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Istituto sull'inquinamento atmosferico

Torna all'elenco Contributi in rivista anno 2017

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Tipo: Articolo in rivista

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

Anno di pubblicazione: 2017

Formato: Elettronico

Autori: Pasini, Antonello; Racca, Paolo; Amendola, Stefano; Cartocci, Giorgio; Cassardo, Claudio

Affiliazioni autori: Institute of Atmospheric Pollution Research, National Research Council, Rome, Italy. Department of Economics and Statistics, University of Turin, Torino, Italy. Department of Mathematics and Physics, Roma Tre University, Rome, Italy. Department of Physics, University of Turin, Torino, Italy. Department of Atmospheric Science and Engineering, Ewha Womans University, Seoul, Korea.

Autori CNR:


Lingua: inglese

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.

Lingua abstract: inglese

Pagine totali: 10


Scientific reports Nature Publishing Group
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 2045-2322

Numero volume: 7

Numero fascicolo: 17681

DOI: 10.1038/s41598-017-18011-8

Referee: Sė: Internazionale

Stato della pubblicazione: Published version

Parole chiave:

  • climate change
  • global warming
  • attribution
  • neural network modelling

URL: http://www.nature.com/articles/s41598-017-18011-8

Data di accettazione: 24/11/2017

Strutture CNR:

Allegati: Attribution of recent temperature behaviour reassessed by a neural-network method (application/pdf)
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