A note on the role of the initial error structure in the tropics on the seasonal-to-decadal forecasting skill in the extratropics

TL;DR

This study investigates how the initial error structure in tropical models influences long-term seasonal-to-decadal forecast skill in the extratropics, highlighting the importance of error control for improved predictability.

Contribution

It demonstrates that perturbing initial errors along the dominant Lyapunov vector enhances forecast accuracy and reduces biases in idealized coupled models.

Findings

Biases develop when perturbations are along all Lyapunov vectors.

Focusing on the dominant Lyapunov vector reduces mean square error.

Proper initial error control improves long-term forecast reliability.

Abstract

The predictability of a coupled system composed by a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order 3-variable tropical recharge-discharge model, is explored with emphasis on the long term forecasting capabilities. Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system, with a specific attention to the structure of the initial errors in the tropical model. Three main types of experiments are explored with random perturbations along the three Lyapunov vectors of the tropical model, along the two dominant Lyapunov vectors, and along the first Lyapunov vector, only. When perturbations are introduced along all vectors, forecasting biases are developing even if in a perfect model framework. Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector. This perturbation strategy allows furthermore for getting a reduced mean square error at long lead times of a few years, and to get reliable ensemble forecasts on the whole time range. These very counterintuitive findings further underline the importance of appropriately control the initial error structure in the tropics through data assimilation.