The Ocean Response to Low-Frequency Interannual Atmospheric Variability in the Mediterranean Sea. Part II: Empirical Orthogonal Functions Analysis

Abstract. This paper presents an analysis of sound speed distribution in the Mediterranean Sea based on climatological temperature and salinity data. In the upper layers, propagation is characterised by upward refraction in winter and an acoustic channel in summer. The seasonal cycle of the Mediterranean and the presence of gyres and fronts create a wide range of spatial and temporal variabilities, with relevant differences between the western and eastern basins. It is shown that the analysis of a climatological data set can help in defining regions suitable for successful monitoring by means of acoustic tomography. Empirical Orthogonal Functions (EOF) decomposition on the profiles, performed on the seasonal cycle for some selected areas, demonstrates that two modes account for more than 98% of the variability of the climatological distribution. Reduced order EOF analysis is able to correctly represent sound speed profiles within each zone, thus providing the a priori knowledge for Matched Field Tomography. It is also demonstrated that salinity can affect the tomographic inversion, creating a higher degree of complexity than in the open oceans.Key words. Oceanography: general (marginal and semi-enclosed seas; ocean acoustics)

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Multilevel Regression Modeling of Nonlinear Processes: Derivation and Applications to Climatic Variability

Journal of Climate ◽

10.1175/jcli3544.1 ◽

2005 ◽

Vol 18 (21) ◽

pp. 4404-4424 ◽

Cited By ~ 91

Author(s):

S. Kravtsov ◽

D. Kondrashov ◽

M. Ghil

Keyword(s):

Nonlinear Dynamics ◽

Polynomial Regression ◽

Climatic Variability ◽

Low Frequency ◽

Inverse Model ◽

Empirical Orthogonal Functions ◽

Multilevel Regression ◽

Coherent Noise ◽

Orthogonal Functions ◽

Constant Matrix

Abstract Predictive models are constructed to best describe an observed field’s statistics within a given class of nonlinear dynamics driven by a spatially coherent noise that is white in time. For linear dynamics, such inverse stochastic models are obtained by multiple linear regression (MLR). Nonlinear dynamics, when more appropriate, is accommodated by applying multiple polynomial regression (MPR) instead; the resulting model uses polynomial predictors, but the dependence on the regression parameters is linear in both MPR and MLR. The basic concepts are illustrated using the Lorenz convection model, the classical double-well problem, and a three-well problem in two space dimensions. Given a data sample that is long enough, MPR successfully reconstructs the model coefficients in the former two cases, while the resulting inverse model captures the three-regime structure of the system’s probability density function (PDF) in the latter case. A novel multilevel generalization of the classic regression procedure is introduced next. In this generalization, the residual stochastic forcing at a given level is subsequently modeled as a function of variables at this level and all the preceding ones. The number of levels is determined so that the lag-0 covariance of the residual forcing converges to a constant matrix, while its lag-1 covariance vanishes. This method has been applied to the output of a three-layer, quasigeostrophic model and to the analysis of Northern Hemisphere wintertime geopotential height anomalies. In both cases, the inverse model simulations reproduce well the multiregime structure of the PDF constructed in the subspace spanned by the dataset’s leading empirical orthogonal functions, as well as the detailed spectrum of the dataset’s temporal evolution. These encouraging results are interpreted in terms of the modeled low-frequency flow’s feedback on the statistics of the subgrid-scale processes.

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Statistical description of the East Australian Current low-frequency variability from the WOCE PCM3 array

Marine and Freshwater Research ◽

10.1071/mf05058 ◽

2006 ◽

Vol 57 (3) ◽

pp. 273 ◽

Cited By ~ 8

Author(s):

Mauricio M. Mata ◽

Susan Wijffels ◽

John A. Church ◽

Matthias Tomczak

Keyword(s):

Ocean Circulation ◽

World Ocean ◽

Low Frequency ◽

General Rule ◽

Empirical Orthogonal Functions ◽

East Australian Current ◽

Orthogonal Functions ◽

The Pacific ◽

Strong Surface ◽

The Impact

The in situ dataset used in the current study consists of the Pacific Current Meter 3 (PCM3) array, which was a significant part of the Australian contribution to the World Ocean Circulation Experiment to study the variability of the East Australian Current (EAC), and was operational between September 1991 and March 1994. Area-preserving spectral analysis has been used to investigate the typical time scales observed by the current meters. As a general rule, the spectra from the top layers of the shallow (1, 2 and 3) and the deep (4, 5 and 6) moorings have a distinct peak in the temporal mesoscale band (periods between 70 and 170 days), with a general redistribution of energy towards the higher-frequencies near the ocean floor. This peak has been linked with eddy variability of the EAC system, which influences the fluctuations of the current main jet. The vertical modes of the velocity profile show that the strong surface-intensified baroclinic signal of the EAC dominated the variability at mooring 4 location. Further offshore the predominant configuration resembles more closely the barotropic mode. Ultimately, spatial empirical orthogonal functions (EOF) analysis point out the impact of the presence/absence of the EAC jet in the array.

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Multiple Regimes and Low-Frequency Oscillations in the Northern Hemisphere’s Zonal-Mean Flow

Journal of the Atmospheric Sciences ◽

10.1175/jas3672.1 ◽

2006 ◽

Vol 63 (3) ◽

pp. 840-860 ◽

Cited By ~ 23

Author(s):

S. Kravtsov ◽

A. W. Robertson ◽

M. Ghil

Keyword(s):

Singular Spectrum Analysis ◽

Low Frequency ◽

Zonal Flow ◽

Empirical Orthogonal Functions ◽

Mean Flow ◽

Orthogonal Functions ◽

Atlantic Sector ◽

Low Frequency Oscillations ◽

Multiple Regimes ◽

Flow States

Abstract This paper studies multiple regimes and low-frequency oscillations in the Northern Hemisphere zonal-mean zonal flow in winter, using 55 yr of daily observational data. The probability density function estimated in the phase space spanned by the two leading empirical orthogonal functions exhibits two distinct, statistically significant maxima. The two regimes associated with these maxima describe persistent zonal-flow states that are characterized by meridional displacements of the midlatitude jet, poleward and equatorward of its time-mean position. The geopotential height anomalies of either regime have a pronounced zonally symmetric component, but largest-amplitude anomalies are located over the Atlantic and Pacific Oceans. High-frequency synoptic transients participate in the maintenance of and transitions between these regimes. Significant oscillatory components with periods of 147 and 72 days are identified by spectral analysis of the zonal-flow time series. These oscillations are described by singular spectrum analysis and the multitaper method. The 147-day oscillation involves zonal-flow anomalies that propagate poleward, while the 72-day oscillation only manifests northward propagation in the Atlantic sector. Both modes mainly describe changes in the midlatitude jet position and intensity. In the horizontal plane though, the two modes exhibit synchronous centers of action located over the Atlantic and Pacific Oceans. The two persistent flow regimes are associated with slow phases of either oscillation.

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Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability

Ocean Dynamics ◽

10.1007/s10236-011-0493-5 ◽

2011 ◽

Vol 62 (1) ◽

pp. 13-30 ◽

Cited By ~ 84

Author(s):

Nikolaos Skliris ◽

Sarantis Sofianos ◽

Athanasios Gkanasos ◽

Anneta Mantziafou ◽

Vasilis Vervatis ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Mediterranean Sea ◽

Sea Surface ◽

Atmospheric Variability ◽

Decadal Scale ◽

The Mediterranean

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Climate Regime Variability for Past and Present Time Slices Simulated by the Fast Ocean Atmosphere Model

Journal of Climate ◽

10.1175/2008jcli2258.1 ◽

2009 ◽

Vol 22 (1) ◽

pp. 58-70 ◽

Cited By ~ 7

Author(s):

Dörthe Handorf ◽

Klaus Dethloff ◽

Andrew G. Marshall ◽

Amanda Lynch

Keyword(s):

Climate Model ◽

Low Frequency ◽

Empirical Orthogonal Functions ◽

Climate Regime ◽

Orthogonal Functions ◽

Medium Resolution ◽

Ocean Atmosphere ◽

Dimensional State Space ◽

Atmosphere Model ◽

Low Dimensional

Abstract This paper presents an analysis of Northern Hemisphere climate regime variability for three different time slices, simulated by the Fast Ocean Atmosphere Model (FOAM). The three time slices are composed of present-day conditions, the mid-Holocene, and the Last Glacial Maximum (LGM). Climate regimes have been determined by analyzing the structure of a spherical probability density function in a low-dimensional state space spanned by the three leading empirical orthogonal functions. This study confirms the ability of the FOAM medium-resolution climate model to reproduce low-frequency climate variability in the form of regime-like behavior. Three to four regimes have been detected for each time slice. Compared with present-day conditions, new climate regimes appeared for the LGM. For the mid-Holocene, which had slightly different boundary conditions and external forcings than the present-day simulation, the frequency of occurrence of the regimes was altered while only slight changes were found in the structure of some regimes.

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Study of the low-frequency variability of the atmospheric general circulation with the use of time-dependent empirical orthogonal functions

Izvestiya Atmospheric and Oceanic Physics ◽

10.1134/s0001433807010021 ◽

2007 ◽

Vol 43 (1) ◽

pp. 15-23 ◽

Cited By ~ 2

Author(s):

M. B. Galin

Keyword(s):

General Circulation ◽

Low Frequency ◽

Empirical Orthogonal Functions ◽

Time Dependent ◽

Orthogonal Functions ◽

Use Of Time ◽

Atmospheric General Circulation ◽

Low Frequency Variability

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Multivariate Empirical Orthogonal Function analysis of the upper thermocline structure of the Mediterranean Sea from observations and model simulations

Annales Geophysicae ◽

10.5194/angeo-21-167-2003 ◽

2003 ◽

Vol 21 (1) ◽

pp. 167-187 ◽

Cited By ~ 19

Author(s):

S. Sparnocchia ◽

N. Pinardi ◽

E. Demirov

Keyword(s):

Mediterranean Sea ◽

Large Scale ◽

Vertical Structure ◽

Model Simulation ◽

Function Analysis ◽

Original Data ◽

Empirical Orthogonal Functions ◽

Data Set ◽

Model Simulations ◽

The Mediterranean

Abstract. Multivariate vertical Empirical Orthogonal Functions (EOF) are calculated for the entire Mediterranean Sea both from observations and model simulations, in order to find the optimal number of vertical modes to represent the upper thermocline vertical structure. For the first time, we show that the large-scale Mediterranean thermohaline vertical structure can be represented by a limited number of vertical multivariate EOFs, and that the "optimal set" can be selected on the basis of general principles. In particular, the EOFs are calculated for the combined temperature and salinity statistics, dividing the Mediterranean Sea into 9 regions and grouping the data seasonally. The criterion used to establish whether a reduced set of EOFs is optimal is based on the analysis of the root mean square residual error between the original data and the profiles reconstructed by the reduced set of EOFs. It was found that the number of EOFs needed to capture the variability contained in the original data changes with geographical region and seasons. In particular, winter data require a smaller number of modes (4–8, depending on the region) than the other seasons (8–9 in summer). Moreover, western Mediterranean regions require more modes than the eastern Mediterranean ones, but this result may depend on the data scarcity in the latter regions. The EOFs computed from the in situ data set are compared to those calculated using data obtained from a model simulation. The main results of this exercise are that the two groups of modes are not strictly comparable but their ability to reproduce observations is the same. Thus, they may be thought of as equivalent sets of basis functions, upon which to project the thermohaline variability of the basin. Key words. Oceanography: general (water masses) – Oceanography: physical (hydrography; instruments and techniques)

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