Towards operational climate prediction: ENSO-related variability as simulated in a set of state-of-the-art seasonal prediction systems

In the last decade, high demands from stakeholders and policymakers have driven unprecedented research efforts directed to improve climate predictability. Nevertheless, attempts to get operational climate predictions on seasonal time scales have been far from skillful for a long time. Based on sources of predictability from the ocean, atmosphere and land processes, current state-of-the-art prediction systems are approaching operational predictability. This work examines and compares the ability of different prediction systems to simulate the variability of sea surface temperatures (SSTs) associated with El Niño-Southern Oscillation (ENSO) and the ENSO-forced response of hydroclimate variability in the North Atlantic-Europe (NAE) region. Seasonal hindcasts derived from two generations of the Norwegian Earth System Model (NorESM1-ME and NorESM2-MM) are used in addition to C3S data to generate time series of year-to-year variability that are validated against observational data. Our results reveal both the advantages and the limitations of these prediction systems to simulate ENSO-related variability, identifying model biases that prevent skillful predictability. Further efforts must be aimed at mitigating these biases in order to achieve fully operational predictions of paramount importance for the benefit of society.