Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles

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Abstract

We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors.

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APA

Kim, O. Y. (2018). Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles. Climate Dynamics, 50(9–10), 3237–3250. https://doi.org/10.1007/s00382-017-3802-y

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