord bathymetry to fill these gaps, greatly improving on previously available datasets. This will assist in future elevation product development until further observations become available.
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A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)
A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)
AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
Geosci. Model Dev., 18, 4045–4074, https://doi.org/10.5194/gmd-18-4045-2025, https://doi.org/10.5194/gmd-18-4045-2025, 2025
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Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian process (GP) emulators to accelerate subglacial drainage modelling by ~ 1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for > 90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
Geosci. Model Dev., 18, 4023–4044, https://doi.org/10.5194/gmd-18-4023-2025, https://doi.org/10.5194/gmd-18-4023-2025, 2025
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform-resolution simulations.
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
Geosci. Model Dev., 18, 3895–3919, https://doi.org/10.5194/gmd-18-3895-2025, https://doi.org/10.5194/gmd-18-3895-2025, 2025
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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix is a computer programme that simulates the movement and temperature evolution in ice sheets. It helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that ice temperatures play an essential role in determining the motion and stability of ice sheets. Nix is a useful tool for learning how climate change affects polar ice sheets.
Constantijn J. Berends, Victor Azizi, Jorge A. Bernales, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 3635–3659, https://doi.org/10.5194/gmd-18-3635-2025, https://doi.org/10.5194/gmd-18-3635-2025, 2025
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Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution that can resolve a lot of detail in areas where lots of changes happen in the ice and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025, https://doi.org/10.5194/gmd-18-3041-2025, 2025
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This study uses ice mass balance buoys – temperature- and height-measuring devices frozen into sea ice – to find how well climate models simulate (1) melt and growth of Arctic sea ice and (2) conduction of heat through Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find that models tend to show more melt, growth or conduction for a given ice thickness than the buoys, although the difference is smaller for models with more physically realistic thermodynamics.
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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The new coupled ice sheet–ocean model addresses challenges related to horizontal resolution through advanced mesh flexibility, enabled by the use of unstructured grids. We describe the new model, verify its functioning in an idealised setting and demonstrate its advantages in a global-ocean–Antarctic ice sheet domain. The results of this study comprise an important step towards improving predictions of the Antarctic contribution to sea level rise over centennial timescales.
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
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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In this work we introduce IceBoost, a machine learning framework to model the ice thickness distribution of all the world's glaciers with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skilful estimates across all regions. This advancement will help in better assessing future sea level changes and freshwater resources, with significance for both the scientific community and society at large.
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
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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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.
Alek Aaron Petty, Christopher Cardinale, and Madison Smith
EGUsphere, https://doi.org/10.5194/egusphere-2025-766, https://doi.org/10.5194/egusphere-2025-766, 2025
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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.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
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.
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.
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.
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.
Cited articles
Adusumilli, S., Fricker, H. A., Medley, B., Padman, L., and Siegfried, M. R.: Interannual variations in meltwater input to the Southern Ocean from Antarctic ice shelves, Nat. Geosci., 13, 616–620, 2020. a
Amante, C. and Eakins, B. W.: ETOPO1 arc-minute global relief model: procedures, data sources and analysis, https://doi.org/10.7289/V5C8276M, 2009. a, b, c, d
Arndt, J. E., Schenke, H. W., Jakobsson, M., Nitsche, F. O., Buys, G., Goleby, B., Rebesco, M., Bohoyo, F., Hong, J., Black, J., and Greku, R.: The International Bathymetric Chart of the Southern Ocean (IBCSO) Version 1.0 A new bathymetric compilation covering circum-Antarctic waters, Geophys. Res. Lett., 40, 3111–3117, 2013. a, b
Assmann, K., Jenkins, A., Shoosmith, D., Walker, D., Jacobs, S., and Nicholls, K.: Variability of circumpolar deep water transport onto the Amundsen Sea continental shelf through a shelf break trough, J. Geophys. Res., 118, 6603–6620, 2013. a
Auger, M., Prandi, P., and Sallée, J.-B.: Southern ocean sea level anomaly in the sea ice-covered sector from multimission satellite observations, Sci. Data, 9, 70, https://doi.org/10.1038/s41597-022-01166-z, 2022a. a
Auger, M., Sallée, J.-B., Prandi, P., and Naveira Garabato, A. C.: Subpolar Southern Ocean Seasonal Variability of the Geostrophic Circulation From Multi-Mission Satellite Altimetry, J. Geophys. Res.-Oceans, 127, e2021JC018096, https://doi.org/10.1002/grl.50413, 2022b. a, b, c
Bailey, S. T., Jones, C. S., Abernathey, R. P., Gordon, A. L., and Yuan, X.: Water mass transformation variability in the Weddell Sea in ocean reanalyses, Ocean Sci., 19, 381–402, https://doi.org/10.5194/os-19-381-2023, 2023. a
Beckmann, A., Hellmer, H. H., and Timmermann, R.: A numerical model of the Weddell Sea: Large-scale circulation and water mass distribution, J. Geophys. Res.-Oceans, 104, 23375–23391, 1999. a
Brancato, V., Rignot, E., Milillo, P., Morlighem, M., Mouginot, J., An, L., Scheuchl, B., Jeong, S., Rizzoli, P., Bueso Bello, J. L., and Prats‐Iraola, P.: Grounding line retreat of Denman Glacier, East Antarctica, measured with COSMO-SkyMed radar interferometry data, Geophys. Res. Lett., 47, e2019GL086291, https://doi.org/10.1029/2019GL086291, 2020. a
Bronselaer, B., Winton, M., Griffies, S. M., Hurlin, W. J., Rodgers, K. B., Sergienko, O. V., Stouffer, R. J., and Russell, J. L.: Change in future climate due to Antarctic meltwater, Nature, 564, 53–58, 2018. a
Carroll, D., Menemenlis, D., Adkins, J. F., Bowman, K. W., Brix, H., Dutkiewicz, S., Fenty, I., Gierach, M. M., Hill, C., Jahn, O., and Landschützer, P.: The ECCO-Darwin data-assimilative global ocean biogeochemistry model: Estimates of seasonal to multidecadal surface ocean pCO2 and air-sea CO2 flux, J. Adv. Model. Earth Sy., 12, e2019MS001888, https://doi.org/10.1029/2019MS001888, 2020. a
Cerovečki, I. and Haumann, F. A.: Decadal reorganization of Subantarctic Mode Water, Geophys. Res. Lett., 50, e2022GL102148, https://doi.org/10.1029/2022GL102148, 2023. a
Chen, Y., Zhang, Z., Wang, X., Liu, X., and Zhou, M.: Interannual variations of heat budget in the lower layer of the eastern Ross Sea shelf and the forcing mechanisms in the Southern Ocean State Estimate, Int. J. Climatol., 43, 5055–5076, https://doi.org/10.1002/joc.8132, 2023. a
Chu, P. C. and Fan, C.: An inverse model for calculation of global volume transport from wind and hydrographic data, J. Mar. Syst., 65, 376–399, 2007. a
Cisewski, B., Strass, V. H., and Leach, H.: Circulation and transport of water masses in the Lazarev Sea, Antarctica, during summer and winter 2006, Deep-Sea Res. Pt. I, 58, 186–199, 2011. a
Darelius, E., Daae, K., Dundas, V., Fer, I., Hellmer, H. H., Janout, M., Nicholls, K. W., Sallée, J.-B., and Østerhus, S.: Observational evidence for on-shelf heat transport driven by dense water export in the Weddell Sea, Nat. Commun., 14, 1022, https://doi.org/10.1038/s41467-023-36580-3, 2023. a
De Rydt, J., Holland, P., Dutrieux, P., and Jenkins, A.: Geometric and oceanographic controls on melting beneath Pine Island Glacier, J. Geophys. Res.-Oceans, 119, 2420–2438, 2014. a
De Verdière, A. C. and Ollitrault, M.: A direct determination of the World Ocean barotropic circulation, J. Phys. Oceanogr., 46, 255–273, 2016. a
Dettling, N., Losch, M., Pollmann, F., and Kanzow, T.: Toward Parameterizing Eddy-Mediated Transport of Warm Deep Water across the Weddell Sea Continental Slope, J. Phys. Oceanogr., 54, 1675–1690, https://doi.org/10.1175/JPO-D-23-0215.1, 2023. a
Dinniman, M. S., Klinck, J. M., and Smith Jr, W. O.: A model study of Circumpolar Deep Water on the West Antarctic Peninsula and Ross Sea continental shelves, Deep-Sea Res. Pt. II, 58, 1508–1523, 2011. a
Dinniman, M. S., Klinck, J. M., Bai, L.-S., Bromwich, D. H., Hines, K. M., and Holland, D. M.: The effect of atmospheric forcing resolution on delivery of ocean heat to the Antarctic floating ice shelves, J. Climate, 28, 6067–6085, 2015. a
Fahrbach, E., Hoppema, M., Rohardt, G., Schröder, M., and Wisotzki, A.: Decadal-scale variations of water mass properties in the deep Weddell Sea, Ocean Dynam., 54, 77–91, 2004. a
FFecher, T., Pail, R., Gruber, T., and the GOCO Consortium: GOCO05c: a new combined gravity field model based on full normal equations and regionally varying weighting, Surv. Geophys., 38, 571–590, https://doi.org/10.1007/s10712-016-9406-y, 2017. a
Feng, Y., Menemenlis, D., Xue, H., Zhang, H., Carroll, D., Du, Y., and Wu, H.: Improved representation of river runoff in Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) simulations: implementation, evaluation, and impacts to coastal plume regions, Geosci. Model Dev., 14, 1801–1819, https://doi.org/10.5194/gmd-14-1801-2021, 2021. a
Foldvik, A., Gammelsrød, T., Østerhus, S., Fahrbach, E., Rohardt, G., Schröder, M., Nicholls, K. W., Padman, L., and Woodgate, R.: Ice shelf water overflow and bottom water formation in the southern Weddell Sea, J. Geophys. Res.-Oceans, 109, C02015, https://doi.org/10.1029/2003JC002008, 2004. a
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N. E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske, D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni, P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel, R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill, W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk, B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto, B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti, A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica, The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, 2013. a
Garnier, F., Fleury, S., Garric, G., Bouffard, J., Tsamados, M., Laforge, A., Bocquet, M., Fredensborg Hansen, R. M., and Remy, F.: Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements, The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, 2021. a
Garnier, F., Bocquet, M., Fleury, S., Bouffard, J., Tsamados, M., Remy, F., Garric, G., and Chenal, A.: Latest Altimetry-Based Sea Ice Freeboard and Volume Inter-Annual Variability in the Antarctic over 2003–2020, Remote Sens., 14, 4741, https://doi.org/10.3390/rs14194741, 2022. a
Gille, S. T.: Warming of the Southern Ocean since the 1950s, Science, 295, 1275–1277, 2002. a
Gutierrez-Villanueva, M. O., Chereskin, T. K., and Sprintall, J.: Compensating transport trends in the Drake Passage frontal regions yield no acceleration in net transport, Nat. Commun., 14, 7792, https://doi.org/10.1038/s41467-023-43499-2, 2023. a
Gwyther, D. E., Galton-Fenzi, B. K., Hunter, J. R., and Roberts, J. L.: Simulated melt rates for the Totten and Dalton ice shelves, Ocean Sci., 10, 267–279, https://doi.org/10.5194/os-10-267-2014, 2014. a
Hammond, M. D. and Jones, D. C.: Freshwater flux from ice sheet melting and iceberg calving in the Southern Ocean, Geosci. Data J., 3, 60–62, https://doi.org/10.1002/gdj3.43, 2017. a, b
Hellmer, H. and Olbers, D.: A two-dimensional model for the thermohaline circulation under an ice shelf, Antarct. Sci., 1, 325–336, 1989. a
Hellmer, H. H., Jacobs, S. S., and Jenkins, A.: Oceanic erosion of a floating Antarctic glacier in the Amundsen Sea, in: Ocean, Ice, and Atmosphere: Interactions at the Antarctic continental margin, edited by: Jacobs, S., and Weiss, R., Antarctic Research Series, AGU, Washington DC, USA, 75, 319–339, https://doi.org/10.1029/AR075p0083, 1998. a
Hellmer, H. H., Kauker, F., Timmermann, R., Determann, J., and Rae, J.: Twenty-first-century warming of a large Antarctic ice-shelf cavity by a redirected coastal current, Nature, 485, 225–228, 2012. a
Hirano, D., Tamura, T., Kusahara, K., Fujii, M., Yamazaki, K., Nakayama, Y., Ono, K., Itaki, T., Aoyama, Y., Simizu, D., Mizobata, K., Ohshima, K. I., Nogi, Y., Rintoul, S. R., Wijk, E., Greenbaum, J. S., Blankenship, D. D., Saito, K., and Aoki, S.: On-shelf circulation of warm water toward the Totten Ice Shelf in East Antarctica, Nat. Commun., 14, 4955, https://doi.org/10.1038/s41467-023-39764-z, 2023. a, b
Hogg, A. M., Meredith, M. P., Chambers, D. P., Abrahamsen, E. P., Hughes, C. W., and Morrison, A. K.: Recent trends in the S outhern O cean eddy field, J. Geophys. Res.-Oceans, 120, 257–267, 2015. a
Holland, D. M. and Jenkins, A.: Modeling thermodynamic ice-ocean interactions at the base of an ice shelf, J. Phys. Oceanogr., 29, 1787–1800, 1999. a
Hou, Y., Nie, Y., Min, C., Shu, Q., Luo, H., Liu, J., and Yang, Q.: Evaluation of Antarctic sea ice thickness and volume during 2003–2014 in CMIP6 using Envisat and CryoSat-2 observations, Environ. Res. Lett., 19, 014067, https://doi.org/10.1088/1748-9326/ad1725, 2024. a, b
Ito, T.: Development of the Regional Carbon Cycle Model in the Central Pacific Sector of the Southern Ocean, J. Adv. Model. Earth Sy., 14, e2021MS002757, https://doi.org/10.1029/2021MS002757, 2022. a
Ito, T. and Marshall, J.: Control of lower-limb overturning circulation in the Southern Ocean by diapycnal mixing and mesoscale eddy transfer, J. Phys. Oceanogr., 38, 2832–2845, 2008. a
Jacobs, S. S., Amos, A. F., and Bruchhausen, P. M.: Ross Sea oceanography and Antarctic bottom water formation, in: Deep Sea Research and Oceanographic Abstracts, Elsevier, vol. 17,935–962, https://doi.org/10.1016/0011-7471(70)90046-X, 1970. a
Jacobs, S. S., Jenkins, A., Giulivi, C. F., and Dutrieux, P.: Stronger ocean circulation and increased melting under Pine Island Glacier ice shelf, Nat. Geosci., 4, 519–523, 2011. a
Janout, M. A., Hellmer, H. H., Hattermann, T., Huhn, O., Sültenfuss, J., Østerhus, S., Stulic, L., Ryan, S., Schröder, M., and Kanzow, T.: FRIS Revisited in 2018: On the Circulation and Water Masses at the Filchner and Ronne Ice Shelves in the Southern Weddell Sea, J. Geophys. Res.-Oceans, 126, e2021JC017269, https://doi.org/10.1029/2021JC017269, 2021. a
Jenkins, A.: A one-dimensional model of ice shelf-ocean interaction, J. Geophys. Res.-Oceans, 96, 20671–20677, 1991. a
Jourdain, N. C., Mathiot, P., Merino, N., Durand, G., Le Sommer, J., Spence, P., Dutrieux, P., and Madec, G.: Ocean circulation and sea-ice thinning induced by melting ice shelves in the Amundsen Sea, J. Geophys. Res.-Oceans, 122, 2550–2573, 2017. a
Jungclaus, J. H., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D., Mikolajewicz, U., Notz, D., and Von Storch, J.: Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model, J. Adv. Model. Earth Sy., 5, 422–446, 2013. a, b
Kim, T. W., Yang, H. W., Dutrieux, P., Wåhlin, A. K., Jenkins, A., Kim, Y. G., Ha, H. K., Kim, C. S., Cho, K. H., Park, T., and Park, J.: Interannual variation of modified circumpolar deep water in the Dotson-Getz Trough, West Antarctica, J. Geophys. Res.-Oceans, 126, e2021JC017491, https://doi.org/10.1029/2021JC017491, 2021. a
Kiss, A. E., Hogg, A. McC., Hannah, N., Boeira Dias, F., Brassington, G. B., Chamberlain, M. A., Chapman, C., Dobrohotoff, P., Domingues, C. M., Duran, E. R., England, M. H., Fiedler, R., Griffies, S. M., Heerdegen, A., Heil, P., Holmes, R. M., Klocker, A., Marsland, S. J., Morrison, A. K., Munroe, J., Nikurashin, M., Oke, P. R., Pilo, G. S., Richet, O., Savita, A., Spence, P., Stewart, K. D., Ward, M. L., Wu, F., and Zhang, X.: ACCESS-OM2 v1.0: a global ocean–sea ice model at three resolutions, Geosci. Model Dev., 13, 401–442, https://doi.org/10.5194/gmd-13-401-2020, 2020. a
Koenig, Z., Provost, C., Ferrari, R., Sennéchael, N., and Rio, M.-H.: Volume transport of the A ntarctic C ircumpolar C urrent: Production and validation of a 20 year long time series obtained from in situ and satellite observations, J. Geophys. Res.-Oceans, 119, 5407–5433, 2014. a
Köhl, A.: GECCO3 Ocean Synthesis (41S6m) (Version 41S6m), Quarterly J. Royal Met. Soc. [data set], https://doi.org/10.25592/uhhfdm.14187, 2024. a
Kusahara, K. and Hasumi, H.: Pathways of basal meltwater from Antarctic ice shelves: A model study, J. Geophys. Res. Lett., 119, 5690–5704, https://doi.org/https://doi.org/10.1002/2014JC009915, 2014. a
Li, Q., England, M. H., Hogg, A. M., Rintoul, S. R., and Morrison, A. K.: Abyssal ocean overturning slowdown and warming driven by Antarctic meltwater, Nature, 615, 841–847, 2023. a
Liao, S., Luo, H., Wang, J., Shi, Q., Zhang, J., and Yang, Q.: An evaluation of Antarctic sea-ice thickness from the Global Ice-Ocean Modeling and Assimilation System based on in situ and satellite observations, The Cryosphere, 16, 1807–1819, https://doi.org/10.5194/tc-16-1807-2022, 2022. a
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M., Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K., Paver, C. R., and Smolyar, I.: World Ocean Atlas 2018, Volume 1: Temperature, NOAA Atlas NESDIS 81, edited by: Mishonov, A., National Oceanic and Atmospheric Administration, U.S. Department of Commerce, 52 pp., 2018. a, b, c
Mack, S. L., Dinniman, M. S., Klinck, J. M., McGillicuddy Jr, D. J., and Padman, L.: Modeling ocean eddies on Antarctica's cold water continental shelves and their effects on ice shelf basal melting, J. Geophys. Res.-Oceans, 124, 5067–5084, 2019. a
Mathiot, P., Jenkins, A., Harris, C., and Madec, G.: Explicit representation and parametrised impacts of under ice shelf seas in the z∗ coordinate ocean model NEMO 3.6, Geosci. Model Dev., 10, 2849–2874, https://doi.org/10.5194/gmd-10-2849-2017, 2017. a
Meier, W. N., Fetterer, F., Windnagel, A. K., and Stewart, J. S.: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, Boulder, Colorado USA. National Snow and Ice Data Center [data set], https://doi.org/10.7265/efmz-2t65, 2021. a
Mensah, V., Nakayama, Y., Fujii, M., Nogi, Y., and Ohshima, K. I.: Dense water downslope flow and AABW production in a numerical model: Sensitivity to horizontal and vertical resolution in the region off Cape Darnley polynya, Ocean Modell., 165, 101843, https://doi.org/10.1016/j.ocemod.2021.101843, 2021. a, b
Millan, R., St-Laurent, P., Rignot, E., Morlighem, M., Mouginot, J., and Scheuchl, B.: Constraining an ocean model under Getz Ice Shelf, Antarctica, using a gravity-derived bathymetry, Geophys. Res. Lett., 47, e2019GL086522, https://doi.org/10.1029/2019GL086522, 2020. a
Nakayama, Y.: ECCOv4r5 output analyzed in “Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean”, Zenodo [code], https://doi.org/10.5281/zenodo.10930853, 2024a. a
Nakayama, Y.: ECCO-LLC270 output analyzed in “Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean” (Part 1), Zenodo [data set], https://doi.org/10.5281/zenodo.10934678, 2024b. a
Nakayama, Y.: ECCO-LLC270 output analyzed in “Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean” (Part 2), Zenodo [data set], https://doi.org/10.5281/zenodo.10935131, 2024c. a
Nakayama, Y.: SOSE output analyzed in “Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean” (Part 1), Zenodo [data set], https://doi.org/10.5281/zenodo.10935396, 2024d. a
Nakayama, Y.: SOSE output analyzed in “Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean” (Part 2-1), Zenodo [data set], https://doi.org/10.5281/zenodo.13117970, 2024e. a
Nakayama, Y. and Malyarenko, A.: Analysis scripts developed for “Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean”, Zenodo [code], https://doi.org/10.5281/zenodo.10958934, 2024. a
Nakayama, Y., Ohshima, K. I., Matsumura, Y., Fukamachi, Y., and Hasumi, H.: A numerical investigation of formation and variability of Antarctic bottom water off Cape Darnley, East Antarctica, J. Phys. Oceanogr., 44, 2921–2937, 2014a. a
Nakayama, Y., Timmermann, R., Rodehacke, C. B., Schröder, M., and Hellmer, H. H.: Modeling the spreading of glacial meltwater from the Amundsen and Bellingshausen Seas, Geophys. Res. Lett., 41, 7942–7949, 2014b. a
Nakayama, Y., Manucharayan, G., Kelin, P., Torres, H. G., Schodlok, M., Rignot, E., Dutrieux, P., and Menemenlis, D.: Pathway of Circumpolar Deep Water into Pine Island and Thwaites ice shelf cavities and to their grounding lines, Sci. Rep., https://doi.org/10.1038/s41598-019-53190-6, 2019. a
Nakayama, Y., Greene, C. A., Paolo, F. S., Mensah, V., Zhang, H., Kashiwase, H., Simizu, D., Greenbaum, J. S., Blankenship, D. D., Abe‐Ouchi, A., and Aoki, S.: Antarctic slope current modulates ocean heat intrusions towards Totten glacier, Geophys. Res. Lett., 48, e2021GL094149, https://doi.org/10.1029/2021GL094149, 2021a. a, b
Nakayama, Y., Menemenlis, D., Wang, O., Zhang, H., Fenty, I., and Nguyen, A. T.: Development of adjoint-based ocean state estimation for the Amundsen and Bellingshausen seas and ice shelf cavities using MITgcm–ECCO (66j), Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, 2021b. a
Nakayama, Y., Wongpan, P., Greenbaum, J. S., Yamazaki, K., Noguchi, T., Simizu, D., Kashiwase, H., Blankenship, D. D., Tamura, T., and Aoki, S.: Helicopter-Based Ocean Observations Capture Broad Ocean Heat Intrusions Toward the Totten Ice Shelf, Geophys. Res. Lett., 50, e2022GL097864, https://doi.org/10.1029/2022GL097864, 2023. a
National Geophysical Data Center: 5-minute Gridded Global Relief Data (ETOPO5), National Geophysical Data Center, NOAA [data set], https://doi.org/10.7289/V5D798BF, 1993. a
Naughten, K. A., Jenkins, A., Holland, P. R., Mugford, R. I., Nicholls, K. W., and Munday, D. R.: Modeling the influence of the Weddell polynya on the Filchner–Ronne ice shelf cavity, J. Climate, 32, 5289–5303, 2019. a
Naughten, K. A., Holland, P. R., Dutrieux, P., Kimura, S., Bett, D. T., and Jenkins, A.: Simulated Twentieth-Century Ocean Warming in the Amundsen Sea, West Antarctica, Geophys. Res. Lett., 49, e2021GL094566, https://doi.org/10.1029/2021GL094566, 2022. a
Neme, J., England, M. H., Hogg, A. M., Khatri, H., and Griffies, S. M.: The Role of Bottom Friction in Mediating the Response of the Weddell Gyre Circulation to Changes in Surface Stress and Buoyancy Fluxes, J. Phys. Oceanogr., 54, 217–236, https://doi.org/10.1175/JPO-D-23-0165.1, 2023. a
Newman, L., Heil, P., Trebilco, R., Katsumata, K., Constable, A., Van Wijk, E., Assmann, K., Beja, J., Bricher, P., Coleman, R., and Costa, D.: Delivering sustained, coordinated, and integrated observations of the Southern Ocean for global impact, Fronti. Mar. Sci., 6, 433, https://doi.org/10.3389/fmars.2019.00433, 2019. a
Nie, Y., Uotila, P., Cheng, B., Massonnet, F., Kimura, N., Cipollone, A., and Lv, X.: Southern Ocean sea ice concentration budgets of five ocean-sea ice reanalyses, Clim. Dynam., 59, 3265–3285, 2022. a
Ohshima, K. I., Fukamachi, Y., Williams, G. D., Nihashi, S., Roquet, F., Kitade, Y., Tamura, T., Hirano, D., Herraiz-Borreguero, L., Field, I. and Hindell, M., Aoki, S., and Wakatsuchi M.: Antarctic Bottom Water production by intense sea-ice formation in the Cape Darnley polynya, Nat. Geosci., 6, 235–240, 2013. a
Orsi, A. H., Johnson, G. C., and Bullister, J. L.: Circulation, mixing, and production of Antarctic Bottom Water, Prog. Oceanogr., 43, 55–109, 1999. a
Paolo, F. S., Gardner, A. S., Greene, C. A., Nilsson, J., Schodlok, M. P., Schlegel, N.-J., and Fricker, H. A.: Widespread slowdown in thinning rates of West Antarctic ice shelves, The Cryosphere, 17, 3409–3433, https://doi.org/10.5194/tc-17-3409-2023, 2023. a
Park, Y.-H. and Gambéroni, L.: Large-scale circulation and its variability in the south Indian Ocean from TOPEX/POSEIDON altimetry, J. Geophys. Res.-Oceans, 100, 24911–24929, 1995. a
Rahmstorf, S.: Climate drift in an ocean model coupled to a simple, perfectly matched atmosphere, Clim. Dynam., 11, 447–458, 1995. a
Reeve, K. A., Boebel, O., Strass, V., Kanzow, T., and Gerdes, R.: Horizontal circulation and volume transports in the Weddell Gyre derived from Argo float data, Prog. Oceanogr., 175, 263–283, 2019. a
Rignot, E., Mouginot, J., Scheuchl, B., van den Broeke, M., van Wessem, M. J., and Morlighem, M.: Four decades of Antarctic Ice Sheet mass balance from 1979–2017, P. Natl. Acad. Sci. USA, 116, 1095–1103, 2019. a
Rintoul, S. R.: The global influence of localized dynamics in the Southern Ocean, Nature, 558, 209–218, 2018. a
Rintoul, S. R., Silvano, A., Pena-Molino, B., van Wijk, E., Rosenberg, M., Greenbaum, J. S., and Blankenship, D. D.: Ocean heat drives rapid basal melt of the Totten Ice Shelf, Sci. Adv., 2, e1601610, https://doi.org/10.1126/sciadv.1601610, 2016. a
Ruan, X., Thompson, A. F., Flexas, M. M., and Sprintall, J.: Contribution of topographically generated submesoscale turbulence to Southern Ocean overturning, Nat. Geosci., 10, 840–845, 2017. a
Ryan, S., Schröder, M., Huhn, O., and Timmermann, R.: On the warm inflow at the eastern boundary of the Weddell Gyre, Deep-Sea Res. Pt. I, 107, 70–81, 2016. a
Sallée, J.-B., Speer, K., and Rintoul, S.: Zonally asymmetric response of the Southern Ocean mixed-layer depth to the Southern Annular Mode, Nat. Geosci., 3, 273–279, 2010. a
Schodlok, M., Menemenlis, D., and Rignot, E.: Ice shelf basal melt rates around Antarctica from simulations and observations, J. Geophys. Res.-Oceans, 121, 1085–1109, https://doi.org/10.1002/2015JC011117, 2015. a
Schodlok, M. P., Menemenlis, D., Rignot, E., and Studinger, M.: Sensitivity of the ice shelf ocean system to the sub-ice shelf cavity shape measured by NASA IceBridge in Pine Island Glacier, West Antarctica, Ann. Glaciol., 53, 156–162, 2012. a
Snow, K., Hogg, A. M., Downes, S. M., Sloyan, B. M., Bates, M. L., and Griffies, S. M.: Sensitivity of abyssal water masses to overflow parameterisations, Ocean Model., 89, 84–103, 2015. a
SO-CHIC Consortium, Salle, J. B., Abrahamsen, E. P., Allaigre, C., Auger, M., Ayres, H., Badhe, R., Boutin, J., Brearley, J. A., de Lavergne, C., ten Doeschate, A. M. M., Droste, E. S., du Plessis, M. D., Ferreira, D., Giddy, I. S., Glk, B., Gruber, N., Hague, M., Hoppema, M., Josey, S. A., Kanzow, T., Kimmritz, M., Lindeman, M. R., Llanillo, P. J., Lucas, N. S., Madec, G., Marshall, D. P., Meijers, A. J. S., Meredith, M. P., Mohrmann, M., Monteiro, P. M. S., Dupin, C. M., Naeck, K., Narayanan, A., Garabato, A. C. N., Nicholson, S.-A., Novellino, A., Dalen, M., Sterhus, S., Park, W., Patmore, R. D., Piedagnel, E., Roquet, F., Rosenthal, H. S., Roy, T., Saurabh, R., Silvy, Y., Spira, T., Steiger, N., Styles, A. F., Swart, S., Vogt, L., Ward, B., and Zhou, S.: Southern ocean carbon and heat impact on climate, Philos. T. Roy. Soc. A, 381, 20220056, https://doi.org/10.1098/RSTA.2022.0056, 2023. a
Stewart, A. L., Klocker, A., and Menemenlis, D.: Acceleration and overturning of the Antarctic Slope Current by winds, eddies, and tides, J. Phys. Oceanogr., 49, 2043–2074, 2019. a
Taewook, P., Yoshihiro, N., and SungHyun, N.: Amundsen Sea Circulation Controls Bottom Upwelling and Antarctic Pine Island and Thwaites Ice Shelf Melting, Nat. Commun., 15, 2946, https://doi.org/10.1038/s41467-024-47084-z, 2024. a
Takahashi, T., Sweeney, C., Hales, B., Chipman, D. W., Newberger, T., Goddard, J. G., Iannuzzi, R. A., and Sutherland, S. C.: The changing carbon cycle in the Southern Ocean, Oceanography, 25, 26–37, 2012. a
Timmermann, R., Danilov, S., Schröter, J., Böning, C., Sidorenko, D., and Rollenhagen, K.: Ocean circulation and sea ice distribution in a finite element global sea ice–ocean model, Ocean Model., 27, 114–129, 2009. a
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A., Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., and Kauker, F.: An assessment of ten ocean reanalyses in the polar regions, Clim. Dynam., 52, 1613–1650, 2019. a
Van Caspel, M., Schröder, M., Huhn, O. and Hellmer, H. H.: Precursors of Antarctic Bottom Water formed on the continental shelf of Larsen Ice Shelf. Deep-Sea Res. Pt. I, 99, 1–9, https://doi.org/10.1016/j.dsr.2015.01.004, 2015. a
Vernet, M., Geibert, W., Hoppema, M., Brown, P. J., Haas, C., Hellmer, H. H., Jokat, W., Jullion, L., Mazloff, M., Bakker, D. C. E., and Brearley, J. A.: The Weddell Gyre, Southern Ocean: present knowledge and future challenges, Rev. Geophys. 57, 623–708, 2019. a
Walker, C. and Gardner, A. S.: Rapid drawdown of Antarctica's Wordie Ice Shelf glaciers in response to ENSO/Southern Annular Mode-driven warming in the Southern Ocean, Earth Planet. Sc. Lett., 476, 100–110, 2017. a
Wang, J., Min, C., Ricker, R., Shi, Q., Han, B., Hendricks, S., Wu, R., and Yang, Q.: A comparison between Envisat and ICESat sea ice thickness in the Southern Ocean, The Cryosphere, 16, 4473–4490, https://doi.org/10.5194/tc-16-4473-2022, 2022. a
Wang, Z. and Meredith, M.: Density-driven Southern Hemisphere subpolar gyres in coupled climate models, Geophys. Res. Lett., 35, L14608, https://doi.org/10.1029/2008GL034344, 2008. a
Webber, B. G., Heywood, K. J., Stevens, D. P., Dutrieux, P., Abrahamsen, E. P., Jenkins, A., Jacobs, S. S., Ha, H. K., Lee, S. H., and Kim, T. W.: Mechanisms driving variability in the ocean forcing of Pine Island Glacier, Nat. Commun., 8, 1–8, 2017. a
Whitworth, T.: Monitoring the transport of the Antarctic circumpolar current at Drake Passage, J. Phys. Oceanogr., 13, 2045–2057, 1983. a
Whitworth, T. and Peterson, R.: Volume transport of the Antarctic Circumpolar Current from bottom pressure measurements, J. Phys. Oceanogr., 15, 810–816, 1985. a
Whitworth III, T., Nowlin Jr, W., and Worley, S.: The net transport of the Antarctic circumpolar current through Drake Passage, J. Phys. Oceanogr., 12, 960–971, 1982. a
Williams, G., Bindoff, N., Marsland, S., and Rintoul, S.: Formation and export of dense shelf water from the Adélie Depression, East Antarctica, J. Geophys. Res.-Oceans, 113, C04039, https://doi.org/10.1029/2007JC004346, 2008. a
Williams, R. G., Ceppi, P., Roussenov, V., Katavouta, A., and Meijers, A. J.: The role of the Southern Ocean in the global climate response to carbon emissions, Philos. T. Roy. Soc. A, 381, 20220062, https://doi.org/10.1098/rsta.2022.0062, 2023. a
Xu, X., Chassignet, E. P., Firing, Y. L., and Donohue, K.: Antarctic circumpolar current transport through Drake Passage: what can we learn from comparing high-resolution model results to observations?, J. Geophys. Res.-Oceans, 125, e2020JC016365, https://doi.org/10.1029/2020JC016365, 2020. a
Zaba, K. D., Rudnick, D. L., Cornuelle, B. D., Gopalakrishnan, G., and Mazloff, M. R.: Annual and interannual variability in the California current system: Comparison of an ocean state estimate with a network of underwater gliders, J. Phys. Oceanogr., 48, 2965–2988, 2018. a
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A., Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K., Paver, C. R., and Smolyar, I.: World ocean atlas 2018, volume 2: Salinity, NOAA Atlas NESDIS 81, edited by: Mishonov, A., National Oceanic and Atmospheric Administration, U.S. Department of Commerce, 50 pp., 2019. a, b, c
Short summary
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.
Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean...
