Impact of atmospheric horizontal resolution on El Nino Southern Oscillation forecasts

ENSO, Southern Oscillation, Forecast

In this paper, we assess the skill of a seasonal forecast system to predict El Nino Southern Oscillation (ENSO) anomalies and the impact that the horizontal resolution of the atmospheric component has on the forecasts of the growing phase of strong ENSO events. The analysis has been carried out on retrospective six-month forecasts for the period 1973-2001 performed with the Istituto Nazionale di Geofisica e Vulcanologia (INGV) coupled ocean-atmosphere general circulation model (SINTEX) in the framework of the DEMETER project. Each forecast is formed by an ensemble of nine integrations that start from perturbed initial conditions. In order to consider the possible impact of the seasonal cycle on the forecasts, four different dates of the year have been chosen as initial conditions. The results indicate that, in general, the model is reasonably good in predicting the distribution of the seasonal anomalies in the tropical Pacific. However, it is also shown that the model predictions underestimate substantially the amplitude of the anomalies during the developing phase of intense El Nino episodes. The forecasts of strong ENSO events in the period here considered have been repeated, increasing the horizontal resolution of the atmospheric component. In these experiments, both the ocean model component and the oceanic initial conditions are unchanged. During the decaying phase of the oscillation, the high-resolution and low-resolution versions of the model produce similar results and the forecasts are in general good agreement with the observations. During the developing phase of ENSO, the low-resolution version of the model is unable to sustain the growth of the coupled disturbance. With the low-resolution atmosphere, the initial perturbation of the coupled system decays and the anomalies in the tropical Pacific tend to vanish very quickly. With the high-resolution atmosphere, on the other hand, the model appears to be able to sustain the growth of the disturbance, improving the quality of the forecasts both for El Nino and La Nina cases.