Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book

Name: Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book
Keywords: Atmosphere, Special Issue, Modelling, Python

Domazetoski, Viktor; Zúñiga-González, Andrés; Allemang, Owen

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Software . 2025

License: https://opensource.org/licenses/MIT

Data sources: Datacite

ZENODO

Software . 2025

License: https://opensource.org/licenses/MIT

Data sources: Datacite

Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 20 Jun 2025Publisher:Zenodo

Authors: Domazetoski, Viktor; Zúñiga-González, Andrés; Allemang, Owen;

doi: 10.5281/zenodo.8301002 , 10.5281/zenodo.15706519

Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book

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Abstract

Notebook developed to demonstrate the computational reproduction of the paper Detection and attribution of climate change: A deep learning and variational approach, published in Environmental Data Science journal.

Please cite the following works when using this project.

Related Organizations

University of Cambridge
United Kingdom
University of Göttingen
Germany

Keywords

Atmosphere, Special Issue, Modelling, Python

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Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book
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