Articles | Volume 13, issue 2
https://doi.org/10.5194/gmd-13-401-2020
© Author(s) 2020. 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-13-401-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
05 Feb 2020
Model description paper |
| 05 Feb 2020
| 05 Feb 2020
ACCESS-OM2 v1.0: a global ocean–sea ice model at three resolutions
Research School of Earth Sciences, Australian National University, Canberra, Australia
ARC Centre of Excellence for Climate Extremes, Australia
Andrew McC. Hogg
Research School of Earth Sciences, Australian National University, Canberra, Australia
ARC Centre of Excellence for Climate Extremes, Australia
Nicholas Hannah
Double Precision, Sydney, Australia
Fabio Boeira Dias
ARC Centre of Excellence for Climate Extremes, Australia
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Gary B. Brassington
Bureau of Meteorology, Melbourne, Australia
Matthew A. Chamberlain
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Christopher Chapman
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Peter Dobrohotoff
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Catia M. Domingues
ARC Centre of Excellence for Climate Extremes, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Earl R. Duran
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Matthew H. England
ARC Centre of Excellence for Climate Extremes, Australia
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Russell Fiedler
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Stephen M. Griffies
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey, USA
Aidan Heerdegen
Research School of Earth Sciences, Australian National University, Canberra, Australia
ARC Centre of Excellence for Climate Extremes, Australia
Petra Heil
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Australian Antarctic Division, Kingston, Tasmania, Australia
Ryan M. Holmes
ARC Centre of Excellence for Climate Extremes, Australia
Climate Change Research Centre, University of New South Wales, Sydney, Australia
School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
Andreas Klocker
ARC Centre of Excellence for Climate Extremes, Australia
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Simon J. Marsland
ARC Centre of Excellence for Climate Extremes, Australia
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Adele K. Morrison
Research School of Earth Sciences, Australian National University, Canberra, Australia
ARC Centre of Excellence for Climate Extremes, Australia
James Munroe
Memorial University of Newfoundland, St John's, Canada
Maxim Nikurashin
ARC Centre of Excellence for Climate Extremes, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Peter R. Oke
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Gabriela S. Pilo
ARC Centre of Excellence for Climate Extremes, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Océane Richet
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Centre for Southern Hemisphere Ocean Research, Hobart, Tasmania, Australia
Abhishek Savita
ARC Centre of Excellence for Climate Extremes, Australia
CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Paul Spence
ARC Centre of Excellence for Climate Extremes, Australia
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Kial D. Stewart
Research School of Earth Sciences, Australian National University, Canberra, Australia
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Marshall L. Ward
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
National Computational Infrastructure, Australian National University, Canberra, Australia
Fanghua Wu
Beijing Climate Centre, Beijing, China
Xihan Zhang
Research School of Earth Sciences, Australian National University, Canberra, Australia
ARC Centre of Excellence for Climate Extremes, Australia
Related authors
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Laurie C. Menviel, Paul Spence, Andrew E. Kiss, Matthew A. Chamberlain, Hakase Hayashida, Matthew H. England, and Darryn Waugh
Biogeosciences, 20, 4413–4431, https://doi.org/10.5194/bg-20-4413-2023, https://doi.org/10.5194/bg-20-4413-2023, 2023
Short summary
Short summary
As the ocean absorbs 25% of the anthropogenic emissions of carbon, it is important to understand the impact of climate change on the flux of carbon between the ocean and the atmosphere. Here, we use a very high-resolution ocean, sea-ice, carbon cycle model to show that the capability of the Southern Ocean to uptake CO2 has decreased over the last 40 years due to a strengthening and poleward shift of the southern hemispheric westerlies. This trend is expected to continue over the coming century.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
Short summary
Short summary
Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Megan Jeffers, Christopher C. Chapman, Bernadette M. Sloyan, and Helen Bostock
Ocean Sci., 21, 537–554, https://doi.org/10.5194/os-21-537-2025, https://doi.org/10.5194/os-21-537-2025, 2025
Short summary
Short summary
This study analyses physical and biogeochemical data collected in the East Australian Current (EAC) to understand variability in the nutrient-poor surface waters. The distribution of nutrients in the water column is influenced by seasonal flow and the EAC's position. An inshore EAC has a deeper nutricline and lower nutrient concentrations compared to an offshore EAC position. This has implications for the marine ecosystems along the coastline.
Fabio Boeira Dias, Matthew H. England, Adele K. Morrison, and Benjamin Galton-Fenzi
EGUsphere, https://doi.org/10.5194/egusphere-2024-3905, https://doi.org/10.5194/egusphere-2024-3905, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The Antarctic Ice Sheet melting dominates the sea-level projection uncertainties. Much uncertainty arises from our limited understanding of how ice shelves melt from below. Using a detailed ocean-ice shelf model, we found that East Antarctic ice shelves experience seasonal melting driven by ocean heat transport variability. In contrast, West Antarctic ice shelves show consistent melting due to a steady supply of warm, deep water, indicating potentially distinct response due to a warming climate.
Pearse James Buchanan, P. Jyoteeshkumar Reddy, Richard J. Matear, Matthew A. Chamberlain, Tyler Rohr, Dougal Squire, and Elizabeth H. Shadwick
EGUsphere, https://doi.org/10.5194/egusphere-2024-4026, https://doi.org/10.5194/egusphere-2024-4026, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We developed a cost-effective method to improve ocean models for studying the global carbon cycle. Using machine learning, we optimized parameters in the WOMBAT-lite model, enhancing its accuracy in predicting chlorophyll levels, air-sea carbon dioxide exchange, and phytoplankton nutrient use. This approach increases model reliability and offers a pathway for scientists to better understand and predict ocean changes, contributing to improved insights into Earth's climate system.
Diana Francis, Ricardo Fonseca, Narendra Nelli, Petra Heil, Jonathan Wille, Irina Gorodetskaya, and Robert Massom
EGUsphere, https://doi.org/10.5194/egusphere-2024-3535, https://doi.org/10.5194/egusphere-2024-3535, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This study investigates the impact of atmospheric rivers and associated atmospheric dynamics on sea-ice thickness and snow depth at a coastal site in East Antarctica during July–November 2022 using in-situ measurements and numerical modelling. The passage of an atmospheric river induced a reduction of up to 0.06 m in both fields. Precipitation occurred from the convergence of katabatic winds with advected low-latitude moist air.
Claire K. Yung, Madelaine G. Rosevear, Adele K. Morrison, Andrew McC Hogg, and Yoshihiro Nakayama
EGUsphere, https://doi.org/10.5194/egusphere-2024-3513, https://doi.org/10.5194/egusphere-2024-3513, 2024
Short summary
Short summary
Ocean models are used to understand how the ocean interacts with the Antarctic Ice Sheet, but they are too coarse in resolution to capture the small-scale ocean processes driving melting and require a parameterisation to predict melt. Previous parameterisations ignore key processes occurring in some regions of Antarctica. We develop a parameterisation with the feedback of stratification on melting and test it in idealised and regional ocean models, finding changes to melt rate and circulation.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-519, https://doi.org/10.5194/essd-2024-519, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Andrew D. King, Tilo Ziehn, Matthew Chamberlain, Alexander R. Borowiak, Josephine R. Brown, Liam Cassidy, Andrea J. Dittus, Michael Grose, Nicola Maher, Seungmok Paik, Sarah E. Perkins-Kirkpatrick, and Aditya Sengupta
Earth Syst. Dynam., 15, 1353–1383, https://doi.org/10.5194/esd-15-1353-2024, https://doi.org/10.5194/esd-15-1353-2024, 2024
Short summary
Short summary
Governments are targeting net-zero emissions later this century with the aim of limiting global warming in line with the Paris Agreement. However, few studies explore the long-term consequences of reaching net-zero emissions and the effects of a delay in reaching net-zero. We use the Australian Earth system model to examine climate evolution under net-zero emissions. We find substantial changes which differ regionally, including continued Southern Ocean warming and Antarctic sea ice reduction.
Toan Bui, Ming Feng, and Chris Chapman
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-449, https://doi.org/10.5194/essd-2024-449, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Time series data are crucial to detect changes in the ocean. Moored instruments have traditionally been used to obtain long-term observations on the continental shelf. However, mooring losses or instrument failures often result in data gaps. Here we present a gap-filled time series dataset of a shelf mooring array off the Western Australian coast, by adopting a machine learning tool to fill the data gaps. The gap-filled data has acceptable errors and shows consistency with observations.
Bartholomé Duboc, Katrin J. Meissner, Laurie Menviel, Nicholas K. H. Yeung, Babette Hoogakker, Tilo Ziehn, and Matthew Chamberlain
EGUsphere, https://doi.org/10.5194/egusphere-2024-2675, https://doi.org/10.5194/egusphere-2024-2675, 2024
Short summary
Short summary
We use an Earth System Model to simulate ocean oxygen during two past warm periods, the Last Interglacial (~129–115 ka) and Marine Isotope Stage (MIS) 9e (~336-321 ka). The global ocean is overall less oxygenated compared to the preindustrial simulation. Large regions in the Mediterranean Sea are oxygen deprived in the Last Interglacial simulation, and to a lesser extent in the MIS 9e simulation, due to an intensification and expansion of the African Monsoon and enhanced river run-off.
Benoît Pasquier, Mark Holzer, and Matthew A. Chamberlain
Biogeosciences, 21, 3373–3400, https://doi.org/10.5194/bg-21-3373-2024, https://doi.org/10.5194/bg-21-3373-2024, 2024
Short summary
Short summary
How do perpetually slower and warmer oceans sequester carbon? Compared to the preindustrial state, we find that biological productivity declines despite warming-stimulated growth because of a lower nutrient supply from depth. This throttles the biological carbon pump, which still sequesters more carbon because it takes longer to return to the surface. The deep ocean is isolated from the surface, allowing more carbon from the atmosphere to pass through the ocean without contributing to biology.
Matthew A. Chamberlain, Tilo Ziehn, and Rachel M. Law
Biogeosciences, 21, 3053–3073, https://doi.org/10.5194/bg-21-3053-2024, https://doi.org/10.5194/bg-21-3053-2024, 2024
Short summary
Short summary
This paper explores the climate processes that drive increasing global average temperatures in zero-emission commitment (ZEC) simulations despite decreasing atmospheric CO2. ACCESS-ESM1.5 shows the Southern Ocean to continue to warm locally in all ZEC simulations. In ZEC simulations that start after the emission of more than 1000 Pg of carbon, the influence of the Southern Ocean increases the global temperature.
Mukund Gupta, Heather Regan, Young Hyun Koo, Sean Minhui Tashi Chua, Xueke Li, and Petra Heil
EGUsphere, https://doi.org/10.5194/egusphere-2024-1329, https://doi.org/10.5194/egusphere-2024-1329, 2024
Short summary
Short summary
The sea ice cover is composed of floes, whose shapes set the material properties of the pack. Here, we use a satellite product (ICESat-2) to investigate these floe shapes within the Weddell Sea. We find that floes tend to become smaller during the melt season, while their thickness distribution exhibits different behavior between the western and southern regions of the pack. These metrics will help calibrate models, and improve our understanding of sea ice physics across scales.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
Short summary
Short summary
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Sina Loriani, Yevgeny Aksenov, David Armstrong McKay, Govindasamy Bala, Andreas Born, Cristiano M. Chiessi, Henk Dijkstra, Jonathan F. Donges, Sybren Drijfhout, Matthew H. England, Alexey V. Fedorov, Laura Jackson, Kai Kornhuber, Gabriele Messori, Francesco Pausata, Stefanie Rynders, Jean-Baptiste Salée, Bablu Sinha, Steven Sherwood, Didier Swingedouw, and Thejna Tharammal
EGUsphere, https://doi.org/10.5194/egusphere-2023-2589, https://doi.org/10.5194/egusphere-2023-2589, 2023
Short summary
Short summary
In this work, we draw on paleoreords, observations and modelling studies to review tipping points in the ocean overturning circulations, monsoon systems and global atmospheric circulations. We find indications for tipping in the ocean overturning circulations and the West African monsoon, with potentially severe impacts on the Earth system and humans. Tipping in the other considered systems is considered conceivable but currently not sufficiently supported by evidence.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Laurie C. Menviel, Paul Spence, Andrew E. Kiss, Matthew A. Chamberlain, Hakase Hayashida, Matthew H. England, and Darryn Waugh
Biogeosciences, 20, 4413–4431, https://doi.org/10.5194/bg-20-4413-2023, https://doi.org/10.5194/bg-20-4413-2023, 2023
Short summary
Short summary
As the ocean absorbs 25% of the anthropogenic emissions of carbon, it is important to understand the impact of climate change on the flux of carbon between the ocean and the atmosphere. Here, we use a very high-resolution ocean, sea-ice, carbon cycle model to show that the capability of the Southern Ocean to uptake CO2 has decreased over the last 40 years due to a strengthening and poleward shift of the southern hemispheric westerlies. This trend is expected to continue over the coming century.
Benoît Pasquier, Mark Holzer, Matthew A. Chamberlain, Richard J. Matear, Nathaniel L. Bindoff, and François W. Primeau
Biogeosciences, 20, 2985–3009, https://doi.org/10.5194/bg-20-2985-2023, https://doi.org/10.5194/bg-20-2985-2023, 2023
Short summary
Short summary
Modeling the ocean's carbon and oxygen cycles accurately is challenging. Parameter optimization improves the fit to observed tracers but can introduce artifacts in the biological pump. Organic-matter production and subsurface remineralization rates adjust to compensate for circulation biases, changing the pathways and timescales with which nutrients return to the surface. Circulation biases can thus strongly alter the system’s response to ecological change, even when parameters are optimized.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Haihan Hu, Jiechen Zhao, Petra Heil, Zhiliang Qin, Jingkai Ma, Fengming Hui, and Xiao Cheng
The Cryosphere, 17, 2231–2244, https://doi.org/10.5194/tc-17-2231-2023, https://doi.org/10.5194/tc-17-2231-2023, 2023
Short summary
Short summary
The oceanic characteristics beneath sea ice significantly affect ice growth and melting. The high-frequency and long-term observations of oceanic variables allow us to deeply investigate their diurnal and seasonal variation and evaluate their influences on sea ice evolution. The large-scale sea ice distribution and ocean circulation contributed to the seasonal variation of ocean variables, revealing the important relationship between large-scale and local phenomena.
Andrew P. Schurer, Gabriele C. Hegerl, Hugues Goosse, Massimo A. Bollasina, Matthew H. England, Michael J. Mineter, Doug M. Smith, and Simon F. B. Tett
Clim. Past, 19, 943–957, https://doi.org/10.5194/cp-19-943-2023, https://doi.org/10.5194/cp-19-943-2023, 2023
Short summary
Short summary
We adopt an existing data assimilation technique to constrain a model simulation to follow three important modes of variability, the North Atlantic Oscillation, El Niño–Southern Oscillation and the Southern Annular Mode. How it compares to the observed climate is evaluated, with improvements over simulations without data assimilation found over many regions, particularly the tropics, the North Atlantic and Europe, and discrepancies with global cooling following volcanic eruptions are reconciled.
Alban Planchat, Lester Kwiatkowski, Laurent Bopp, Olivier Torres, James R. Christian, Momme Butenschön, Tomas Lovato, Roland Séférian, Matthew A. Chamberlain, Olivier Aumont, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, Tatiana Ilyina, Hiroyuki Tsujino, Kristen M. Krumhardt, Jörg Schwinger, Jerry Tjiputra, John P. Dunne, and Charles Stock
Biogeosciences, 20, 1195–1257, https://doi.org/10.5194/bg-20-1195-2023, https://doi.org/10.5194/bg-20-1195-2023, 2023
Short summary
Short summary
Ocean alkalinity is critical to the uptake of atmospheric carbon and acidification in surface waters. We review the representation of alkalinity and the associated calcium carbonate cycle in Earth system models. While many parameterizations remain present in the latest generation of models, there is a general improvement in the simulated alkalinity distribution. This improvement is related to an increase in the export of biotic calcium carbonate, which closer resembles observations.
Na Li, Ruibo Lei, Petra Heil, Bin Cheng, Minghu Ding, Zhongxiang Tian, and Bingrui Li
The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, https://doi.org/10.5194/tc-17-917-2023, 2023
Short summary
Short summary
The observed annual maximum landfast ice (LFI) thickness off Zhongshan (Davis) was 1.59±0.17 m (1.64±0.08 m). Larger interannual and local spatial variabilities for the seasonality of LFI were identified at Zhongshan, with the dominant influencing factors of air temperature anomaly, snow atop, local topography and wind regime, and oceanic heat flux. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
Short summary
Short summary
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Qianjiang Xing, David Munday, Andreas Klocker, Isabel Sauermilch, and Joanne Whittaker
Clim. Past, 18, 2669–2693, https://doi.org/10.5194/cp-18-2669-2022, https://doi.org/10.5194/cp-18-2669-2022, 2022
Short summary
Short summary
A high-resolution ocean model and realistic paleo-bathymetry are applied to obtain accurate simulation results. We firstly propose that the alignment of the maximum wind stress with a deep Tasmanian Gateway and Drake Passage is a trigger for the proto-Antarctic Circumpolar Current (proto-ACC) and the cooling of the Eocene Southern Ocean. We use zonal momentum budget analysis to explore the nature of the proto-ACC and the sensitivity of its transport through gateways to doubled wind stress.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
Short summary
Short summary
The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
Short summary
Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Gustavo M. Marques, Nora Loose, Elizabeth Yankovsky, Jacob M. Steinberg, Chiung-Yin Chang, Neeraja Bhamidipati, Alistair Adcroft, Baylor Fox-Kemper, Stephen M. Griffies, Robert W. Hallberg, Malte F. Jansen, Hemant Khatri, and Laure Zanna
Geosci. Model Dev., 15, 6567–6579, https://doi.org/10.5194/gmd-15-6567-2022, https://doi.org/10.5194/gmd-15-6567-2022, 2022
Short summary
Short summary
We present an idealized ocean model configuration and a set of simulations performed using varying horizontal grid spacing. While the model domain is idealized, it resembles important geometric features of the Atlantic and Southern oceans. The simulations described here serve as a framework to effectively study mesoscale eddy dynamics, to investigate the effect of mesoscale eddies on the large-scale dynamics, and to test and evaluate eddy parameterizations.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chao-Yuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere, 16, 1873–1887, https://doi.org/10.5194/tc-16-1873-2022, https://doi.org/10.5194/tc-16-1873-2022, 2022
Short summary
Short summary
The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Zhongshan Station in the Antarctic. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modeling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Tian R. Tian, Alexander D. Fraser, Noriaki Kimura, Chen Zhao, and Petra Heil
The Cryosphere, 16, 1299–1314, https://doi.org/10.5194/tc-16-1299-2022, https://doi.org/10.5194/tc-16-1299-2022, 2022
Short summary
Short summary
This study presents a comprehensive validation of a satellite observational sea ice motion product in Antarctica by using drifting buoys. Two problems existing in this sea ice motion product have been noticed. After rectifying problems, we use it to investigate the impacts of satellite observational configuration and timescale on Antarctic sea ice kinematics and suggest the future improvement of satellite missions specifically designed for retrieval of sea ice motion.
Dipayan Choudhury, Laurie Menviel, Katrin J. Meissner, Nicholas K. H. Yeung, Matthew Chamberlain, and Tilo Ziehn
Clim. Past, 18, 507–523, https://doi.org/10.5194/cp-18-507-2022, https://doi.org/10.5194/cp-18-507-2022, 2022
Short summary
Short summary
We investigate the effects of a warmer climate from the Earth's paleoclimate (last interglacial) on the marine carbon cycle of the Southern Ocean using a carbon-cycle-enabled state-of-the-art climate model. We find a 150 % increase in CO2 outgassing during this period, which results from competition between higher sea surface temperatures and weaker oceanic circulation. From this we unequivocally infer that the carbon uptake by the Southern Ocean will reduce under a future warming scenario.
Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
Short summary
Short summary
We have shown through field experiments that the amount of wave energy dissipated in landfast ice, sea ice attached to land, is much larger than in broken ice. By comparing our measurements against predictions of contemporary wave–ice interaction models, we determined which models can explain our observations and which cannot. Our results will improve our understanding of how waves and ice interact and how we can model such interactions to better forecast waves and ice in the polar regions.
Matthew A. Chamberlain, Peter R. Oke, Russell A. S. Fiedler, Helen M. Beggs, Gary B. Brassington, and Prasanth Divakaran
Earth Syst. Sci. Data, 13, 5663–5688, https://doi.org/10.5194/essd-13-5663-2021, https://doi.org/10.5194/essd-13-5663-2021, 2021
Short summary
Short summary
BRAN2020 is a dynamical reconstruction of the ocean, combining observations with a high-resolution global ocean model. BRAN2020 currently spans January 1993 to December 2019, assimilating in situ temperature and salinity, as well as satellite-based sea level and sea surface temperature. A new multiscale approach to data assimilation constrains the broad-scale ocean properties and turbulent mesoscale dynamics in two steps, showing closer agreement to observations than all previous versions.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
Short summary
Short summary
Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Trevor J. McDougall, Paul M. Barker, Ryan M. Holmes, Rich Pawlowicz, Stephen M. Griffies, and Paul J. Durack
Geosci. Model Dev., 14, 6445–6466, https://doi.org/10.5194/gmd-14-6445-2021, https://doi.org/10.5194/gmd-14-6445-2021, 2021
Short summary
Short summary
We show that the way that the air–sea heat flux is treated in ocean models means that the model's temperature variable should be interpreted as being Conservative Temperature, irrespective of whether the equation of state used in an ocean model is EOS-80 or TEOS-10.
Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu, and Aixue Hu
Geosci. Model Dev., 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021, https://doi.org/10.5194/gmd-14-2977-2021, 2021
Short summary
Short summary
This paper presents the high-resolution version of the Beijing Climate Center (BCC) Climate System Model, BCC-CSM2-HR, and describes its climate simulation performance including the atmospheric temperature and wind; precipitation; and the tropical climate phenomena such as TC, MJO, QBO, and ENSO. BCC-CSM2-HR is our model version contributing to the HighResMIP. We focused on its updates and differential characteristics from its predecessor, the medium-resolution version BCC-CSM2-MR.
Diana Francis, Kyle S. Mattingly, Stef Lhermitte, Marouane Temimi, and Petra Heil
The Cryosphere, 15, 2147–2165, https://doi.org/10.5194/tc-15-2147-2021, https://doi.org/10.5194/tc-15-2147-2021, 2021
Short summary
Short summary
The unexpected September 2019 calving event from the Amery Ice Shelf, the largest since 1963 and which occurred almost a decade earlier than expected, was triggered by atmospheric extremes. Explosive twin polar cyclones provided a deterministic role in this event by creating oceanward sea surface slope triggering the calving. The observed record-anomalous atmospheric conditions were promoted by blocking ridges and Antarctic-wide anomalous poleward transport of heat and moisture.
Chia-Wei Hsu, Jianjun Yin, Stephen M. Griffies, and Raphael Dussin
Geosci. Model Dev., 14, 2471–2502, https://doi.org/10.5194/gmd-14-2471-2021, https://doi.org/10.5194/gmd-14-2471-2021, 2021
Short summary
Short summary
The new surface forcing from JRA55-do (OMIP II) significantly improved the underestimated sea level trend across the entire Pacific Ocean along 10° N in the simulation forced by CORE (OMIP I). We summarize and list out the reasons for the existing sea level biases across all studied timescales as a reference for improving the sea level simulation in the future. This study on the evaluation and improvement of ocean climate models should be of broad interest to a large modeling community.
Nicholas King-Hei Yeung, Laurie Menviel, Katrin J. Meissner, Andréa S. Taschetto, Tilo Ziehn, and Matthew Chamberlain
Clim. Past, 17, 869–885, https://doi.org/10.5194/cp-17-869-2021, https://doi.org/10.5194/cp-17-869-2021, 2021
Short summary
Short summary
The Last Interglacial period (LIG) is characterised by strong orbital forcing compared to the pre-industrial period (PI). This study compares the mean climate state of the LIG to the PI as simulated by the ACCESS-ESM1.5, with a focus on the southern hemispheric monsoons, which are shown to be consistently weakened. This is associated with cooler terrestrial conditions in austral summer due to decreased insolation, and greater pressure and subsidence over land from Hadley cell strengthening.
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Bradley N. Opdyke, and Stephen M. Eggins
Clim. Past, 17, 171–201, https://doi.org/10.5194/cp-17-171-2021, https://doi.org/10.5194/cp-17-171-2021, 2021
Short summary
Short summary
We undertake a model–data study of the last glacial–interglacial cycle of atmospheric CO2, spanning 0–130 ka. We apply a carbon cycle box model, constrained with glacial–interglacial observations, and solve for optimal model parameter values against atmospheric and ocean proxy data. The results indicate that the last glacial drawdown in atmospheric CO2 was delivered mainly by slowing ocean circulation, lower sea surface temperatures and also increased Southern Ocean biological productivity.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
Short summary
Short summary
The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Joey J. Voermans, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Aleksey Marchenko, Clarence O. Collins III, Mohammed Dabboor, Graig Sutherland, and Alexander V. Babanin
The Cryosphere, 14, 4265–4278, https://doi.org/10.5194/tc-14-4265-2020, https://doi.org/10.5194/tc-14-4265-2020, 2020
Short summary
Short summary
In this work we demonstrate the existence of an observational threshold which identifies when waves are most likely to break sea ice. This threshold is based on information from two recent field campaigns, supplemented with existing observations of sea ice break-up. We show that both field and laboratory observations tend to converge to a single quantitative threshold at which the wave-induced sea ice break-up takes place, which opens a promising avenue for operational forecasting models.
Hiroyuki Tsujino, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro Iovino, Who M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, and Zipeng Yu
Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, https://doi.org/10.5194/gmd-13-3643-2020, 2020
Short summary
Short summary
The OMIP-2 framework for global ocean–sea-ice model simulations is assessed by comparing multi-model means from 11 CMIP6-class global ocean–sea-ice models calculated separately for the OMIP-1 and OMIP-2 simulations. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet key improvements in transitioning from OMIP-1 to OMIP-2 are also identified. Thus, the present assessment justifies that future ocean–sea-ice model development and analysis studies use the OMIP-2 framework.
Vivek K. Arora, Anna Katavouta, Richard G. Williams, Chris D. Jones, Victor Brovkin, Pierre Friedlingstein, Jörg Schwinger, Laurent Bopp, Olivier Boucher, Patricia Cadule, Matthew A. Chamberlain, James R. Christian, Christine Delire, Rosie A. Fisher, Tomohiro Hajima, Tatiana Ilyina, Emilie Joetzjer, Michio Kawamiya, Charles D. Koven, John P. Krasting, Rachel M. Law, David M. Lawrence, Andrew Lenton, Keith Lindsay, Julia Pongratz, Thomas Raddatz, Roland Séférian, Kaoru Tachiiri, Jerry F. Tjiputra, Andy Wiltshire, Tongwen Wu, and Tilo Ziehn
Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, https://doi.org/10.5194/bg-17-4173-2020, 2020
Short summary
Short summary
Since the preindustrial period, land and ocean have taken up about half of the carbon emitted into the atmosphere by humans. Comparison of different earth system models with the carbon cycle allows us to assess how carbon uptake by land and ocean differs among models. This yields an estimate of uncertainty in our understanding of how land and ocean respond to increasing atmospheric CO2. This paper summarizes results from two such model intercomparison projects that use an idealized scenario.
Lester Kwiatkowski, Olivier Torres, Laurent Bopp, Olivier Aumont, Matthew Chamberlain, James R. Christian, John P. Dunne, Marion Gehlen, Tatiana Ilyina, Jasmin G. John, Andrew Lenton, Hongmei Li, Nicole S. Lovenduski, James C. Orr, Julien Palmieri, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Charles A. Stock, Alessandro Tagliabue, Yohei Takano, Jerry Tjiputra, Katsuya Toyama, Hiroyuki Tsujino, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, and Tilo Ziehn
Biogeosciences, 17, 3439–3470, https://doi.org/10.5194/bg-17-3439-2020, https://doi.org/10.5194/bg-17-3439-2020, 2020
Short summary
Short summary
We assess 21st century projections of marine biogeochemistry in the CMIP6 Earth system models. These models represent the most up-to-date understanding of climate change. The models generally project greater surface ocean warming, acidification, subsurface deoxygenation, and euphotic nitrate reductions but lesser primary production declines than the previous generation of models. This has major implications for the impact of anthropogenic climate change on marine ecosystems.
Rui Yang, Marshall Ward, and Ben Evans
Geosci. Model Dev., 13, 1885–1902, https://doi.org/10.5194/gmd-13-1885-2020, https://doi.org/10.5194/gmd-13-1885-2020, 2020
Short summary
Short summary
Parallel I/O is implemented in the Modular Ocean Model (MOM) with optimal performance over a range of tuning parameters of model configuration, netCDF, MPI-IO and Lustre filesystem. The scalable parallel I/O performance is observed at 0.1° resolution global model, and it could achieve up to 60 times faster in write speed relative to serial single-file I/O running on 960 PEs.
Tongwen Wu, Fang Zhang, Jie Zhang, Weihua Jie, Yanwu Zhang, Fanghua Wu, Laurent Li, Jinghui Yan, Xiaohong Liu, Xiao Lu, Haiyue Tan, Lin Zhang, Jun Wang, and Aixue Hu
Geosci. Model Dev., 13, 977–1005, https://doi.org/10.5194/gmd-13-977-2020, https://doi.org/10.5194/gmd-13-977-2020, 2020
Short summary
Short summary
This paper describes the first version of the Beijing Climate Center (BCC) fully coupled Earth System Model with interactive atmospheric chemistry and aerosols (BCC-ESM1). It is one of the models at the BCC for the Coupled Model Intercomparison Project Phase 6 (CMIP6). The CMIP6 Aerosol Chemistry Model Intercomparison Project (AerChemMIP) experiment using BCC-ESM1 has been finished. The evaluations show an overall good agreement between BCC-ESM1 simulations and observations in the 20th century.
Tongwen Wu, Yixiong Lu, Yongjie Fang, Xiaoge Xin, Laurent Li, Weiping Li, Weihua Jie, Jie Zhang, Yiming Liu, Li Zhang, Fang Zhang, Yanwu Zhang, Fanghua Wu, Jianglong Li, Min Chu, Zaizhi Wang, Xueli Shi, Xiangwen Liu, Min Wei, Anning Huang, Yaocun Zhang, and Xiaohong Liu
Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, https://doi.org/10.5194/gmd-12-1573-2019, 2019
Short summary
Short summary
This work presents advancements of the BCC model transition from CMIP5 to CMIP6, especially in the model resolution and its physics. Compared with BCC CMIP5 models, the BCC CMIP6 model shows significant improvements in historical simulations in many aspects including tropospheric air temperature and circulation at global and regional scales in East Asia, climate variability at different timescales (QBO, MJO, and diurnal cycle of precipitation), and the long-term trend of global air temperature.
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Stephen M. Eggins, and Bradley N. Opdyke
Geosci. Model Dev., 12, 1541–1572, https://doi.org/10.5194/gmd-12-1541-2019, https://doi.org/10.5194/gmd-12-1541-2019, 2019
Short summary
Short summary
The [simple carbon project] model v1.0 (SCP-M) was constructed for simulations of the paleo and modern carbon cycle. In this paper we show its application to the carbon cycle transition from the Last Glacial Maximum to the Holocene period. Our model–data experiment uses SCP-M's fast run time to cover a large range of possible inputs. The results highlight the role of varying the strength of ocean circulation to account for large fluctuations in atmospheric CO2 across the two periods.
Daniel Boettger, Robin Robertson, and Gary B. Brassington
Geosci. Model Dev., 11, 3795–3805, https://doi.org/10.5194/gmd-11-3795-2018, https://doi.org/10.5194/gmd-11-3795-2018, 2018
Short summary
Short summary
This study focuses on the impact of the model vertical mixing parameterisation on the representation of the mixed layer depth (MLD) in ocean forecast models. We compare data from two recent versions of the OceanMAPS forecast system, and find that while there were large improvements in the later version of the model, the skill of each parameterisation varies with spatial location.
Kaitlin A. Naughten, Katrin J. Meissner, Benjamin K. Galton-Fenzi, Matthew H. England, Ralph Timmermann, Hartmut H. Hellmer, Tore Hattermann, and Jens B. Debernard
Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, https://doi.org/10.5194/gmd-11-1257-2018, 2018
Short summary
Short summary
MetROMS and FESOM are two ocean/sea-ice models which resolve Antarctic ice-shelf cavities and consider thermodynamics at the ice-shelf base. We simulate the period 1992–2016 with both models, and with two options for resolution in FESOM, and compare output from the three simulations. Ice-shelf melt rates, sub-ice-shelf circulation, continental shelf water masses, and sea-ice processes are compared and evaluated against available observations.
Mabel Costa Calim, Paulo Nobre, Peter Oke, Andreas Schiller, Leo San Pedro Siqueira, and Guilherme Pimenta Castelão
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-5, https://doi.org/10.5194/gmd-2018-5, 2018
Revised manuscript not accepted
Short summary
Short summary
A new tool inspired on tides is introduced. The Spectral Taylor Diagram designed for evaluating and monitoring models performance in frequency domain calculates the degree of correspondence between simulated and observed fields for a given frequency (or a band of frequencies). It's a powerful tool to detect co-oscillating patterns in multi scale analysis, without using filtering techniques.
Rachel M. Law, Tilo Ziehn, Richard J. Matear, Andrew Lenton, Matthew A. Chamberlain, Lauren E. Stevens, Ying-Ping Wang, Jhan Srbinovsky, Daohua Bi, Hailin Yan, and Peter F. Vohralik
Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017, https://doi.org/10.5194/gmd-10-2567-2017, 2017
Short summary
Short summary
The paper describes a version of the Australian Community Climate and Earth System Simulator that has been enabled to simulate the carbon cycle, which is designated ACCESS-ESM1. The model performance for pre-industrial conditions is assessed and land and ocean carbon fluxes are found to be simulated realistically.
James C. Orr, Raymond G. Najjar, Olivier Aumont, Laurent Bopp, John L. Bullister, Gokhan Danabasoglu, Scott C. Doney, John P. Dunne, Jean-Claude Dutay, Heather Graven, Stephen M. Griffies, Jasmin G. John, Fortunat Joos, Ingeborg Levin, Keith Lindsay, Richard J. Matear, Galen A. McKinley, Anne Mouchet, Andreas Oschlies, Anastasia Romanou, Reiner Schlitzer, Alessandro Tagliabue, Toste Tanhua, and Andrew Yool
Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, https://doi.org/10.5194/gmd-10-2169-2017, 2017
Short summary
Short summary
The Ocean Model Intercomparison Project (OMIP) is a model comparison effort under Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its physical component is described elsewhere in this special issue. Here we describe its ocean biogeochemical component (OMIP-BGC), detailing simulation protocols and analysis diagnostics. Simulations focus on ocean carbon, other biogeochemical tracers, air-sea exchange of CO2 and related gases, and chemical tracers used to evaluate modeled circulation.
Emlyn M. Jones, Mark E. Baird, Mathieu Mongin, John Parslow, Jenny Skerratt, Jenny Lovell, Nugzar Margvelashvili, Richard J. Matear, Karen Wild-Allen, Barbara Robson, Farhan Rizwi, Peter Oke, Edward King, Thomas Schroeder, Andy Steven, and John Taylor
Biogeosciences, 13, 6441–6469, https://doi.org/10.5194/bg-13-6441-2016, https://doi.org/10.5194/bg-13-6441-2016, 2016
Short summary
Short summary
Marine biogeochemical models are often used to understand water quality, nutrient and blue-carbon dynamics at scales that range from estuaries and bays, through to the global ocean. We introduce a new methodology allowing for the assimilation of observed remote sensing reflectances, avoiding the need to use empirically derived chlorophyll-a concentrations. This method opens up the possibility to assimilate of reflectances from a variety of missions and potentially non-satellite platforms.
Jonathan M. Gregory, Nathaelle Bouttes, Stephen M. Griffies, Helmuth Haak, William J. Hurlin, Johann Jungclaus, Maxwell Kelley, Warren G. Lee, John Marshall, Anastasia Romanou, Oleg A. Saenko, Detlef Stammer, and Michael Winton
Geosci. Model Dev., 9, 3993–4017, https://doi.org/10.5194/gmd-9-3993-2016, https://doi.org/10.5194/gmd-9-3993-2016, 2016
Short summary
Short summary
As a consequence of greenhouse gas emissions, changes in ocean temperature, salinity, circulation and sea level are expected in coming decades. Among the models used for climate projections for the 21st century, there is a large spread in projections of these effects. The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate and explain this spread by prescribing a common set of changes in the input of heat, water and wind stress to the ocean in the participating models.
Colette Kerry, Brian Powell, Moninya Roughan, and Peter Oke
Geosci. Model Dev., 9, 3779–3801, https://doi.org/10.5194/gmd-9-3779-2016, https://doi.org/10.5194/gmd-9-3779-2016, 2016
Short summary
Short summary
Ocean circulation drives weather and climate and supports marine ecosystems, so providing accurate predictions is important. The ocean circulation is complex, 3-D and highly variable, and its prediction requires advanced numerical models combined with real-time measurements. Focusing on the dynamic East Austr. Current, we use novel mathematical techniques to combine an ocean model with measurements to better estimate the circulation. This is an important step towards improving ocean forecasts.
Peter R. Oke, Roger Proctor, Uwe Rosebrock, Richard Brinkman, Madeleine L. Cahill, Ian Coghlan, Prasanth Divakaran, Justin Freeman, Charitha Pattiaratchi, Moninya Roughan, Paul A. Sandery, Amandine Schaeffer, and Sarath Wijeratne
Geosci. Model Dev., 9, 3297–3307, https://doi.org/10.5194/gmd-9-3297-2016, https://doi.org/10.5194/gmd-9-3297-2016, 2016
Short summary
Short summary
The Marine Virtual Laboratory (MARVL) is designed to help ocean modellers hit the ground running. Usually, setting up an ocean model involves a handful of technical steps that time and effort. MARVL provides a user-friendly interface that allows users to choose what options they want for their model, including the region, time period, and input data sets. The user then hits "go", and MARVL does the rest – delivering a "take-away bundle" that contains all the files needed to run the model.
Stephen M. Griffies, Gokhan Danabasoglu, Paul J. Durack, Alistair J. Adcroft, V. Balaji, Claus W. Böning, Eric P. Chassignet, Enrique Curchitser, Julie Deshayes, Helge Drange, Baylor Fox-Kemper, Peter J. Gleckler, Jonathan M. Gregory, Helmuth Haak, Robert W. Hallberg, Patrick Heimbach, Helene T. Hewitt, David M. Holland, Tatiana Ilyina, Johann H. Jungclaus, Yoshiki Komuro, John P. Krasting, William G. Large, Simon J. Marsland, Simona Masina, Trevor J. McDougall, A. J. George Nurser, James C. Orr, Anna Pirani, Fangli Qiao, Ronald J. Stouffer, Karl E. Taylor, Anne Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Maria Valdivieso, Qiang Wang, Michael Winton, and Stephen G. Yeager
Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, https://doi.org/10.5194/gmd-9-3231-2016, 2016
Short summary
Short summary
The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This document defines OMIP and details a protocol both for simulating global ocean/sea-ice models and for analysing their output.
Willem P. Sijp and Matthew H. England
Clim. Past, 12, 543–552, https://doi.org/10.5194/cp-12-543-2016, https://doi.org/10.5194/cp-12-543-2016, 2016
Short summary
Short summary
The polar warmth of the greenhouse climates in the Earth's past represents a fundamentally different climate state to that of today, with a strongly reduced temperature difference between the Equator and the poles. It is commonly thought that this would lead to a more quiescent ocean, with much reduced ventilation of the abyss. Surprisingly, using a Cretaceous cimate model, we find that ocean overturning is not weaker under a reduced temperature gradient arising from amplified polar heat.
X. Zhang, P. R. Oke, M. Feng, M. A. Chamberlain, J. A. Church, D. Monselesan, C. Sun, R. J. Matear, A. Schiller, and R. Fiedler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-17, https://doi.org/10.5194/gmd-2016-17, 2016
Revised manuscript not accepted
Short summary
Short summary
Eddy-resolving global ocean models are highly desired, but expensive to run, and also subject to many problems including drift. Here we modified a near-global eddy-resolving OGCM for climate studies with some novel strategies. We demonstrated that the historical experiment driven by Japanese atmospheric reanalysis product, didn't have significant drifts, and also provided an eddy-resolving simulation of the global ocean over 1979–2014. Our experiences can be helpful to other modelling groups.
G. S. Pilo, M. M. Mata, and J. L. L. Azevedo
Ocean Sci., 11, 629–641, https://doi.org/10.5194/os-11-629-2015, https://doi.org/10.5194/os-11-629-2015, 2015
Short summary
Short summary
Oceanic eddies are closed circulation features that transport water between regions, taking part in the ocean's heat and salt balance. We perform a comparative eddy census in the East Australian, Agulhas and Brazil currents. We find that eddy propagation in all systems is steered by the local mean flow and bathymetry. Also, eddies present a geographic segregation according to size. Investigating eddy propagation helps us to better understand their effect in local mixing.
L. Menviel, A. Timmermann, T. Friedrich, and M. H. England
Clim. Past, 10, 63–77, https://doi.org/10.5194/cp-10-63-2014, https://doi.org/10.5194/cp-10-63-2014, 2014
S. McGregor, A. Timmermann, M. H. England, O. Elison Timm, and A. T. Wittenberg
Clim. Past, 9, 2269–2284, https://doi.org/10.5194/cp-9-2269-2013, https://doi.org/10.5194/cp-9-2269-2013, 2013
P. R. Oke, D. A. Griffin, A. Schiller, R. J. Matear, R. Fiedler, J. Mansbridge, A. Lenton, M. Cahill, M. A. Chamberlain, and K. Ridgway
Geosci. Model Dev., 6, 591–615, https://doi.org/10.5194/gmd-6-591-2013, https://doi.org/10.5194/gmd-6-591-2013, 2013
Related subject area
Climate and Earth system modeling
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
Investigating Carbon and Nitrogen Conservation in Reported CMIP6 Earth System Model Data
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
PaleoSTeHM v1.0-rc: a modern, scalable spatio-temporal hierarchical modeling framework for paleo-environmental data
From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
Historical Trends and Controlling Factors of Isoprene Emissions in CMIP6 Earth System Models
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
Short summary
Short summary
The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-183, https://doi.org/10.5194/gmd-2024-183, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we developed and applied a weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. It also enhances the simulation of interannual precipitation and temperature variability over the southern US. This system is to support future predictability studies.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2183, https://doi.org/10.5194/egusphere-2024-2183, 2024
Short summary
Short summary
PaleoSTeHM v1.0-rc is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
Short summary
Short summary
Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Cited articles
Adcroft, A. and Campin, J.-M.: Rescaled height coordinates for accurate
representation of free-surface flows in ocean circulation models, Ocean
Modell., 7, 269–284, 2004. a
Annamalai, H., Liu, P., and Xie, S. P.: Southwest Indian Ocean SST
variability: Its local effect and remote influence on Asian monsoons,
J. Climate, 18, 4150–4167, https://doi.org/10.1175/JCLI3533.1, 2005. a
Ansorge, I. J., Froneman, P. W., and Durgadoo, J. V.: The Marine Ecosystem of
the Sub-Antarctic, Prince Edward Islands, in: Marine Ecosystems, edited by:
Cruzado, A., chap. 3, IntechOpen, Rijeka, https://doi.org/10.5772/36676, 2012. a
Bamber, J., van den Broeke, M., Ettema, J., Lenaerts, J., and Rignot, E.:
Recent large increases in freshwater fluxes from Greenland into the North
Atlantic, Geophys. Res. Lett., 39, L19501,
https://doi.org/10.1029/2012gl052552, 2012. a
Barnier, B., Blaker, A., Biastoch, A., Böning, C., Coward, A., Deshayes,
J., Hirshi, J., Le Sommer, J., Madec, G., Maze, G., Molines, J., New, A.,
Penduff, T., Scheinert, M., Talandier, C., and Treguier, A.-M.: DRAKKAR:
developing high resolution ocean components for European Earth system
models, CLIVAR Exch., 19, 18–21, 2014. a
Beckmann, A. and Döscher, R.: A Method for Improved Representation of Dense
Water Spreading over Topography in Geopotential-Coordinate Models, J.
Phys. Oceanogr., 27, 581–591,
https://doi.org/10.1175/1520-0485(1997)027<0581:amfiro>2.0.co;2,
1997. a
Behera, K. S., Luo, J.-J., Masson, S., Delecluse, P., Gualdi, S., Navarra, A.,
and Toshio, Y.: Paramount Impact of the Indian Ocean Dipole on the East
African Short Rains : A CGCM Study, J. Climate, 18, 4514–4530,
2005. a
Bentley, J. L.: Multidimensional Binary Search Trees Used for Associative
Searching, Commun. ACM, 18, 509–517, https://doi.org/10.1145/361002.361007, 1975. a
Bi, D. and Marsland, S.: Australian Climate Ocean Model (AusCOM) users guide,
CAWCR Technical Report 27, The Centre for Australian Weather and Climate
Research,
available at: http://www.cawcr.gov.au/technical-reports/CTR_027.pdf (last access: 21 January 2020), 2010. a
Bi, D., Dix, M., Marsland, S. J., O'Farrell, S., Rashid, H. A., Uotila, P.,
Hirst, A. C., Kowalczyk, E., Golebiewski, M., Sullivan, A., Yan, H., Hannah,
N., Franklin, C., Sun, Z., Vohralik, P., Watterson, I., Zhou, X., Fiedler,
R., Collier, M., Ma2, Y., Noonan, J., Stevens, L., Uhe, P., Zhu, H.,
Griffies, S. M., Hill, R., Harris, C., and Puri, K.: The ACCESS coupled
model: description, control climate and evaluation, Aust. Met. Ocean. J.,
63, 61–64, 2013a. a
Bintanja, R., van Oldenborgh, G., and Katsman, C.: The effect of increased
fresh water from Antarctic ice shelves on future trends in Antarctic sea
ice, Ann. Glaciol., 56, 120–126, https://doi.org/10.3189/2015aog69a001, 2015. a
Bishop, S. P., Gent, P. R., Bryan, F. O., Thompson, A. F., Long, M. C., and
Abernathey, R.: Southern Ocean Overturning Compensation in an
Eddy-Resolving Climate Simulation, J. Phys. Oceanogr., 46,
1575–1592, https://doi.org/10.1175/JPO-D-15-0177.1, 2016. a
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of
sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677,
https://doi.org/10.1029/1999jc900100, 1999. a
Campin, J.-M. and Goosse, H.: Parameterization of density-driven downsloping
flow for a coarse-resolution ocean model in z-coordinate, Tellus A: Dynam.
Meteorol. Oceanogr., 51, 412–430,
https://doi.org/10.3402/tellusa.v51i3.13468, 1999. a
Chambers, D. P.: Using kinetic energy measurements from altimetry to detect
shifts in the positions of fronts in the Southern Ocean, Ocean Sci., 14,
105–116, https://doi.org/10.5194/os-14-105-2018, 2018. a
Chassignet, W. and Marshall, D.: Gulf Stream separation in numerical ocean
models, Geophys. Monogr. Ser., 177, 39–61, 2008. a
Colella, P. and Woodward, P. R.: The Piecewise Parabolic Method (PPM) for
gas-dynamical simulations, J. Comput. Phys., 54, 174–201,
https://doi.org/10.1016/0021-9991(84)90143-8, 1984. a
Colin de Verdière, A. and Ollitrault, M.: A Direct Determination of the
World Ocean Barotropic Circulation, J. Phys. Oceanogr., 46,
255–273, https://doi.org/10.1175/jpo-d-15-0046.1, 2016. a, b
COSIMA: COSIMA Model Output Collection, available at: https://doi.org/10.4225/41/5a2dc8543105a (last access: 21 January 2020), 2019.
Craig, A. P., Mickelson, S. A., Hunke, E. C., and Bailey, D. A.: Improved
parallel performance of the CICE model in CESM1, Int. J. High Perform.
Comput. Appl., 29, 154–165, https://doi.org/10.1177/1094342014548771, 2015. a, b
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., Nurser, A. G., 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 1: Mean States, Ocean Modell., 73, 76–107, https://doi.org/10.1016/j.ocemod.2013.10.005, 2014. a, b, c, d
Delworth, T. L., Rosati, A., Anderson, W., Adcroft, A. J., Balaji, V., Benson,
R., Dixon, K., Griffies, S. M., Lee, H.-C., Pacanowski, R. C., Vecchi, G. A., Wittenberg, A. T., Zeng, F., and Zhang, R.:
Simulated Climate and Climate Change in the GFDL CM2.5 High-Resolution
Coupled Climate Model, J. Climate, 25, 2755–2781,
https://doi.org/10.1175/jcli-d-11-00316.1, 2012. a
de Miranda, A. P., Barnier, B., and Dewar, W. K.: On the dynamics of the
Zapiola Anticyclone, J. Geophys. Res.-Oceans, 104,
21137–21149, https://doi.org/10.1029/1999JC900042, 1999. a
de Ruijter, W. P. M., Ridderinkhof, H., Lutjeharms, J. R. E., Schouten,
M. W., and Veth, C.: Observations of the flow in the Mozambique Channel,
Geophys. Res. Lett., 29, 1502, https://doi.org/10.1029/2001GL013714, 2002. a
Dencausse, G., Arhan, M., and Speich, S.: Routes of Agulhas rings in the
southeastern Cape Basin, Deep Sea Res. Part I,
57, 1406–1421, https://doi.org/10.1016/j.dsr.2010.07.008, 2010. a
Depoorter, M. A., Bamber, J. L., Griggs, J. A., Lenaerts, J. T. M., Ligtenberg,
S. R. M., van den Broeke, M. R., and Moholdt, G.: Calving fluxes and basal
melt rates of Antarctic ice shelves, Nature, 502, 89–92,
https://doi.org/10.1038/nature12567, 2013. a, b
Dewar, W. K.: Topography and barotropic transport control by bottom friction,
J. Mar. Res., 56, 295–328, https://doi.org/10.1357/002224098321822320, 1998. a
Donohue, K. A., Tracey, K. L., Watts, D. R., Chidichimo, M. P., and Chereskin, T. K.: Mean Antarctic Circumpolar Current transport measured in Drake Passage, Geophys. Res. Lett., 43, 11760–11767,
https://doi.org/10.1002/2016GL070319, 2016. a, b
Döscher, R. and Beckmann, A.: Effects of a Bottom Boundary Layer
Parameterization in a Coarse-Resolution Model of the North Atlantic Ocean,
J. Atmos. Ocean. Technol., 17, 698–707,
https://doi.org/10.1175/1520-0426(2000)017<0698:eoabbl>2.0.co;2,
2000. a
Downes, S. M., Farneti, R., Uotila, P., Griffies, S. M., Marsland, S. J., Bailey, D., Behrens, E., Bentsen, M., Bi, D., Biastoch, A., Böning, C., Bozec, A., Canuto, V. M., Chassignet, E., Danabasoglu, G., Danilov, S., Diansky, N., Drange, H., Fogli, P. G., Gusev, A., Howard, A., Ilicak, M., Jung, T., Kelley, M., Large, W. G., Leboissetier, A., Long, M., Lu, J., Masina, S., Mishra, A., Navarra, A., Nurser, A. G., Patara, L., Samuels, B. L., Sidorenko, D., Spence, P., Tsujino, H., Wang, Q., and Yeager, S. G.: An
assessment of Southern Ocean water masses and sea ice during 1988-2007 in a
suite of interannual CORE-II simulations, Ocean Modell., 94, 67–94,
https://doi.org/10.1016/j.ocemod.2015.07.022, 2015. 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. Clim., 30, 7719–7737,
https://doi.org/10.1175/jcli-d-16-0586.1, 2017. a
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O.,
Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp, L.,
Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L.,
Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L.,
Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A.,
Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F.,
Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D.,
Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate
change projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1,
2013. a
Dukowicz, J. K. and Baumgardner, J. R.: Incremental Remapping as a
Transport/Advection Algorithm, J. Comput. Phys., 160,
318–335, https://doi.org/10.1006/jcph.2000.6465, 2000. a
Farneti, R., Downes, S. M., Griffies, S. M., Marsland, S. J., Behrens, E.,
Bentsen, M., Bi, D., Biastoch, A., Böning, C., Bozec, A., Canuto,
V. M., Chassignet, E., Danabasoglu, G., Danilov, S., Diansky, N., Drange, H.,
Fogli, P. G., Gusev, A., Hallberg, R. W., Howard, A., Ilicak, M., Jung, T.,
Kelley, M., Large, W. G., Leboissetier, A., Long, M., Lu, J., Masina, S.,
Mishra, A., Navarra, A., George Nurser, A. J., Patara, L., Samuels, B. L.,
Sidorenko, D., Tsujino, H., Uotila, P., Wang, Q., and Yeager, S. G.: An
assessment of Antarctic Circumpolar Current and Southern Ocean meridional
overturning circulation during 1958-2007 in a suite of interannual CORE-II
simulations, Ocean Modell., 93, 84–120,
https://doi.org/10.1016/j.ocemod.2015.07.009, 2015. a
Ferrari, R., Griffies, S. M., Nurser, A. G., and Vallis, G. K.: A
boundary-value problem for the parameterized mesoscale eddy transport, Ocean
Modell., 32, 143–156, https://doi.org/10.1016/j.ocemod.2010.01.004, 2010. a
Fetterer, F., Knowles, K., Meier, W., Savoie, M., and Windnagel, A. K.: Sea Ice
Index, Version 3, Tech. rep., NSIDC: National Snow and Ice Data Center,
Boulder, Colorado, USA, https://doi.org/10.7265/N5K072F8, 2017, updated daily. a
Fox-Kemper, B., Ferrari, R., and Hallberg, R.: Parameterization of mixed layer
eddies. Part I: Theory and diagnosis, J. Phys. Oceanogr., 38, 1145–1165,
https://doi.org/10.1175/2007JPO3792.1, 2008. a
Fu, L. L.: Pathways of eddies in the South Atlantic Ocean revealed from
satellite altimeter observations, Geophys. Res. Lett., 33, 1–5,
https://doi.org/10.1029/2006GL026245, 2006. a
Ganachaud, A. and Wunsch, C.: Large-Scale Ocean Heat and Freshwater Transports
during the World Ocean Circulation Experiment, J. Climate, 16,
696–705, https://doi.org/10.1175/1520-0442(2003)016<0696:LSOHAF>2.0.CO;2, 2003. a, b
GEBCO: The GEBCO_2014 Grid, available at: https://www.gebco.net/ (last access: 21 January 2020), 2014. 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, b
Griffies, S. and Hallberg, R.: Biharmonic friction with a Smagorinsky-like
viscosity for use in large-scale eddy-permitting ocean models, Mon.
Weather Rev., 128, 2935–2946, 2000. a
Griffies, S. M.: The Gent-McWilliams Skew Flux, J. Phys. Oceanogr., 28,
831–841, https://doi.org/10.1175/1520-0485(1998)028<0831:TGMSF>2.0.CO;2, 1998. a
Griffies, S. M., Gnanadesikan, A., Pacanowski, R. C., Larichev, V. D.,
Dukowicz, J. K., and Smith, R. D.: Isoneutral Diffusion in a z-Coordinate
Ocean Model, J. Phys. Oceanogr., 28, 805–830,
https://doi.org/10.1175/1520-0485(1998)028<0805:IDIAZC>2.0.CO;2, 1998. a, b
Griffies, S. M., Gnanadesikan, A., Dixon, K. W., Dunne, J. P., Gerdes, R.,
Harrison, M. J., Rosati, A., Russell, J. L., Samuels, B. L., Spelman, M. J.,
Winton, W., and Zhang, R.: Formulation of an ocean model for global climate
simulations, Ocean Sci., 1, 45–79, https://doi.org/10.5194/os-1-45-2005, 2005. a, b, c
Griffies, S. M., Biastoch, A., Böning, C. W., Bryan, F., Danabasoglu, G.,
Chassignet, E., 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., Sen Gupta, A., Severijns, C. A., Simmons, H. L.,
Treguier, A. M., Winton, M., Yeager, S., and Yin, J.: Coordinated Ocean-ice
Reference Experiments (COREs), Ocean Modell., 26, 1–46,
https://doi.org/10.1016/j.ocemod.2008.08.007, 2009. a, b, c, d, e, f
Griffies, S. M., Yin, J., Durack, P. J., Goddard, P., Bates, S. C., Behrens,
E., Bentsen, M., Bi, D., Biastoch, A., Böning, C. W., Bozec, A.,
Chassignet, E., Danabasoglu, G., Danilov, S., Domingues, C. M., Drange, H.,
Farneti, R., Fernandez, E., Greatbatch, R. J., Holland, D. M., Ilicak, M.,
Large, W. G., Lorbacher, K., Lu, J., Marsland, S. J., Mishra, A., Nurser,
A. G., Salas y Mélia, D., Palter, J. B., Samuels, B. L., Schröter, J.,
Schwarzkopf, F. U., Sidorenko, D., Treguier, A. M., Tseng, Y.-H., Tsujino,
H., Uotila, P., Valcke, S., Voldoire, A., Wang, Q., Winton, M., and Zhang,
X.: An assessment of global and regional sea level for years 1993–2007 in a
suite of interannual CORE-II simulations, Ocean Modell., 78, 35–89,
https://doi.org/10.1016/j.ocemod.2014.03.004, 2014. a, b
Griffies, S. M., Winton, M., Anderson, W. G., Benson, R., Delworth, T. L.,
Dufour, C. O., Dunne, J. P., Goddard, P., Morrison, A. K., Rosati, A.,
Wittenberg, A. T., Yin, J., and Zhang, R.: Impacts on Ocean Heat from
Transient Mesoscale Eddies in a Hierarchy of Climate Models, J.
Climate, 28, 952–977, 2015. a, b, c, d, e, f, g
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016. a
Haidvogel, D., McWilliams, J., and Gent, P.: Boundary current separation in a
quasigeostrophic, eddy-resolving ocean circulation model, J. Phys. Oceanogr.,
22, 882–902, 1992. a
Hallberg, R.: Using a resolution function to regulate parameterizations of
oceanic mesoscale eddy effects, Ocean Modell., 72, 92–103,
https://doi.org/10.1016/j.ocemod.2013.08.007, 2013. a, b
Hallberg, R.: Numerical instabilities of the ice/ocean coupled system, CLIVAR
Exchanges, 65, 38–42,
available at: http://www.clivar.org/sites/default/files/documents/exchanges65_0.pdf (last access: 21 January 2020),
2014. a
Han, W., Meehl, G. A., Stammer, D., Hu, A., Hamlington, B., Kenigson, J.,
Palanisamy, H., and Thompson, P.: Spatial Patterns of Sea Level Variability
Associated with Natural Internal Climate Modes, Surv. Geophys., 38,
217–250, https://doi.org/10.1007/s10712-016-9386-y, 2017. a
Hannah, N., Kiss, A. E., Heerdegen, A., Ward, M. L., Fiedler, R., Hogg, A. M., Griffies, S. M., and Holmes, R. M.: The ACCESS-OM2 global ocean – sea ice coupled model (version 1.0),
Zenodo, https://doi.org/10.5281/zenodo.2653246, 2019. a
Hermes, J. C. and Reason, C. J. C.: Annual cycle of the South Indian Ocean
(Seychelles-Chagos) thermocline ridge in a regional ocean model, J.
Geophys. Res.-Oceans, 113, 1–10, https://doi.org/10.1029/2007JC004363, 2008. a
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
Heuzé, C., Heywood, K. J., Stevens, D. P., and Ridley, J.: Changes in
Global Ocean Bottom Properties and Volume Transports in CMIP5 Models under
Climate Change Scenarios, J. Climate, 28, 2917–2944,
https://doi.org/10.1175/JCLI-D-14-00381.1, 2015a. a
Heuzé, C., Ridley, J. K., Calvert, D., Stevens, D. P., and Heywood, K. J.: Increasing vertical mixing to reduce Southern Ocean deep convection in NEMO3.4, Geosci. Model Dev., 8, 3119–3130, https://doi.org/10.5194/gmd-8-3119-2015, 2015b. a
Hewitt, H. T., Roberts, M. J., Hyder, P., Graham, T., Rae, J., Belcher, S. E., Bourdallé-Badie, R., Copsey, D., Coward, A., Guiavarch, C., Harris, C., Hill, R., Hirschi, J. J.-M., Madec, G., Mizielinski, M. S., Neininger, E., New, A. L., Rioual, J.-C., Sinha, B., Storkey, D., Shelly, A., Thorpe, L., and Wood, R. A.: The impact of resolving the Rossby radius at mid-latitudes in the ocean: results from a high-resolution version of the Met Office GC2 coupled model, Geosci. Model Dev., 9, 3655–3670, https://doi.org/10.5194/gmd-9-3655-2016, 2016. a, b
Hibler, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys.
Oceanogr., 9, 815–846,
https://doi.org/10.1175/1520-0485(1979)009<0815:adtsim>2.0.co;2, 1979. a
Hobbs, W., Palmer, M. D., and Monselesan, D.: An energy conservation analysis
of ocean drift in the CMIP5 global coupled models, J. Climate, 29,
1639–1653, https://doi.org/10.1175/JCLI-D-15-0477.1, 2016. a
Hogg, A. McC., Meredith, M. P., Chambers, D. P., Abrahamsen, E. P., Hughes,
C. W., and Morrison, A. K.: Recent trends in the Southern Ocean eddy field,
J. Geophys. Res., 120, 257–267, https://doi.org/10.1002/2014JC010470, 2015. a
Holland, P. R. and Kwok, R.: Wind-driven trends in Antarctic sea-ice drift,
Nat. Geosci., 5, 872–875, https://doi.org/10.1038/ngeo1627, 2012. a
Holmes, R. M., Zika, J. D., and England, M. H.: Diathermal Heat Transport in a Global Ocean Model, J. Phys. Oceanogr., 49, 141–161,
https://doi.org/10.1175/jpo-d-18-0098.1, 2019. a
Huang, B., Banzon, V. F., Freeman, E., Lawrimore, J., Liu, W., Peterson, T. C.,
Smith, T. M., Thorne, P. W., Woodruff, S. D., and Zhang, H.-M.: Extended
Reconstructed Sea Surface Temperature (ERSST), Version 4 [Annual and Global
Average], https://doi.org/10.7289/V5KD1VVF, 2019. a
Hunke, E. C.: Viscous–Plastic Sea Ice Dynamics with the EVP Model:
Linearization Issues, J. Comput. Phys., 170, 18–38,
https://doi.org/10.1006/jcph.2001.6710, 2001. a
Hunke, E. C.: Thickness sensitivities in the CICE sea ice model, Ocean
Modell., 34, 137–149, https://doi.org/10.1016/j.ocemod.2010.05.004, 2010. a
Hunke, E. C. and Dukowicz, J. K.: An Elastic–Viscous–Plastic Model for Sea
Ice Dynamics, J. Phys. Oceanogr., 27, 1849–1867,
https://doi.org/10.1175/1520-0485(1997)027<1849:aevpmf>2.0.co;2,
1997. a
Hunke, E. C. and Dukowicz, J. K.: The Elastic–Viscous–Plastic Sea Ice
Dynamics Model in General Orthogonal Curvilinear Coordinates on a
Sphere – Incorporation of Metric Terms, Mon. Weather Rev., 130,
1848–1865, https://doi.org/10.1175/1520-0493(2002)130<1848:tevpsi>2.0.co;2,
2002. a, b
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.:
CICE: the Los Alamos Sea Ice Model Documentation and Software User's
Manual Version 5.1, Tech. Rep. LA-CC-06-012, Los Alamos National Laboratory,
Los Alamos NM 87545,
available at: http://oceans11.lanl.gov/trac/CICE/attachment/wiki/WikiStart/cicedoc.pdf?format=raw (last access: 21 January 2020),
2015. a
Hutchings, J. K., Heil, P., and Hibler, W. D.: Modeling Linear Kinematic
Features in Sea Ice, Mon. Weather Rev., 133, 3481–3497,
https://doi.org/10.1175/mwr3045.1,
2005. a
Hutter, N., Losch, M., and Menemenlis, D.: Scaling Properties of Arctic Sea
Ice Deformation in a High-Resolution Viscous-Plastic Sea Ice Model and in
Satellite Observations, J. Geophys. Res.-Oceans, 123,
672–687, https://doi.org/10.1002/2017jc013119, 2018. a
Hyder, P., Edwards, J. M., Allan, R. P., Hewitt, H. T., Bracegirdle, T. J.,
Gregory, J. M., Wood, R. A., Meijers, A. J. S., Mulcahy, J., Field, P.,
Furtado, K., Bodas-Salcedo, A., Williams, K. D., Copsey, D., Josey, S. A.,
Liu, C., Roberts, C. D., Sanchez, C., Ridley, J., Thorpe, L., Hardiman,
S. C., Mayer, M., Berry, D. I., and Belcher, S. E.: Critical Southern Ocean
climate model biases traced to atmospheric model cloud errors, Nat.
Commun., 9, 3625, https://doi.org/10.1038/s41467-018-05634-2, 2018. a, b, c
Ivanova, N., Pedersen, L. T., Tonboe, R. T., Kern, S., Heygster, G., Lavergne, T., Sørensen, A., Saldo, R., Dybkjær, G., Brucker, L., and Shokr, M.: Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations, The Cryosphere, 9, 1797–1817, https://doi.org/10.5194/tc-9-1797-2015, 2015. a
Izumo, T., Montégut, C. B., Luo, J.-J., Behera, S. K., Masson, S., and
Yamagata, T.: The Role of the Western Arabian Sea Upwelling in Indian
Monsoon Rainfall Variability, J. Climate, 21, 5603–5623,
https://doi.org/10.1175/2008JCLI2158.1, 2008. a
Jackett, D. R., McDougall, T. J., Feistel, R., Wright, D. G., and Griffies,
S. M.: Algorithms for Density, Potential Temperature, Conservative
Temperature, and the Freezing Temperature of Seawater, J. Atmos.
Ocean. Technol., 23, 1709–1728, https://doi.org/10.1175/jtech1946.1, 2006. a
Jochum, M.: Impact of latitudinal variations in vertical diffusivity on climate
simulations, J. Geophys. Res.-Oceans, 114, C01010,
https://doi.org/10.1029/2008JC005030, 2009. a
Johns, W. E., Lee, T. N., Zhang, D., Zantopp, R., Liu, C.-T., and Yang, Y.: The
Kuroshio East of Taiwan: Moored Transport Observations from the WOCE
PCM-1 Array, J. Phys. Oceanogr., 31, 1031–1053,
https://doi.org/10.1175/1520-0485(2001)031<1031:tkeotm>2.0.co;2,
2001. a
Johnson, G. C., Sloyan, B. M., Kessler, W. S., and McTaggart, K. E.: Direct
measurements of upper ocean currents and water properties across the tropical
Pacific during the 1990s, Prog. Oceanogr., 52, 31–61,
https://doi.org/10.1016/s0079-6611(02)00021-6, 2002. a, b
Kawabe, M.: Variations of Current Path, Velocity, and Volume Transport of
the Kuroshio in Relation with the Large Meander, J. Phys.
Oceanogr., 25, 3103–3117,
https://doi.org/10.1175/1520-0485(1995)025<3103:VOCPVA>2.0.CO;2, 1995. a
Khoei, A. R. and Gharehbaghi, S. A.: The Superconvergence Patch Recovery
Technique and Data Transfer Operators in 3D Plasticity Problems, Finite Elem.
Anal. Des., 43, 630–648, https://doi.org/10.1016/j.finel.2007.01.002, 2007. a
Kimmritz, M., Danilov, S., and Losch, M.: On the convergence of the modified
elastic–viscous–plastic method for solving the sea ice momentum equation,
J. Comput. Phys., 296, 90–100,
https://doi.org/10.1016/j.jcp.2015.04.051, 2015. a
Kimmritz, M., Losch, M., and Danilov, S.: A comparison of viscous-plastic sea
ice solvers with and without replacement pressure, Ocean Modell., 115,
59–69, https://doi.org/10.1016/j.ocemod.2017.05.006, 2017. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., and Al., E.: The JRA-55
Reanalysis: General Specifications and Basic Characteristics, J.
Meteorol. Soc. Japan. Ser. II, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015. a
Kritsikis, E., Aechtner, M., Meurdesoif, Y., and Dubos, T.: Conservative interpolation between general spherical meshes, Geosci. Model Dev., 10, 425–431, https://doi.org/10.5194/gmd-10-425-2017, 2017. a
Large, W. G. and Yeager, S.: Diurnal to decadal global forcing for ocean and
sea-ice models: The data sets and flux climatologies, Technical Note
NCAR/TN-460+STR, NCAR, https://doi.org/10.5065/D6KK98Q6, 2004. a, b, c
Large, W. G. and Yeager, S. G.: 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, b
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, https://doi.org/10.1029/94RG01872, 1994. a
Large, W. G., Danabasoglu, G., McWilliams, J. C., Gent, P. R., and Bryan,
F. O.: Equatorial Circulation of a Global Ocean Climate Model with
Anisotropic Horizontal Viscosity, J. Phys. Oceanogr., 31,
518–536, https://doi.org/10.1175/1520-0485(2001)031<0518:ECOAGO>2.0.CO;2, 2001. a
Laurindo, L. C., Mariano, A. J., and Lumpkin, R.: An improved near-surface
velocity climatology for the global ocean from drifter observations, Deep Sea
Res. Part I, 124, 73–92,
https://doi.org/10.1016/j.dsr.2017.04.009, 2017. a
Lee, H.-C., Rosati, A., and Spelman, M. J.: Barotropic tidal mixing effects in a coupled climate model: Oceanic conditions in the Northern Atlantic, Ocean Modell., 11, 464–477, https://doi.org/10.1016/j.ocemod.2005.03.003, 2006. a
Lemieux, J.-F., Knoll, D. A., Tremblay, B., Holland, D. M., and Losch, M.: A
comparison of the Jacobian-free Newton–Krylov method and the EVP model
for solving the sea ice momentum equation with a viscous-plastic formulation:
A serial algorithm study, J. Comput. Phys., 231, 5926–5944,
https://doi.org/10.1016/j.jcp.2012.05.024, 2012. a
Lemieux, J.-F., Beaudoin, C., Dupont, F., Roy, F., Smith, G. C., Shlyaeva, A.,
Buehner, M., Caya, A., Chen, J., Carrieres, T., Pogson, L., DeRepentigny, P., Plante, A., Pestieau, P., Pellerin, P., Ritchie, H., Garric, G., and Ferry N.: The Regional Ice
Prediction System (RIPS): verification of forecast sea ice concentration,
Q. J. Roy. Meteorol. Soc., 142, 632–643,
https://doi.org/10.1002/qj.2526, 2015. a
Li, Y. and Han, W.: Decadal Sea Level Variations in the Indian Ocean
Investigated with HYCOM: Roles of Climate Modes, Ocean Internal
Variability, and Stochastic Wind Forcing, J. Climate, 28, 9143–9165,
https://doi.org/10.1175/JCLI-D-15-0252.1, 2015. a
Lipscomb, W. H. and Hunke, E. C.: Modeling Sea Ice Transport Using Incremental
Remapping, Mon. Weather Rev., 132, 1341–1354,
https://doi.org/10.1175/1520-0493(2004)132<1341:msitui>2.0.co;2,
2004. a
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.: Ridging,
strength, and stability in high-resolution sea ice models, J.
Geophys. Res., 112, C03S91, https://doi.org/10.1029/2005jc003355, 2007. a
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E.,
Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R.,
Hamilton, M., and Seidov, D.: World Ocean Atlas 2013, Volume 1: Temperature,
NOAA Atlas NESDIS 73, 2013. a
Losch, M. and Danilov, S.: On solving the momentum equations of dynamic sea ice
models with implicit solvers and the elastic–viscous–plastic technique,
Ocean Modell., 41, 42–52, https://doi.org/10.1016/j.ocemod.2011.10.002, 2012. a
Lumpkin, R. and Speer, K.: Global Ocean Meridional Overturning, J.
Phys. Oceanogr., 37, 2550–2562, 2007. a
Manizza, M., Le Quéré, C., Watson, A. J., and Buitenhuis, E. T.:
Bio-optical feedbacks among phytoplankton, upper ocean physics and sea-ice in
a global model, Geophys. Res. Lett., 32, L05603, https://doi.org/10.1029/2004gl020778, 2005. a
Marshall, J. and Speer, K.: Closure of the meridional overturning circulation
through Southern Ocean upwelling, Nat. Geosci., 5, 171–180, 2012. a
McCarthy, G., Smeed, D., Johns, W., Frajka-Williams, E., Moat, B., Rayner, D.,
Baringer, M., Meinen, C., Collins, J., and Bryden, H.: Measuring the
Atlantic Meridional Overturning Circulation at 26∘ N, Prog.
Oceanogr., 130, 91–111, https://doi.org/10.1016/j.pocean.2014.10.006, 2015. a, b
McDougall, T. J. and McIntosh, P. C.: The Temporal-Residual-Mean Velocity.
Part II: Isopycnal Interpretation and the Tracer and Momentum Equations,
J. Phys. Oceanogr., 31, 1222–1246,
https://doi.org/10.1175/1520-0485(2001)031<1222:ttrmvp>2.0.co;2,
2001. a
Meier, W., Fetterer, F., Savoie, M., Mallory, S., Duerr, R., and Stroeve, J.:
NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration,
Version 3, Tech. rep., NSIDC: National Snow and Ice Data Center, Boulder,
Colorado USA, https://doi.org/10.7265/N59P2ZTG, 2017. a
Meier, W. N., Peng, G., Scott, D. J., and Savoie, M. H.: Verification of a new
NOAA/NSIDC passive microwave sea-ice concentration climate record, Polar
Res., 33, 21004, https://doi.org/10.3402/polar.v33.21004, 2014. a
Meinen, C. S., Baringer, M. O., and Garcia, R. F.: Florida Current transport
variability: An analysis of annual and longer-period signals, Deep Sea
Res. Part I, 57, 835–846,
https://doi.org/10.1016/j.dsr.2010.04.001, 2010. a
Mu, D., Yan, H., and Feng, W.: Assessment of sea level variability
derived by EOF reconstruction, Geophys. J. Int., 214,
79–87, https://doi.org/10.1093/gji/ggy126, 2018. a
Murray, R. J.: Explicit Generation of Orthogonal Grids for Ocean Models,
J. Comput. Phys., 126, 251–273,
https://doi.org/10.1006/jcph.1996.0136, 1996. a
Oke, P. R., Griffin, D. A., Schiller, A., Matear, R. J., Fiedler, R., Mansbridge, J., Lenton, A., Cahill, M., Chamberlain, M. A., and Ridgway, K.: Evaluation of a near-global eddy-resolving ocean model, Geosci. Model Dev., 6, 591–615, https://doi.org/10.5194/gmd-6-591-2013, 2013. a
Pacanowski, R. C. and Gnanadesikan, A.: Transient Response in a Z-Level Ocean
Model That Resolves Topography with Partial Cells, Monthly Weather Review,
126, 3248–3270, https://doi.org/10.1175/1520-0493(1998)126<3248:triazl>2.0.co;2,
1998. 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
Potemra, J. T. and Lukas, R.: Seasonal to interannual modes of sea level
variability in the western Pacific and eastern Indian oceans, Geophys.
Res. Lett., 26, 365–368, https://doi.org/10.1029/1998GL900280, 1999. a
Pringle, D. J., Eicken, H., Trodahl, H. J., and Backstrom, L. G. E.: Thermal
conductivity of landfast Antarctic and Arctic sea ice, J.
Geophys. Res., 112, C04017, https://doi.org/10.1029/2006jc003641, 2007. a
Redi, M. H.: Oceanic Isopycnal Mixing by Coordinate Rotation, J. Phys.
Oceanogr., 12, 1154–1158,
https://doi.org/10.1175/1520-0485(1982)012<1154:OIMBCR>2.0.CO;2, 1982. a, b
Renault, L., Molemaker, M. J., McWilliams, J. C., Shchepetkin, A. F.,
Lemarié, F., Chelton, D., Illig, S., and Hall, A.: Modulation of Wind
Work by Oceanic Current Interaction with the Atmosphere, J. Phys.
Oceanogr., 46, 1685–1704, https://doi.org/10.1175/JPO-D-15-0232.1, 2016. a, b
Ridgway, K. R. and Dunn, J. R.: Mesoscale structure of the mean East
Australian Current System and its relationship with topography,
Prog. Oceanogr., 56, 189–222,
https://doi.org/10.1016/S0079-6611(03)00004-1, 2003. a
Rio, M. H., Guinehut, S., and Larnicol, G.: New CNES-CLS09 global mean
dynamic topography computed from the combination of GRACE data, altimetry,
and in situ measurements, J. Geophys. Res.-Oceans, 116, C07018,
https://doi.org/10.1029/2010JC006505, 2011. a
Rossby, T.: The North Atlantic Current and surrounding waters: At the
crossroads, Rev. Geophys., 34, 463–481, 1996. a
Sallée, J. B., Shuckburgh, E., Bruneau, N., Meijers, A. J., Bracegirdle,
T. J., and Wang, Z.: Assessment of Southern Ocean mixed-layer depths in
CMIP5 models: Historical bias and forcing response, J. Geophys. Res.-Ocean.,
118, 1845–1862, https://doi.org/10.1002/jgrc.20157, 2013. a
Schlosser, E., Haumann, F. A., and Raphael, M. N.: Atmospheric influences on the anomalous 2016 Antarctic sea ice decay, The Cryosphere, 12, 1103–1119, https://doi.org/10.5194/tc-12-1103-2018, 2018. a
Schmidt, M.: A benchmark for the parallel code used in FMS and MOM-4, Ocean
Modell., 17, 49–67, https://doi.org/10.1016/j.ocemod.2006.11.002, 2007. a
Sen Gupta, A., Muir, L. C., Brown, J. N., Phipps, S. J., Durack, P. J.,
Monselesan, D., and Wijffels, S. E.: Climate Drift in the CMIP3 Models,
J. Climate, 25, 4621–4640, https://doi.org/10.1175/JCLI-D-11-00312.1,
2013. a, b
Shirasawa, K. and Ingram, R. G.: Currents and turbulent fluxes under the
first-year sea ice in Resolute Passage, Northwest Territories, Canada,
J. Mar. Syst., 11, 21–32, https://doi.org/10.1016/s0924-7963(96)00024-3, 1997. a
Simmons, H. L., Jayne, S. R., Laurent, L. C. S., and Weaver, A. J.: Tidally
driven mixing in a numerical model of the ocean general circulation, Ocean
Model., 6, 245–263, https://doi.org/10.1016/S1463-5003(03)00011-8, 2004. a
Sloyan, B. M. and Rintoul, S. R.: The Southern Ocean limb of the global deep
overturning circulation, J. Phys. Oceanogr., 31, 143–173, 2001. a
Sloyan, B. M., Ridgway, K. R., and Cowley, R.: The East Australian Current
and property transport at 27 S from 2012 to 2013, J. Phys.
Oceanogr., 46, 993–1008, 2016. a
Sprintall, J., Wijffels, S. E., Molcard, R., and Jaya, I.: Direct estimates of
the Indonesian Throughflow entering the Indian Ocean: 2004–2006, J.
Geophys. Res., 114, C07001, https://doi.org/10.1029/2008JC005257, 2009. a, b
Stacey, M. W., Pond, S., and Nowak, Z. P.: A Numerical Model of the Circulation
in Knight Inlet, British Columbia, Canada, J. Phys.
Oceanogr., 25, 1037–1062, 1995. a
Stewart, K., Hogg, A. McC., Griffies, S., Heerdegen, A., Ward, M., Spence, P., and
England, M.: Vertical resolution of baroclinic modes in global ocean models,
Ocean Modell., 113, 50–65, https://doi.org/10.1016/j.ocemod.2017.03.012, 2017. a, b
Stewart, K. D., Kim, W., Urakawa, S., Hogg, A. McC., Yeager, S., Tsujino, H.,
Nakano, H., Kiss, A. E., and Danabasoglu, G.: JRA55-based Repeat Year
Forcing datasets for driving ocean-sea-ice models, Ocean Modell., 147, 101557, https://doi.org/10.1016/j.ocemod.2019.101557, 2020. a, b
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae, J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, 2018. a, b
Stössel, A., Zhang, Z., and Vihma, T.: The effect of alternative real-time
wind forcing on Southern Ocean sea ice simulations, J. Geophys.
Res., 116, C11021, https://doi.org/10.1029/2011jc007328, 2011. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all
seasons, Environ. Res. Lett., 13, 103001,
https://doi.org/10.1088/1748-9326/aade56, 2018. a
Stroeve, J. C., Markus, T., Boisvert, L., Miller, J., and Barrett, A.: Changes
in Arctic melt season and implications for sea ice loss, Geophys.
Res. Lett., 41, 1216–1225, https://doi.org/10.1002/2013gl058951, 2014. a
Suresh, A. and Huynh, H.: Accurate Monotonicity-Preserving Schemes with
Runge-Kutta Time Stepping, J. Comput. Phys., 136, 83–99,
https://doi.org/10.1006/jcph.1997.5745, 1997. a
Suzuki, T., Yamazaki, D., Tsujino, H., Komuro, Y., Nakano, H., and Urakawa, S.:
A dataset of continental river discharge based on JRA-55 for use in a
global ocean circulation model, J. Oceanogr., 74, 421–429,
https://doi.org/10.1007/s10872-017-0458-5, 2018. a
Sweeney, C., Gnanadesikan, A., Griffies, S. M., Harrison, M. J., Rosati, A. J.,
and Samuels, B. L.: Impacts of Shortwave Penetration Depth on Large-Scale
Ocean Circulation and Heat Transport, J. Phys. Oceanogr., 35, 1103–1119,
https://doi.org/10.1175/JPO2740.1, 2005. a
Taboada, F. G., Stock, C. A., Griffies, S. M., Dunne, J., John, J. G., Small,
R. J., and Tsujino, H.: Surface winds from atmospheric reanalysis lead to
contrasting oceanic forcing and coastal upwelling patterns, Ocean Modell.,
133, 79–111, https://doi.org/10.1016/j.ocemod.2018.11.003, 2019. a, b
Talley, L. D.: Closure of the Global Overturning Circulation Through the
Indian, Pacific, and Southern Oceans: Schematics and Transports,
Oceanography, 26, 80–97, https://doi.org/10.5670/oceanog.2013.07, 2013. a
Taschetto, A. S., Sen Gupta, A., Hendon, H. H., Ummenhofer, C. C., and England, M. H.: The contribution of Indian Ocean sea surface temperature anomalies on Australian summer rainfall during EL Niño events, J. Climate, 24, 3734–3747, https://doi.org/10.1175/2011JCLI3885.1, 2011. a
Thompson, A. F., Stewart, A. L., and Bischoff, T.: A Multibasin Residual-Mean
Model for the Global Overturning Circulation, J. Phys.
Oceanogr., 46, 2583–2604, https://doi.org/10.1175/JPO-D-15-0204.1, 2016. a
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness
distribution of sea ice, J. Geophys. Res., 80, 4501–4513,
https://doi.org/10.1029/jc080i033p04501, 1975. a
Trenberth, K. E. and Caron, J. M.: Estimates of Meridional Atmosphere and Ocean
Heat Transports, J. Climate, 14, 3433–3443,
https://doi.org/10.1175/1520-0442(2001)014<3433:EOMAAO>2.0.CO;2, 2001. a
Tseng, Y.-H., Lin, H., Chen, H.-C., Thompson, K., Bentsen, M., W. Böning, C., Bozec, A., Cassou, C., Chassignet, E., Chow, C. H., Danabasoglu, G., Danilov, S., Farneti, R., Fogli, P. G., Fujii, Y., Griffies, S., Ilicak, M., Jung, T., Masina, S., Navarra, A., Patara, L., Samuels, B. L., Scheinert, M., Sidorenko, D., Sui, C.-H., Tsujino, H., Valcke, S., Voldoire, A., Wang, Q., and Yeager, S.: North and Equatorial Pacific Ocean
Circulation in the CORE-II Hindcast Simulations, Ocean Modell., 104, 143–170,
https://doi.org/10.1016/j.ocemod.2016.06.003, 2016. a, b
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 Modell., 130, 79–139, https://doi.org/10.1016/j.ocemod.2018.07.002, 2018. a, b, c, d, e, f, g, h, i
Turner, A. K., Hunke, E. C., and Bitz, C. M.: Two modes of sea-ice gravity
drainage: A parameterization for large-scale modeling, J. Geophys.
Res.-Oceans, 118, 2279–2294, https://doi.org/10.1002/jgrc.20171, 2013. a
Turner, J. and Comiso, J.: Solve Antarctica's sea-ice puzzle, Nature, 547,
275–277, https://doi.org/10.1038/547275a, 2017. a
Valcke, S., Craig, T., and Coquart, L.: OASIS3-MCT User Guide: OASIS3-MCT
2.0, Cerfacs/cnrs suc ura no1875, cerfacs tr/cmgc/13/17, CERFACS/CNRS,
available at: http://www.cerfacs.fr/oa4web/oasis3-mct/oasis3mct_UserGuide.pdf (last access: 21 January 2020),
2013.
a
Valdivieso, M., Haines, K., Balmaseda, M., Chang, Y.-S., Drevillon, M., Ferry,
N., Fujii, Y., Köhl, A., Storto, A., Toyoda, T., Wang, X., Waters, J.,
Xue, Y., Yin, Y., Barnier, B., Hernandez, F., Kumar, A., Lee, T., Masina, S.,
and Andrew Peterson, K.: An assessment of air–sea heat fluxes from ocean and
coupled reanalyses, Clim. Dynam., 49, 983–1008,
https://doi.org/10.1007/s00382-015-2843-3, 2017. 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 Modell., 99,
110–132, https://doi.org/10.1016/j.ocemod.2015.12.008, 2016. a
Wenegrat, J. O., Thomas, L. N., Gula, J., and McWilliams, J. C.: Effects of the
Submesoscale on the Potential Vorticity Budget of Ocean Mode Waters, J. Phys. Oceanogr., 48, 2141–2165, https://doi.org/10.1175/jpo-d-17-0219.1,
2018. a
Xie, S. P., Annamalai, H., Schott, F. A., and McCreary, J. P.: Structure and
mechanisms of South Indian Ocean climate variability, J. Climate,
15, 864–878, https://doi.org/10.1175/1520-0442(2002)015<0864:SAMOSI>2.0.CO;2, 2002. a, b
Yokoi, T., Tozuka, T., and Yamagata, T.: Seasonal variation of the Seychelles
Dome, J. Climate, 21, 3740–3754, https://doi.org/10.1175/2008JCLI1957.1,
2008. 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, R., Delworth, T. L., Rosati, A., Anderson, W. G., Dixon, K. W., Lee,
H. C., and Zeng, F.: Sensitivity of the North Atlantic Ocean Circulation to
an abrupt change in the Nordic Sea overflow in a high resolution global
coupled climate model, J. Geophys. Res.-Oceans, 116, 1–14,
https://doi.org/10.1029/2011JC007240, 2011. a, b
Zhang, Z., Vihma, T., Stössel, A., and Uotila, P.: The role of wind forcing
from operational analyses for the model representation of Antarctic coastal
sea ice, Ocean Modell., 94, 95–111, https://doi.org/10.1016/j.ocemod.2015.07.019,
2015. a
Zweng, M., Reagan, J., Antonov, J., Locarnini, R., Mishonov, A., Boyer, T., Garcia, H., Baranova, O., Johnson, D., Seidov, D., and Biddle, M.: World Ocean
Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS 74, 2013. a
Short summary
We describe new computer model configurations which simulate the global ocean and sea ice at three resolutions. The coarsest resolution is suitable for multi-century climate projection experiments, whereas the finest resolution is designed for more detailed studies over time spans of decades. The paper provides technical details of the model configurations and an assessment of their performance relative to observations.
We describe new computer model configurations which simulate the global ocean and sea ice at...
