1CSIRO, Melbourne, VIC
El Niño‒Southern Oscillation (ENSO) is the dominant mode of tropical interannual climate variability, with large influence on global weather and climate. Here we construct an empirical dynamical model of tropical Pacific air-sea interactions to investigate various factors affecting ENSO prediction skill. A hierarchy of models with increasing complexity have been constructed using data for 1958-1990 and retrospective forecasts are made for 1991- 2017 for each of the models. The model with the best ENSO prediction skill is then chosen as a reference model. The reference model’s predictability limit, defined here as the forecast lead month of 0.5 anomaly correlation (AC), is around 11 months. After establishing the suitability of this model by comparing its simulated ENSO properties with the observed, we use it to determine the relative importance of several factors affecting the model’s ENSO prediction skill. In particular, we examine the extent to which ENSO prediction skill is affected by the main atmosphere-ocean interaction processes―thermocline and zonal wind feedbacks and zonal wind forcing―on ENSO predictability. We find that all these processes significantly affect ENSO predictability and extend the predictability limit by up to five months, with the largest effect coming from the thermocline feedback. The other processes with progressively smaller effects are the total zonal wind forcing, zonal wind feedback and external zonal wind forcing. This result suggests that the dynamical seasonal prediction models must have a good representation of the major ENSO processes in order to have good ENSO prediction skills.
Dr Harun Rashid is a senior research scientist in CSIRO Climate Science Centre of the Oceans and Atmosphere BU. His expertise is in understanding and predicting climate variability using dynamical and empirical models of the earth’s climate system. His current interest is modelling El Niño‒Southern Oscillation (ENSO) using Coupled Global Climate Model (CGCM) and Empirical Dynamical Models (EDMs).