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Preprint withdrawn
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.
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
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Ocean models are used to understand how the ocean interacts with the Antarctic Ice Sheet, but they are too coarse in resolution to capture the small-scale ocean processes driving melting and require a parameterisation to predict melt. Previous parameterisations ignore key processes occurring in some regions of Antarctica. We develop a parameterisation with the feedback of stratification on melting and test it in idealised and regional ocean models, finding changes to melt rate and circulation.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
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
Short summary
Short summary
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
Short summary
Short summary
Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Bradley N. Opdyke, and Stephen M. Eggins
Clim. Past, 17, 171–201, https://doi.org/10.5194/cp-17-171-2021, https://doi.org/10.5194/cp-17-171-2021, 2021
Short summary
Short summary
We undertake a model–data study of the last glacial–interglacial cycle of atmospheric CO2, spanning 0–130 ka. We apply a carbon cycle box model, constrained with glacial–interglacial observations, and solve for optimal model parameter values against atmospheric and ocean proxy data. The results indicate that the last glacial drawdown in atmospheric CO2 was delivered mainly by slowing ocean circulation, lower sea surface temperatures and also increased Southern Ocean biological productivity.
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
Short summary
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.
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Stephen M. Eggins, and Bradley N. Opdyke
Geosci. Model Dev., 12, 1541–1572, https://doi.org/10.5194/gmd-12-1541-2019, https://doi.org/10.5194/gmd-12-1541-2019, 2019
Short summary
Short summary
The [simple carbon project] model v1.0 (SCP-M) was constructed for simulations of the paleo and modern carbon cycle. In this paper we show its application to the carbon cycle transition from the Last Glacial Maximum to the Holocene period. Our model–data experiment uses SCP-M's fast run time to cover a large range of possible inputs. The results highlight the role of varying the strength of ocean circulation to account for large fluctuations in atmospheric CO2 across the two periods.
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
Climate and Earth system modeling
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Historical Trends and Controlling Factors of Isoprene Emissions in CMIP6 Earth System Models
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – 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)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5): Model formulation and validation
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
The Earth Science Box Modeling Toolkit (ESBMTK)
High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Presentation, Calibration and Testing of the DCESS II Earth System Model of Intermediate Complexity (version 1.0)
Baseline Climate Variables for Earth System Modelling
The DOE E3SM Version 2.1: Overview and Assessment of the Impacts of Parameterized Ocean Submesoscales
Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous 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 sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used 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. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model 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.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged 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.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
Short summary
Short summary
We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so 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.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Esteban Fernández and Gary Shaffer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-122, https://doi.org/10.5194/gmd-2024-122, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Here we describe, calibrate and test DCESS II, a new, broad, adaptable and fast Earth System Model. DCESS II has been designed for global simulations over time scales 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 should prove to be a useful, computational-friendly tool for simulations of past climates as well as for future Earth System projections.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2363, https://doi.org/10.5194/egusphere-2024-2363, 2024
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 132 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most heavily used variables from Earth System Models, based on an assessment of data publication and download records from the largest archive of global climate projects.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum 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. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. 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).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
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...
