Articles | Volume 9, issue 8
https://doi.org/10.5194/gmd-9-2793-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2793-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
24 Aug 2016
Model description paper |
| 24 Aug 2016
The Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0
Royal Meteorological Institute of Belgium, Avenue Circulaire 3, 1180 Brussels, Belgium
Jonathan Demaeyer
Royal Meteorological Institute of Belgium, Avenue Circulaire 3, 1180 Brussels, Belgium
Stéphane Vannitsem
Royal Meteorological Institute of Belgium, Avenue Circulaire 3, 1180 Brussels, Belgium
Viewed
Total article views: 4,982 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 03 May 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,003 | 1,741 | 238 | 4,982 | 456 | 257 | 245 |
- HTML: 3,003
- PDF: 1,741
- XML: 238
- Total: 4,982
- Supplement: 456
- BibTeX: 257
- EndNote: 245
Total article views: 4,505 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Aug 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,770 | 1,503 | 232 | 4,505 | 336 | 252 | 238 |
- HTML: 2,770
- PDF: 1,503
- XML: 232
- Total: 4,505
- Supplement: 336
- BibTeX: 252
- EndNote: 238
Total article views: 477 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 03 May 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 233 | 238 | 6 | 477 | 120 | 5 | 7 |
- HTML: 233
- PDF: 238
- XML: 6
- Total: 477
- Supplement: 120
- BibTeX: 5
- EndNote: 7
Cited
25 citations as recorded by crossref.
- Impact of tropical teleconnections on the long-range predictability of the atmosphere at midlatitudes: a reduced-order multi-scale model perspective S. Vannitsem 10.1088/2632-072X/ad04e8
- Lyapunov analysis of multiscale dynamics: the slow bundle of the two-scale Lorenz 96 model M. Carlu et al. 10.5194/npg-26-73-2019
- The Structure of Climate Variability Across Scales C. Franzke et al. 10.1029/2019RG000657
- Multiple Equilibria in a Land–Atmosphere Coupled System D. Li et al. 10.1007/s13351-018-8012-y
- GeophysicalFlows.jl: Solvers for geophysical fluid dynamics problems in periodic domains on CPUs GPUs N. Constantinou et al. 10.21105/joss.03053
- Projected data assimilation using sliding window proper orthogonal decomposition A. Albarakati et al. 10.1016/j.jcp.2024.113235
- On the use of near-neutral Backward Lyapunov Vectors to get reliable ensemble forecasts in coupled ocean–atmosphere systems S. Vannitsem & W. Duan 10.1007/s00382-020-05313-3
- Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model J. Demaeyer & S. Vannitsem 10.5194/npg-25-605-2018
- Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics S. Vannitsem 10.1063/1.4979042
- qgs: A flexible Python framework of reduced-order multiscale climate models J. Demaeyer et al. 10.21105/joss.02597
- Multiscale fractal dimension analysis of a reduced order model of coupled ocean–atmosphere dynamics T. Alberti et al. 10.5194/esd-12-837-2021
- A new mathematical framework for atmospheric blocking events V. Lucarini & A. Gritsun 10.1007/s00382-019-05018-2
- Variability and predictability of a reduced-order land–atmosphere coupled model A. Xavier et al. 10.5194/esd-15-893-2024
- Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study S. Vannitsem et al. 10.1029/2021MS002530
- Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
- Exploring the Lyapunov instability properties of high-dimensional atmospheric and climate models L. De Cruz et al. 10.5194/npg-25-387-2018
- Correcting for model changes in statistical postprocessing – an approach based on response theory J. Demaeyer & S. Vannitsem 10.5194/npg-27-307-2020
- Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model S. Penny et al. 10.1029/2019MS001652
- On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter M. Tondeur et al. 10.1007/s10955-020-02525-z
- Identifying Efficient Ensemble Perturbations for Initializing Subseasonal‐To‐Seasonal Prediction J. Demaeyer et al. 10.1029/2021MS002828
- Multistability in a coupled ocean–atmosphere reduced‐order model: Nonlinear temperature equations O. Hamilton et al. 10.1002/qj.4564
- Review article: Interdisciplinary perspectives on climate sciences – highlighting past and current scientific achievements V. Galfi et al. 10.5194/npg-31-185-2024
- Routes to long‐term atmospheric predictability in reduced‐order coupled ocean–atmosphere systems: Impact of the ocean basin boundary conditions S. Vannitsem et al. 10.1002/qj.3594
- Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning E. Kalnay et al. 10.5194/npg-30-217-2023
- Evidence of coupling in ocean‐atmosphere dynamics over the North Atlantic S. Vannitsem & M. Ghil 10.1002/2016GL072229
24 citations as recorded by crossref.
- Impact of tropical teleconnections on the long-range predictability of the atmosphere at midlatitudes: a reduced-order multi-scale model perspective S. Vannitsem 10.1088/2632-072X/ad04e8
- Lyapunov analysis of multiscale dynamics: the slow bundle of the two-scale Lorenz 96 model M. Carlu et al. 10.5194/npg-26-73-2019
- The Structure of Climate Variability Across Scales C. Franzke et al. 10.1029/2019RG000657
- Multiple Equilibria in a Land–Atmosphere Coupled System D. Li et al. 10.1007/s13351-018-8012-y
- GeophysicalFlows.jl: Solvers for geophysical fluid dynamics problems in periodic domains on CPUs GPUs N. Constantinou et al. 10.21105/joss.03053
- Projected data assimilation using sliding window proper orthogonal decomposition A. Albarakati et al. 10.1016/j.jcp.2024.113235
- On the use of near-neutral Backward Lyapunov Vectors to get reliable ensemble forecasts in coupled ocean–atmosphere systems S. Vannitsem & W. Duan 10.1007/s00382-020-05313-3
- Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model J. Demaeyer & S. Vannitsem 10.5194/npg-25-605-2018
- Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics S. Vannitsem 10.1063/1.4979042
- qgs: A flexible Python framework of reduced-order multiscale climate models J. Demaeyer et al. 10.21105/joss.02597
- Multiscale fractal dimension analysis of a reduced order model of coupled ocean–atmosphere dynamics T. Alberti et al. 10.5194/esd-12-837-2021
- A new mathematical framework for atmospheric blocking events V. Lucarini & A. Gritsun 10.1007/s00382-019-05018-2
- Variability and predictability of a reduced-order land–atmosphere coupled model A. Xavier et al. 10.5194/esd-15-893-2024
- Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study S. Vannitsem et al. 10.1029/2021MS002530
- Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
- Exploring the Lyapunov instability properties of high-dimensional atmospheric and climate models L. De Cruz et al. 10.5194/npg-25-387-2018
- Correcting for model changes in statistical postprocessing – an approach based on response theory J. Demaeyer & S. Vannitsem 10.5194/npg-27-307-2020
- Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model S. Penny et al. 10.1029/2019MS001652
- On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter M. Tondeur et al. 10.1007/s10955-020-02525-z
- Identifying Efficient Ensemble Perturbations for Initializing Subseasonal‐To‐Seasonal Prediction J. Demaeyer et al. 10.1029/2021MS002828
- Multistability in a coupled ocean–atmosphere reduced‐order model: Nonlinear temperature equations O. Hamilton et al. 10.1002/qj.4564
- Review article: Interdisciplinary perspectives on climate sciences – highlighting past and current scientific achievements V. Galfi et al. 10.5194/npg-31-185-2024
- Routes to long‐term atmospheric predictability in reduced‐order coupled ocean–atmosphere systems: Impact of the ocean basin boundary conditions S. Vannitsem et al. 10.1002/qj.3594
- Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning E. Kalnay et al. 10.5194/npg-30-217-2023
1 citations as recorded by crossref.
Latest update: 24 Apr 2025
Short summary
Large-scale weather patterns such as the North Atlantic Oscillation, which dictates the harshness of European winters, vary over the course of years. By recreating it in a simple ocean-atmosphere model, we hope to understand what drives this slow, hard-to-predict variability. MAOOAM is such a model, in which the resolution and included physical processes can easily be modified. The modular system allowed us to show the robustness of the slow variability against changes in model resolution.
Large-scale weather patterns such as the North Atlantic Oscillation, which dictates the...
