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.

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 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.

A Comparison of the Influence of Additive and Multiplicative Stochastic Forcing on a Coupled Model of ENSO

2005 ◽  
Vol 18 (23) ◽  
pp. 5066-5085 ◽  
Author(s):  
Cristina L. Perez ◽  
Andrew M. Moore ◽  
Javier Zavala-Garay ◽  
Richard Kleeman
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Coupled Model ◽  
Deterministic System ◽  
Low Frequency Oscillation ◽  
Stochastic Forcing ◽  
Integration Period ◽  
Atmospheric Phenomena

Abstract A currently popular idea is that El Niño–Southern Oscillation (ENSO) can be viewed as a linear deterministic system forced by noise representing processes with periods shorter than ENSO. Also, there is observational evidence to suggest that the Madden–Julian oscillation (MJO) acts to trigger and/or amplify the warm phase of ENSO in this way. The feedback of the slower process, ENSO, to higher-frequency atmospheric phenomena, of which a large part of the variability in the intraseasonal band is due to the MJO, has received little attention. This paper considers the hypothesis that the probability of an El Niño event is modified by high MJO activity and that, in turn, the MJO is regulated by ENSO activity. If this is indeed the case, then viewing ENSO as a low-frequency oscillation forced by additive stochastic noise would not present a complete picture. This paper tests the above hypothesis using a stochastically forced intermediate coupled model by allowing ENSO to directly influence the stochastic forcing. The model response to a variety of stochastic forcing types is found to be sensitive to the type of forcing applied. When the model is operated beyond its intrinsic Hopf bifurcation, its probability distribution function (PDF) is fundamentally altered when the stochastic forcing is changed from additive to multiplicative. The model integration period also influences the shape of the PDF, which is also compared to the PDF derived from observations. It is found that multiplicative stochastic forcing reproduces some measures of the observations better than the additive stochastic forcing.

scholarly journals The Role of Stochastic Forcing in Generating ENSO Diversity

2018 ◽  
Vol 31 (22) ◽  
pp. 9125-9150 ◽  
Author(s):  
Erin E. Thomas ◽  
Daniel J. Vimont ◽  
Matthew Newman ◽  
Cécile Penland ◽  
Cristian Martínez-Villalobos
Keyword(s):  
Initial Conditions ◽  
Southern Oscillation ◽  
Model Framework ◽  
Central Pacific ◽  
Stochastic Forcing ◽  
Enso Events ◽  
Enso Predictability ◽  
Enso Diversity ◽  
Noise Forcing ◽  
Linear Inverse Model

Abstract Numerous oceanic and atmospheric phenomena influence El Niño–Southern Oscillation (ENSO) variability, complicating both prediction and analysis of the mechanisms responsible for generating ENSO diversity. Predictability of ENSO events depends on the characteristics of both the forecast initial conditions and the stochastic forcing that occurs subsequent to forecast initialization. Within a linear inverse model framework, stochastic forcing reduces ENSO predictability when it excites unpredictable growth or interference after the forecast is initialized, but also enhances ENSO predictability when it excites optimal initial conditions that maximize deterministic ENSO growth. Linear inverse modeling (LIM) allows for straightforward separation between predictable signal and unpredictable noise and so can diagnose its own skill. While previous LIM studies of ENSO focused on deterministic dynamics, here we explore how noise forcing influences ENSO diversity and predictability. This study identifies stochastic forcing details potentially contributing to the development of central Pacific (CP) or eastern Pacific (EP) ENSO characteristics. The technique is then used to diagnose the relative roles of initial conditions and noise forcing throughout the evolution of several ENSO events. LIM results show varying roles of noise forcing for any given event, highlighting its utility in separating deterministic from noise-forced contributions to the evolution of individual ENSO events. For example, the strong 1982 event was considerably more influenced by noise forcing late in its evolution than the strong 1997 event, which was more predictable with long lead times due to its deterministic growth. Furthermore, the 2014 deterministic trajectory suggests that a strong event in 2014 was unlikely.

Relative contributions of North and South Pacific sea surface temperature anomalies to ENSO

2020 ◽  
Author(s):  
Ruiqiang Ding ◽  
Yu-heng Tseng ◽  
Jianping Li
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
South Pacific ◽  
Sea Surface ◽  
Coupling Mechanism ◽  
The South ◽  
Enso Events ◽  
The North ◽  
Sst Forcing

<p>Variations in the sea surface temperature (SST) field in both the North Pacific [represented by the Victoria mode (VM)] and the South Pacific [represented by the South Pacific Quadrapole (SPQ) mode] are related to the state of the El Niño-Southern Oscillation (ENSO) three seasons later. Here, with the aid of observational data and numerical experiments, we demonstrate that both VM and SPQ SST forcing can influence the development of ENSO events through a similar air–sea coupling mechanism. By comparing ENSO amplitudes induced by the VM and SPQ, as well as the percentages of strong ENSO events followed by the VM and SPQ events, we find that the VM and SPQ make comparable contributions and therefore have similar levels of importance to ENSO. Additional analysis indicates that although VM or SPQ SST forcing alone may serve as a good predictor for ENSO events, it is more effective to consider their combined influence. A prediction model based on both VM and SPQ indices is developed, which is capable of yielding skillful forecasts for ENSO at lead times of three seasons.</p>

Ocean Salinity Aspects of the Ningaloo Niño

2021 ◽  
pp. 1
Author(s):  
Yaru Guo ◽  
Yuanlong Li ◽  
Fan Wang ◽  
Yuntao Wei
Keyword(s):  
Fresh Water ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Near Surface ◽  
Ocean Salinity

AbstractNingaloo Niño – the interannually occurring warming episode in the southeast Indian Ocean (SEIO) – has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced fresh-water transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward fresh-water advection near the eastern boundary, which is critical in causing the strong freshening (> 0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with the El Niño-Southern Oscillation (0.57, 0.77, and 0.70 with Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (-0.27, -0.42, and -0.35) during 1993-2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.

scholarly journals Formation Mechanism of Barrier Layer in the Subtropical Pacific

2015 ◽  
Vol 45 (11) ◽  
pp. 2790-2805 ◽  
Author(s):  
Shota Katsura ◽  
Eitarou Oka ◽  
Kanako Sato
Keyword(s):  
Formation Mechanism ◽  
Barrier Layer ◽  
Seasonal Variations ◽  
Southern Oscillation ◽  
World Ocean ◽  
South Pacific ◽  
Sea Surface ◽  
Layer Depth ◽  
Isothermal Layer ◽  
North And South

AbstractSeasonal and interannual variations of the barrier layer (BL) and its formation mechanism in the subtropical North and South Pacific were investigated by using raw and gridded Argo profiling float data and various surface flux data in 2003–12 and hydrographic section data from the World Ocean Circulation Experiment Hydrographic Programme. BLs detected by raw Argo profiles, which existed within the sea surface salinity (SSS) front located on the equator side of SSS maxima, were thickest and most frequent in winter and had a temporal scale shorter than 10 days, indicating their transient nature. Surface and subsurface processes for the BL formation suggested by previous studies were evaluated. Poleward Ekman advection of fresher water was dominant as the surface freshening process but cannot explain the observed seasonal variations of the BL. Subsurface equatorward intrusion of high-salinity tropical water was too deep to produce salinity stratification within isothermal layers. These results strongly suggest that BLs in the subtropical Pacific are formed mainly through tilting of the SSS front due to the poleward Ekman flow near the sea surface and the equatorward geostrophic flow in the subsurface. This idea is supported by the dominant contribution of the meridional SSS gradient to the meridional sea surface density gradient within the SSS front and the correspondence between the seasonal variations of the BL and isothermal layer depth. On an interannual time scale, the winter BL thickness in the North and South Pacific was related to the Pacific decadal oscillation and the El Niño–Southern Oscillation, respectively, through the intensity of trade winds controlling isothermal layer depth.

scholarly journals Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO

2008 ◽  
Vol 21 (2) ◽  
pp. 385-402 ◽  
Author(s):  
Michael A. Alexander ◽  
Ludmila Matrosova ◽  
Cécile Penland ◽  
James D. Scott ◽  
Ping Chang
Keyword(s):  
Inverse Model ◽  
Enso Events ◽  
Enso Teleconnections ◽  
Four Seasons ◽  
Global Trend ◽  
The Pacific ◽  
Model Predictions ◽  
Sea Surface Temperature Anomalies ◽  
Linear Inverse Model ◽  
The Individual

Abstract A linear inverse model (LIM) is used to predict Pacific (30°S–60°N) sea surface temperature anomalies (SSTAs), including the Pacific decadal oscillation (PDO). The LIM is derived from the observed simultaneous and lagged covariance statistics of 3-month running mean Pacific SSTA for the years 1951–2000. The model forecasts exhibit significant skill over much of the Pacific for two to three seasons in advance and up to a year in some locations, particulary for forecasts initialized in winter. The predicted and observed PDO are significantly correlated at leads of up to four seasons, for example, the correlation exceeds 0.6 for 12-month forecasts initialized in January–March (JFM). The LIM-based PDO forecasts are more skillful than persistence or a first-order autoregressive model, and have comparable skill to LIM forecasts of El Niño SSTAs. Filtering the data indicates that much of the PDO forecast skill is due to ENSO teleconnections and the global trend. Within LIM, SST anomalies can grow due to constructive interference of the empirically determined modes, even though the individual modes are damped over time. For the Pacific domain, the basinwide SST variance can grow for ∼14 months, consistent with the skill of the actual predictions. The optimal structure (OS), the initial SSTA pattern that LIM indicates should increase the most rapidly with time, is clearly relevant to the predictions, as the OS develops into a mature ENSO and PDO event 6–10 months later. The OS is also consistent with the seasonal footprinting mechanism (SFM) and the meridional mode (MM); the SFM and MM involve a set of atmosphere–ocean interactions that have been hypothesized to initiate ENSO events.

scholarly journals Tropical Cyclone Climatology of the South Pacific Ocean and Its Relationship to El Niño–Southern Oscillation

2012 ◽  
Vol 25 (18) ◽  
pp. 6108-6122 ◽  
Author(s):  
Andrew J. Dowdy ◽  
Lixin Qi ◽  
David Jones ◽  
Hamish Ramsay ◽  
Robert Fawcett ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
El Niño ◽  
Large Scale ◽  
Critical Line ◽  
El Nino ◽  
Southern Oscillation ◽  
South Pacific ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
The South

Abstract Climatological features of tropical cyclones in the South Pacific Ocean have been analyzed based on a new archive for the Southern Hemisphere. A vortex tracking and statistics package is used to examine features such as climatological maps of system intensity and the change in intensity with time, average tropical cyclone system movement, and system density. An examination is presented of the spatial variability of these features, as well as changes in relation to phase changes of the El Niño–Southern Oscillation phenomenon. A critical line is defined in this study based on maps of cyclone intensity to describe the statistical geographic boundary for cyclone intensification. During El Niño events, the critical line shifts equatorward, while during La Niña events the critical line is generally displaced poleward. Regional variability in tropical cyclone activity associated with El Niño–Southern Oscillation phases is examined in relation to the variability of large-scale atmospheric or oceanic variables associated with tropical cyclone activity. Maps of the difference fields between different phases of El Niño–Southern Oscillation are examined for sea surface temperature, vertical wind shear, lower-tropospheric vorticity, and midtropospheric relative humidity. Results are also examined in relation to the South Pacific convergence zone. The common region where each of the large-scale variables showed favorable conditions for cyclogenesis coincided with the location of maximum observed cyclogenesis for El Niño events as well as for La Niña years.

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