Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-2681-2023
© 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/tc-17-2681-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Southern Ocean polynyas and dense water formation in a high-resolution, coupled Earth system model
Institute of Ocean and Atmospheric Sciences (IOAS), Hanyang University, Ansan, South Korea
Department of Ocean Science and Technology, Hanyang University, Ansan, South Korea
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Adrian K. Turner
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Andrew F. Roberts
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Milena Veneziani
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Stephen F. Price
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Xylar S. Asay-Davis
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Luke P. Van Roekel
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Wuyin Lin
Brookhaven National Laboratory, Upton, New York, USA
Peter M. Caldwell
Lawrence Livermore National Laboratory, Livermore, California, USA
Hyo-Seok Park
Institute of Ocean and Atmospheric Sciences (IOAS), Hanyang University, Ansan, South Korea
Department of Ocean Science and Technology, Hanyang University, Ansan, South Korea
Jonathan D. Wolfe
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Azamat Mametjanov
Argonne National Laboratory, Lemont, Illinois, USA
Related authors
No articles found.
Trevor R. Hillebrand, Matthew J. Hoffman, Holly K. Han, Mauro Perego, Alexander O. Hager, Andrew Nolan, Xylar Asay-Davis, Stephen F. Price, Jerry Watkins, and Max Carlson
EGUsphere, https://doi.org/10.5194/egusphere-2025-3942, https://doi.org/10.5194/egusphere-2025-3942, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We present new simulations that complement our contribution to the ISMIP6-Antarctica-2300 ensemble. We find significant mass loss after 2300 under both low-emissions and present-day forcing. Thermal evolution is extremely important, with fixed temperature yielding up to twice as much mass loss as simulations with evolving temperature. External forcing uncertainty dominates the ensemble spread after 2050. Initial condition uncertainty could explain the inter-model spread in the ISMIP6 ensembles.
Claire K. Yung, Xylar S. Asay-Davis, Alistair Adcroft, Christopher Y. S. Bull, Jan De Rydt, Michael S. Dinniman, Benjamin K. Galton-Fenzi, Daniel Goldberg, David E. Gwyther, Robert Hallberg, Matthew Harrison, Tore Hattermann, David M. Holland, Denise Holland, Paul R. Holland, James R. Jordan, Nicolas C. Jourdain, Kazuya Kusahara, Gustavo Marques, Pierre Mathiot, Dimitris Menemenlis, Adele K. Morrison, Yoshihiro Nakayama, Olga Sergienko, Robin S. Smith, Alon Stern, Ralph Timmermann, and Qin Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2025-1942, https://doi.org/10.5194/egusphere-2025-1942, 2025
Short summary
Short summary
ISOMIP+ compares 12 ocean models that simulate ice-ocean interactions in a common, idealised, static ice shelf cavity setup, aiming to assess and understand inter-model variability. Models simulate similar basal melt rate patterns, ocean profiles and circulation but differ in ice-ocean boundary layer properties and spatial distributions of melting. Ice-ocean boundary layer representation is a key area for future work, as are realistic-domain ice sheet-ocean model intercomparisons.
Naser Mahfouz, Hassan Beydoun, Johannes Mülmenstädt, Noel Keen, Adam C. Varble, Luca Bertagna, Peter Bogenschutz, Andrew Bradley, Matthew W. Christensen, T. Conrad Clevenger, Aaron Donahue, Jerome Fast, James Foucar, Jean-Christophe Golaz, Oksana Guba, Walter Hannah, Benjamin Hillman, Robert Jacob, Wuyin Lin, Po-Lun Ma, Yun Qian, Balwinder Singh, Christopher Terai, Hailong Wang, Mingxuan Wu, Kai Zhang, Andrew Gettelman, Mark Taylor, L. Ruby Leung, Peter Caldwell, and Susannah Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2025-1868, https://doi.org/10.5194/egusphere-2025-1868, 2025
Short summary
Short summary
Our study assesses the aerosol effective radiative forcing in a global cloud-resolving atmosphere model at ultra-high resolution. We demonstrate that global ERFaer signal can be robustly reproduced across resolutions when aerosol activation processes are carefully parameterized. Further, we argue that simplified prescribed aerosol schemes will open the door for further process/mechanism studies under controlled conditions.
John D. Jakeman, Mauro Perego, D. Thomas Seidl, Tucker A. Hartland, Trevor R. Hillebrand, Matthew J. Hoffman, and Stephen F. Price
Earth Syst. Dynam., 16, 513–544, https://doi.org/10.5194/esd-16-513-2025, https://doi.org/10.5194/esd-16-513-2025, 2025
Short summary
Short summary
This study investigated the computational benefits of using multiple models of varying cost and accuracy to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. Despite some models being incapable of capturing the local features of the ice-flow fields, using multiple models reduced the error in the estimated statistics by over an order of magnitude when compared to an approach that only used a single accurate model.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam 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. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
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 bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Benjamin Keith Galton-Fenzi, Richard Porter-Smith, Sue Cook, Eva Cougnon, David E. Gwyther, Wilma G. C. Huneke, Madelaine G. Rosevear, Xylar Asay-Davis, Fabio Boeira Dias, Michael S. Dinniman, David Holland, Kazuya Kusahara, Kaitlin A. Naughten, Keith W. Nicholls, Charles Pelletier, Ole Richter, Helene L. Seroussi, and Ralph Timmermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-4047, https://doi.org/10.5194/egusphere-2024-4047, 2025
Short summary
Short summary
Melting beneath Antarctica’s floating ice shelves is key to future sea-level rise. We compare several different ocean simulations with satellite measurements, and provide the first multi-model average estimate of melting and refreezing driven by both ocean temperature and currents beneath ice shelves. The multi-model average can provide a useful tool for better understanding the role of ice shelf melting in present-day and future ice-sheet changes and informing coastal adaptation efforts.
Irena Vaňková, Xylar Asay-Davis, Carolyn Branecky Begeman, Darin Comeau, Alexander Hager, Matthew Hoffman, Stephen F. Price, and Jonathan Wolfe
The Cryosphere, 19, 507–523, https://doi.org/10.5194/tc-19-507-2025, https://doi.org/10.5194/tc-19-507-2025, 2025
Short summary
Short summary
We study the effect of subglacial discharge on basal melting for Antarctic ice shelves. We find that the results from previous studies of vertical ice fronts and two-dimensional ice tongues do not translate to the rotating ice-shelf framework. The melt rate dependence on discharge is stronger in the rotating framework. Further, there is a substantial melt-rate sensitivity to the location of the discharge along the grounding line relative to the directionality of the Coriolis force.
Sanket Jantre, Matthew J. Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro Perego, Stephen Price, and John D. Jakeman
The Cryosphere, 18, 5207–5238, https://doi.org/10.5194/tc-18-5207-2024, https://doi.org/10.5194/tc-18-5207-2024, 2024
Short summary
Short summary
We investigate potential sea-level rise from Antarctica's Lambert Glacier, once considered stable but now at risk due to projected ocean warming by 2100. Using statistical methods and limited supercomputer simulations, we calibrated our ice-sheet model using three observables. We find that, under high greenhouse gas emissions, glacier retreat could raise sea levels by 46–133 mm by 2300. This study highlights the need for better observations to reduce uncertainty in ice-sheet model projections.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
Short summary
Short summary
Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
Short summary
Short summary
Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Matthew J. Hoffman, Carolyn Branecky Begeman, Xylar S. Asay-Davis, Darin Comeau, Alice Barthel, Stephen F. Price, and Jonathan D. Wolfe
The Cryosphere, 18, 2917–2937, https://doi.org/10.5194/tc-18-2917-2024, https://doi.org/10.5194/tc-18-2917-2024, 2024
Short summary
Short summary
The Filchner–Ronne Ice Shelf in Antarctica is susceptible to the intrusion of deep, warm ocean water that could increase the melting at the ice-shelf base by a factor of 10. We show that representing this potential melt regime switch in a low-resolution climate model requires careful treatment of iceberg melting and ocean mixing. We also demonstrate a possible ice-shelf melt domino effect where increased melting of nearby ice shelves can lead to the melt regime switch at Filchner–Ronne.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
Short summary
Short summary
We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Sara Calandrini, Darren Engwirda, and Luke Van Roekel
EGUsphere, https://doi.org/10.5194/egusphere-2024-472, https://doi.org/10.5194/egusphere-2024-472, 2024
Preprint withdrawn
Short summary
Short summary
Most modern ocean circulation models only consider the hydrostatic pressure, but for coastal phenomena nonhydrostatic effects become important, creating the need to include the nonhydrostatic pressure. In this work, we present a nonhydrostatic formulation for MPAS-Ocean (MPAS-O NH) and show its correctness on idealized benchmark test cases. MPAS-O NH is the first global nonhydrostatic model at variable resolution and is the first nonhydrostatic ocean model to be fully coupled in a climate model.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
Short summary
Short summary
Mass loss from Antarctica is a key contributor to sea level rise over the 21st century, and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Olawale James Ikuyajolu, Luke Van Roekel, Steven R. Brus, Erin E. Thomas, Yi Deng, and Sarat Sreepathi
Geosci. Model Dev., 16, 1445–1458, https://doi.org/10.5194/gmd-16-1445-2023, https://doi.org/10.5194/gmd-16-1445-2023, 2023
Short summary
Short summary
Wind-generated waves play an important role in modifying physical processes at the air–sea interface, but they have been traditionally excluded from climate models due to the high computational cost of running spectral wave models for climate simulations. To address this, our work identified and accelerated the computationally intensive section of WAVEWATCH III on GPU using OpenACC. This allows for high-resolution modeling of atmosphere–wave–ocean feedbacks in century-scale climate integrations.
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, https://doi.org/10.5194/gmd-16-1297-2023, 2023
Short summary
Short summary
Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding and erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this paper we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tide–ice interactions.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Trevor R. Hillebrand, Matthew J. Hoffman, Mauro Perego, Stephen F. Price, and Ian M. Howat
The Cryosphere, 16, 4679–4700, https://doi.org/10.5194/tc-16-4679-2022, https://doi.org/10.5194/tc-16-4679-2022, 2022
Short summary
Short summary
We estimate that Humboldt Glacier, northern Greenland, will contribute 5.2–8.7 mm to global sea level in 2007–2100, using an ensemble of model simulations constrained by observations of glacier retreat and speedup. This is a significant fraction of the 40–140 mm from the whole Greenland Ice Sheet predicted by the recent ISMIP6 multi-model ensemble, suggesting that calibrating models against observed velocity changes could result in higher estimates of 21st century sea-level rise from Greenland.
Alexander O. Hager, Matthew J. Hoffman, Stephen F. Price, and Dustin M. Schroeder
The Cryosphere, 16, 3575–3599, https://doi.org/10.5194/tc-16-3575-2022, https://doi.org/10.5194/tc-16-3575-2022, 2022
Short summary
Short summary
The presence of water beneath glaciers is a control on glacier speed and ocean-caused melting, yet it has been unclear whether sizable volumes of water can exist beneath Antarctic glaciers or how this water may flow along the glacier bed. We use computer simulations, supported by observations, to show that enough water exists at the base of Thwaites Glacier, Antarctica, to form "rivers" beneath the glacier. These rivers likely moderate glacier speed and may influence its rate of retreat.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Xue Zheng, Qing Li, Tian Zhou, Qi Tang, Luke P. Van Roekel, Jean-Christophe Golaz, Hailong Wang, and Philip Cameron-Smith
Geosci. Model Dev., 15, 3941–3967, https://doi.org/10.5194/gmd-15-3941-2022, https://doi.org/10.5194/gmd-15-3941-2022, 2022
Short summary
Short summary
We document the model experiments for the future climate projection by E3SMv1.0. At the highest future emission scenario, E3SMv1.0 projects a strong surface warming with rapid changes in the atmosphere, ocean, sea ice, and land runoff. Specifically, we detect a significant polar amplification and accelerated warming linked to the unmasking of the aerosol effects. The impact of greenhouse gas forcing is examined in different climate components.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
Short summary
Short summary
We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Milena Veneziani, Wieslaw Maslowski, Younjoo J. Lee, Gennaro D'Angelo, Robert Osinski, Mark R. Petersen, Wilbert Weijer, Anthony P. Craig, John D. Wolfe, Darin Comeau, and Adrian K. Turner
Geosci. Model Dev., 15, 3133–3160, https://doi.org/10.5194/gmd-15-3133-2022, https://doi.org/10.5194/gmd-15-3133-2022, 2022
Short summary
Short summary
We present an Earth system model (ESM) simulation, E3SM-Arctic-OSI, with a refined grid to better resolve the Arctic ocean and sea-ice system and low spatial resolution elsewhere. The configuration satisfactorily represents many aspects of the Arctic system and its interactions with the sub-Arctic, while keeping computational costs at a fraction of those necessary for global high-resolution ESMs. E3SM-Arctic can thus be an efficient tool to study Arctic processes on climate-relevant timescales.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
Short summary
Short summary
We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Carolyn Branecky Begeman, Xylar Asay-Davis, and Luke Van Roekel
The Cryosphere, 16, 277–295, https://doi.org/10.5194/tc-16-277-2022, https://doi.org/10.5194/tc-16-277-2022, 2022
Short summary
Short summary
This study uses ocean modeling at ultra-high resolution to study the small-scale ocean mixing that controls ice-shelf melting. It offers some insights into the relationship between ice-shelf melting and ocean temperature far from the ice base, which may help us project how fast ice will melt when ocean waters entering the cavity warm. This study adds to a growing body of research that indicates we need a more sophisticated treatment of ice-shelf melting in coarse-resolution ocean models.
Gunter R. Leguy, William H. Lipscomb, and Xylar S. Asay-Davis
The Cryosphere, 15, 3229–3253, https://doi.org/10.5194/tc-15-3229-2021, https://doi.org/10.5194/tc-15-3229-2021, 2021
Short summary
Short summary
We present numerical features of the Community Ice Sheet Model in representing ocean termini glaciers. Using idealized test cases, we show that applying melt in a partly grounded cell is beneficial, in contrast to recent studies. We confirm that parameterizing partly grounded cells yields accurate ice sheet representation at a grid resolution of ~2 km (arguably 4 km), allowing ice sheet simulations at a continental scale. The choice of basal friction law also influences the ice flow.
Steven R. Brus, Phillip J. Wolfram, Luke P. Van Roekel, and Jessica D. Meixner
Geosci. Model Dev., 14, 2917–2938, https://doi.org/10.5194/gmd-14-2917-2021, https://doi.org/10.5194/gmd-14-2917-2021, 2021
Short summary
Short summary
Wind-generated waves are an important process in the global climate system. They mediate many interactions between the ocean, atmosphere, and sea ice. Models which describe these waves are computationally expensive and have often been excluded from coupled Earth system models. To address this, we have developed a capability for the WAVEWATCH III model which allows model resolution to be varied globally across the coastal open ocean. This allows for improved accuracy at reduced computing time.
Qing Li and Luke Van Roekel
Geosci. Model Dev., 14, 2011–2028, https://doi.org/10.5194/gmd-14-2011-2021, https://doi.org/10.5194/gmd-14-2011-2021, 2021
Short summary
Short summary
Physical processes in the ocean span multiple spatial and temporal scales. Simultaneously resolving all these in a simulation is computationally challenging. Here we develop a more efficient technique to better study the interactions across scales, particularly focusing on the ocean surface turbulent mixing, by coupling a global ocean circulation model MPAS-Ocean and a large eddy simulation model PALM. The latter is customized and ported on a GPU to further accelerate the computation.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
Short summary
Short summary
A stochastic deep convection parameterization is implemented into the US Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of
too much light rain and too little heavy rainis largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in the deep convective parameterization.
William H. Lipscomb, Gunter R. Leguy, Nicolas C. Jourdain, Xylar Asay-Davis, Hélène Seroussi, and Sophie Nowicki
The Cryosphere, 15, 633–661, https://doi.org/10.5194/tc-15-633-2021, https://doi.org/10.5194/tc-15-633-2021, 2021
Short summary
Short summary
This paper describes Antarctic climate change experiments in which the Community Ice Sheet Model is forced with ocean warming predicted by global climate models. Generally, ice loss begins slowly, accelerates by 2100, and then continues unabated, with widespread retreat of the West Antarctic Ice Sheet. The mass loss by 2500 varies from about 150 to 1300 mm of equivalent sea level rise, based on the predicted ocean warming and assumptions about how this warming drives melting beneath ice shelves.
Tong Zhang, Stephen F. Price, Matthew J. Hoffman, Mauro Perego, and Xylar Asay-Davis
The Cryosphere, 14, 3407–3424, https://doi.org/10.5194/tc-14-3407-2020, https://doi.org/10.5194/tc-14-3407-2020, 2020
Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, https://doi.org/10.5194/gmd-13-4443-2020, 2020
Short summary
Short summary
This paper documents a tool that has been developed that can be used to accelerate the development and understanding of climate models. This version of the model, known as a the single-column model, is much faster to run than the full climate model, and we demonstrate that this tool can be used to quickly exploit model biases that arise due to physical processes. We show examples of how this single-column model can directly benefit the field.
Nicolas C. Jourdain, Xylar Asay-Davis, Tore Hattermann, Fiammetta Straneo, Hélène Seroussi, Christopher M. Little, and Sophie Nowicki
The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, https://doi.org/10.5194/tc-14-3111-2020, 2020
Short summary
Short summary
To predict the future Antarctic contribution to sea level rise, we need to use ice sheet models. The Ice Sheet Model Intercomparison Project for AR6 (ISMIP6) builds an ensemble of ice sheet projections constrained by atmosphere and ocean projections from the 6th Coupled Model Intercomparison Project (CMIP6). In this work, we present and assess a method to derive ice shelf basal melting in ISMIP6 from the CMIP6 ocean outputs, and we give examples of projected melt rates.
Hélène Seroussi, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, https://doi.org/10.5194/tc-14-3033-2020, 2020
Short summary
Short summary
The Antarctic ice sheet has been losing mass over at least the past 3 decades in response to changes in atmospheric and oceanic conditions. This study presents an ensemble of model simulations of the Antarctic evolution over the 2015–2100 period based on various ice sheet models, climate forcings and emission scenarios. Results suggest that the West Antarctic ice sheet will continue losing a large amount of ice, while the East Antarctic ice sheet could experience increased snow accumulation.
Cited articles
Abernathey, R., Cerovecki, I., Holland, P., Newsom, E., Mazloff, M., and Talley, L.:
Water-mass transformation by sea ice in the upper branch of the Southern Ocean overturning, Nat. Geosci., 9, 596, https://doi.org/10.1038/ngeo2749, 2016. a
Aguiar, W., Mata, M. M., and Kerr, R.:
On deep convection events and Antarctic Bottom Water formation in ocean reanalysis products, Ocean Sci., 13, 851–872, https://doi.org/10.5194/os-13-851-2017, 2017. a, b, c, d
Årthun, M., Holland, P. R., Nicholls, K. W., and Feltham, D. L.:
Eddy-driven exchange between the open ocean and a sub-ice shelf cavity, J. Phys. Oceanogr., 43, 2372–2387, https://doi.org/10.1175/JPO-D-13-0137.1, 2013. a
Bromwich, D. H. and Kurtz, D. D.:
Katabatic wind forcing of the Terra Nova Bay polynya, J. Geophys. Res.-Oceans, 89, 3561–3572, https://doi.org/10.1029/JC089iC03p03561, 1984. a
Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, C., Lin, W., Bader, D. C., Keen, N. D., Feng, Y., Jacob, R., Maltrud, M. E., Roberts, A. F., Taylor, M. A., Veneziani, M., Wang, H., Wolfe, J. D., Balaguru, K., Cameron-Smith, Dong, L., Klein, S. A., Leung, L. R., Li, H.-Y., Li, Q., Liu, X., Neale, R. B., Pinheiro, M., Qian, Y., Ullrich, P. A., Xie, S., Yang, Y., Zhang, Y., Zhang, K., and Zhou, T.:
The DOE E3SM Coupled Model Version 1: Description and results at High Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2019MS001870, 2019. a, b, c, d, e, f
Campbell, E. C., Wilson, E. A., Moore, G. W. L., Riser, S. C., Brayton, C. E., Mazloff, M. R., and Talley, L. D.:
Antarctic offshore polynyas linked to Southern Hemisphere climate anomalies, Nature, 570, 319–325, https://doi.org/10.1038/s41586-019-1294-0, 2019. a
Cheon, W. G. and Gordon, A. L.:
Open-ocean polynyas and deep convection in the Southern Ocean, Sci. Rep.-UK, 9, 1–9, 2019. a
Cheon, W. G., Park, Y.-G., Toggweiler, J., and Lee, S.-K.:
The Relationship of Weddell Polynya and Open-Ocean Deep Convection to the Southern Hemisphere Westerlies, J. Phys. Oceanogr., 44, 694–713, https://doi.org/10.1175/JPO-D-13-0112.1, 2014. a
Craig, A. P., Vertenstein, M., and Jacob, R.:
A new flexible coupler for earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012. a
Diao, X., Stössel, A., Chang, P., Danabasoglu, G., Yeager, S. G., Gopal, A., Wang, H., and Zhang, S.:
On the Intermittent Occurrence of Open-Ocean Polynyas in a Multi-Century High-Resolution Preindustrial Earth System Model Simulation, J. Geophys. Res.-Oceans, 127, e2021JC017672, https://doi.org/10.1029/2021JC017672, 2022. a
Dinniman, M. S., Asay-Davis, X. S., Galton-Fenzi, B. K., Holland, P. R., Jenkins, A., and Timmermann, R.:
Modeling Ice Shelf/Ocean Interaction in Antarctica: A Review, Oceanography, 29, 144–153, https://doi.org/10.5670/oceanog.2016.106, 2016. a
Dufour, C. O., Morrison, A. K., Griffies, S. M., Frenger, I., Zanowski, H., and Winton, M.:
Preconditioning of the Weddell Sea polynya by the ocean mesoscale and dense water overflows, J. Climate, 30, 7719–7737, 2017. a
E3SM Project: Energy Exascale Earth System Model v1.0, DOE [code], https://doi.org/10.11578/E3SM/dc.20180418.36, 2018. a
Foppert, A., Rintoul, S. R., and England, M. H.:
Along-slope variability of cross-slope Eddy transport in East Antarctica, Geophys. Res. Lett., 46, 8224–8233, 2019. a
Fraser, A. D., Massom, R. A., Michael, K. J., Galton-Fenzi, B. K., and Lieser, J. L.:
East antarctic landfast sea ice distribution and variability, 2000-08, J. Climate, 25, 1137–1156, https://doi.org/10.1175/JCLI-D-10-05032.1, 2012. a
Gent, P. R. and Mcwilliams, J. C.:
Isopycnal Mixing in Ocean Circulation Models, J. Phys. Oceanogr., 20, 150–155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a
Golaz, C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y.,
Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. R., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N.,
Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.:
The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a, b, c, d, e
Gordon, A. L. and Huber, B. A.:
Southern Ocean winter mixed layer, J. Geophys. Res.-Oceans, 95, 11655–11672, https://doi.org/10.1029/JC095iC07p11655, 1990. a
Groeskamp, S., Griffies, S. M., Iudicone, D., Marsh, R., Nurser, A. G., and Zika, J. D.:
The water mass transformation framework for ocean physics and biogeochemistry, Annu. Rev. Mar. Sci., 11, 271–305, 2019. a
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.:
High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a
Hallberg, R.:
Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects, Ocean Model., 72, 92–103, https://doi.org/10.1016/j.ocemod.2013.08.007, 2013. a
Hersbach, H., Bell, B., Berrisford, P., Horányi, A., Sabater, J. M., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.:
Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF Newsletter, 159, 17–24, https://doi.org/10.21957/vf291hehd7, 2019. a, b
Heuzé, C., Heywood, K. J., Stevens, D. P., and Ridley, J. K.:
Southern Ocean bottom water characteristics in CMIP5 models, Geophys. Res. Lett., 40, 1409–1414, https://doi.org/10.1002/grl.50287, 2013. a, b
Jeong, H., Asay-Davis, X. S., Turner, A. K., Comeau, D. S., Price, S. F., Abernathey, R. P., Veneziani, M., Petersen, M. R., Hoffman, M. J., Mazloff, M. R., and Ringler, T. D.:
Impacts of Ice-Shelf Melting on Water Mass Transformation in the Southern Ocean from E3SM Simulations, J. Climate, 33, 5787–5807, https://doi.org/10.1175/JCLI-D-19-0683.1, 2020. a, b, c, d, e, f
Johnson, G. C.:
Quantifying Antarctic Bottom Water and North Atlantic Deep Water volumes, J. Geophys. Res.-Oceans, 113, C05027, https://doi.org/10.1029/2007JC004477, 2008. a
Kurtakoti, P., Veneziani, M., Stössel, A., Weijer, W., and Maltrud, M.:
On the Generation of Weddell Sea Polynyas in a High-Resolution Earth System Model, J. Climate, 34, 2491–2510, https://doi.org/10.1175/JCLI-D-20-0229.1, 2021. a
Kusahara, K., Hasumi, H., and Tamura, T.:
Modeling sea ice production and dense shelf water formation in coastal polynyas around East Antarctica, J. Geophys. Res.-Oceans, 115, C10006, https://doi.org/10.1029/2010JC006133, 2010. a, b, c
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen, P.:
A coupled data assimilation system for climate reanalysis, Q. J. Roy. Meteor. Soc., 142, 65–78, https://doi.org/10.1002/qj.2629, 2016. a, b
Large, W. and Yeager, S.:
The global climatology of an interannually varying air–sea flux data set, Clim. Dynam., 33, 341–364, https://doi.org/10.1007/s00382-008-0441-3, 2009. a
Lee, D. Y., Petersen, M. R., and Lin, W.:
The Southern Annular Mode and Southern Ocean Surface Westerly Winds in E3SM, Earth Space Sci., 6, 2624–2643, https://doi.org/10.1029/2019EA000663, 2019. a
Lemieux, J., Lei, J., Dupont, F., Roy, F., Losch, M., Lique, C., and Laliberté, F.:
The Impact of Tides on Simulated Landfast Ice in a Pan-Arctic Ice-Ocean Model, J. Geophys. Res.-Oceans, 123, 2018JC014080, https://doi.org/10.1029/2018JC014080, 2018. a
Li, H., Wigmosta, M. S., Wu, H., Huang, M., Ke, Y., Coleman, A. M., and Leung, L. R.:
A Physically Based Runoff Routing Model for Land Surface and Earth System Models, J. Hydrometeorol., 14, 808–828, https://doi.org/10.1175/JHM-D-12-015.1, 2013. a
Li, H.-Y., Leung, L. R., Getirana, A., Huang, M., Wu, H., Xu, Y., Guo, J., and Voisin, N.:
Evaluating Global Streamflow Simulations by a Physically Based Routing Model Coupled with the Community Land Model, J. Hydrometeorol., 16, 948–971, https://doi.org/10.1175/JHM-D-14-0079.1, 2015. a
Marshall, J. and Speer, K.:
Closure of the meridional overturning circulation through Southern Ocean upwelling, Nat. Geosci., 5, 171–180, https://doi.org/10.1038/ngeo1391, 2012. a
Marsland, S. J., Bindoff, N., Williams, G., and Budd, W.:
Modeling water mass formation in the Mertz Glacier Polynya and Adélie Depression, East Antarctica, J. Geophys. Res.-Oceans, 109, C11003, https://doi.org/10.1029/2004JC002441, 2004. a
Mazloff, M. R., 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. a, b, c
Menary, M. B., Kuhlbrodt, T., Ridley, J., Andrews, M. B., Dimdore-Miles, O. B., Deshayes, J., Eade, R., Gray, L., Ineson, S., Mignot, J., Roberts, C. D.,
Robson, J., Wood, R. A., and Xavier, P.:
Preindustrial control simulations with HadGEM3-GC3. 1 for CMIP6, J. Adv. Model. Earth Sy., 10, 3049–3075, https://doi.org/10.1029/2018MS001495, 2018. a
Minnett, P. and Key, E.:
Chapter 4 Meteorology and Atmosphere–Surface Coupling in and around Polynyas, in: Polynyas: Windows to the World, edited by: Smith, W. and Barber, D., vol. 74 of Elsevier Oceanography Series, Elsevier, 127–161, https://doi.org/10.1016/S0422-9894(06)74004-1, 2007. a
Morales Maqueda, M., Willmott, A., and Biggs, N.:
Polynya Dynamics: A Review of Observations and Modeling, Rev. Geophys., 42, RG1004, https://doi.org/10.1029/2002RG000116, 2004. a
Notz, D., Haumann, F. A., Haak, H., Jungclaus, J. H., and Marotzke, J.:
Arctic sea-ice evolution as modeled by Max Planck Institute for Meteorology's Earth system model, J. Adv. Model. Earth Sy., 5, 173–194, https://doi.org/10.1002/jame.20016, 2013. a
Ohshima, K. I., Fukamachi, Y., Williams, G. D., Nihashi, S., Roquet, F., Kitade, Y., Tamura, T.,
Hirano, D., Herraiz-Borreguero, L., Field, I.,
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, https://doi.org/10.1038/ngeo1738, 2013. a
Ohshima, K. I., Nihashi, S., and Iwamoto, K.:
Global view of sea-ice production in polynyas and its linkage to dense/bottom water formation, Geoscience Letters, 3, 13, https://doi.org/10.1186/s40562-016-0045-4, 2016. a, b, c, d
Orsi, A. H., Johnson, G. C., and Bullister, J. L.:
Circulation, mixing, and production of Antarctic Bottom Water, Prog. Oceanogr., 43, 55–109, https://doi.org/10.1016/S0079-6611(99)00004-X, 1999. a, b
Peng, G., Meier, W. N., Scott, D. J., and Savoie, M. H.:
A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring, Earth Syst. Sci. Data, 5, 311–318, https://doi.org/10.5194/essd-5-311-2013, 2013. a, b
Penny, S. G., Akella, S., Balmaseda, M. A., Browne, P., Carton, J. A., Chevallier, M., Counillon, F., Domingues, C., Frolov, S., Heimbach, P., Hogan, P., Hoteit, I., Iovino, D., Laloyaux, P., Martin, M. J., Masina, S., Moore, A. M.,
de Rosnay, P.,
Schepers, D.,
Sloyan, B. M.,
Storto, A.,
Subramanian, A.,
Nam, S.,
Vitart, F.,
Yang, C.,
Fujii, Y.,
Zuo, H.,
O'Kane, T.,
Sandery, P.,
Moore, T.,
Chapman, C. C.: Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction, Front. Mar. Sci., 6, 434319,, https://doi.org/10.3389/fmars.2019.00391, 2019. a
Petersen, M. R., Jacobsen, D. W., Ringler, T. D., Hecht, M. W., and Maltrud, M. E.:
Evaluation of the arbitrary Lagrangian-Eulerian vertical coordinate method in the MPAS-Ocean model, Ocean Model., 86, 93–113, https://doi.org/10.1016/j.ocemod.2014.12.004, 2015. a
Petersen, M. R., Asay-Davis, X. S., Berres, A. S., Chen, Q., Feige, N., Hoffman, M. J., Jacobsen, D. W., Jones, P. W., Maltrud, M. E., Price, S. F., Ringler, T. D., Streletz, G. J., Turner, A. K., Van Roekel, L. P., Veneziani, M., Wolfe, J. D., Wolfram, P. J., and Woodring, J. L.:
An Evaluation of the Ocean and Sea Ice Climate of E3SM Using MPAS and Interannual CORE-II Forcing, J. Adv. Model. Earth Sy., 11, 1438–1458, https://doi.org/10.1029/2018MS001373, 2019. a, b, c
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S. M., Caldwell, P., Zhang, K.,
Easter, R. C., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D., Flanner, M., Foucar, J. G., Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An Overview of the Atmospheric Component of the Energy Exascale Earth System Model, J. Adv. Model. Earth Sy., 11, 2377–2411, https://doi.org/10.1029/2019MS001629, 2019. a, b
Reckinger, S. M., Petersen, M. R., and Reckinger, S. J.:
A study of overflow simulations using MPAS-Ocean: Vertical grids, resolution, and viscosity, Ocean Model., 96, 291–313, https://doi.org/10.1016/j.ocemod.2015.09.006, 2015. a
Ringler, T., Petersen, M., Higdon, R., Jacobsen, D., Jones, P., and Maltrud, M.:
A multi-resolution approach to global ocean modeling, Ocean Model., 69, 211–232, https://doi.org/10.1016/j.ocemod.2013.04.010, 2013. a
Roberts, A., Allison, I., and Lytle, V. I.:
Sensible-and latent-heat-flux estimates over the Mertz Glacier polynya, East Antarctica, from in-flight measurements, Ann. Glaciol., 33, 377–384, https://doi.org/10.3189/172756401781818112, 2001. a, b
Santer, B. D., Thorne, P. W., Haimberger, L., Taylor, K. E., L. Wigley, T. M., Lanzante, J. R., Solomon, S., Free, M., Gleckler, P. J., Jones, P. D., Karl, T. R., Klein, S. A., Mears, C., Nychka, D., Schmidt, G. A., Sherwood, S. C., and Wentz, F. J. :
Consistency of modelled and observed temperature trends in the tropical troposphere, Int. J. Climatol., 28, 1703–1722, https://doi.org/10.1002/joc.1756, 2008. a
Sigman, D. M. and Boyle, E. A.:
Glacial/interglacial Variations in Atmospheric Carbon Dioxide, Nature, 407, 859–869, https://doi.org/10.1038/35038000, 2000. a
Silvano, A., Rintoul, S. R., Pe na-Molino, B., Hobbs, W. R., van Wijk, E., Aoki, S., Tamura, T., and Williams, G. D.:
Freshening by glacial meltwater enhances melting of ice shelves and reduces formation of Antarctic Bottom Water, Science Advances, 4, eaap9467, https://doi.org/10.1126/sciadv.aap9467, 2018. a
Solodoch, A., Stewart, A. L., Hogg, A. M., Morrison, A. K., Kiss, A. E., Thompson, A. F., Purkey, S. G., and Cimoli, L.:
How does Antarctic Bottom Water cross the Southern Ocean?, Geophys. Res. Lett., 49, e2021GL097211, https://doi.org/10.1029/2021GL097211, 2022. a
St-Laurent, P., Yager, P., Sherrell, R., Oliver, H., Dinniman, M., and Stammerjohn, S.:
Modeling the seasonal cycle of iron and carbon fluxes in the Amundsen Sea Polynya, Antarctica, J. Geophys. Res.-Oceans, 124, 1544–1565, 2019. a
Steele, M., Morley, R., and Ermold, W.:
PHC: A Global Ocean Hydrography with a High-Quality Arctic Ocean, J. Climate, 14, 2079–2087, https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2, 2001. a
Stewart, A. L. and Thompson, A. F.:
Eddy-mediated transport of warm Circumpolar Deep Water across the Antarctic shelf break, Geophys. Res. Lett., 42, 432–440, 2015. 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, https://doi.org/10.1175/JPO-D-18-0221.1, 2019. a
Stewart, K., Hogg, A. M., Griffies, S., Heerdegen, A., Ward, M., Spence, P., and England, M.:
Vertical resolution of baroclinic modes in global ocean models, Ocean Model., 113, 50–65, https://doi.org/10.1016/j.ocemod.2017.03.012, 2017. a
Stössel, A. and Markus, T.:
Using satellite-derived ice concentration to represent Antarctic coastal polynyas in ocean climate models, J. Geophys. Res.-Oceans, 109, C2, https://doi.org/10.1029/2003JC001779, 2004. a
Swart, N. C., Fyfe, J. C., Hawkins, E., Kay, J. E., and Jahn, A.:
Influence of internal variability on Arctic sea-ice trends, Nat. Clim. Change, 5, 86–89, https://doi.org/10.1038/nclimate2483, 2015. a
Tamura, T., Ohshima, K. I., Enomoto, H., Tateyama, K., Muto, A., Ushio, S., and Massom, R. A.:
Estimation of thin sea-ice thickness from NOAA AVHRR data in a polynya off the Wilkes Land coast, East Antarctica, Ann. Glaciol., 44, 269–274, https://doi.org/10.3189/172756406781811745, 2006. a, b
Tamura, T., Ohshima, K. I., and Nihashi, S.:
Mapping of sea ice production for Antarctic coastal polynyas, Geophys. Res. Lett., 35, L07606, https://doi.org/10.1029/2007GL032903, 2008. a, b
Tamura, T., Ohshima, K. I., Fraser, A. D., and Williams, G. D.:
Sea ice production variability in Antarctic coastal polynyas, J. Geophys. Res.-Oceans, 121, 2967–2979, 2016. a
The NCAR Command Language: Version 6.6.2,
Boulder, Colorado, UCAR/NCAR/CISL/TDD [code], https://doi.org/10.5065/D6WD3XH5, 2019. a
Turner, A. K., Lipscomb, W. H., Hunke, E. C., Jacobsen, D. W., Jeffery, N., Engwirda, D., Ringler, T. D., and Wolfe, J. D.:
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes, Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, 2022.
a
Walin, G.:
On the relation between sea-surface heat flow and thermal circulation in the ocean, Tellus, 34, 187–195, https://doi.org/10.1111/j.2153-3490.1982.tb01806.x, 1982. a
Whitworth III, T., Orsi, A. H., Kim, S.-J., Nowlin Jr., W. D., and Locarnini, R. A.:
Water Masses and Mixing Near the Antarctic Slope Front, vol. 75, pp. 1–27, American Geophysical Union (AGU), https://doi.org/10.1029/AR075p0001, 1998. a, b, c
Williams, G. D., Roquet, F., Tamura, T., Ohshima, K. I., Fukamachi, Y., Fraser, A. D., Gao, L., Chen, H., McMahon, C. R., Harcourt, R., and Hindell, M.:
The suppression of Antarctic bottom water formation by melting ice shelves in Prydz Bay, Nat. Commun., 7, 1–9, https://doi.org/10.1038/ncomms12577, 2016. a, b
Williams, W., Carmack, E., and Ingram, R.:
Physical Oceanography of Polynyas, Chapter 2 in: Polynyas: Windows to the World, edited by: Smith, W. and Barber, D., vol. 74 of Elsevier Oceanography Series, Elsevier, 55–85, https://doi.org/10.1016/S0422-9894(06)74002-8, 2007. a, b
Xie, S., Lin, W., Rasch, P. J., Ma, L., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P. A., Caldwell, P., Cameron-Smith, P., Golaz, C., Mahajan, S., Singh, B., Tang, Q., Wang, H., Yoon, H., Zhang, K., and Zhang, Y.: Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model, J. Adv. Model. Earth Sy., 10, 2618–2644, https://doi.org/10.1029/2018MS001350, 2018. a
Short summary
We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the high amount of coastal sea ice production, the densest water masses are formed in the open ocean. Biases related to the lack of dense water formation are associated with overly strong atmospheric polar easterlies. Our results indicate that the large-scale polar atmospheric circulation must be accurately simulated in models to properly reproduce Antarctic dense water formation.
We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the...