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

Demaeyer, Jonathan; Cruz, Lesley De; Vannitsem, Stéphane

doi:10.21105/joss.02597

Cited by 14 publications

(20 citation statements)

References 25 publications

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“…The VDDG model, as implemented in this study, can be obtained by using the Python qgs framework (Demaeyer et al., 2020). The corresponding Python code is available on Zenodo (Demaeyer & De Cruz, 2021), as well as at https://github.com/Climdyn/qgs.…”

Section: Data Availability Statementmentioning

confidence: 99%

“…The VDDG model, as implemented in this study, can be obtained by using the Python qgs framework (Demaeyer et al, 2020…”

Section: Appendix A: Dynamical Systems Tools: a Primermentioning

confidence: 99%

“…Associate Editor Terence O'Kane and two anonymous reviewers have substantially helped improve the presentation of the material. The VDDG model, as implemented in this paper, can be obtained by using the Python qgs framework (Demaeyer et al, 2020). The corresponding Python code is available on Zenodo (Demaeyer…”

Section: Accepted Articlementioning

confidence: 99%

See 2 more Smart Citations

Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study

Vannitsem

Demaeyer

Ghil

2021

J Adv Model Earth Syst

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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 ENSO forcing is used to mimick 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 (PBAs). This analysis focuses on two types of ENSO forcing generated by the tropical module, one periodic and the other aperiodic. For a substantial range of the ENSO forcing, two chaotic PBAs are found to coexist for the same set of parameter values. Different types of extratropical low‐frequency variability (LFV) are associated with either PBA over the parameter ranges explored. For periodic ENSO forcing, the coexisting PBAs exhibit only weak nonlinear instability. For chaotic forcing, though, they are quite unstable and certain extratropical perturbations induce transitions between the two PBAs. These distinct stability properties may have profound consequences for extratropical climate predictions: in particular, ensemble averaging may no longer help isolate the LFV signal.

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Section: Data Availability Statementmentioning

confidence: 99%

“…The VDDG model, as implemented in this study, can be obtained by using the Python qgs framework (Demaeyer et al, 2020…”

Section: Appendix A: Dynamical Systems Tools: a Primermentioning

confidence: 99%

See 1 more Smart Citation

Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study

Vannitsem

Demaeyer

Ghil

2021

J Adv Model Earth Syst

Self Cite

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show abstract

“…Experiments are conducted with a coupled ocean-atmosphere model that was first introduced by Vannitsem et al (2015), and was further generalized by De Cruz et al (2016) and Demaeyer et al (2020). It consists of a two-layer quasi-geostrophic atmospheric model coupled both thermally and mechanically to a shallow-water oceanic component on a beta plane.…”

Section: The Coupled Ocean-atmosphere Modelmentioning

confidence: 99%

Identifying efficient ensemble perturbations for initializing subseasonal-to-seasonal prediction

Demaeyer,

Penny,

Vannitsem

2021

Preprint

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The prediction of the weather at subseasonal-to-seasonal (S2S) timescales is dependent on both initial and boundary conditions. An open question is how to best initialize a relatively small-sized ensemble of numerical model integrations to produce reliable forecasts at these timescales. Reliability in this case means that the statistical properties of the ensemble forecast are consistent with the actual uncertainties about the future state of the geophysical system under investigation. In the present work, a method is introduced to construct initial conditions that produce reliable ensemble forecasts by projecting onto the eigenfunctions of the Koopman or the Perron-Frobenius operators, which describe the time-evolution of observables and probability distributions of the system dynamics, respectively. These eigenfunctions can be approximated from data by using the Dynamic Mode Decomposition (DMD) algorithm. The effectiveness of this approach is illustrated in the framework of a low-order ocean-atmosphere model exhibiting multiple characteristic timescales, and is compared to other ensemble initialization methods based on the Empirical Orthogonal Functions (EOFs) of the model trajectory and on the backward and covariant Lyapunov vectors of the model dynamics. Projecting initial conditions onto a subset of the Koopman or Perron-Frobenius eigenfunctions that are characterized by time scales with fast-decaying oscillations is found to produce highly reliable forecasts at all lead times investigated, ranging from one week to two months. Reliable forecasts are also obtained with the adjoint covariant Lyapunov vectors, which are the eigenfunctions of the Koopman operator in the tangent space. The advantages of these different methods are discussed.

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“…MAOOAM (De Cruz et al, 2016) (Fortran, Python, and Lua) and its expanded Python implementation qgs(Demaeyer et al, 2020) MAOOAM and qgs simulate two atmospheric layers with QG dynamics, above either land or an oceanic fluid layer with reduced-gravity QG dynamics. The dynamics of individual layers have overlap with the MultiLayerQG and SingleLayerQG modules, however the layer configuration of MOAAM and qgs is specifically designed to study the dynamics of Earth's mid-latitude atmosphere.…”

mentioning

confidence: 99%

GeophysicalFlows.jl: Solvers for geophysical fluid dynamics problems in periodic domains on CPUs GPUs

Constantinou¹,

Wagner²,

Siegelman³

et al. 2021

JOSS

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GeophysicalFlows.jl is a Julia (Bezanson et al., 2017) package that contains partial differential equations solvers for a collection of geophysical fluid systems in periodic domains. All modules use Fourier-based pseudospectral numerical methods and leverage the framework provided by the FourierFlows.jl (Constantinou et al., 2021) Julia package for time-stepping, custom diagnostics, and saving output. Constantinou et al., (2021). GeophysicalFlows.jl: Solvers for geophysical fluid dynamics problems in periodic domains on CPUs & GPUs.

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

Cited by 14 publications

References 25 publications

Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study

Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study

Identifying efficient ensemble perturbations for initializing subseasonal-to-seasonal prediction

GeophysicalFlows.jl: Solvers for geophysical fluid dynamics problems in periodic domains on CPUs GPUs

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