Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system
Abstract A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the...
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ftopenaccessrep:oai:zenodo.org:135875 2023-10-29T02:38:27+01:00 Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system F. Ameli Massimo Lorè Antonello Pasini 2006-01-01 https://www.openaccessrepository.it/record/135875 https://doi.org/10.1016/j.ecolmodel.2005.08.012 eng eng url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/135875 doi:10.1016/j.ecolmodel.2005.08.012 info:eu-repo/semantics/openAccess Energy Research Ecological Modeling info:eu-repo/semantics/article publication-article 2006 ftopenaccessrep https://doi.org/10.1016/j.ecolmodel.2005.08.012 2023-10-03T22:19:19Z Abstract A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Nino southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository Ecological Modelling 191 1 58 67 |
institution |
Open Polar |
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Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository |
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ftopenaccessrep |
language |
English |
topic |
Energy Research Ecological Modeling |
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Energy Research Ecological Modeling F. Ameli Massimo Lorè Antonello Pasini Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system |
topic_facet |
Energy Research Ecological Modeling |
description |
Abstract A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Nino southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models. |
format |
Article in Journal/Newspaper |
author |
F. Ameli Massimo Lorè Antonello Pasini |
author_facet |
F. Ameli Massimo Lorè Antonello Pasini |
author_sort |
F. Ameli |
title |
Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system |
title_short |
Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system |
title_full |
Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system |
title_fullStr |
Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system |
title_full_unstemmed |
Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system |
title_sort |
neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system |
publishDate |
2006 |
url |
https://www.openaccessrepository.it/record/135875 https://doi.org/10.1016/j.ecolmodel.2005.08.012 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/135875 doi:10.1016/j.ecolmodel.2005.08.012 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1016/j.ecolmodel.2005.08.012 |
container_title |
Ecological Modelling |
container_volume |
191 |
container_issue |
1 |
container_start_page |
58 |
op_container_end_page |
67 |
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1781064490766303232 |