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Istituto sull'inquinamento atmosferico

Torna all'elenco Contributi in rivista anno 2015

Contributo in rivista

Tipo: Articolo in rivista

Titolo: A multi-approach strategy in climate attribution studies: is it possible to apply a robustness framework?

Anno di pubblicazione: 2015

Formato: Elettronico Cartaceo

Autori: Antonello Pasini (1); Fulvio Mazzocchi (2)

Affiliazioni autori: (1) CNR, Institute of Atmospheric Pollution Research, Via Salaria Km 29,300, I-00015 Monterotondo St., Rome, Italy (2) CNR, Institute for Complex Systems, Via Salaria Km 29,300, I-00015 Monterotondo St., Rome, Italy

Autori CNR:

  • FULVIO MAZZOCCHI
  • ANTONELLO PASINI

Lingua: inglese

Abstract: Attribution studies investigate the causes of recent global warming. For a few decades the scientific community generally adopted dynamical models - the so-called Global Climate Models (GCMs) - for such an investigation. These models show the essential role of anthropogenic forcings in driving the temperature behaviour of the last half century. In the last period even other (data-driven) methodological approaches were adopted for attribution studies. This allows the scientific community to compare the results coming from these different approaches and to possibly increase their robustness. For such a purpose, the paper explores the possibility of applying a robustness framework, so far used only in the case of multi-model GCM ensembles, to a strategy including models from different methodological orientations, assessing such an application especially in the light of the independence issue.

Lingua abstract: inglese

Pagine da: 191

Pagine a: 199

Rivista:

Environmental science & policy Elsevier Science,
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1462-9011

Numero volume: 50

Numero fascicolo: June

DOI: 10.1016/j.envsci.2015.02.018

Referee: Sė: Internazionale

Parole chiave:

  • climate change
  • climate modelling
  • attribution
  • scientific uncertainty
  • robustness analysis
  • dynamical modelling
  • multi-model ensembles
  • data-driven modelling
  • neural networks
  • Granger causality
  • complex systems.

URL: http://www.sciencedirect.com/science/article/pii/S1462901115000490

Altre informazioni: Available online 22 March 2015.

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