Published

2010-07-01

ESTIMACIÓN PROBABILÍSTICA DEL CAMBIO CLIMÁTICO EN VENEZUELA MEDIANTE UN ENFOQUE BAYESIANO

PROBABILISTIC ESTIMATION OF CLIMATE CHANGE IN VENEZUELA USING A BAYESIAN APPROACH

Keywords:

estimación Bayes, inferencia posterior, modelo probabilístico (es)
Bayes estimation, Probabilistic model, Posterior inference (en)

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Authors

  • Alexis Durán Universidad Experimental Ezequiel Zamora
  • Lelys Guenni Universidad Simón Bolívar
El problema del cambio climático es uno de los grandes problemas ambientales que enfrenta la humanidad, ya que ligeras variaciones en las variables climáticas pueden traer graves consecuencias en las actividades económicas y el bienestar humano en general. Hoy en día los modelos de circulación general (MCG) de la atmósfera son la principal herramienta para estudiar los cambios climáticos. El Ministerio del Ambiente y de los Recursos Naturales (MARN) lideró en el año 2005 la Primera Comunicación Nacional en Cambio Climático de Venezuela, utilizando salidas de 16 MCGs a escala global (resolución de 5°x 5°), cuyas proyecciones estiman incrementos para la temperatura y disminución en la precipitación para los próximos años. Cada MCG arroja diferentes resultados generando incertidumbre en la señal del cambio climático futuro. Este trabajo utiliza un enfoque Bayesiano y una extensión del método Reliability Ensemble Average (REA) (Tebaldi et al. 2005), combinando las salidas (presente y futura) de precipitación y temperatura de los 16 MCG con observaciones de las condiciones climáticas actuales, con el fin de determinar las distribuciones de probabilidad del cambio climático futuro para estas dos variables climáticas en nueve regiones de Venezuela. Para el estudio se toman en cuenta dos criterios: sesgo, el cual considera la diferencia entre las salidas de los modelos y el clima actual, y convergencia, que cuantifica las diferencias en los cambios simulados por los múltiples modelos del clima futuro. El principal resultado obtenido del trabajo es que aún existe considerable incertidumbre en las proyecciones de los MCG, ya que estos no incluyen todos los aspectos sobre el funcionamiento del sistema climático. También se pudo establecer que mientras menor sea la variabilidad natural de la variable climática, más efectiva será su proyección.
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°x 5°) 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.

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