Séminaire
First, two large ensembles are used to quantify the extent to which internal variability can contribute to long-term changes in El Niño-Southern Oscillation (ENSO) characteristics. We diagnose changes that are externally forced and distinguish between multi-model simulation results that differ by chance and those that differ due to different model physics. The range of simulated ENSO amplitude changes in the large-ensemble historical simulations encompasses 90% of the Coupled Model Intercomparison Project 5 historical simulations and 80% of moderate (RCP4.5) and strong (RCP8.5) warming scenarios. When considering projected ENSO pattern changes, model differences are also important. We find that ENSO has high internal variability, and that single realizations of a model can produce very different results to the ensemble mean response. Due to this variability, 30-40 ensemble members of a single model are needed to robustly compute absolute ENSO variance to a 10% error when 30-year analysis periods are used.
Secondly we use machine learning tools to build a robust ENSO classification algorithm. We use the observational record, where ENSO has been manually classified, to train seven classification algorithms. Given the relatively short record and limited observations of strong ENSO events, we extend the training data by using five different sea surface temperature observational products (OISST, HadISST, COBE, GODAS and AMSRE). Of the seven classifiers tested, the Neural Network classifier has the best performance when tested on a sixth observational product (ERSST). Preliminary results show that there is no change in the mean frequency of each type of ENSO event under any of the forcing scenarios when the algorithm is applied to five large ensembles.
jv@locean-ipsl.upmc.fr