Intraseasonal Variability of Rainfall over Tanzania During March-April-May (MAM) Season of 2022

This study investigates the intraseasonal variability (ISV) of rainfall in Tanzania during the March-April-May (MAM) season, specifically identifying the dominant peaks of ISV in rainfall for that period. The 5-day running mean during the MAM season reveals that Tanzania experienced an irregular pattern of wet and dry days in the year 2022, indicating the presence of ISV that led to fluctuations in weather patterns. Moreover, the study identifies the dominant peak date, where a significant peak was observed in the 10 - 25-day range, showing that ISV exhibits a quasi-biweekly oscillation around 17 days, with composite evolution from day ？8 to day +8 after filtering, and day 0 marking peak rainfall. Furthermore, composite atmospheric circulation analysis reveals critical interactions with ISV. Geopotential height wind patterns at 850 hPa indicate that negative/positive geopotential height anomalies over the Western Indian Ocean and Mozambique Channel enhance low-level convergence/divergence of moisture, resulting in wet/dry phase, meanwhile strong positive geopotential height anomalies at 200 hPa are associated with the upper-level divergence that supports peak rainfall (day 0). During Lag ？4 to Lag 0, the results revealed dominant negative OLR anomalies (？18 to ？20 W/m²) indicating peak dates of ISV of rainfall while the transition to positive OLR anomalies after Lag +2 showed the starting point of a dry phase of ISV. Also, at the initial phase (Lag ？8 to Lag ？6), weak positive and limited moisture flux anomalies were observed over the region, while in the peak phase (Lag 0), strong positive anomalies dominated, reflecting intense moisture convergence from both the South West Indian Ocean (SWIO) and the Congo Basin, associated with maximum ISV of rainfall activity. After lag 0, transition into the dry phase (Lag +6 to Lag +8), negative anomalies developed as moisture transport diminishes and winds shift, suppressing convergence over Tanzania, leading to the dry phase. The results highlight the significance of integrating ISV patterns into weather forecasting and disaster preparedness to reduce the risks associated with extreme rainfall events like floods and droughts. Additionally, the findings offer valuable insights for managing water resources, planning agriculture, and enhancing climate resilience in areas of Tanzania that depend on rainfall.