Interannual variability of the early and late-rainy seasons in the Caribbean

The Caribbean seasonal rainfall cycle and its characteristics are heavily relied upon by the region's inhabitants for their socioeconomic needs; the prediction of its variability would be valuable to society. An important way to understand the predictability of the Caribbean rainfall cycle is to study its interannual variability. Previous studies vary as to how and what large-scale climate driver(s) affect the interannual variability of rainfall and its associated dynamical mechanisms in the Caribbean. To address this, this study investigates wet and dry Caribbean early-rainy seasons (ERS; mid-April to mid-June) and late-rainy seasons (LRS; late August to mid-November) by conducting the following: (1) a spatial composite of rainfall from 34 Caribbean rainfall stations using daily data; and, (2) spatial composites of sea-surface temperature, sea-level pressure, and mean flow moisture convergence and transports. The ERS and LRS are impacted in distinctly different ways by two different, and largely independent, dominant large-scale phenomena: the North Atlantic Oscillation (NAO) and the El Nino-Southern Oscillation (ENSO), respectively. Dry ERS years are associated with a persistent dipole of cold and warm SSTs over the Caribbean Sea and Gulf of Mexico, respectively, that were caused by a preceding positive NAO state. This setting involves a wind-evaporation-SST (WES) feedback expressed in enhanced trade winds and consequently, moisture transport divergence over all of the Caribbean, except in portions of the NW Caribbean in May. A contribution from the preceding winter cold ENSO event is also discernible during dry ERS years. Dry LRS years are due to the summertime onset of an El Nino event, developing an inter-basin SLP pattern that fluxes moisture out of the Caribbean, except in portions of the NW Caribbean in November. Both large-scale climate drivers would have the opposite effect during their opposite phases leading to wet years for both seasons. The two rainy seasons are independent because the main drivers of their variability are independent. This has implications for prediction.