Abstract
This paper investigates the optimal observational array for improving the El Niño-Southern Oscillation (ENSO) prediction by exploring sensitive areas for target observations of two types of El Niño events in the whole Pacific. A target observation method based on the particle filter and pre-industrial control runs from six coupled model outputs in Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments are used to quantify the relative importance of the initial accuracy of sea surface temperature (SST) in different Pacific areas. The initial accuracy of the tropical Pacific, subtropical Pacific, and extratropical Pacific can all exert influences on both types of El Niño predictions. The relative importance of different areas changes along with different lead times of predictions. Tropical Pacific observations are crucial for decreasing the root mean square error of predictions of all lead times. Subtropical and extratropical observations play an important role in decreasing the prediction uncertainty, especially when the prediction is made before and throughout the boreal spring. To consider different El Niño types and different start months for predictions, a quantitative frequency method based on frequency distribution is applied to determine the optimal observations of ENSO predictions. The final optimal observational array contains 31 grid points, including 21 grid points in the equatorial Pacific and 10 grid points in the North Pacific, suggesting the importance of the initial SST conditions for ENSO predictions not only in the tropical Pacific but also in the area outside the tropics. Furthermore, the predictions made by assimilating SST in sensitive areas have better prediction skills in the verification experiment, which can indicate the validity of the optimal observational array designed in this study.
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Data availability
The Climate Model Intercomparison Project (CMIP) datasets applied in this study are available online (https://esgf-node.llnl.gov/search/cmip5/).
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Acknowledgements
The authors thank two anonymous reviewers who provided constructive comments that greatly improved the overall quality of the paper. All figures in this study were generated by the NCAR Command Language (version 6.4.0, https://doi.org/10.5065/D6WD3XH5).
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This study is supported by the National Natural Science Foundation of China (Grant Nos. 42130409, 42106004, and 41606012).
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Hou, M., Tang, Y., Duan, W. et al. Toward an optimal observational array for improving two flavors of El Niño predictions in the whole Pacific. Clim Dyn 60, 831–850 (2023). https://doi.org/10.1007/s00382-022-06342-w
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DOI: https://doi.org/10.1007/s00382-022-06342-w