Seasonal prediction of the Caribbean rainfall cycle

Abstract

Rainfall in the Caribbean is an important resource for numerous stakeholders in the region. Based on previous work, which identified several variables that could provide predictive skill of rainfall in the region, canonical correlation analysis is applied to assess forecast skill for station-averaged sub-regional frequency and intensity of the Early-Rainy Season (ERS) and Late-Rainy Season (LRS) wet days, and magnitude of the Mid-Summer Drought (MSD). Predictor fields are explored from the ERA-Interim and North American Multi-Model Ensemble (NMME). The use of sea-level pressure (SLP), 850 hPa zonal winds (u850), vertically integrated zonal (UQ), and meridional (VQ) moisture fluxes show comparable, if not better, forecast skill than sea-surface temperatures (SSTs), which is generally the commonly-used predictor in a given region’s seasonal climate forecasts. Generally, the highest predictive skill is found for the frequency of wet days. Rainfall characteristics in the Central and Eastern Caribbean have significant predictive skill. Forecast skill of rainfall characteristics in the Northwestern and Western Caribbean are lower and less consistent. The sub-regional differences and consistently significant skill across lead times up to at least two months can be attributed to persistent SST/SLP anomalies during the ERS that resemble the North Atlantic Oscillation, and that resembles the summer-time onset of the El Niño-Southern Oscillation during the LRS. The anomalous spatial patterns during the MSD bear resemblance to both the ERS and LRS signals. The results provide additional variables that can be used to forecast rainfall characteristics in the Caribbean and a way to tailor seasonal forecasts for each sub-region.

Keywords

• Caribbean rainfall
• S2S
• ENSO
• NAO
• Prediction
• Climate service