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

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

SUBMITTER: Pasini A 

PROVIDER: S-EPMC5732275 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature