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Disentangling Internal and Forced Climate Variability with Convolutional Neural Networks using Multivariate Fields — arXiv:2609.05359

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05359
License (as archived): CC0-1.0
Archived: 2026-10-17T00:10:49.252Z
SHA-256 of the archived text: eaa891a99d0159201de9c6f0f1d1956beb1c18c976d5a084dbbe79b61549cc6a
Stable URL of this page: https://metraipolis.com/documento/dc_320365f4ba91

How to cite

Disentangling Internal and Forced Climate Variability with Convolutional Neural Networks using Multivariate Fields — arXiv:2609.05359 — arXiv (en), https://arxiv.org/abs/2609.05359 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_320365f4ba91 (Metraipolis archive dc_320365f4ba91, sha256 eaa891a99d0159201de9c6f0f1d1956beb1c18c976d5a084dbbe79b61549cc6a)

BibTeX

@misc{dc320365f4ba91,
  title = {Disentangling Internal and Forced Climate Variability with Convolutional Neural Networks using Multivariate Fields — arXiv:2609.05359},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05359},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_320365f4ba91; sha256 eaa891a99d0159201de9c6f0f1d1956beb1c18c976d5a084dbbe79b61549cc6a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05359. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Long-term climate data exhibit variations composed of internal and forced components. Internal variability arises from natural processes that could occur within a stable climate. Forced variability, on the other hand, reflects climate changes induced, for example, by anthropogenic greenhouse gas and aerosol emissions. Accurately distinguishing between these types of variability is crucial for attributing climate fluctuations and understanding internal variability processes and impacts. In this study, we apply a U-Net convolutional network, a model commonly used in computer vision, to separate forced and internal climate variability from 1950 to 2022 using a multi-model dataset. The dataset includes multiple fields from five single-model initial-condition large ensembles. Cross-validation is conducted by training the U-Net using the data from the ensembles of four models, leaving out the data from one model to assess performance. Validation results yield errors ranging from 0.1°C to 0.4°C for the forced variability of local-scale monthly surface air temperature, which is no more than half the magnitude of external forcing. The U-Net achieves better performance than a simple approach based on a fourth-order polynomial trend for estimating forced variability. The error is mainly due to insufficient sampling and poor agreement among the models, as the U-Net underestimates the warming for the model with the highest transient climate sensitivity during validation. Performance is lower for variables like sea-level pressure and precipitation because of their low ratio of forced to internal variability. This framework might be enhanced by incorporating a larger multi-model dataset in the training and validation.

Tema degli esploratori: terra, clima ed energia (retro-catalogo arXiv: annunci del 2026-09-07 in physics.ao-ph).
Categorie arXiv: Atmospheric and Oceanic Physics (physics.ao-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.05359 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T23:52:50.006Z dalla pagina https://arxiv.org/catchup/physics.ao-ph/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).