scholarly journals A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

2020 ◽  
Vol 33 (11) ◽  
pp. 4537-4554 ◽  
Author(s):  
Jiale Lou ◽  
Terence J. O’Kane ◽  
Neil J. Holbrook
Keyword(s):  
Pacific Ocean ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
South Pacific ◽  
Inverse Model ◽  
Deterministic System ◽  
The South ◽  
Seasonal Predictability ◽  
South Pacific Ocean ◽  
Linear Inverse Model

AbstractA 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.

scholarly journals A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

2020 ◽  
Author(s):  
Jiale Lou ◽  
Terence O'Kane ◽  
Neil Holbrook
Keyword(s):  
Pacific Ocean ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
South Pacific ◽  
Inverse Model ◽  
Deterministic System ◽  
The South ◽  
Seasonal Predictability ◽  
South Pacific Ocean ◽  
Linear Inverse Model

<p>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 extra-tropical 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 the 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 the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the mid-latitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the atmospheric high-frequency variability of the Pacific South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in Mar-May (MAM) but nevertheless displays 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.</p>

scholarly journals South Pacific Ocean Dipole: A Predictable Mode on Multiseasonal Time Scales

2014 ◽  
Vol 27 (4) ◽  
pp. 1648-1658 ◽  
Author(s):  
Yuanhong Guan ◽  
Jieshun Zhu ◽  
Bohua Huang ◽  
Zeng-Zhen Hu ◽  
James L. Kinter III
Keyword(s):  
Pacific Ocean ◽  
Time Scales ◽  
Southern Oscillation ◽  
Austral Summer ◽  
South Pacific ◽  
The South ◽  
Active Centers ◽  
Austral Spring ◽  
South Pacific Ocean ◽  
South Indian

Abstract Evaluating the climate hindcasts for 1982–2009 from the NCEP CFS Reanalysis and Reforecast (CFSRR) project using the Climate Forecast System, version 2 (CFSv2), this study identifies substantial areas of high prediction skill of the sea surface temperature (SST) in the South Pacific. The skill is the highest in the extratropical oceans on seasonal-to-interannual time scales, and it is only slightly lower than that for the El Niño–Southern Oscillation (ENSO). Two regions with the highest prediction skills in the South Pacific in both the CFSv2 and persistence hindcasts coincide with the active centers of opposite signs in the South Pacific Ocean dipole (SPOD) mode, a seesaw between the subtropical and extratropical SST in the South Pacific with a strong phase locking to austral summer. Interestingly, the CFSv2 prediction exhibits skillful predictions made three seasons ahead, more superior to the persistence forecast, suggesting significant dynamical predictability of the SPOD. An austral “spring predictability barrier” is noted in both the dynamical and persistence hindcasts. An analysis of the observational and model data suggests that the SPOD mode is significantly associated with ENSO, as an oceanic response to the atmospheric planetary wave trains forced by the anomalous atmospheric heating in the western Pacific. Although previous studies have demonstrated that the pattern of subtropical SST dipole is ubiquitous in the Southern Ocean, the SPOD has been least known and studied, compared with its counterparts in the south Indian and Atlantic Oceans. Since the SPOD is the most predictable oceanic mode in the whole Southern Hemisphere, its climate effects for local and remote regions should be further studied.

scholarly journals A Linear Inverse Model of Tropical and South Pacific Climate Variability: Optimal Structure and Stochastic Forcing

2021 ◽  
Vol 34 (1) ◽  
pp. 143-155
Author(s):  
Jiale Lou ◽  
Terence J. O’Kane ◽  
Neil J. Holbrook
Keyword(s):  
Southern Oscillation ◽  
Gaussian White Noise ◽  
South Pacific ◽  
Ocean Model ◽  
Inverse Model ◽  
Difference Approximation ◽  
Deterministic System ◽  
Stochastic Forcing ◽  
Sst Forcing ◽  
Linear Inverse Model

AbstractA stochastically forced linear inverse model (LIM) of the combined modes of variability from the tropical and South Pacific Oceans is used to investigate the linear growth of optimal initial perturbations and to identify the spatiotemporal features of the stochastic forcing associated with the atmospheric Pacific–South American patterns 1 and 2 (PSA1 and PSA2). Optimal initial perturbations are shown to project onto El Niño–Southern Oscillation (ENSO) and South Pacific decadal oscillation (SPDO), where the inclusion of subsurface South Pacific Ocean temperature variability significantly increases the multiyear linear predictability of the deterministic system. We show that the optimal extratropical sea surface temperature (SST) precursor is associated with the South Pacific meridional mode, which takes from 7 to 9 months to linearly evolve into the final ENSO and SPDO peaks in both the observations and as simulated in an atmosphere-forced ocean model. The optimal subsurface precursor resembles its peak phase, but with a weak amplitude, representing oceanic Rossby waves in the extratropical South Pacific. The stochastic forcing is estimated as the residual by removing the deterministic dynamics from the actual tendency under a centered difference approximation. The resulting stochastic forcing time series satisfies the Gaussian white noise assumption of the LIM. We show that the PSA-like variability is strongly associated with stochastic SST forcing in the tropical and South Pacific Oceans and contributes not only to excite the optimal initial perturbations associated with ENSO and the SPDO but in general to activate the entire stochastic SST forcing, especially in austral summer.

Seasonal forecasting of tropical cyclone activity in the Australian and the South Pacific Ocean regions

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
J.S. Wijnands ◽  
G. Qian ◽  
K.L. Shelton ◽  
R.J.B. Fawcett ◽  
J.C.L. Chan ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Tropical Cyclone ◽  
Southern Oscillation ◽  
South Pacific ◽  
Seasonal Forecasting ◽  
Support Vector ◽  
Seasonal Forecasts ◽  
The South ◽  
Explanatory Variables ◽  
South Pacific Ocean

AbstractThe Australian Bureau of Meteorology (Bureau) issues operational tropical cyclone (TC) seasonal forecasts for the Australian region (AR) and the South Pacific Ocean (SPO) and subregions therein. The forecasts are issued in October, ahead of the Southern Hemisphere TC season (November to April). Improvement of operational TC seasonal forecasts can lead to more accurate warnings for coastal communities to prepare for TC hazards. This study investigates the use of support vector regression (SVR) models, exploring new explanatory variables and non-linear relationships between them, the use of model averaging, and lastly the integration of forecast intervals based on a bias-corrected and accelerated non-parametric bootstrap. Hindcasting analyses show that the SVR model outperforms several benchmark methods. Analysis of the generated models shows that the Dipole Mode Index, 5VAR index and the Southern Oscillation Index are the most frequently selected as explanatory variables for TC seasonal forecasting in all regions. The usage of ENSOrelated covariates implies that definitions of regions and subregions may have to be updated to achieve optimal forecasting performance. Overall, the new SVR methodology is an improvement over the current linear discriminant analysis models and has the potential to increase accuracy of TC seasonal forecasts in the AR and SPO.

Carbon monoxide in the South Pacific Ocean

Tellus ◽  
1974 ◽  
Vol 26 (1-2) ◽  
pp. 136-142 ◽  
Author(s):  
J. W. Swinnerton ◽  
R. A. Lamontagne
Keyword(s):  
Carbon Monoxide ◽  
Pacific Ocean ◽  
South Pacific ◽  
The South ◽  
South Pacific Ocean

Daily accumulation rates of marine litter on the shores of Rapa Nui (Easter Island) in the South Pacific Ocean

2021 ◽  
Vol 169 ◽  
pp. 112535
Author(s):  
Martin Thiel ◽  
Bárbara Barrera Lorca ◽  
Luis Bravo ◽  
Iván A. Hinojosa ◽  
Hugo Zeballos Meneses
Keyword(s):  
Pacific Ocean ◽  
South Pacific ◽  
Easter Island ◽  
Accumulation Rates ◽  
The South ◽  
Rapa Nui ◽  
Marine Litter ◽  
South Pacific Ocean

scholarly journals The Maximum Salinity Core Layer Spreading in the South Pacific Ocean

2018 ◽  
Author(s):  
Keitapu Maamaatuaiahutapu ◽  
Jan Witting ◽  
Elodie Martinez
Keyword(s):  
Pacific Ocean ◽  
Core Layer ◽  
South Pacific ◽  
The South ◽  
South Pacific Ocean
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