Utilization of El Nino-Southern Oscillation projected by climate models in improvement of seasonal precipitation predictability

Improved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Nino-Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision-making processes.