A model-independent strategy for the targeted observation analysis and its application in ENSO prediction

The model-dependency has been a challenging issue for traditional data assimilation (DA)-based targeted observational method. This study developed a new strategy to address this challenge using multiple-model prediction ensemble. It was found that while the ensemble size reaches a sufficiently large number the optimal observational sites detected tend to stable and model-independent. This new finding answers the long-standing challenge question on the model dependence in targeted observational analysis, offering an efficient and objective way to identify optimal observational sites.

With this strategy, we designed an optimal observational array in the tropical Pacific for the El Niño-Southern Oscillation (ENSO) prediction using the multiple historical simulation datasets from Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis datasets. Sensitive experiments show that while number of datasets reaches 12, a robust optimal observational array is obtained. The first 10 optimal observational sites, mostly located in the equatorial central eastern Pacific, can reduce initial uncertainties by 67%. This was further confirmed by the observation system simulation experiments (OSSE), which is implemented by the EAKF (Ensemble Adjustment Kalman Filter) assimilation system developed in the Community Earth System Model (CESM). This newly developed model-independent strategy makes it feasible to design a robust oceanic observational network for ENSO prediction even using the current targeted observational algorithm, well serving the goal of international Tropical Pacific Observation System (TPOS) 2020 project.