Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-3133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An evaluation of the E3SMv1 Arctic ocean and sea-ice regionally refined model
Los Alamos National Laboratory, Los Alamos, NM, USA
Wieslaw Maslowski
Naval Postgraduate School, Monterey, CA, USA
Younjoo J. Lee
Naval Postgraduate School, Monterey, CA, USA
Gennaro D'Angelo
Los Alamos National Laboratory, Los Alamos, NM, USA
Robert Osinski
Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland
Mark R. Petersen
Los Alamos National Laboratory, Los Alamos, NM, USA
Wilbert Weijer
Los Alamos National Laboratory, Los Alamos, NM, USA
Anthony P. Craig
independent researcher
John D. Wolfe
Los Alamos National Laboratory, Los Alamos, NM, USA
Darin Comeau
Los Alamos National Laboratory, Los Alamos, NM, USA
Adrian K. Turner
Los Alamos National Laboratory, Los Alamos, NM, USA
Related authors
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
Short summary
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.
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
Nat. Hazards Earth Syst. Sci., 25, 3619–3639, https://doi.org/10.5194/nhess-25-3619-2025, https://doi.org/10.5194/nhess-25-3619-2025, 2025
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.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
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.
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.
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
Short summary
Short summary
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.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
Short summary
Short summary
Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
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.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
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.
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
Short summary
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.
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
Short summary
Short summary
FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
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.
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023, https://doi.org/10.5194/tc-17-233-2023, 2023
Short summary
Short summary
During 1979–2020, four winter polynyas occurred in December 1986 and February 2011, 2017, and 2018 north of Greenland. Instead of ice melting due to the anomalous warm air intrusion, the extreme wind forcing resulted in greater ice transport offshore. Based on the two ensemble runs, representing a 1980s thicker ice vs. a 2010s thinner ice, a dominant cause of these winter polynyas stems from internal variability of atmospheric forcing rather than from the forced response to a warming climate.
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.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
Short summary
Short summary
Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
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.
Jaclyn Clement Kinney, Karen M. Assmann, Wieslaw Maslowski, Göran Björk, Martin Jakobsson, Sara Jutterström, Younjoo J. Lee, Robert Osinski, Igor Semiletov, Adam Ulfsbo, Irene Wåhlström, and Leif G. Anderson
Ocean Sci., 18, 29–49, https://doi.org/10.5194/os-18-29-2022, https://doi.org/10.5194/os-18-29-2022, 2022
Short summary
Short summary
We use data crossing Herald Canyon in the Chukchi Sea collected in 2008 and 2014 together with numerical modelling to investigate the circulation in the western Chukchi Sea. A large fraction of water from the Chukchi Sea enters the East Siberian Sea south of Wrangel Island and circulates in an anticyclonic direction around the island. To assess the differences between years, we use numerical modelling results, which show that high-frequency variability dominates the flow in Herald Canyon.
Cited articles
Adcroft, A. and Campin, J.-M.: Rescaled height coordinates for accurate
representation of free-surface flows in ocean circulation models, Ocean Model., 7, 269–284, https://doi.org/10.1016/j.ocemod.2003.09.003, 2004. a
Beszczynska-Möller, A., Woodgate, R. A., Lee, C. M., Melling, H., and
Karcher, M.: A Synthesis of Exchanges Through the Main Oceanic Gateways to
the Arctic Ocean, Oceanography, 24, 82–99, https://doi.org/10.5670/oceanog.2011.59, 2011. a
Beszczynska-Möller, A., Fahrbach, E., Schauer, U., and Hansen, E.:
Variability in Atlantic water temperature and transport at the entrance to
the Arctic Ocean, 1997–2010, ICES J. Marine Sci., 69, 852–863,
https://doi.org/10.1093/icesjms/fss056, 2012. a
Brunke, M. A., Cassano, J. J., Dawson, N., DuVivier, A. K., Gutowski Jr., W. J., Hamman, J., Maslowski, W., Nijssen, B., Reeves Eyre, J. E. J., Renteria, J. C., Roberts, A., and Zeng, X.: Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations, Geosci. Model Dev., 11, 4817–4841, https://doi.org/10.5194/gmd-11-4817-2018, 2018. a
Bryan, F. O., Gent, P. R., and Tomas, R.: Can Southern Ocean Eddy Effects Be
Parameterized in Climate Models?, J. Climate, 27, 411–425,
https://doi.org/10.1175/JCLI-D-12-00759.1, 2014. a
Bunzel, F., Notz, D., and Pedersen, L. T.: Retrievals of Arctic Sea-Ice
Volume and Its Trend Significantly Affected by Interannual Snow Variability,
Geophys. Res. Lett., 45, 11751–11759, https://doi.org/10.1029/2018GL078867,
2018. a
Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, J.-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, P., 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, 4095–4146,
https://doi.org/10.1029/2019MS001870, 2019. a, b
Cassano, J. J., DuVivier, A., Roberts, A., Hughes, M., Seefeldt, M., Brunke,
M., Craig, A., Fisel, B., Gutowski, W., Hamman, J., Higgins, M., Maslowski,
W., Nijssen, B., Osinski, R., and Zeng, X.: Development of the Regional
Arctic System Model (RASM): Near-Surface Atmospheric Climate Sensitivity,
J. Climate, 30, 5729–5753, https://doi.org/10.1175/JCLI-D-15-0775.1, 2017. a
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., Comiso, J. C., and Zwally,
H. J.: Deriving long-term time series of sea ice cover from satellite passive
microwave multisensor data sets, J. Geophys. Res.-Oceans,
104, 15803–15814, https://doi.org/10.1029/1999JC900081, 1999. a
Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii,
Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda, T.,
Valdivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S., Ferry,
N., Garric, G., Haines, K., Keeley, S., Kovach, R. M., Kuragano, T., Masina,
S., Tang, Y., Tsujino, H., and Wang, X.: Intercomparison of the Arctic sea
ice cover in global ocean–sea ice reanalyses from the ORA-IP project,
Clim. Dynam., 49, 1107–1136, https://doi.org/10.1007/s00382-016-2985-y, 2017. a
Clement Kinney, J., Maslowski, W., Aksenov, Y., de Cuevas, B., Jakacki, J.,
Nguyen, A., Osinski, R., Steele, M., Woodgate, R. A., and Zhang, J.: On the
Flow Through Bering Strait: A Synthesis of Model Results and Observations,
167–198, Springer Netherlands, Dordrecht,
https://doi.org/10.1007/978-94-017-8863-2_7, 2014. a
Cunningham, S. A., Kanzow, T., Rayner, D., Baringer, M. O., Johns, W. E.,
Marotzke, J., Longworth, H. R., Grant, E. M., Hirschi, J. J.-M., Beal, L. M.,
Meinen, C. S., and Bryden, H. L.: Temporal Variability of the Atlantic
Meridional Overturning Circulation at 26.5∘ N, Science, 317,
935–938, https://doi.org/10.1126/science.1141304, 2007. a
Cuny, J., Rhines, P. B., and Kwok, R.: Davis Strait volume, freshwater and
heat fluxes, Deep-Sea Res. Pt. I, 52,
519–542, https://doi.org/10.1016/j.dsr.2004.10.006, 2005. a, b
Curry, B., Lee, C. M., Petrie, B., Moritz, R. E., and Kwok, R.: Multiyear
Volume, Liquid Freshwater, and Sea Ice Transports through Davis Strait,
2004–10, J. Phys. Oceanogr., 44, 1244–1266,
https://doi.org/10.1175/JPO-D-13-0177.1, 2014. a, b, c, d
Dai, A., Luo, D., Song, M., and Liu, J.: Arctic amplification is caused by
sea-ice loss under increasing CO2, Nat. Commun., 10, 121,
https://doi.org/10.1038/s41467-018-07954-9, 2019. a
Danabasoglu, G., Yeager, S. G., Bailey, D., Behrens, E., Bentsen, M., Bi, D.,
Biastoch, A., Böning, C., Bozec, A., Canuto, V. M., Cassou, C.,
Chassignet, E., Coward, A. C., Danilov, S., Diansky, N., Drange, H., Farneti,
R., Fernandez, E., Fogli, P. G., Forget, G., Fujii, Y., Griffies, S. M.,
Gusev, A., Heimbach, P., Howard, A., Jung, T., Kelley, M., Large, W. G.,
Leboissetier, A., Lu, J., Madec, G., Marsland, S. J., Masina, S., Navarra,
A., George Nurser, A., Pirani, A., Salas y Mélia, D., Samuels, B. L.,
Scheinert, M., Sidorenko, D., Treguier, A.-M., Tsujino, H., Uotila, P.,
Valcke, S., Voldoire, A., and Wang, Q.: North Atlantic simulations in
Coordinated Ocean-ice Reference Experiments phase II (CORE-II).
Part I: Mean states, Ocean Model., 73, 76–107,
https://doi.org/10.1016/j.ocemod.2013.10.005, 2014. a
de Steur, L.: Fram Strait freshwater transport and gridded monthly mean
velocity and salinity 1997–2015, Norwegian Polar Institute [data set], https://doi.org/10.21334/npolar.2018.9e01a801, 2018. a
de Steur, L., Hansen, E., Gerdes, R., Karcher, M., Fahrbach, E., and Holfort,
J.: Freshwater fluxes in the East Greenland Current: A decade of
observations, Geophys. Res. Lett., 36, L23611,
https://doi.org/10.1029/2009GL041278, 2009. 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, J.-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. S.,
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., 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. J., 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
Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G.,
Chassignet, E. P., England, M. H., Gerdes, R., Haak, H., Hallberg, R. W.,
Hazeleger, W., Jungclaus, J., Large, W. G., Madec, G., Pirani, A., Samuels,
B. L., Scheinert, M., Gupta, A. S., Severijns, C. A., Simmons, H. L.,
Treguier, A. M., Winton, M., Yeager, S., and Yin, J.: Coordinated Ocean-ice
Reference Experiments (COREs), Ocean Model., 26, 1–46,
https://doi.org/10.1016/j.ocemod.2008.08.007, 2009. a, b
Haine, T. W., Curry, B., Gerdes, R., Hansen, E., Karcher, M., Lee, C., Rudels,
B., Spreen, G., de Steur, L., Stewart, K. D., and Woodgate, R.: Arctic
freshwater export: Status, mechanisms, and prospects, Global Planet.
Change, 125, 13–35, https://doi.org/10.1016/j.gloplacha.2014.11.013, 2015. a
Häkkinen, S.: An Arctic source for the great salinity anomaly: A simulation
of the Arctic ice-ocean system for 1955–1975, J. Geophys.
Res.-Oceans, 98, 16397–16410, https://doi.org/10.1029/93JC01504, 1993. a
Hamman, J., Nijssen, B., Brunke, M., Cassano, J., Craig, A., DuVivier, A.,
Hughes, M., Lettenmaier, D. P., Maslowski, W., Osinski, R., Roberts, A., and
Zeng, X.: Land surface climate in the Regional Arctic System Model, J. Climate, 29, 6543–6562, https://doi.org/10.1175/JCLI-D-15-0415.1, 2016. a
Ilicak, M., Drange, H., Wang, Q., Gerdes, R., Aksenov, Y., Bailey, D., Bentsen,
M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet, E.,
Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E., Fogli,
P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T., Large,
W. G., Lee, C., Lique, C., Lu, J., Masina, S., George Nurser, A., Roth, C.,
Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H., Valcke, S.,
Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the Arctic
Ocean in a suite of interannual CORE-II simulations. Part III:
Hydrography and fluxes, Ocean Model., 100, 141–161,
https://doi.org/10.1016/j.ocemod.2016.02.004, 2016. a, b
jedwards4b, Foucar, J., Mametjanov, A., Jacob, R., Taylor, M., singhbalwinder, Sacks, B., mvertens, Wolfe, J., jayeshkrishna, Paul, K., noel, onguba, fischer-ncar, Hartnett, E., Deakin, M., Jacobsen, D., Shollenberger, J., susburrows, Wilke, A., Bertini, A., jqyin, Norman, M., Thayer-Calder, K., Petersen, M., Hillman, B. R., Sarich, J., Hoffman, M., Salinger, A., Sreepathi, S.: milenaveneziani/E3SM: e3sm-arctic-osi-2019 (e3sm-arctic-2019), Zenodo [code], https://doi.org/10.5281/zenodo.5548434, 2020. a
Kurtz, N. and Harbeck, J.: CryoSat-2 Level-4 Sea Ice Elevation, Freeboard, and
Thickness, Version 1, Boulder, Colorado USA. NASA National Snow and Ice Data
Center Distributed Active Archive Center, https://doi.org/10.5067/96JO0KIFDAS8 (last access: May 2019), 2017. a
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A
review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, 1994. a
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M.,
Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K., Paver, C. R., and
Smolyar, I.: World Ocean Atlas 2018, Volume 1: Temperature, Tech. rep.,
NOAA Atlas NESDIS, Silver Spring, MD, edited by: Mishonov, A., 2018. a
Maslowski, W., Clement Kinney, J., Higgins, M., and Roberts, A.: The Future of
Arctic Sea Ice, Annual Review of Earth Planet. Sci., 40,
625–654, https://doi.org/10.1146/annurev-earth-042711-105345, 2012. a
McCarthy, G. D., Brown, P. J., Flagg, C. N., Goni, G., Houpert, L., Hughes,
C. W., Hummels, R., Inall, M., Jochumsen, K., Larsen, K. M. H., Lherminier,
P., Meinen, C. S., Moat, B. I., Rayner, D., Rhein, M., Roessler, A., Schmid,
C., and Smeed, D. A.: Sustainable Observations of the AMOC: Methodology
and Technology, Rev. Geophys., 58, e2019RG000654,
https://doi.org/10.1029/2019RG000654, 2020. a, b
Münchow, A.: Volume and Freshwater Flux Observations from Nares Strait to
the West of Greenland at Daily Time Scales from 2003 to 2009, J.
Phys. Oceanogr., 46, 141–157, https://doi.org/10.1175/JPO-D-15-0093.1, 2016. a, b, c, d
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, 3122, https://doi.org/10.1029/2001JC000976, 2002. a
Perovich, D., Meier, W., Tschudi, M., Farrell, S., Hendricks, S., Gerland, S.,
Kaleschke, L., Ricker, R., Tian-Kunze, X., Webster, M., and Wood, K.: Sea
Ice, Tech. rep., Arctic Report Card 2019,
http://www.arctic.noaa.gov/Report-Card (last access: 7 April 2022), 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, d, e
Polyakov, I. V., Pnyushkov, A. V., Alkire, M. B., Ashik, I. M., Baumann, T. M.,
Carmack, E. C., Goszczko, I., Guthrie, J., Ivanov, V. V., Kanzow, T.,
Krishfield, R., Kwok, R., Sundfjord, A., Morison, J., Rember, R., and Yulin,
A.: Greater role for Atlantic inflows on sea-ice loss in the Eurasian
Basin of the Arctic Ocean, Science, 356, 285–291,
https://doi.org/10.1126/science.aai8204, 2017. a, b
Prinsenberg, S. J. and Hamilton, J.: Monitoring the volume, freshwater and heat
fluxes passing through Lancaster sound in the Canadian Arctic
Archipelago, Atmosphere-Ocean, 43, 1–22, https://doi.org/10.3137/ao.430101, 2005. a, b, c, d
Proshutinsky, A., Krishfield, R., Timmermans, M.-L., Toole, J., Carmack, E.,
McLaughlin, F., Williams, W. J., Zimmermann, S., Itoh, M., and Shimada, K.:
Beaufort Gyre freshwater reservoir: State and variability from
observations, J. Geophys. Res.-Oceans, 114, C00A10,
https://doi.org/10.1029/2008JC005104, 2009. a
Proshutinsky, A., Krishfield, R., Toole, J. M., Timmermans, M.-L., Williams,
W., Zimmermann, S., Yamamoto-Kawai, M., Armitage, T. W. K., Dukhovskoy, D.,
Golubeva, E., Manucharyan, G. E., Platov, G., Watanabe, E., Kikuchi, T.,
Nishino, S., Itoh, M., Kang, S.-H., Cho, K.-H., Tateyama, K., and Zhao, J.:
Analysis of the Beaufort Gyre Freshwater Content in 2003–2018, J. Geophys. Res.-Oceans, 124, 9658–9689, https://doi.org/10.1029/2019JC015281, 2019. a, b
Rabe, B., Karcher, M., Kauker, F., Schauer, U., Toole, J. M., Krishfield,
R. A., Pisarev, S., Kikuchi, T., and Su, J.: Arctic Ocean basin liquid
freshwater storage trend 1992–2012, Geophys. Res. Lett., 41,
961–968, https://doi.org/10.1002/2013GL058121, 2014. a
Ringler, T., Petersen, M., Higdon, R. L., Jacobsen, D., Jones, P. W., 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., Craig, A., Maslowski, W., Osinski, R., Duvivier, A., Hughes, M.,
Nijssen, B., Cassano, J., and Brunke, M.: Simulating transient ice-ocean
Ekman transport in the Regional Arctic System Model and Community Earth
System Model, Ann. Glaciol., 56, 211–228,
https://doi.org/10.3189/2015AoG69A760, 2015. a
Roemmich, D. and Gilson, J.: The 2004–2008 mean and annual cycle of
temperature, salinity, and steric height in the global ocean from the Argo
Program, Prog. Oceanogr., 82, 81–100,
https://doi.org/10.1016/j.pocean.2009.03.004, 2009. a
Rudels, B.: Arctic Ocean circulation and variability – advection and external forcing encounter constraints and local processes, Ocean Sci., 8, 261–286, https://doi.org/10.5194/os-8-261-2012, 2012. a
Schauer, U. and Beszczynska-Möller, A.: Problems with estimation and interpretation of oceanic heat transport – conceptual remarks for the case of Fram Strait in the Arctic Ocean, Ocean Sci., 5, 487–494, https://doi.org/10.5194/os-5-487-2009, 2009. a, b
Schauer, U., Beszczynska-Möller, A., Walczowski, W., Fahrbach, E.,
Piechura, J., and Hansen, E.: Variation of Measured Heat Flow Through the
Fram Strait Between 1997 and 2006, 65–85, Springer Netherlands,
Dordrecht, https://doi.org/10.1007/978-1-4020-6774-7_4, 2008. a, b, c, d
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys.
Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. a
Serreze, M. C., Barrett, A. P., Slater, A. G., Woodgate, R. A., Aagaard, K.,
Lammers, R. B., Steele, M., Moritz, R., Meredith, M., and Lee, C. M.: The
large-scale freshwater cycle of the Arctic, J. Geophys.
Res.-Oceans, 111, C11010, https://doi.org/10.1029/2005JC003424, 2006. a, b
SIMIP Community: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47,
e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020. a
Smedsrud, L. H., Ingvaldsen, R., Nilsen, J. E. Ø., and Skagseth, Ø.: Heat in the Barents Sea: transport, storage, and surface fluxes, Ocean Sci., 6, 219–234, https://doi.org/10.5194/os-6-219-2010, 2010. a, b
Smedsrud, L. H., Esau, I., Ingvaldsen, R. B., Eldevik, T., Haugan, P. M., Li,
C., Lien, V. S., Olsen, A., Omar, A. M., Otterȧ, O. H., Risebrobakken,
B., Sandø, A. B., Semenov, V. A., and Sorokina, S. A.: The role of the
Barents Sea in the Arctic Climate System, Rev. Geophys.,
51, 415–449, https://doi.org/10.1002/rog.20017, 2013. a, b, c
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
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland,
M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and
observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676,
2012. a
Tang, C. C. L., Ross, C. K., Yao, T., Petrie, B., DeTracey, B. M., and Dunlap,
E.: The circulation, water masses and sea-ice of Baffin Bay, Prog. Oceanogr., 63, 183–228, https://doi.org/10.1016/j.pocean.2004.09.005, 2004. a
Tilling, R. L., Ridout, A., and Shepherd, A.: Estimating Arctic sea ice
thickness and volume using CryoSat-2 radar altimeter data, Adv. Space
Res., 62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051, 2018. a
Timmermans, M.-L., Toole, J., and Krishfield, R.: Warming of the interior
Arctic Ocean linked to sea ice losses at the basin margins, Sci.
Adv., 4, eaat6773, https://doi.org/10.1126/sciadv.aat6773, 2018. a
Toole, J. M., Krishfield, R. A., Timmermans, M.-L., and Proshutinsky, A.: The
Ice-Tethered Profiler: Argo of the Arctic, Oceanography, 24, 126–135,
https://doi.org/10.5670/oceanog.2011.64, 2011. a
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G.,
Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W.,
Bozec, A., Chassignet, E. P., Curchitser, E., Dias, F. B., Durack, P. J.,
Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C.,
Kobayashi, S., Komuro, Y., Large, W. G., Sommer, J. L., Marsland, S. J.,
Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.:
JRA-55 based surface dataset for driving ocean–sea-ice models
(JRA55-do), Ocean Model., 130, 79–139,
https://doi.org/10.1016/j.ocemod.2018.07.002, 2018. a
Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., Arsouze, T., Bentsen, M., Bernardello, R., Böning, C. W., Bozec, A., Chassignet, E. P., Danilov, S., Dussin, R., Exarchou, E., Fogli, P. G., Fox-Kemper, B., Guo, C., Ilicak, M., Iovino, D., Kim, W. M., Koldunov, N., Lapin, V., Li, Y., Lin, P., Lindsay, K., Liu, H., Long, M. C., Komuro, Y., Marsland, S. J., Masina, S., Nummelin, A., Rieck, J. K., Ruprich-Robert, Y., Scheinert, M., Sicardi, V., Sidorenko, D., Suzuki, T., Tatebe, H., Wang, Q., Yeager, S. G., and Yu, Z.: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, 2020. 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. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-355, in review, 2021. a, b
Veneziani, M.: Model data in support of the E3SMv1-Arctic-OSI GMD manuscript (2021) (Version v1), Zenodo [data set], https://doi.org/10.5281/zenodo.5548528, 2021. a
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A.,
Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet,
E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E.,
Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T.,
Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. G., Rabe,
B., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H.,
Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the
Arctic Ocean in a suite of interannual CORE-II simulations. Part
II: Liquid freshwater, Ocean Model., 99, 86–109,
https://doi.org/10.1016/j.ocemod.2015.12.009, 2016a. a
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A.,
Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet,
E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E.,
Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T.,
Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. G., Rabe,
B., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H.,
Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the
Arctic Ocean in a suite of interannual CORE-II simulations. Part
I: Sea ice and solid freshwater, Ocean Model., 99, 110–132,
https://doi.org/10.1016/j.ocemod.2015.12.008, 2016b. a
Wang, Q., Wekerle, C., Danilov, S., Wang, X., and Jung, T.: A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4, Geosci. Model Dev., 11, 1229–1255, https://doi.org/10.5194/gmd-11-1229-2018, 2018. a
Watts, M., Maslowski, W., Lee, Y. J., Kinney, J. C., and Osinski, R.: A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations, J. Climate, https://doi.org/10.1175/JCLI-D-20-0491.1, 2022. a
Weijer, W., Cheng, W., Garuba, O. A., Hu, A., and Nadiga, B. T.: CMIP6 models
predict significant 21st Century secline of the Atlantic Meridional
Overturning Circulation, Geophys. Res. Lett., 47, e2019GL086075,
https://doi.org/10.1029/2019GL086075, 2020. a
Wekerle, C., Wang, Q., Danilov, S., Jung, T., and Schröter, J.: The Canadian
Arctic Archipelago throughflow in a multiresolution global model: Model
assessment and the driving mechanism of interannual variability, J.
Geophys. Res.-Oceans, 118, 4525–4541, 2013. a
Wekerle, C., Wang, Q., Danilov, S., Schourup-Kristensen, V., von Appen, W.-J.,
and Jung, T.: Atlantic Water in the Nordic Seas: Locally eddy-permitting
ocean simulation in a global setup, J. Geophys. Res.-Oceans,
122, 914–940, https://doi.org/10.1002/2016JC012121, 2017a. a
Wekerle, C., Wang, Q., von Appen, W.-J., Danilov, S., Schourup-Kristensen, V.,
and Jung, T.: Eddy-Resolving Simulation of the Atlantic Water Circulation
in the Fram Strait With Focus on the Seasonal Cycle, J. Geophys.
Res.-Oceans, 122, 8385–8405, https://doi.org/10.1002/2017JC012974,
2017b. a
Woodgate, R. A.: Increases in the Pacific inflow to the Arctic from 1990 to
2015, and insights into seasonal trends and driving mechanisms from
year-round Bering Strait mooring data, Prog. Oceanogr., 160,
124–154, https://doi.org/10.1016/j.pocean.2017.12.007, 2018. a
Woodgate, R. A. and Aagaard, K.: Revising the Bering Strait freshwater flux
into the Arctic Ocean, Geophys. Res. Lett., 32, L02602,
https://doi.org/10.1029/2004GL021747, 2005. a, b, c, d
Woodgate, A. R. and Peralta-Ferriz, C.: Warming and Freshening of the Pacific
Inflow to the Arctic From 1990–2019 Implying Dramatic Shoaling in Pacific
Winter Water Ventilation of the Arctic Water Column, Geophys. Res.
Lett., 48, e2021GL092528, https://doi.org/10.1029/2021GL092528,
2021. a
Woodgate, R. A., Weingartner, T., and Lindsay, R.: The 2007 Bering Strait
oceanic heat flux and anomalous Arctic sea-ice retreat, Geophys. Res. Lett., 37, L01602, https://doi.org/10.1029/2009GL041621, 2010. a, b
Woodgate, R. A., Stafford, K. M., and Prahl, F. G.: A Synthesis of Year-Round
Interdisciplinary Mooring Measurements in the Bering Strait (1990–2014)
and the RUSALCA Years (2004–2011), Oceanography, 28, 46–67,
https://doi.org/10.5670/oceanog.2015.57, 2015. a
Yi, D. and Zwally, H. J.: Arctic Sea Ice Freeboard and Thickness, Version 1,
Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed
Active Archive Center, https://doi.org/10.5067/SXJVJ3A2XIZT (last access: April 2014),
2009, updated 15 April 2014. a
Zhang, J. and Rothrock, D. A.: Modeling Global Sea Ice with a Thickness and
Enthalpy Distribution Model in Generalized Curvilinear Coordinates, Mon.
Weather Rev., 131, 845–861,
https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2, 2003. a
Zhang, J., Weijer, W., Steele, M., Cheng, W., Verma, T., and Veneziani, M.:
Labrador Sea freshening linked to Beaufort Gyre freshwater release,
Nat. Commun., 12, 1229, https://doi.org/10.1038/s41467-021-21470-3, 2021.
a
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A.,
Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K., Paver, C. R.,
and Smolyar, I.: World Ocean Atlas 2018, Volume 2: Salinity, Tech. rep.,
NOAA Atlas NESDIS, Silver Spring, MD, edited by: Mishonov, A., 2018. a
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014. a
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.
We present an Earth system model (ESM) simulation, E3SM-Arctic-OSI, with a refined grid to...