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Attribution of recent temperature behaviour reassessed by a neural-network method

Title
Attribution of recent temperature behaviour reassessed by a neural-network method
Authors
Pasini A.Racca P.Amendola S.Cartocci G.Cassardo C.
Ewha Authors
Claudio Cassardo
Issue Date
2017
Journal Title
Scientific Reports
ISSN
2045-2322JCR Link
Citation
Scientific Reports vol. 7, no. 1
Publisher
Nature Publishing Group
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Attribution studies on recent global warming by Global Climate Model (GCM) ensembles converge in showing the fundamental role of anthropogenic forcings as primary drivers of temperature in the last half century. However, despite their differences, all these models pertain to the same dynamical approach and come from a common ancestor, so that their very similar results in attribution studies are not surprising and cannot be considered as a clear proof of robustness of the results themselves. Thus, here we adopt a completely different, non-dynamical, data-driven and fully nonlinear approach to the attribution problem. By means of neural network (NN) modelling, and analysing the last 160 years, we perform attribution experiments and find that the strong increase in global temperature of the last half century may be attributed basically to anthropogenic forcings (with details on their specific contributions), while the Sun considerably influences the period 1910-1975. Furthermore, the role of sulphate aerosols and Atlantic Multidecadal Oscillation for better catching interannual to decadal temperature variability is clarified. Sensitivity analyses to forcing changes are also performed. The NN outcomes both corroborate our previous knowledge from GCMs and give new insight into the relative contributions of external forcings and internal variability to climate. © 2017 The Author(s).
DOI
10.1038/s41598-017-18011-8
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엘텍공과대학 > 기후·에너지시스템공학전공 > Journal papers
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