Abstract

DURAN, ALEXIS  and  GUENNI, LELYS. Probabilistic Estimation of Climate Change in Venezuela using a Bayesian approach. Rev.Colomb.Estad. [online]. 2010, vol.33, n.2, pp.191-218. ISSN 0120-1751.

The changing climate is one of the main environmental problems facing humanity, since slight variations in the climate variables might have terrible consequences in the economic activities and human well-being. Nowadays atmospheric Global Circulation Models (GCMs) are the main tools to study changing climate. The Ministry of Environment and Natural Resources (MENR) led in 2005 the First Communication in Climate Change of Venezuela, using the outputs of 16 GCMs at a global scale (resolution of 5^\circ \times 5^\circ) whose projections estimate increasing temperature and diminishing precipitation in the coming years. Each GCM gives different results, generating uncertainty in the future changing climate signal. This work uses a Bayesian approach and an extension of the Reliability Ensemble Average (REA) (Tebaldi et al. 2005) method, combining the outputs (present and future) of precipitation and temperature of the 16 GCMs with observations of present climate conditions, to determine the probability distributions of future changing climate change for these two climate variables in 9 regions in Venezuela. For this study, two criteria are used: bias, which considers the difference between the model outputs and the present climate; and convergence, which quantifies the differences among the simulated changes of future climate by multiple models. The main result of this work is that a large amount of uncertainty still exists in the GCMs projections, since they as yet do not include all aspects of the climate system functioning. It was also concluded that the lower the natural variability in the climate variable, the more effective is its projection.

Keywords : Bayes estimation; Probabilistic model; Posterior inference.

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