LISTEN designs and implements two innovative initialisation techniques: the first one is the quantile matching method, that aims at tackling the drift and the potential inconsistencies between the observed/model distribution of variability. The drift is avoided by choosing an initial state that belongs to the model attractor (i.e. the ensemble of all the model trajectorie) and the variability amplitude incompatibilities are corrected by matching the observed and model statistical distributions.
The most remarkable impacts of the quantile matching technique are found in the North Atlantic. First of all, the quantile matching predictions overcome the issue of the deep convection collapse in the Labrador Sea, which the standard full field predictions experience. As an effect of the correct representation of the convection, the skill of the barotropic stream function in the Western subpolar North Atlantic sector is significant throughout the whole forecast period and is the highest compared to that found in historical simulations and full field decadal predictions. Also the Atlantic Meridional Overturning Circulation is skillfully predicted by the quantile matching predictions throughout the whole forecast time, and the main improvements of sea surface temperature and ocean heat content skill are found in the North Atlantic subpolar gyre sector.
The second innovative experiment implemented in LISTEN is the analogue method, which attempts to address the issue of the geographical mismatch between the model and the observed variability modes.
The analogue method consists in choosing an initial state that belongs to the model attractor and whose amplitude of the main variability modes is as close as possible to the amplitude of the corresponding reference modes, at the initialisation time. The initialisation method has been implemented and the production of the decadal climate predictions initialised with the analogue method is currently ongoing.
The analysis of the weather regimes has shown a remarkable ability of the quantile matching predictions in reproducing the regimes patterns. Moreover, the predictions also show a good performance in capturing the regime frequency and persistence, with a tendency to slightly overestimate short events and underestimate longer events.
