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A linear inverse model of tropical and South Pacific seasonal predictability

Citation

Lou, J and O'Kane, TJ and Holbrook, NJ, A linear inverse model of tropical and South Pacific seasonal predictability, Journal of Climate, 33, (11) pp. 4537-4554. ISSN 0894-8755 (2020) [Refereed Article]


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Copyright Statement

Copyright 2020 American Meteorological Society

DOI: doi:10.1175/JCLI-D-19-0548.1

Abstract

A multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extratropical sea surface temperature, and South Pacific Ocean vertically averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble El Niño–Southern Oscillation (ENSO) and the South Pacific decadal oscillation (SPDO). We show that although the oscillatory periods of ENSO and SPDO are distinct, they have very close damping time scales, indicating that the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the midlatitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the high-frequency atmospheric variability of the Pacific–South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in March–May (MAM) but displays a significant correlation skill of up to ~3 months. For the SPDO, the predictability barrier tends to appear in June–September (JAS), indicating remote but delayed influences from the tropics. We demonstrate that subsurface processes in the South Pacific Ocean are the main source of decadal variability and further that by characterizing the upper ocean temperature contribution in the LIM, the seasonal predictability of both ENSO and the SPDO variability is increased.

Item Details

Item Type:Refereed Article
Keywords:Linear Inverse Model, tropical and South Pacific, seasonal predictability
Research Division:Earth Sciences
Research Group:Oceanography
Research Field:Physical oceanography
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Understanding climate change
Objective Field:Climate variability (excl. social impacts)
UTAS Author:Lou, J (Mr Jiale LOU)
UTAS Author:Holbrook, NJ (Professor Neil Holbrook)
ID Code:139175
Year Published:2020
Web of Science® Times Cited:1
Deposited By:Oceans and Cryosphere
Deposited On:2020-05-29
Last Modified:2021-04-28
Downloads:0

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