Predicción estacional del clima en Centroamérica mediante la reducción de escala dinámica. Parte II: aplicación del modelo MM5V3

In the first part of this work it was determined that general circulation model(GCM) ECHAM4.5 shows more ability than CCM3.6 to simulate key climate featuresof Central America. For such reason, output from ECHAM4.5 was used to perform adynamical downscaling experiment using the regional model MM5v3,...

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Autores Principales: Rivera Fernández, Erick, Amador Astúa, Jorge Alberto
Formato: Artículo
Idioma: Español
Publicado: 2015
Acceso en línea: http://revistas.ucr.ac.cr/index.php/matematica/article/view/1420
http://hdl.handle.net/10669/12948
Sumario: In the first part of this work it was determined that general circulation model(GCM) ECHAM4.5 shows more ability than CCM3.6 to simulate key climate featuresof Central America. For such reason, output from ECHAM4.5 was used to perform adynamical downscaling experiment using the regional model MM5v3, in which a setof high-resolution simulations (of up to 30-km horizontal resolution) was generated forJanuary 2000.The results of the dynamical downscaling allow to conclude that MM5v3 is able tosuitably reproduce aspects of the Central American climate that GCMs cannot capturebecause of their coarse horizontal resolution, their limitations in representing both theregional topography and the mesoscale dynamical interactions. Comparison with dataderived from observations indicates that the MM5v3 simulates the region of maximumlow-level wind that is related to the Intra-Americas Seas Low Level Jet, although theregional model underestimates its intensity. Regarding the precipitation patterns, theyagree with those derived from the observations (drier areas in the Pacific, wetter areasin the Caribbean). Nevertheless, there is a generalized overestimation in the amountof simulated rain. The analysis of the standard deviation for a twelve-member sampleshows areas in which MM5v3 has greater dispersion or uncertainty (mainly to thesouth of Panama).Keywords: numerical models, seasonal climate prediction, dynamical downscaling, climate,climate variability.