The early rainy season in Central America: the role of the tropical North Atlantic SSTs: EARLY RAINY SEASON IN CENTRAL AMERICA AND THE TNA SST
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We explored the relationship between the precipitation anomalies during May to June as the first peak of the rainy season in the Pacific slope of Central America, and sea surface temperature (SST ) fluctuations in the surrounding oceans, using canonical correlation analysis (CCA ). With this approach, we studied variations in total precipitation, frequency of rainy days and the monthly occurrence of days with rainfall above (below) the 80th (20th) percentile, due to changes in the nearby SST . Composites of the sea‐level pressure (SLP ), geopotential heights (200 hPa ), relative humidity (700 hPa ), horizontal moisture flux and wind at 850 hPa were estimated to provide a dynamical analysis. The composites are calculated using the information obtained with CCA . In addition, we used a general circulation model forced with fixed SST to explore the sensitivity of the model to the SST patterns found using CCA . The results show that the SST over the tropical North Atlantic controls the precipitation fluctuations at interannual scales, due to its connection with the tropical upper tropospheric trough. Warmer (colder) temperatures result in SLP below normal in the Caribbean region, associated with an increase in the heights at 200 hPa . This vertical configuration reduces the wind shear between 850 and 200 hPa and increases the input of humidity to mid‐levels, creating favourable conditions for deep convection, and favouring the generation of tropical cyclone activity. In the Pacific, a positive anomalous low‐level moisture flux is observed from the ocean to the continental parts of the region and may enhance the formation of mesoscale systems. The classic prediction schemes show a lead time of 1 or 2 months; this is an advantage for climate services operative work. The atmospheric model outcomes replicate the main results found in the composite analysis, reflecting its potential use for model output statistics predictive schemes.
