Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3355-2023
© Author(s) 2023. 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-16-3355-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
14 Jun 2023
Development and technical paper |
| 14 Jun 2023
| 14 Jun 2023
Conservation of heat and mass in P-SKRIPS version 1: the coupled atmosphere–ice–ocean model of the Ross Sea
Alena Malyarenko
CORRESPONDING AUTHOR
National Institute of Water and Atmospheric Research | Taihoro Nukurangi, 301 Evans Bay Parade, Hataitai, Te Whanganui-a-Tara / Wellington, New Zealand
Antarctic Research Centre | Te Puna Pātiotio, Victoria University of Wellington, CO 549, Cotton Building, Gate 7, Kelburn Parade, Te Whanganui-a-Tara / Wellington 6012, New Zealand
Alexandra Gossart
CORRESPONDING AUTHOR
Antarctic Research Centre | Te Puna Pātiotio, Victoria University of Wellington, CO 549, Cotton Building, Gate 7, Kelburn Parade, Te Whanganui-a-Tara / Wellington 6012, New Zealand
Scripps Institution of Oceanography, La Jolla, California, USA
Mario Krapp
GNS Science | Te Pū Ao, 30 Gracefield Rd, Gracefield, Te Awa Kairangi ki Tai / Lower Hutt 5010, New Zealand
Antarctic Research Centre | Te Puna Pātiotio, Victoria University of Wellington, CO 549, Cotton Building, Gate 7, Kelburn Parade, Te Whanganui-a-Tara / Wellington 6012, New Zealand
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The Cryosphere, 16, 3723–3738, https://doi.org/10.5194/tc-16-3723-2022, https://doi.org/10.5194/tc-16-3723-2022, 2022
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Understanding how the Ross Ice Shelf will evolve in a warming world is important to the future stability of Antarctica. It remains unclear what changes could drive the largest mass loss in the future and where places are most likely to trigger larger mass losses. Sensitivity maps are modelled showing that the RIS is sensitive to changes in environmental and glaciological controls at regions which are currently experiencing changes. These regions need to be monitored in a warming world.
Francesca Baldacchino, Nicholas R. Golledge, Mathieu Morlighem, Huw Horgan, Alanna V. Alevropoulos-Borrill, Alena Malyarenko, Alexandra Gossart, Daniel P. Lowry, and Laurine van Haastrecht
The Cryosphere, 19, 107–127, https://doi.org/10.5194/tc-19-107-2025, https://doi.org/10.5194/tc-19-107-2025, 2025
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Understanding how the Ross Ice Shelf flow is changing in a warming world is important for predicting ice sheet change. Field measurements show clear intra-annual variations in ice flow; however, it is unclear what mechanisms drive this variability. We show that local perturbations in basal melt can have a significant impact on ice flow speed, but a combination of forcings is likely driving the observed variability in ice flow.
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Positive degree-day (PDD) schemes are widely used in many Antarctic numerical ice sheet models. However, the PDD approach has not been systematically explored for its application in Antarctica. We have constructed a novel grid-cell-level spatially distributed PDD (dist-PDD) model and assessed its accuracy. We suggest that an appropriately parameterized dist-PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
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In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
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The Cryosphere, 16, 3723–3738, https://doi.org/10.5194/tc-16-3723-2022, https://doi.org/10.5194/tc-16-3723-2022, 2022
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Understanding how the Ross Ice Shelf will evolve in a warming world is important to the future stability of Antarctica. It remains unclear what changes could drive the largest mass loss in the future and where places are most likely to trigger larger mass losses. Sensitivity maps are modelled showing that the RIS is sensitive to changes in environmental and glaciological controls at regions which are currently experiencing changes. These regions need to be monitored in a warming world.
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The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, https://doi.org/10.5194/tc-15-3751-2021, 2021
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Even the most sophisticated global climate models are known to have significant biases in the way they simulate the climate system. Correcting model biases is therefore essential for creating realistic reconstructions of past climate that can be used, for example, to study long-term ecological dynamics. Here, we evaluated three widely used bias correction methods by means of a global dataset of empirical temperature and precipitation records from the last 125 000 years.
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.
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Clim. Past Discuss., https://doi.org/10.5194/cp-2019-91, https://doi.org/10.5194/cp-2019-91, 2019
Revised manuscript not accepted
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The local response of the global climate system to the external drivers of the glacial–interglacial climates throughout the Quaternary can be approximated by a simple linear regression model. Based on numerical climate model simulations for the last glacial cycle, our global climate model emulator (GCMET) is able to reconstruct the climate of the last 800 000 years, in good agreement with long-term terrestrial and marine proxy records.
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The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-25, https://doi.org/10.5194/tc-2019-25, 2019
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Niels Souverijns, Alexandra Gossart, Stef Lhermitte, Irina V. Gorodetskaya, Jacopo Grazioli, Alexis Berne, Claudio Duran-Alarcon, Brice Boudevillain, Christophe Genthon, Claudio Scarchilli, and Nicole P. M. van Lipzig
The Cryosphere, 12, 3775–3789, https://doi.org/10.5194/tc-12-3775-2018, https://doi.org/10.5194/tc-12-3775-2018, 2018
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Snowfall observations over Antarctica are scarce and currently limited to information from the CloudSat satellite. Here, a first evaluation of the CloudSat snowfall record is performed using observations of ground-based precipitation radars. Results indicate an accurate representation of the snowfall climatology over Antarctica, despite the low overpass frequency of the satellite, outperforming state-of-the-art model estimates. Individual snowfall events are however not well represented.
Niels Souverijns, Alexandra Gossart, Irina V. Gorodetskaya, Stef Lhermitte, Alexander Mangold, Quentin Laffineur, Andy Delcloo, and Nicole P. M. van Lipzig
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This work is the first to gain insight into the local surface mass balance over Antarctica using accurate long-term snowfall observations. A non-linear relationship between accumulation and snowfall is discovered, indicating that total surface mass balance measurements are not a good proxy for snowfall over Antarctica. Furthermore, the meteorological drivers causing changes in the local SMB are identified.
Alexandra Gossart, Niels Souverijns, Irina V. Gorodetskaya, Stef Lhermitte, Jan T. M. Lenaerts, Jan H. Schween, Alexander Mangold, Quentin Laffineur, and Nicole P. M. van Lipzig
The Cryosphere, 11, 2755–2772, https://doi.org/10.5194/tc-11-2755-2017, https://doi.org/10.5194/tc-11-2755-2017, 2017
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Blowing snow plays an important role on local surface mass balance of Antarctica. We present here the blowing snow detection algorithm, to retrieve blowing snow occurrence from the attenuated backscatter signal of ceilometers set up at two station. There is a good correspondence in detection of heavy blowing snow by the algorithm and the visual observations performed at Neumayer station. Moreover, most of the blowing snow occurs during events bringing precipitation from the coast inland.
Mario Krapp, Alexander Robinson, and Andrey Ganopolski
The Cryosphere, 11, 1519–1535, https://doi.org/10.5194/tc-11-1519-2017, https://doi.org/10.5194/tc-11-1519-2017, 2017
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We present the snowpack model SEMIC. It calculates snow height, surface temperature, surface albedo, and the surface mass balance of snow- and ice-covered surfaces while using meteorological data as input. In this paper we describe how SEMIC works and how well it compares with snowpack data of a more sophisticated regional climate model applied to the Greenland ice sheet. Because of its simplicity and efficiency, SEMIC can be used as a coupling interface between atmospheric and ice sheet models.
Johannes Gütschow, M. Louise Jeffery, Robert Gieseke, Ronja Gebel, David Stevens, Mario Krapp, and Marcia Rocha
Earth Syst. Sci. Data, 8, 571–603, https://doi.org/10.5194/essd-8-571-2016, https://doi.org/10.5194/essd-8-571-2016, 2016
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This paper provides the methodology for the creation of historical country-resolved time series of greenhouse gas emissions from different datasets which are individually incomplete in terms of years, gases, and/or countries. The combination of datasets is carried out using the PRIMAP model (www.primap.org). The resulting time series can be viewed interactively on www.pik-potsdam.de/primap-live. It will be used for climate policy analysis, e.g. the historical responsibility for climate change.
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
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CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
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The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
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This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
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The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
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HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present day and future projections, and to build links with other communities to provide more robust climate information.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
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We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
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1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the north and south Atlantic (south Pacific and Indian Ocean).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
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A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
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Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
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Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
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We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
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Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
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Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
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The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
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Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-119, https://doi.org/10.5194/gmd-2024-119, 2024
Revised manuscript accepted for GMD
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10-15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100-km and a 25-km grid. The three models are compared with observations to study the improvements thanks to the increased in the resolution.
Cited articles
Agosta, C., Fettweis, X., and Datta, R.: Evaluation of the CMIP5 models in the aim of regional modelling of the Antarctic surface mass balance, The Cryosphere, 9, 2311–2321, https://doi.org/10.5194/tc-9-2311-2015, 2015. a
Agosta, C., Amory, C., Kittel, C., Orsi, A., Favier, V., Gallée, H., van den Broeke, M. R., Lenaerts, J. T. M., van Wessem, J. M., van de Berg, W. J., and Fettweis, X.: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes, The Cryosphere, 13, 281–296, https://doi.org/10.5194/tc-13-281-2019, 2019. a, b
Alam, A. and Curry, J.: Lead-induced atmospheric circulations, J.
Geophys. Res.-Oceans, 100, 4643–4651,
https://doi.org/10.1029/94JC02562, 1995. a
Arduini, G., Keeley, S., Day, J., Sandu, I., Zampieri, L., and Balsamo, G.: On
the importance of representing snow over sea-ice for simulating the Arctic
boundary layer, J. Adv. Model. Earth Syst., 14, e2021MS00277,
https://doi.org/10.1029/2021MS002777, 2022. a, b
Arndt, J., Schenke, H., Jakobsson, M., Nitsche, F., Buys, G., Goleby, B.,
Rebesco, M., Bohoyo, F., Hong, J. andBlack, J., Greku, R., Udintsev, G.,
Barrios, F., Reynoso-Peralta, W., Taisei, M., and Wigley, 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, https://doi.org/10.1002/grl.50413, 2013. a
Azaneu, M., Kerr, R., and Mata, M. M.: Assessment of the representation of Antarctic Bottom Water properties in the ECCO2 reanalysis, Ocean Sci., 10, 923–946, https://doi.org/10.5194/os-10-923-2014, 2014. a
Banwell, A., MacAyeal, D., and Sergienko, O.: Breakup of the Larsen B Ice Shelf
triggered by chain reaction drainage of supraglacial lakes, Geophys. Res.
Lett., 40, 5872–5876, https://doi.org/10.1002/2013GL057694, 2013. a
Beadling, R., Russell, J., Stouffer, R., Mazloff, N., Talley, L., Goodman, P.,
Sallee, J., Hewitt, H., Hyder, P., and Pandde, A.: Representation of Southern
Ocean Properties across Coupled Model Intercomparison Project Generations:
CMIP3 to CMIP6, J. Climate, 33, 6555–6581, https://doi.org/10.1175/JCLI-D-19-0970.1,
2020. a
Bell, R., Banwell, A., Trusel, L., and Kingslake, J.: Antarctic surface
hydrology and impacts on ice-sheet mass balance, Nat. Clim. Change, 8,
1044–1052, https://doi.org/10.1038/s41558-018-0326-3, 2018. a
Bozkurt, D., Bromwich, D., Carrasco, J., and Ronadnelli, R.: Temperature and
precipitation projections for the Antarctic Peninsula over the next two
decades: contrasting global and regional climate model simulations, Clim.
Dynam., 56, 3853–3874, https://doi.org/10.1007/s00382-021-05667-2,
2021. a, b
Bromwich, D.: Snowfall in High Southern Latitudes, Rev. Geophys., 26, 149–168,
https://doi.org/10.1029/RG026i001p00149, 1988. a
Bromwich, D., Otieno, F., Hines, K., Manning, K., and Sihlo, E.: Comprehensive
evaluation of polar weather research and forecasting model performance in the
Antarctic, J. Geophys. Res.-Atmos., 118, 274–292, https://doi.org/10.1029/2012JD018139,
2013. a, b, c
Carrasco, J. and Bromwich, D.: Mesoscale Cyclogenesis Dynamics Over the
Southwestern Ross Sea, Antarctica, J. Geophys. Res., 98, 12973–12995,
https://doi.org/10.1029/92JD02821, 1993. a
Carrasco, J., Bromwich, D., and Monaghan, A.: Distribution and Characteristics
of Mesoscale Cyclones in the Antarctic: Ross Sea Eastward to the Weddell Sea,
Mon. Weather Rev., 131, 289–301,
https://doi.org/10.1175/1520-0493(2003)131<0289:DACOMC>2.0.CO;2, 2003. a
Christie, F., Benham, T., Batchelor, C., Rack, W., Montelli, A., and
Dowdeswell, J.: Antarctic ice-shelf advance driven by anomalous atmospheric
and sea-ice circulation, Nat. Geosci., 15, 356–362,
https://doi.org/10.1038/s41561-022-00938-x, 2022. a
DeConto, R. and Pollard, D.: Contribution of Antarctica to past and future
sea-level rise, Nature, 531, 591–597, https://doi.org/10.1038/nature17145, 2016. a
Dorn, W., Rinke, A., Köberle, C., Dethloff, K., and Gerdes, R.: HIRHAM–NAOSIM 2.0: The upgraded version of the coupled regional atmosphere-ocean-sea ice model for Arctic climate studies, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2018-278, 2018. a
Dorn, W., Rinke, A., Köberle, C., Dethloff, K., and Gerdes, G.: Evaluation of
the Sea-Ice Simulation in the Upgraded Version of the COupled Regional
Atmosphere-Ocean-Sea Ice Model HIRHAM-NAOSIM 2.0, Atmosphere, 10, 431,
https://doi.org/10.3390/atmos10080431, 2019. a
Etourneau, J., Sgubin, G., Crosta, X., Swingedouw, D., Willmott, V., Barbara,
L., Houssais, M.-N., Schouten, S., Sinninghe Damsté, J., Goosse, H.,
Escutia, C., Crespin, J., Massé, G., and Kim, J.-H.: Ocean temperature
impact on ice shelf extent in the eastern Antarctic Peninsula, Nat. Commun.,
10, 304, https://doi.org/10.1038/s41467-018-08195-6, 2019. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017. 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 (data available at: https://www.bas.ac.uk/data/our-data/maps/thematic-maps/bedmap2/, last access: October 2021). a, b
Gerber, F. and Lehning, M.: REMA topography and AntarcticaLC2000 for WRF, EnviDat [data set],
https://doi.org/10.16904/envidat.190, 2020. a
Golledge, N., Kowalewski, D., Naish, T., Levy, R., Fogwill, C., and Gasson, E.:
The multi-millennial Antarctic commitment to future sea-level rise, Nature,
526, 421–425, https://doi.org/10.1038/nature15706, 2015. a
Golledge, N., Keller, E., Gomez, N., Naughten, N., Bernales, J., Trusel, L.,
and Edwards, T.: Global environmental consequences of twenty-first century
ice-sheet melt, Nature, 566, 65–75, https://doi.org/10.1038/s41586-019-0889-9, 2019. a
Goosse, H., Kay, J., Armour, K., Bodas-Salcedo, A., Chepfer, H., Docquier, D.,
Jonko, A., Kushner, P., Lecomte, O., Massonnet, F., Park, H.-S., Pithan, F.,
Svensson, G., and Vancoppenolle, M.: Quantifying climate feedbacks in polar
regions, Nat. Commun., 9, 1919, https://doi.org/10.1038/s41467-018-04173-0, 2018. a
Greene, C., Gwyther, D., and Blankenship, D.: Antarctic Mapping Tools for
Matlab, Comput. Geosci., 104, 151–157, https://doi.org/10.1016/j.cageo.2016.08.003,
2017. a
Hellmer, 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, https://doi.org/10.1038/nature11064,
2012. a
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., 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. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023a. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023b. a
Hill, C., DeLuca, C., Balaji, Suarez, M., and Silva, A.: The architecture of
the Earth system modeling framework, Computing in Science and Engineering, 6,
18–28, https://doi.org/10.1109/MCISE.2004.1255817, 2004. a
Hines, K. and Bromwich, D.: Development and Testing of Polar Weather Research
and Forecasting (WRF) Model, Part I: Greenland Ice Sheet Meteorology, Mon.
Weather Rev., 136, 1971–1989, https://doi.org/10.1175/2007mwr2112.1, 2008. a, b
Holland, D., Nicholls, K. W., and Basinski, A.: The Southern Ocean and its
interaction with the Antarctic Ice Sheet, Science, 367, 1326–1330,
https://doi.org/10.1126/science.aaz5491, 2020. a
Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665–674, https://doi.org/10.5194/tc-13-665-2019, 2019. a
Hubbard, B., Luckman, A., Ashmore, D., Bevan, S., Kulessa, B., Kuipers-Munneke,
P., Philippe, M., Jansen, D., Booth, A., Sevestre, H., Tison, J.-L.,
O’Leary, M., and Rutt, I.: Massive subsurface ice formed by refreezing of
ice-shelf melt ponds, Nat. Commun., 7, 11897, https://doi.org/10.1038/ncomms11897, 2016. a
Hui, F., Kang, J., Liu, Y., Cheng, X., Gong, P., Wang, F., Li, Z., Ye, Y., and
Guo, Z.: AntarcticaLC2000: The new Antarctic land cover database for the year
2000, Sci. China Earth Sci., 60, 686–696, https://doi.org/10.1007/s11430-016-0029-2,
2017. a
Iacono, M., Delamere, J., Mlawer, E., Shepherd, M., Clough, S., and Collins,
W.: Radiative forcing by long-lived greenhouse gases: Calculations with the
AER radiative transfer models, J. Geophys. Res., 113, D13103,
https://doi.org/10.1029/2008JD009944, 2008. a
Janjic, Z. I.: Nonsingular Implementation of the Mellor-Yamada Level 2.5 Scheme in
the NCEP Meso model, NCEP Office Note No. 437, 61 pp., 2002. a
Jeffries, M. O., Li, S., Jaña, R. A., Krouse, H. R., and Hurst-Cushing, B.:
Late Winter First-Year Ice Floe Thickness Variability, Seawater Flooding and
Snow Ice Formation in the Amundsen and Ross Seas, in: Antarctic Sea Ice:
Physical Processes, Interactions and Variability, edited by: Jeffries, M. O.,
American Geophysical Union (AGU), 69—87, https://doi.org/10.1029/AR074p0069,
1998. a
Jourdain, N., Mathiot, P., Gallée, H., and Barnier, B.: Influence of coupling
on atmosphere, sea ice and ocean regional models in the Ross Sector,
Antarctica, Clim. Dynam., 36, 1523–1543, https://doi.org/10.1007/s00382-010-0889-9,
2011. a
Kingslake, J., Ely, J., Das, I., and Bell, R.: Widespread movement of meltwater
onto and across Antarctic ice shelves, Nature, 544, 349–352,
https://doi.org/10.1038/nature22049, 2017. a
Kittel, C., Amory, C., Agosta, C., Delhasse, A., Doutreloup, S., Huot, P.-V., Wyard, C., Fichefet, T., and Fettweis, X.: Sensitivity of the current Antarctic surface mass balance to sea surface conditions using MAR, The Cryosphere, 12, 3827–3839, https://doi.org/10.5194/tc-12-3827-2018, 2018. a, b
Krinner, G., Magand, O., Simmonds, I., Genthon, C., and Dufesne, J.-L.:
Simulated Antarctic precipitation and surface mass balance at the end of the
twentieth and twenty-first centuries, Clim. Dynam., 28, 215–230,
https://doi.org/10.1007/s00382-006-0177-x, 2007. a
Leeson, A., Forster, E., Rice, A., Gourmelen, N., and van Wessem, J.: Evolution
of supraglacial lakes on the Larsen B ice shelf in the decades before it
collapsed, Geophys. Res. Lett., 47, e2019GL085591,
https://doi.org/10.1029/2019GL085591, 2020. a
Li, S., Huang, G., Li, X., Liu, J., and Fan, G.: An Assessment of the Antarctic
Sea Ice Mass Budget Simulation in CMIP6 Historical Experiment, Front. Earth
Sci., 9, 649743, https://doi.org/10.3389/feart.2021.649743, 2021. a
Losch, M.: Modeling ice shelf cavities in a z coordinate ocean general
circulation model, J. Geophys. Res., 113, C08043, https://doi.org/10.1029/2007JC004368, 2008. a
Losch, M., Menemenlis, D., Heimbach, P., Campin, J.-M., and Hill, C.: On the
formulation of sea-ice models: Part 1: Effects of different solver
implementations and parameterizations, Ocean Modell., 33, 129–144,
https://doi.org/10.1016/j.ocemod.2009.12.008, 2010. a
Lucas-Picher, P., Wulff-Nielsen, M., Christensen, J. H., Aðalgeirsdóttir, G.,
Mottram, R., and Simonsen, S. B.: Very high resolution regional climate model
simulations over Greenland: Identifying added value, J. Geophys. Res.-Atmos.,
117, D02108, https://doi.org/10.1029/2011JD016267, 2012. a
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri, D.,
Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Ethé, C.,
Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D., Lévy, C., Lovato,
T., Martin, N., Masson, S., Mocavero, S., Paul, J., Rousset, C., Storkey, D.,
Storto, A., and Vancoppenolle, M.: NEMO ocean engine. In Notes du Pôle de modélisation de l'Institut Pierre-Simon Laplace (IPSL) (v3.6-patch, Number 27), Zenodo [code], https://doi.org/10.5281/zenodo.3248739, 2017. a
Malyarenko, A. and Gossart, A.: P-SKRIPS Version 1, Zenodo [data set], https://doi.org/10.5281/zenodo.7739063, 2023a. a, b
Malyarenko, A. and Gossart, A.: PSKRIPS Version 1 – Data for January Case, Zenodo [data set], https://doi.org/10.5281/zenodo.7739059, 2023b. a, b
Mazloff, M., Heimbach, P., and Wunsch, C.: An Eddy-Permitting Southern Ocean
State Estimate, J. Phys. Oceanogr., 40, 880–899,
https://doi.org/10.1175/2009JPO4236.1, 2010 (data available at: http://sose.ucsd.edu/SO6/ITER135/, last access: March 2021). a, b
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.:
Intercomparison makes for a better climate model, Eos, 78, 445–51,
https://doi.org/10.1029/97EO00276, 1997. a
Mohrmann, M., Heuzé, C., and Swart, S.: Southern Ocean polynyas in CMIP6 models, The Cryosphere, 15, 4281–4313, https://doi.org/10.5194/tc-15-4281-2021, 2021. a
Moorman, R., Morrison, A. K., and Hogg, A. M.: Thermal responses to Antarctic
ice shelf melt in an eddy rich global ocean–sea-ice model, J. Climate, 33,
6599–6620, https://doi.org/10.1175/jcli-d-19-0846.1, 2020. a
Morrison, A. K., Frölicher, T. L., and Sarmiento, J. L.: Upwelling in the
Southern Ocean, Phys. Today, 68, 27–32, https://doi.org/10.1063/PT.3.2654, 2015. a
Morrison, A. K., Hogg, A., England, M. H., and Spence, P.: Warm Circumpolar
Deep Water transport toward Antarctica driven by local dense water export in
canyons, Sci. Adv., 6, eaav2516, https://doi.org/10.1126/sciadv.aav2516, 2020. a
Nakayama, Y., Menemenlis, D., Schodlok, M., and Rignot, E.: Amundsen and
Bellingshausen Seas simulation with optimized ocean, sea ice, and
thermodynamic ice shelf model parameters, J. Geophys. Res.-Oceans, 122,
6180–6195, https://doi.org/10.1002/2016jc012538, 2017. a
Naughten, K., De Rydt, J., Rosier, S., Jenkins, A., Holland, P., and Ridley,
J.: Two-timescale response of a large Antarctic ice shelf to climate change,
Nat. Commun., 12, 1991, https://doi.org/10.1038/s41467-021-22259-0, 2021. 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
Niu, G.-Y., Yang, Z.-L., Mitchell, K., Chen, F., Ek, M., Barlage, M., Kumar,
A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: he
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale measurements,
J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a, b
Orsi, A. H., Smethie Jr., W. M., and Bullister, J. L.: On the total input of
Antarctic waters to the deep ocean: A preliminary estimate from
chlorofluorocarbon measurements, J. Geophys. Res.-Oceans, 107, 31-1–31-14,
https://doi.org/10.1029/2001JC000976, 2002. a
Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, https://doi.org/10.5194/gmd-15-553-2022, 2022. a
Ren, S., Liang, X., Sun, Q., Yu, H., Tremblay, L. B., Lin, B., Mai, X., Zhao, F., Li, M., Liu, N., Chen, Z., and Zhang, Y.: A fully coupled Arctic sea-ice–ocean–atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description and preliminary results, Geosci. Model Dev., 14, 1101–1124, https://doi.org/10.5194/gmd-14-1101-2021, 2021. a, b
Richter, O., Gwyther, D. E., Galton-Fenzi, B. K., and Naughten, K. A.: The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation, Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, 2022. a
Rignot, E.: Accelerated ice discharge from the Antarctic Peninsula following
the collapse of Larsen B ice shelf, Geophys. Res. Lett., 31, L18401,
https://doi.org/10.1029/2004GL020697, 2004. a
Roach, L. A., Dörr, J., Holmes, C., Massonnet, F., Blockley, E. W., Notz, D.,
Rackow, T., Raphael, M., O’Farrell, S., Bailey, D., , and Bitz, C.:
Antarctic sea ice area in CMIP6, Geophys. Res. Lett., 47, e2019GL086729,
https://doi.org/10.1029/2019GL086729, 2020. a
Rott, H., Skvarca, P., and Nagler, T.: Rapid collapse of Northern Larsen Ice
Shelf, Antarctica, Science, 271, 788–792,
https://doi.org/10.1126/science.271.5250.788, 1996. a
Scambos, T., Hulbe, C., Fahnestock, M., and Bohlander, J.: The link between
climate wargaming and break-up of ice shelves in the Antarctic Peninsula, J.
Glaciol., 46, 516–530, https://doi.org/10.3189/172756500781833043, 2000. a, b
Shepherd, A., Wingham, D., and Rignot, E.: Warm ocean is eroding West Antarctic
Ice Sheet, Geophys. Res. Lett., 31, L23402, https://doi.org/10.1029/2004GL021106,
2004. a
Sitz, L., Di Sante, F., Farnetti, R., Fuentes-Franco, R., Coppola, E.,
Mariotti, L., Reale, M., Sannino, G., Bareiro, M., Norgherotto, R., Giuliani,
G., Graffino, G., Solidoro, C., Cossarini, G., and Giorgi, F.: Description
and evaluation of the Earth System Regional Climate Model (RegCM-ES), J. Adv. Model. Earth Sy., 9, 1863–1886,
https://doi.org/10.1002/2017MS000933, 2017. a
Skamarok, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W.,
Powers, J., Duda, M., Barker, D., and Huang, X.-Y.: A description of the
Advanced Research WRF Version 4, Tech. Rep. NCAR TechnicalNote:
NCAR/TN-556+STR, NCAR, https://doi.org/10.5065/1dfh-6p97, 2019. a
Skinner, L., Menviel, L., Broadfield, L., Gottshalk, J., and Greaves, M.:
Southern Ocean convection amplified past Antarctic warming and atmospheric
CO2 rise during Heinrich Stadial 4., Commun. Earth Environ., 1, 23,
https://doi.org/10.1038/s43247-020-00024-3, 2020. a
Smith, R. S., Mathiot, P., Siahaan, A., Lee, V., Cornford, S. L., Gregory,
J. M., Payne, A. J., Jenkins, A., Holland, P. R., Ridley, J. K., and Jones,
C. G.: Coupling the U.K. Earth System Model to Dynamic Models of the
Greenland and Antarctic Ice Sheets, J. Adv. Model. Earth Sy., 13,
e2021MS002520, https://doi.org/10.1029/2021MS002520, 2021. a
Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J. T. M., Medley, B.,
Gorodetskaya, I. V., Vanden Broucke, S., and van Lipzig, N. P. M.: A New
Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-Year Hindcast
with COSMO-CLM2 Over Antarctica, J. Geophys. Res.-Atmos., 124, 1405–1427,
https://doi.org/10.1029/2018JD028862, 2019. a
Sun, R.: SKRIPS Model v2.0b (v2.0b), Zenodo [code], https://doi.org/10.5281/zenodo.7336070, 2022. a
Sun, R., Subramanian, A. C., Miller, A. J., Mazloff, M. R., Hoteit, I., and Cornuelle, B. D.: SKRIPS v1.0: a regional coupled ocean–atmosphere modeling framework (MITgcm–WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea, Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, 2019. a, b
Toyota, T., Massom, R., Tateyama, K., Tamura, T., and Fraser, A.: Properties of
snow overlying the sea ice off East Antarctica in late winter 2007, Deep-Sea
Res. Pt. II, 58, 1137–1148,
https://doi.org/10.1016/j.dsr2.2010.12.002, 2011. a
Toyota, T., Massom, R., Lecomte, O., Nomura, D., Heil, P., Tamura, T., and
Fraser, A.: On the extraordinary snow on the sea ice off East Antarctica in
late winter, 2012, Deep-Sea Res. Pt. II, 131, 53–67,
https://doi.org/10.1016/j.dsr2.2016.02.003, 2016. a
van den Broeke, M.: Strong surface melting preceded collapse of Antarctic
Peninsula ice shelf, Geophys. Res. Lett., 32, L12815,
https://doi.org/10.1029/2005GL023247, 2005. a
van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018.
a, b
Wang, H., Fyke, J. G., Lenaerts, J. T. M., Nusbaumer, J. M., Singh, H., Noone, D., Rasch, P. J., and Zhang, R.: Influence of sea-ice anomalies on Antarctic precipitation using source attribution in the Community Earth System Model, The Cryosphere, 14, 429–444, https://doi.org/10.5194/tc-14-429-2020, 2020. a
Wang, W., Bruyère, C., Duda, M., Dudhia, J., Gill, D., Kavulich, M., Werner,
K., Chen, M., Lin, H.-C., Michalakes, J., Rizvi, S., Zhang, X., Berner, J.,
Munoz-Esparza, D., Reen, B., Ha, S., and Fossell, K.: WRF users' guide,
https://www2.mmm.ucar.edu/wrf/users/docs/user_guide_v4/v4.1/contents.html (last access: December 2020,
2019. a
Worby, A., Jeffries, M., Weeks, W., Morris, K., and Jana, R.: The thickness
distribution of sea ice and snow cover during late winter in the
Bellingshausen and Amundsen Seas, Antarctica, J. Geophys. Res., 101,
28441–28455, https://doi.org/10.1029/96JC02737, 1996. a
Wu, X. G., Simmonds, I., and Budd, W. F.: Southern hemisphere climate system
recovery from “instantaneous” sea-ice removal, Q. J. Roy. Meteor. Soc., 122,
1501–1520, https://doi.org/10.1002/qj.49712253503, 1996. a
Yang, Z.-L. and Niu, G.-Y.: The versatile integrator of surface and atmosphere
processes (VISA) part I: Model description, Global Planet. Change, 38,
175–189, https://doi.org/10.1016/S0921-8181(03)00028-6, 2003. a
Short summary
Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for...
