Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7449-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7449-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Oct 2022
Development and technical paper |
| 07 Oct 2022
| 07 Oct 2022
The Moist Quasi-Geostrophic Coupled Model: MQ-GCM 2.0
Sergey Kravtsov
Department of Mathematical Sciences, University of
Wisconsin–Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USA
Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow,
117218, Russia
Institute of Applied Physics, Russian Academy of Sciences, Nizhny
Novgorod, 603155, Russia
Ilijana Mastilovic
CORRESPONDING AUTHOR
Department of Mathematical Sciences, University of
Wisconsin–Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USA
Andrew McC. Hogg
Research School of Earth Sciences and ARC Centre of Excellence for
Climate Extremes, Australian National University, Canberra, Australia
William K. Dewar
Department of Earth, Ocean and Atmospheric Science, Florida State
University, Tallahassee, FL 32304, USA
Laboratoire de Glaciologie et Geophysique de l'Environnement, CNRS,
Grenoble, France
Jeffrey R. Blundell
Ocean and Earth Science, National Oceanography Centre Southampton,
University of Southampton, Southampton SO14 3ZH, United Kingdom
Related authors
S. Kravtsov, N. Sugiyama, and A. A. Tsonis
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npgd-1-1905-2014, https://doi.org/10.5194/npgd-1-1905-2014, 2014
Preprint withdrawn
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We studied transient behavior in numerical simulations of the three-variable Lorenz model initialized far away from the region of its asymptotic attractor. These transients were shown to have a range of durations, with the longest transients corresponding to the trajectories having largest average Lyapunov exponents and complex routes emulating sensitivity to initial conditions, as well as exhibiting the “ghost” attractors akin to their asymptotic siblings.
Wilma G. C. Huneke, Andy McC. Hogg, Martin Dix, Daohua Bi, Arnold Sullivan, Shayne McGregor, Chiara Holgate, Siobhan P. O'Farrell, and Micael J. T. Oliveira
EGUsphere, https://doi.org/10.5194/egusphere-2025-1006, https://doi.org/10.5194/egusphere-2025-1006, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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A new configuration of the Australian Community Climate and Earth System Simulator coupled model, ACCESS-CM2, with a higher resolution ocean-sea ice component is introduced. The new version of the coupled climate model was designed to better capture smaller-scale ocean motions. While this configuration improves the representation of many aspects of the climate system, some biases from the existing lower-resolution version persist.
Claire K. Yung, Madelaine G. Rosevear, Adele K. Morrison, Andrew McC Hogg, and Yoshihiro Nakayama
EGUsphere, https://doi.org/10.5194/egusphere-2024-3513, https://doi.org/10.5194/egusphere-2024-3513, 2024
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Ocean models are used to understand how the ocean interacts with the Antarctic Ice Sheet, but they are too coarse in resolution to capture the small-scale ocean processes driving melting and require a parameterisation to predict melt. Previous parameterisations ignore key processes occurring in some regions of Antarctica. We develop a parameterisation with the feedback of stratification on melting and test it in idealised and regional ocean models, finding changes to melt rate and circulation.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
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.
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, https://doi.org/10.5194/gmd-15-5829-2022, 2022
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Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
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.
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.
Andrew E. Kiss, Andrew McC. Hogg, Nicholas Hannah, Fabio Boeira Dias, Gary B. Brassington, Matthew A. Chamberlain, Christopher Chapman, Peter Dobrohotoff, Catia M. Domingues, Earl R. Duran, Matthew H. England, Russell Fiedler, Stephen M. Griffies, Aidan Heerdegen, Petra Heil, Ryan M. Holmes, Andreas Klocker, Simon J. Marsland, Adele K. Morrison, James Munroe, Maxim Nikurashin, Peter R. Oke, Gabriela S. Pilo, Océane Richet, Abhishek Savita, Paul Spence, Kial D. Stewart, Marshall L. Ward, Fanghua Wu, and Xihan Zhang
Geosci. Model Dev., 13, 401–442, https://doi.org/10.5194/gmd-13-401-2020, https://doi.org/10.5194/gmd-13-401-2020, 2020
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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.
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.
S. Kravtsov, N. Sugiyama, and A. A. Tsonis
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npgd-1-1905-2014, https://doi.org/10.5194/npgd-1-1905-2014, 2014
Preprint withdrawn
Short summary
Short summary
We studied transient behavior in numerical simulations of the three-variable Lorenz model initialized far away from the region of its asymptotic attractor. These transients were shown to have a range of durations, with the longest transients corresponding to the trajectories having largest average Lyapunov exponents and complex routes emulating sensitivity to initial conditions, as well as exhibiting the “ghost” attractors akin to their asymptotic siblings.
Related subject area
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Yong-He Liu and Zong-Liang Yang
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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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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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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.
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.
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.
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 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, among others, 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.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
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. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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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.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Sian Megan Chilcott and Malte Meinshausen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-203, https://doi.org/10.5194/gmd-2024-203, 2025
Revised manuscript accepted for GMD
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Climate models are expensive to run and often underestimate how sensitive Arctic sea ice is to climate change. To address this, we developed a simple model that emulates the response of sea ice to global warming. We find the remaining carbon dioxide (CO2) emissions that will avoid a seasonally ice-free Arctic Ocean is lower than previous estimates of 821 Gigatonnes of CO2. Our model also provides insights into the future of winter sea ice, examining a larger ensemble than previously possible.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
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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 key 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 irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Revised manuscript accepted for GMD
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We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Cited articles
Barsugli, J. J. and Battisti, D. S.: The basic effects of atmosphere–ocean
thermal coupling on midlatitude variability, J. Atmos. Sci., 55, 477–493,
https://doi.org/10.1175/1520-0469(1998)055<0477:TBEOAO>2.0.CO;2, 1998.
Berloff, P. and McWilliams, J.: Large-scale low-frequency variability in
wind-driven ocean gyres, J. Phys. Oceanogr., 29, 1925–1949, 1999.
Berloff, P., Hogg, A., and Dewar, W.: The turbulent oscillator: A mechanism
of low-frequency variability of wind-driven ocean gyres, J. Phys. Oceanogr.,
37, 2363–2386, 2007.
Bolton, D.: The computation of equivalent potential temperature, Mon. Weather
Rev., 108, 1046–1053, 1980.
Brachet, S., Codron, F., Feliks, Y., Ghil, M., Le Treut, H., and Simonnet,
E.: Atmospheric circulations induced by a midlatitude SST front: a GCM
study, J. Climate, 25, 1847–1853, 2012.
Bryan, F. O., Tomas, R., Dennis, J. M., Chelton, D. B., Loeb, N. G., and
McClean, J. L.: Frontal scale air–sea interaction in high-resolution
coupled climate models, J. Climate, 23, 6277–6291,
https://doi.org/10.1175/2010JCLI3665.1, 2010.
Chelton D.: Ocean–atmosphere coupling: Mesoscale eddy effects, Nat.
Geosci., 6, 594–595, 2013.
Chelton, D. and Xie, S.-P.: Coupled ocean-atmosphere interaction at oceanic
mesoscales, Oceanography, 23, 52–69, https://doi.org/10.5670/oceanog.2010.05, 2010.
Czaja, A. and Blunt, N.: A new mechanism for ocean–atmosphere coupling in
midlatitudes, Q. J. Roy. Meteor. Soc., 137, 1095–1101, 2011.
Czaja, A. and Marshall, J.: Observations of atmosphere–ocean coupling in
the North Atlantic, Q. J. Roy. Meteor. Soc., 127, 1893–1916, 2001.
Deremble, B., Lapeyre, G., and Ghil, M.: Atmospheric Dynamics Triggered by
an Oceanic SST Front in a Moist Quasigeostrophic Model, J. Atmos. Sci., 69,
1617–1632, https://doi.org/10.1175/JAS-D-11-0288.1, 2012.
Deremble, B., Wienders, N., and Dewar, W. K.: Cheapaml: a simple atmospheric
boundary layer model for use in ocean-only calculations, Mon. Weather Rev., 141, 12, https://doi.org/10.1175/MWR-D-11-00254.1, 2013.
Deser, C. and Blackmon, M. L.: Surface climate variations over the North
Atlantic Ocean during winter: 1900–1989, J. Climate, 6, 1743–1753, 1993.
Dewar, W. and Flierl, G.: Some effects of the wind on rings, J. Phys.
Oceanogr., 17, 1653–1667, https://doi.org/10.1175/1520-0485(1987)017<1653:SEOTWO>2.0.CO;2, 1987.
Fan, M. and Schneider, E. K.: Observed Decadal North Atlantic Tripole SST
Variability. Part I: Weather Noise Forcing and Coupled Response, J. Atmos.
Sci., 69, 35–50, https://doi.org/10.1175/JAS-D-11-018.1, 2012.
Feliks, Y., Ghil, M., and Simonnet, E.: Low-frequency variability in the
midlatitude atmosphere induced by an oceanic thermal front, J. Atmos. Sci.,
61, 961, https://doi.org/10.1175/1520-0469(2004)061<0961:LVITMA>2.0.CO;2, 2004.
Feliks, Y., Ghil, M., and Simonnet, E.: Low-frequency variability in the
midlatitude baroclinic atmosphere induced by an oceanic thermal front, J.
Atmos. Sci., 64, 97–116, 2007.
Feliks, Y., Ghil, M., and Robertson, A. W.: The atmospheric circulation over
the North Atlantic as induced by the SST field, J. Climate, 24, 522–542,
https://doi.org/10.1175/2010JCLI3859.1, 2011.
Foussard, A., Lapeyre, G., and Plougonven, R.: Storm Track Response to
Oceanic Eddies in Idealized Atmospheric Simulations, J. Climate, 32,
445–463, https://doi.org/10.1175/JCLI-D-18-0415.1, 2019.
Frankignoul, C.: Sea surface temperature anomalies, planetary waves, and
air-sea feedback in the middle latitudes, Rev. Geophys., 23, 357–390,
https://doi.org/10.1029/RG023i004p00357, 1985.
Frankignoul, C. and Hasselmann, K.: Stochastic climate models, part II:
Application to sea-surface temperature anomalies and thermocline
variability, Tellus, 29A, 289–305, https://doi.org/10.1111/j.2153-3490.1977.tb00740.x,
1977.
Frenger, I., Gruber, N., Knutti, R., and Munnich, M.: Imprint of Southern
Ocean eddies on winds, clouds and rainfall, Nat. Geosci., 6, 608–612,
https://doi.org/10.1038/ngeo1863, 2013.
Gaube, P., Chelton, D. B., Strutton, P. G., and Behrenfeld, M. J.: Satellite
observations of chlorophyll, phytoplankton biomass, and Ekman pumping in
nonlinear mesoscale eddies, J. Geophys. Res., 118, 6349–6370, 2013.
Gaube, P., Chelton, D. B., Samelson, R. M., Schlax, M. G., and O'Neill, L. W.:
Satellite observations of mesoscale eddy-induced Ekman pumping, J. Phys.
Oceanogr., 45, 104–132, 2015.
Gill, A. E.: Atmosphere–Ocean Dynamics, Academic Press, 662 pp., ISBN 9780122835223,
eBook ISBN 9780080570525, 1982.
Hasselmann, K.: Stochastic climate models: Part I. Theory, Tellus, 28A,
473–485, https://doi.org/10.1111/j.2153-3490.1976.tb00696.x, 1976.
Hogg, A. M. and Blundell, J. R.: Interdecadal variability of the Southern
Ocean, J. Phys. Oceanogr., 36, 1626–1645, 2006.
Hogg, A. M., Dewar, W. K., Killworth, P. D., and Blundell, J. R.: A
quasigeostrophic coupled model (Q-GCM), Mon. Weather Rev., 131, 2261–2278,
2003.
Hogg, A. M., Killworth, P. D., Blundell, J. R., and Dewar, W. K.: Mechanisms
of decadal variability of the wind-driven ocean circulation, J. Phys.
Oceanogr., 35, 512–531, 2005.
Hogg, A. M., Dewar, W. K., Killworth, P. D., and Blundell, J. R.: Decadal
variability of the midlatitude climate system driven by the ocean
circulation, J. Climate, 19, 1149–1166, 2006.
Hogg, A. M., Killworth, P. D., Blundell, J. R., and Dewar, W. K.: Low
Frequency Ocean Variability: Feedbacks Between Eddies and the Mean Flow, in: Turbulence and Coherent Structures
in Fluids, Plasmas and Granular Flows, edited by:
Fredriksen, J. and Denier, J., 171–185, World Scientific, 2007.
Hogg, A. M., Meredith, M. P., Blundell, J. R., and Wilson, C.: Eddy heat
flux in the Southern Ocean: Response to variable wind forcing, J. Climate,
21, 608–620, 2008.
Hogg, A. M., Dewar, W. K., Berloff, P. S., Kravtsov, S., and Hutchinson, D.
K.: The effects of mesoscale ocean-atmosphere coupling on the large-scale
ocean circulation, J. Climate, 22, 4066–4082, 2009.
Hogg, A. M., Blundell, J. R., Dewar, W. K., and Killworth, P. D.:
Formulation and users' guide for Q-GCM, Version 1.5.0,
http://www.q-gcm.org/downloads/q-gcm-v1.5.0.pdf (last access: 10 May 2022), 2014.
Hutchinson, D. K., Hogg, A. McC., and Blundell, J. R.: Southern Ocean
response to relative velocity wind stress forcing, J. Phys. Oceanogr., 40,
326–339, https://doi.org/10.1175/ 2009JPO4240.1, 2010.
Kelly, K. A., Small, R. J., Samelson, R. M., Qiu, B., Joyce, T. M., Kwon, Y., and Cronin, M. F.: Western Boundary Currents and Frontal Air–Sea Interaction: Gulf Stream and Kuroshio Extension, J. Climate, 23, 5644–5667, https://doi.org/10.1175/2010JCLI3346.1, 2010.
Kravtsov, S., Robertson, A. W., and Ghil, M.: Bimodal behavior in the zonal
mean flow of a baroclinic-channel model, J. Atmos. Sci., 62, 1746–1769,
2005.
Kravtsov, S., Berloff, P., Dewar, W. K., Ghil, M., and McWilliams, J. C.:
Dynamical origin of low-frequency variability in a highly nonlinear
mid-latitude coupled model, J. Climate, 19, 6391–6408, 2006.
Kravtsov, S., Dewar, W. K., Berloff, P., McWilliams, J. C., and Ghil, M.: A
highly nonlinear coupled mode of decadal variability in a mid-latitude
ocean–atmosphere model, Dyn. Atmos.-Oceans, 43, 123–150,
https://doi.org/10.1016/j.dynatmoce.2006.08.001, 2007.
Kravtsov, S. K., Dewar, W. K., Ghil, M., Berloff, P. S., and McWilliams, J. C.: North Atlantic climate variability in coupled models and data, Nonlin. Processes Geophys., 15, 13–24, https://doi.org/10.5194/npg-15-13-2008, 2008.
Kravtsov, S., Kamenkovich, I., Hogg, A. M., and Peters, J. M.: On the
mechanisms of late 20th century sea-surface temperature trends over the
Antarctic Circumpolar Current, J. Geophys. Res.-Oceans, 116, C11034, https://doi.org/10.1029/2011JC007473, 2011.
Kravtsov, S., Mastilovic, I., Hogg, A., Dewar, W. K., and Blundell, J. R.:
MQ-GCM2.0 model, Zenodo [code], https://doi.org/10.5281/zenodo.4916720, 2021a.
Kravtsov, S., Mastilovic, I., Hogg, A. M., Dewar, W. K., Blundell, J. R., and Killworth, P.: MQ-GCM (v2.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.5250828, 2021b.
Kushnir, Y.: Interdecadal variations in North Atlantic sea surface
temperature and associated atmospheric circulation, J. Climate, 7, 141–157,
1994.
Kuwano-Yoshida, A., Minobe, S., and Xie, S.-P.: Precipitation response to
the Gulf Stream in an atmospheric GCM, J. Climate, 23, 3676–3698,
https://doi.org/10.1175/2010jcli3261.1, 2010.
Laîné, A., Lapeyre, G., and Rivière, G.: A Quasigeostrophic
Model for Moist Storm Tracks, J. Atmos. Sci., 68,
1306–1322, https://doi.org/10.1175/2011JAS3618.1, 2011.
Lindzen, R. S. and Nigam, S.: On the role of sea surface temperature
gradients in forcing low-level winds and convergence in the tropics, J. Atmos.
Sci., 44, 2418–2436, 1987.
Ma, X., Chang, P., Saravanan, R., Montuoro, R., Hsieh, J.-S., Wu, D., Lin,
X., Wu, L., and Jing, Z.: Distant influence of Kuroshio eddies on North
Pacific weather patterns?, Sci. Rep.-UK, 5, 17785, https://doi.org/10.1038/srep17785, 2015.
Ma, X., Chang, P., Saravanan, R., Montuoro, R., Nakamura, H., Wu, D., Lin,
X., and Wu, L.: Importance of Resolving Kuroshio Front and Eddy Influence in
Simulating the North Pacific Storm Track, J. Climate, 30, 1861–1880, https://doi.org/10.1175/JCLI-D-16-0154.1, 2017.
Maloney, E. D. and Chelton, D. B.: An assessment of sea surface temperature
influence on surface winds in numerical weather prediction and climate
models. J. Climate, 19, 2743–2762, 2006.
Manabe, S. and Strickler, R. F.: Thermal Equilibrium of the Atmosphere with
a Convective Adjustment, J. Atmos. Sci., 21,
361–385, https://doi.org/10.1175/1520-0469(1964)021<0361:TEOTAW>2.0.CO;2, 1964.
Manabe, S. and Wetherald, R. T.: Thermal Equilibrium of the Atmosphere with
a Given Distribution of Relative Humidity, J. Atmos. Sci., 24,
241–259, https://doi.org/10.1175/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2, 1967.
Marshall, J. and Molteni, F.: Toward a Dynamical Understanding of
Planetary-Scale Flow Regimes, J. Atmos. Sci., 50,
1792–1818, https://doi.org/10.1175/1520-0469(1993)050<1792:TADUOP>2.0.CO;2, 1993.
Martin, P. E., Arbic, B. K., Hogg, A. McC., Kiss, A.E., Munroe, J. R., and
Blundell, J. R.: Frequency-domain analysis of the energy budget in an
idealized coupled ocean–atmosphere model, J. Climate, 33, 707–726,
2020.
Mastilovic, I. and Kravtsov, S.: Climatic effects of mesoscale
ocean–atmosphere interaction in an idealized coupled model, Geophys.
Res. Abstr., 21, EGU2019-8383, EGU General Assembly 2019,
https://meetingorganizer.copernicus.org/EGU2019/EGU2019-8383.pdf (last access: 10 May 2022), 2019.
McDougall, T. and Dewar, W.: Vertical mixing and cabbeling in layered
models, J. Phys. Oceanogr., 28, 1458–1480, 1998.
Meredith, M. P. and Hogg, A. M.: Circumpolar response of Southern Ocean eddy
activity to a change in the Southern Annular Mode, Geophys. Res. Lett., 33,
https://doi.org/10.1029/2006GL026499, 2006.
Miller, A. J. and Schneider, N.: Interdecadal climate regime dynamics in the
North Pacific Ocean: Theories, observations and ecosystem impacts, Prog.
Oceanogr., 47, 355–379, https://doi.org/10.1016/S0079-6611(00)00044-6, 2000.
Minobe, S., Kuwano-Yoshida, A. , Komori, N., Xie, S.-P., and Small, R. J.:
Influence of the Gulf Stream on the troposphere, Nature, 452, 206–209,
https://doi.org/10.1038/nature06690, 2008.
Nakamura, H. and Yamane, S.: Dominant anomaly patterns in the near-surface
baroclinicity and accompanying anomalies in the atmosphere and oceans. Part
I: North Atlantic Basin. J. Climate, 22, 880–904,
https://doi.org/10.1175/2008JCLI2297.1, 2009.
Nakamura, H., Sampe, T., Goto, A., Ohfuchi, W., and Xie, S.-P.: On the
importance of midlatitude oceanic frontal zones for the mean state and
dominant variability in the tropospheric circulation, Geophys. Res. Lett.,
35, L15709, https://doi.org/10.1029/2008GL034010, 2008.
O'Neill, L. W., Chelton, D. B., and Esbensen, S. K.: The effects of
SST-induced surface wind speed and direction gradients on midlatitude
surface vorticity and divergence, J. Climate, 23, 255–281, https://doi.org/10.1175/2009JCLI2613.1, 2010.
O'Neill, L. W., Chelton, D. B., and Esbensen, S. K.: Covariability of
surface wind and stress responses to sea surface temperature fronts, J.
Climate, 25, 5916–5942, https://doi.org/10.1175/JCLI-D-11-00230.1, 2012.
O'Reilly, C. H. and Czaja, A.: The response of the Pacific storm track and
atmospheric circulation to Kuroshio Extension variability, Q. J. Roy.
Meteor. Soc., 141, 52–66, https://doi.org/10.1002/qj.2334, 2015.
Parfitt, R., Czaja, A., Kwon, Y.-O.: The impact of SST resolution change in
the ERA-Interim reanalysis on wintertime Gulf Stream frontal air-sea
interaction, Geophys. Res. Lett., 44, 3246–3254, 2017.
Perlin, N., De Szoeke, S. P., Chelton, D. B., Samelson, R. S., Skyllingstad,
E. D., and O'Neill, L. W.: Modeling the atmospheric boundary layer wind response
to mesoscale sea surface temperature perturbations, Mon. Weather Rev., 142, 4284–4307,
https://doi.org/10.1175/MWR-D-13-00332.1, 2014.
Piazza, M., Terray, L., Boé, J., Maisonnave, E., and Sanchez-Gomez, E.:
Influence of small-scale North Atlantic sea surface temperature patterns on
the marine boundary layer and free troposphere: A study using the
atmospheric ARPEGE model, Clim. Dynam., 46, 1699–1717, https://doi.org/10.1007/s00382-015-2669-z, 2016.
Primeau, F. W.: Multiple equilibria and low-frequency variability of the
wind-driven ocean circulation, J. Phys. Oceanogr., 32, 2236–2252, 2002.
Putrasahan, D. A., Miller, A. J., and Seo, H.: Isolating mesoscale coupled
ocean–atmosphere interactions in the Kuroshio Extension region, Dyn. Atmos.
Oceans, 63, 60–78, https://doi.org/10.1016/j.dynatmoce.2013.04.001, 2013.
Putrasahan, D. A., Kamenkovich, I., Le Henaff, M., and Kirtman, B. P.:
Importance of oceanic mesoscale variability for air-sea interactions in the
Gulf of Mexico, Geophys. Res. Lett., 44, 6352–6362, https://doi.org/10.1002/2017GL072884, 2017.
Ramanathan, V. and Coakley, J. A.: Climate modeling through
radiative-convective models, Rev. Geophys., 16, 465–489,
https://doi.org/10.1029/RG016i004p00465, 1978.
Schneider, N. and Qiu, B.: The atmospheric response to weak sea surface
temperature fronts, J. Atmos. Sci., 72, 3356–3377,
https://doi.org/10.1175/JAS-D-14-0212.1, 2015.
Seo, H., Miller, A. J., and Norris, J. R.: Eddy-wind interaction in the
California Current System: dynamics and impacts, J. Phys. Oceanogr., 46,
439–459, 2016.
Shevchenko, I., Berloff, P., Guerrero-Lopez, D., and Roman, J.: On
low-frequency variability of the midlatitude ocean gyres, J. Fluid Mech.,
795, 423–442, 2016.
Siqueira, L. and Kirtman, B. P.: Atlantic near-term climate variability and
the role of a resolved Gulf Stream, Geophys. Res. Lett., 43, 3964–3972,
https://doi.org/10.1002/2016GL068694, 2016.
Small, R. J., De Szoeke, S. P., Xie, S.-P., O'Neill, L., Seo, H., Song, Q.,
Cornillon, P., Spall, M., and Minobe, S.: Air–sea interaction over ocean
fronts and eddies, Dyn. Atmos. Oceans, 45, 274–319,
https://doi.org/10.1016/j.dynatmoce.2008.01.001, 2008.
Small, R., Tomas, A., and Bryan, F. O.: Storm track response to ocean fronts
in a global high-resolution climate model, Clim. Dynam., 43, 805–828,
https://doi.org/10.1007/s00382-013-1980-9, 2014.
Taguchi, B., Nakamura, H., Nonaka, M., and Xie, S.-P.: Influences of the
Kuroshio/Oyashio Extensions on air–sea heat exchanges and storm-track
activity as revealed in regional atmospheric model simulations for the
2003/04 cold season, J. Climate, 22, 6536–6560, https://doi.org/10.1175/2009JCLI2910.1,
2009.
Wallace, J. M., Mitchell, T. P., and Deser, C.: The influence of sea-surface
temperature on surface wind in the eastern equatorial Pacific: seasonal and
interannual variability, J. Climate, 2, 1492–1499, 1989.
Willison, J., Robinson, W. A., and Lackmann, G. M.: The importance of
resolving mesoscale latent heating in the North Atlantic storm track, J.
Atmos. Sci., 70, 2234–2250, https://doi.org/10.1175/JAS-D-12-0226.1, 2013.
Xie, S.-P.: Satellite observations of cool ocean–atmosphere interaction,
B. Am. Meteorol. Soc., 85, 195–208, https://doi.org/10.1175/ BAMS-85-2-195, 2004.
Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Climate is a complex system whose behavior is shaped by multitudes of processes operating on...
