This study investigates the dynamics of seasonal precipitation in Suriname with the help of moisture tracking and Sea Surface Temperature correlations. Prolonged drought periods, heavy precipitation events and accompanying flash floods and landslides are expected to increase in Suriname in the coming years. Reliable seasonal precipitation forecasts are therefore of great value to the agriculture, livestock and energy sectors in Suriname, but also for drought and flood mitigation. The objective of this study is to look into the sources of the predictive skill for seasonal precipitation forecasts for Suriname.

First, Spearman rank correlation analysis between sea surface temperature anomalies and Suriname precipitation anomalies was performed to establish the potential for statistical seasonal precipitation forecasts from SSTs. Second, moisture tracking with the model WAM2layers was applied to find the origin areas of evaporation for precipitation in Suriname. At last, these patterns of correlation and moisture tracking were combined with composite data to determine which potential drivers are influencing the precipitation anomalies in Suriname.

The results show that the ASON season displays the largest SST correlations (0.4 for Nino3 and Nino3.4 index with a lag of 3 months) and the longest lag time at which correlations are visible. This is due to the significant influence of ENSO on the ASON season precipitation. The DJFM season shows a clear link towards the Tropical Atlantic Variability and especially the AMM mode. Positive (negative) SST in the tropical north Atlantic cause a northward (southward) shift of the ITCZ over
Suriname. The AMJJ season reveals the smallest skill to predict seasonal precipitation anomalies. This could possibly be enhanced by performing a sub-seasonal analysis.

The processes of potential skill found in this thesis report can be of help to forecasters for the improvement of seasonal precipitation forecasts for Suriname