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.

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

Pasini A;Mazzocchi F
2015

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.
2015
Istituto sull'Inquinamento Atmosferico - IIA
Istituto dei Sistemi Complessi - ISC
climate change
climate modelling
attribution
scientific uncertainty
robustness analysis
dynamical modelling
multi-model ensembles
data-driven modelling
neural networks
Granger causality
complex systems.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/282479
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