Improvements in SINTEX‐F2 seasonal prediction by implementing an atmospheric nudging scheme

• Published in: International journal of climatology Vol. 43; no. 14; pp. 6900 - 6923
• Main Author: Baba, Yuya
• Format: Journal Article
• Language: English
• Published: 30.11.2023
• ISSN: 0899-8418; 1097-0088; 1097-0088
• DOI: 10.1002/joc.8242

Improvements in the seasonal prediction system Scale Interaction Experiment‐Frontier version 2 (SINTEX‐F2) achieved via atmospheric initialisation were evaluated using hindcast experiments. The atmospheric initialisation in the system was implemented using an atmospheric nudging scheme. The prediction skill of SINTEX‐F2 with and without initialisation (new and original systems) for nine climate indices that represent the interannual variability were evaluated against the observational data. It was found that the prediction skill for some climate indices in the Tropics and most of the climate indices in the midlatitude including subtropics was improved by the atmospheric initialisation. The skills for El Niño–Southern Oscillation (ENSO) and ENSO Modoki, however, remained comparable with the original system. Further investigation for the prediction skill revealed that the sea level pressure (SLP) in the midlatitude was predicted better by the new system than in the original system. The 2‐m temperature was better predicted by the new system, due to the improved SLP prediction. The precipitation prediction was improved in regions where the improved interannual variability had an influence on the variability of precipitation. The difference in air–sea coupling regimes was found to be effective for improving the prediction skill in different latitudes. The atmospheric nudging enhances correct atmospheric feedback on the ocean in midlatitude leading to better subsurface ocean properties; thus, it can improve the prediction skill there. However, by neglecting the ocean side feedback, it deteriorates the prediction skill in Tropics where the sea surface temperature (SST) feedback on the atmosphere is dominant. The finding for the prediction skill is general and can be used for other prediction systems to increase their prediction skill using an atmospheric nudging scheme.