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Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model
Global Climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict climate change. We perform our study using a fully coupled mo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250835/ https://www.ncbi.nlm.nih.gov/pubmed/32457414 http://dx.doi.org/10.1038/s41598-020-65297-2 |
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author | Lembo, Valerio Lucarini, Valerio Ragone, Francesco |
author_facet | Lembo, Valerio Lucarini, Valerio Ragone, Francesco |
author_sort | Lembo, Valerio |
collection | PubMed |
description | Global Climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict climate change. We perform our study using a fully coupled model - MPI-ESM v.1.2 - and for the first time we prove the effectiveness of response theory in predicting future climate response to CO(2) increase on a vast range of temporal scales, from inter-annual to centennial, and for very diverse climatic variables. We investigate within a unified perspective the transient climate response and the equilibrium climate sensitivity, and assess the role of fast and slow processes. The prediction of the ocean heat uptake highlights the very slow relaxation to a newly established steady state. The change in the Atlantic Meridional Overturning Circulation (AMOC) and of the Antarctic Circumpolar Current (ACC) is accurately predicted. The AMOC strength is initially reduced and then undergoes a slow and partial recovery. The ACC strength initially increases due to changes in the wind stress, then undergoes a slowdown, followed by a recovery leading to a overshoot with respect to the initial value. Finally, we are able to predict accurately the temperature change in the North Atlantic. |
format | Online Article Text |
id | pubmed-7250835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72508352020-06-04 Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model Lembo, Valerio Lucarini, Valerio Ragone, Francesco Sci Rep Article Global Climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict climate change. We perform our study using a fully coupled model - MPI-ESM v.1.2 - and for the first time we prove the effectiveness of response theory in predicting future climate response to CO(2) increase on a vast range of temporal scales, from inter-annual to centennial, and for very diverse climatic variables. We investigate within a unified perspective the transient climate response and the equilibrium climate sensitivity, and assess the role of fast and slow processes. The prediction of the ocean heat uptake highlights the very slow relaxation to a newly established steady state. The change in the Atlantic Meridional Overturning Circulation (AMOC) and of the Antarctic Circumpolar Current (ACC) is accurately predicted. The AMOC strength is initially reduced and then undergoes a slow and partial recovery. The ACC strength initially increases due to changes in the wind stress, then undergoes a slowdown, followed by a recovery leading to a overshoot with respect to the initial value. Finally, we are able to predict accurately the temperature change in the North Atlantic. Nature Publishing Group UK 2020-05-26 /pmc/articles/PMC7250835/ /pubmed/32457414 http://dx.doi.org/10.1038/s41598-020-65297-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lembo, Valerio Lucarini, Valerio Ragone, Francesco Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model |
title | Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model |
title_full | Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model |
title_fullStr | Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model |
title_full_unstemmed | Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model |
title_short | Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model |
title_sort | beyond forcing scenarios: predicting climate change through response operators in a coupled general circulation model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250835/ https://www.ncbi.nlm.nih.gov/pubmed/32457414 http://dx.doi.org/10.1038/s41598-020-65297-2 |
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