Lagged ensembles in sub‐seasonal predictions
Frédéric Vitart, Yuhei Takaya
Abstract
Abstract The benefits of using lagged ensembles for S2S predictions have been evaluated using the ECMWF sub‐seasonal to seasonal (S2S) forecasts. The ECMWF S2S forecasts are currently produced twice a week from a 51‐member ensemble. An experiment where the ECMWF forecasts are produced daily instead of twice weekly has been performed to identify the minimum lagged‐ensemble configuration which will produce probabilistic forecasts at least as skilful as the current configuration. Results suggest that the extended‐range prediction of the Madden–Julian Oscillation (MJO) is not deteriorated with the daily lagged ensemble approach, but there is a slight degradation during the first 10 days. In the northern Extratropics, the probabilistic skill scores are improved after week 2 when using lagged ensembles, but only if the daily ensemble size exceeds 20 members. The lagged ensemble approach is more beneficial in the Tropics than in the northern Extratropics, showing even benefits in forecast week 1 over the Tropics, despite the 24 hr difference between two consecutive forecasts. As expected, the daily lagged ensemble method is more beneficial for longer lead times and the optimal lagged ensemble window increases from 2 days for week 2 to 4 days for week 4. An idealized numerical simulation, based on a Monte Carlo approach, confirms the benefits of lagged ensembles. The results obtained are generally consistent with the ECMWF forecast experiment, suggesting that such a simulation could be used as a cheap alternative to address the benefit of lagged versus burst ensembles.