Keywords
precipitation
cluster analysis
trend analysis
unsupervised machine learning
gap statistic
climate variability
América Central
precipitación
análisis de conglomerados
aprendizaje automatizado
estadístico de brecha
análisis de tendencias
variabilidad climática
How to Cite
Abstract
The geographical location of Central America plays a significant role in describing the climate variability of the region. It is surrounded by two large water masses, the Eastern Tropical Pacific ocean on the western side and the Caribbean Sea on the eastern side. The region is sensitive to the effect of both large-scale and regional-scale dynamical systems acting in its vicinity, being the topography the main local modulator of the variability in the region. To account this spatial climate variability we used 57 meteorological stations with daily precipitation data in Central America for the period 1976-2015. Monthly indices were defined to describe how much and how it rains: monthly total accumulated (ACU), number of days with rain (DCP), percentage of days that do not exceed the 20th percentile (dry extremes), and the percentage of days that exceeds the percentile 80th (wet extreme). Using automated learning techniques (cluster analysis), an optimal number of groups was estimated for each variable. Optimization was performed using the gap statistic. A pattern of groups located primarily on the Pacific and Caribbean slopes was found, while in all variables a group located in the Caribbean region of Costa Rica was identified. When analyzing the changes in the 40 years of analysis, no significant changes or trends were found in the monthly seasonal or annual time scales or at the station, group or regional level.
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