scholarly journals Extratropical low‐frequency variability with ENSO forcing: A reduced‐order coupled model study

2021 ◽  
Author(s):  
Stéphane Vannitsem ◽  
Jonathan Demaeyer ◽  
Michael Ghil
Keyword(s):  
Low Frequency ◽  
Coupled Model ◽  
Reduced Order ◽  
Model Study ◽  
Low Frequency Variability

scholarly journals Extratropical low-frequency variability with ENSO forcing: a reduced-order coupled model study  

2021 ◽  
Author(s):  
Stéphane Vannitsem ◽  
Jonathan Demaeyer ◽  
Michael Ghil
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Coupled Model ◽  
Ensemble Averaging ◽  
Reduced Order ◽  
Low Frequency Variability ◽  
Coupling Parameters ◽  
Parameter Ranges ◽  
The Impact

<p>The impact of the El Niño-Southern Oscillation (ENSO) on the extratropics is investigated in an idealized, reduced-order model that has a tropical and an extratropical module. Unidirectional forcing is used to mimic the atmospheric bridge between the tropics and the extratropics. The variability of the coupled ocean--atmosphere extratropical module is then investigated through the analysis of its pullback attractors (PBA). This analysis focuses on two ENSO-type forcings generated by the tropical module, one periodic and one aperiodic.</p><p> </p><p>For a substantial range of coupling parameters, multiple chaotic PBAs are found to coexist for the same set of parameter values. Different types of extratropical low-frequency variability are associated with each PBA over the parameter ranges explored. For periodic ENSO forcing, the coexisting PBAs are nonlinearly stable, while for the chaotic forcing, they are unstable and certain extratropical perturbations induce transitions between the PBAs. These distinct stability properties may have profound consequences for extratropical climate predictions, provided they are confirmed by studies using comprehensive climate models. Thus, for instance, ensemble averaging may no longer be a valid approach to isolate the low-frequency variability signal.</p>

qgs: A flexible Python framework of reduced-order multiscale climate models

2021 ◽  
Author(s):  
Lesley De Cruz ◽  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Open Source Software ◽  
Climate Models ◽  
Low Frequency ◽  
Atmospheric Dynamics ◽  
The Other ◽  
Research Use ◽  
Good Representation ◽  
Atmospheric Variability ◽  
Reduced Order ◽  
Low Frequency Variability

<p>In atmospheric and climate sciences, research and development is often first conducted with a simple idealized system like the Lorenz-<em>N</em> models (<em>N ∈</em> {63, 84, 96}) which are toy models of atmospheric variability. On the other hand, reduced-order spectral quasi-geostrophic models of the atmosphere with a sufficient number of modes offer a good representation of the dry atmospheric dynamics. They allow one to identify typical features of the atmospheric circulation, such as blocked and zonal circulation regimes, and low-frequency variability. However, these models are less often considered in literature, despite their demonstration of more realistic behavior.</p><p><strong>qgs</strong> (Demaeyer et al., 2020) aims to popularize these systems by providing a fast and easy-to-use Python framework for researchers and teachers to integrate this kind of model. The documentation makes it clear and efficient to handle the model, by explaining the equations and parameters and linking these to the code. </p><p>The choice to use Python was specifically made to facilitate its use in Jupyter Notebooks and with the multiple recent machine learning libraries that are available in this language.</p><p>In this talk, we will present the modeling capabilities of <strong>qgs</strong> and show its usage in a varieties of didactical and research use cases.</p><p><strong>Reference</strong></p><p>Demaeyer, J., De Cruz, L., & Vannitsem, S. (2020). qgs: A flexible Python framework of reduced-order multiscale climate models. Journal of Open Source Software, 5(56), 2597, https://doi.org/10.21105/joss.02597 .</p>

scholarly journals A Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0

2016 ◽  
Author(s):  
Lesley De Cruz ◽  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Arbitrary Order ◽  
Low Frequency ◽  
Spectral Expansion ◽  
Truncation Level ◽  
Reduced Order ◽  
New Model ◽  
Low Frequency Variability ◽  
Model Dynamics ◽  
Ocean Atmosphere ◽  
Atmosphere Model

Abstract. This paper describes a reduced-order quasi-geostrophic coupled ocean-atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined "Modular Arbitrary-Order Ocean-Atmosphere Model" (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher resolution versions. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An "optimal" version is proposed for which the ocean resolution is sufficiently high while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics.

scholarly journals Coupled Model Biases Breed Spurious Low‐Frequency Variability in the Tropical Pacific Ocean

2018 ◽  
Vol 45 (19) ◽  
Author(s):  
Dhrubajyoti Samanta ◽  
Kristopher B. Karnauskas ◽  
Nathalie F. Goodkin ◽  
Sloan Coats ◽  
Jason E. Smerdon ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Low Frequency ◽  
Coupled Model ◽  
Tropical Pacific ◽  
Tropical Pacific Ocean ◽  
Model Biases ◽  
Low Frequency Variability ◽  
The Tropical Pacific

scholarly journals The Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0

2016 ◽  
Vol 9 (8) ◽  
pp. 2793-2808 ◽  
Author(s):  
Lesley De Cruz ◽  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Arbitrary Order ◽  
Low Frequency ◽  
Spectral Expansion ◽  
Truncation Level ◽  
Reduced Order ◽  
New Model ◽  
Low Frequency Variability ◽  
Model Dynamics ◽  
Ocean Atmosphere ◽  
Atmosphere Model

Abstract. This paper describes a reduced-order quasi-geostrophic coupled ocean–atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined the "Modular Arbitrary-Order Ocean-Atmosphere Model" (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher-resolution configurations. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An "optimal" configuration is proposed for which the ocean resolution is sufficiently high, while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics.

scholarly journals A Kuroshio Extension System Model Study: Decadal Chaotic Self-Sustained Oscillations

2006 ◽  
Vol 36 (8) ◽  
pp. 1605-1625 ◽  
Author(s):  
Stefano Pierini
Keyword(s):  
Decadal Variability ◽  
Kuroshio Extension ◽  
Low Frequency ◽  
Internal Oscillation ◽  
Extension System ◽  
Model Study ◽  
Low Frequency Variability ◽  
South Of Japan ◽  
Altimetric Measurements ◽  
The Kuroshio

Abstract A model study of the Kuroshio Extension system, in which forcing is provided by a time-independent climatological wind, yields a mean meandering path and a decadal variability of the jet in significant agreement with in situ and altimetric measurements. A reduced-gravity primitive equation ocean model is implemented in a box spanning the whole North Pacific, including a schematic coastline at the western side, and an analytical wind forcing is determined according to the ECMWF and Comprehensive Ocean–Atmosphere Data Set (COADS) climatologies. The modeled time-averaged Kuroshio Extension shows meanders, northern and southern recirculation regions, and a jet penetration that are in good agreement with the corresponding observed climatological features. This result suggests that intrinsic nonlinear mechanisms are likely to play a major role in determining the meander pattern of the mean flow. The internal low-frequency variability is found to be a chaotic bimodal self-sustained oscillation between an energetic meandering state and a much weaker state with a reduced zonal penetration of the jet. These high and low energy states are found to be very similar to the “elongated” and “contracted” modes of the Kuroshio Extension detected through altimetric measurements; moreover, the characteristic period (of around 10 yr), flow patterns, and transition details of a typical bimodal cycle are found to be in significant agreement with altimeter observations for the period 1992–2004. A complex dynamical mechanism supporting this internal oscillation, and involving the bimodal behavior of the Kuroshio south of Japan, is proposed and discussed. On the basis of these modeling results and of their validation with altimeter data, it is hypothesized that the observed bimodal decadal variability of the Kuroshio Extension is basically due to a self-sustained internal oscillation related to the instability of the Kuroshio south of Japan without any crucial intervention of wind-driven Sverdrup transport fluctuations and of topographic interactions, although such effects certainly play an important role in shaping the finer structure of Kuroshio Extension changes. Finally, in a preliminary analysis of the variability in the framework of nonlinear dynamical systems theory it is suggested that the strange attractor corresponding to the modeled low-frequency variability is associated with a homoclinic orbit produced by a global bifurcation; moreover, transitions between oscillations of different character found for slightly different values of the lateral eddy viscosity and forcing amplitude are conjectured to be due to heteroclinic connections.

Sign in / Sign up

Export Citation Format

Share Document