Skillful long-range forecasts of El Niño-Southern Oscillation (ENSO) are still in high demand.
After decades of extensive efforts, dynamical models nowadays represent the best available tools to issue ENSO forecasts at lead times of up to two seasons, although they are still largely constrained by the lack of complete understanding of the physics of the phenomenon, by problems arising from the initialization of the components of the climate system, or by the need for accurate parameterization of important physical processes (Barnston et al. 2012).
Statistical models, on the other hand, largely depend on the availability of ocean and atmosphere historical data, so that the longer the length of the data, the more robust is the predictor-predictand relationship identified by the model (Barnston et al. 2012). In addition to these factors, the low signal-tonoise ratio in boreal spring (Sarachik and Cane 2010), the influence of high-frequency atmospheric winds (Fedorov et al. 2003, 2015), and the natural irregularity of the climate system (Wittenberg 2009) all limit the long-term dynamical and statistical forecasting of the phenomenon.
In the Kalman filter NL statistical model with dynamic components time series we are testing the added skill of El Niño. The distinctive feature of this type of models is that they decompose the time series of interest into dynamic components that represent linear stochastic processes with separate evolutions (Durbin and Koopman 2012). In a new development, we attempt to improve our first versions of all model, by replacing the previously fixed seasonal component with two slowly varying annual and semiannual periodic components (Petrova et al., 2024), and also by including one additional timevarying cycle component corresponding to ENSO as the explanatory interannual covariate.
To this end, we are testing to potential contribution of different climate indices representing variable and differing portions of the complex ENSO mechanism. This way, knowing that El Niño Modoki events -that is central TPAC developing events- appear to be more frequent in the recent decades, we test for the climate of Thailand, their contributing role. in a first attempt, we can see how when comparing Niño3 and Nino 4 indices, we observe differential impacts on the temperature in the country in the interval 1980-2000(Fig. 1).

Same as for precipitation (Fig. 2).

With the impact of the central TPAC on the climate of Thailand being more pronounced for the most recent period (Fig. 3 and 4).

We are now testing the effects that the introduction of such an additional explanatory covariate has on the skill and performance of the Kalman configuration to advance and increase predictability of dengue outbreaks in Thailand.