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Evaluation and application of Bayesian multi-model estimation in temperature simulations

Auteur(s) et Affiliation(s)

MIAO, C.
Normal Univ., Beijing, Chine
DUAN, Q.
Normal Univ., Beijing, Chine
SUN, Q.
Normal Univ., Beijing, Chine
LI, J.
Normal Univ., Beijing, Chine


Description :
This paper assesses the performance of multi-model ensembles in simulating global land temperature from 1960 to 1999, using Nash-Sutcliffe model efficiency and Taylor diagrams. The future trends of temperature for different scales and emission scenarios are projected based on the posterior model probabilities estimated by Bayesian methods. The results show that ensemble prediction can improve the accuracy of simulations of the spatiotemporal distribution of global temperature. The performance of Bayesian model averaging (BMA) at simulating the annual temperature dynamic is significantly better than single climate models and their simple model averaging (SMA). However, BMA simulation can demonstrate the temperature trend on the decadal scale, but its annual assessment of accuracy is relatively weak. The ensemble prediction presents dissimilarly accurate descriptions in different regions, and the best performance appears in Australia. The results also indicate that future temperatures in northern Asia rise with the greatest speed in some scenarios, and Australia is the most sensitive region for the effects of greenhouse gas emissions.


Type de document :
Article de périodique

Source :
Progress in physical geography, issn : 0309-1333, 2013, vol. 37, n°. 6, p. 727-744, nombre de pages : 18, Références bibliographiques : 2 p.

Date :
2013

Editeur :
Pays édition : Royaume-Uni, London, Sage Publications

Langue :
Anglais
Droits :
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