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
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22 citations as recorded by crossref.
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- 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
- Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study S. Vannitsem et al. 10.1029/2021MS002530
- The Structure of Climate Variability Across Scales C. Franzke et al. 10.1029/2019RG000657
- Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
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- GeophysicalFlows.jl: Solvers for geophysical fluid dynamics problems in periodic domains on CPUs GPUs N. Constantinou et al. 10.21105/joss.03053
- 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
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- 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
- Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model J. Demaeyer & S. Vannitsem 10.5194/npg-25-605-2018
- 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
- Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics S. Vannitsem 10.1063/1.4979042
- Review article: Interdisciplinary perspectives on climate sciences – highlighting past and current scientific achievements V. Galfi et al. 10.5194/npg-31-185-2024
- 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
- 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: 15 Apr 2024
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...
