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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"

Authors: de Melo Viríssimo, Francisco; Stainforth, David A.; Bröcker, Jochen;

Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"

Abstract

This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (Van Veen et al, 2001; Daron and Stainforth, 2013). To run these simulations, we used the Low-EFFourth ensemble generator (de Melo Viríssimo, 2025a; de Melo Viríssimo, 2025b), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (de Melo Viríssimo and Stainforth, 2023). These model outputs are presented and discussed in the article "The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example", published by Chaos (de Melo Viríssimo et al., 2024). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to Daron and Stainforth (2013). All files uploaded were generated from simulations run by the authors. For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me. Note: This version (v1.1) is the same version as v1.0 but with the correct README file.

Version (v1.1) is the same version as v1.0 but with the correct README file.

Related Organizations
Keywords

non-autonomous systems, climate change, climate prediction, chaotic dynamical systems, climate modelling, pullback attractor

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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