Abstract
Atlantic Niño, lasting approximately 3 months, manifests as pronounced sea surface temperature (SST) anomalies in the eastern equatorial Atlantic on the interannual time scale. There are two primary types of Atlantic Niño events: one peaking in boreal summer and the other in boreal winter. Sources of dynamical predictability for the two types of Atlantic Niño remain elusive. Through the analysis of seasonal forecasts and hindcasts from various climate models, the present study uncovers distinct sources of dynamical predictability for the boreal summer and winter Atlantic Niño. The prediction skill of the boreal summer Atlantic Niño is closely associated with SST anomalies in the Angola coast, especially those tied to the Benguela Niño. In contrast, the prediction skill of the boreal winter type is significantly influenced by the Indian Ocean Dipole (IOD) and El Niño–Southern Oscillation (ENSO). Due to the superior predictability of the IOD and ENSO in boreal autumn, there is an enhanced prediction skill for the boreal winter Atlantic Niño. Conversely, climate models often struggle to predict the Benguela Niño, leading to a diminished prediction accuracy for the boreal summer Atlantic Niño. Further analysis reveals that the strength of the simulated Atlantic–Benguela Niño connection is sensitive to the Benguela Niño-related surface wind anomalies over the equatorial western–central Atlantic and Angola coast. These results imply that the prediction skill of the Atlantic Niño, especially for the boreal summer type, might be further improved through better capturing the Atlantic–Benguela Niño connection in the models.
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Data availability
The HadISST dataset is available at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. The GODAS dataset is available at https://climatedataguide.ucar.edu/climate-data/godas-ncep-global-ocean-data-assimilation-system. The ERA5 reanalysis dataset and the EUROSIP seasonal forecasts and hindcasts are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/. The NMME model datasets can be obtained from http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/, and the CMME model datasets are available from the corresponding author on reasonable request.
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The authors are grateful to the two anonymous reviewers for their insightful comments, which helped us improve the quality of this paper.
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This work is jointly supported by the National Natural Science Foundations of China (41975102, 42375064, U2142211), the Joint Research Project for Meteorological Capacity Improvement (22NLTSZ002), the National Key Research and Development Program of China (2018YFC1506003), and the China Meteorological Administration Key Innovation Team for Climate Prediction (CMA2023ZD03).
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Liu, A., Zuo, J., Chen, L. et al. Distinct sources of dynamical predictability for two types of Atlantic Niño. Clim Dyn (2024). https://doi.org/10.1007/s00382-024-07169-3
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DOI: https://doi.org/10.1007/s00382-024-07169-3