On the use of near-neutral Backward Lyapunov Vectors to get reliable ensemble forecasts in coupled ocean-atmosphere systems

TL;DR

This paper demonstrates that near-neutral Backward Lyapunov Vectors are most effective for initializing ensemble forecasts in coupled ocean-atmosphere models, improving the spread and transfer of errors across scales.

Contribution

It introduces the use of near-neutral BLVs for coupled forecast initialization, showing their advantage over other vectors in a reduced-order model.

Findings

Near-neutral BLVs have larger projections on ocean variables.

These BLVs facilitate rapid error transfer to unstable atmospheric modes.

Using near-neutral BLVs improves ensemble forecast reliability at multiple scales.

Abstract

The use of coupled Backward Lyapunov Vectors (BLV) for ensemble forecast is demonstrated in a coupled ocean-atmosphere system of reduced order, the Modular Arbitrary Order Ocean-Atmosphere Model (MAOOAM). It is found that overall the best set of BLVs to initialize a (multiscale) coupled ocean-atmosphere forecasting system are the ones associated with near-neutral or slightly negative Lyapunov exponents. This unexpected result is related to the fact that these sets display larger projections on the ocean variables than the others, leading to an appropriate spread for the ocean, and at the same time a rapid transfer of these errors toward the most unstable BLVs affecting predominantly the atmosphere is experienced. The latter dynamics is a natural property of any generic perturbation in nonlinear chaotic dynamical systems, allowing for a reliable spread with the atmosphere too. Furthermore, this specific choice becomes even more crucial when the goal is the forecasting of low-frequency variability at annual and decadal time scales. The implications of these results for operational ensemble forecasts in coupled ocean-atmosphere systems are briefly discussed.