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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
OpenBench: a land models evaluation system
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
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)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Evaluation of Ozone and its Precursors using the Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) during the Michigan-Ontario Ozone Source Experiment (MOOSE)
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
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
Statistical summaries for streamed data from climate simulations: One-pass algorithms (v0.6.2)
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025, https://doi.org/10.5194/gmd-18-4399-2025, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
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This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
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This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
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This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
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Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with vital physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
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Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
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We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
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As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
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We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
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NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
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We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
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The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
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PaleoSTeHM v1.0 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.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here 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. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new 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. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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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 to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. 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.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
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Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is 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.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
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We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
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
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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
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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.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
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Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Noribeth Mariscal, Louisa K. Emmons, Duseong S. Jo, Ying Xiong, Laura M. Judd, Scott J. Janz, Jiajue Chai, and Yaoxian Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-228, https://doi.org/10.5194/egusphere-2025-228, 2025
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The distribution of ozone (O3) and its precursors (NOx, VOCs) is explored using the chemistry-climate model, MUSICAv0, and evaluated using measurements from the Michigan-Ontario Ozone Source Experiment. A custom grid of ~7 km was created over Michigan. A sector-based diurnal cycle for anthropogenic nitric oxide was included in the model. This work shows that grid resolution played a more important role for O3 precursors, and the diurnal cycle significantly impacted nighttime O3 formation.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
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We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
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
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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
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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
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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.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
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To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
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...
