Open AccessTwo Approaches to Quantifying Uncertainty in Global Temperature Changes
Author(s) -
Ana María López,
Claudia Tebaldi,
Mark New,
David A. Stainforth,
Myles Allen,
J. Kettleborough
Publication year - 2006
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/jcli3895.1
Subject(s) - probabilistic logic , general circulation model , climatology , gcm transcription factors , bayesian probability , forcing (mathematics) , climate change , statistical model , probability distribution , environmental science , global temperature , climate model , econometrics , computer science , mathematics , global warming , statistics , geology , oceanography
A Bayesian statistical model developed to produce probabilistic projections of regional climate change using observations and ensembles of general circulation models (GCMs) is applied to evaluate the probability distribution of global mean temperature change under different forcing scenarios. The results are compared to probabilistic projections obtained using optimal fingerprinting techniques that constrain GCM projections by observations. It is found that, due to the different assumptions underlying these statistical approaches, the predicted distributions differ significantly in particular in their uncertainty ranges. Results presented herein demonstrate that probabilistic projections of future climate are strongly dependent on the assumptions of the underlying methodologies.