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

2009 ◽  
Vol 22 (1) ◽  
pp. 58-70 ◽  
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.

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

2005 ◽  
Vol 18 (21) ◽  
pp. 4404-4424 ◽  
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.

Multi-scale ocean colour synergy products for coastal water quality monitoring

2020 ◽  
Author(s):  
Dimitry Van der Zande ◽  
Aida Alvera-Azcárate ◽  
Charles Troupin ◽  
João Cardoso Dos Santos ◽  
Dries Van den Eynde
Keyword(s):  
Spatial Resolution ◽  
Spectral Characteristics ◽  
Empirical Orthogonal Functions ◽  
High Temporal Resolution ◽  
Ocean Colour ◽  
Orthogonal Functions ◽  
Multi Scale ◽  
Medium Resolution ◽  
Sentinel 2

<p>High-quality satellite-based ocean colour products can provide valuable support and insights in the management and monitoring of coastal ecosystems. Today’s availability of Earth Observation (EO) data is unprecedented including medium resolution ocean colour systems (e.g. Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI) and geostationary satellites (e.g. MSG/SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics. In the Multi-Sync project, we developed advanced ocean colour products (i.e. remote sensing reflectance, turbidity, and chlorophyll a concentration) through the synergetic use of these multi-scale EO data taking advantage of spectral characteristics of traditional medium resolution sensors, the high spatial resolution of some land sensors and the high temporal resolution of geostationary sensors.</p><p>To achieve this goal a multi-scale DINEOF (Data Interpolating Empirical Orthogonal Functions) approach was developed to reconstruct missing data using empirical orthogonal functions (EOF), reduce noise and exploit spatio-temporal coherency by joining several spatial and temporal resolutions. Here we present the capacity of DINEOF to extract multi-scale information through the integration of Sentinel-3, Sentinel-2 and SEVIRI datasets.</p><p>The functionality of the advanced multi-scale products will be demonstrated in a case study for the Belgian Coastal Zone (BCZ) highly relevant to the user community: sediment transport modelling near the harbour of Zeebrugge in support of dredging operations. As stated in the OSPAR treaty (1992), Belgium is obliged to monitor and evaluate the effects of all human activities on the marine ecosystem. Dredging activities in and near Belgian harbors fall under this treaty and are performed daily to ensure accessibility of the port by ships. Optimization of these dredging activities requires monitoring data which is typically acquired through in situ observations or modelling data. In this case study we take advantage of Sentinel-3, Sentinel-2 and SEVIRI data characteristics to provide a satellite product that meets the end user requirements in terms of product quality and temporal/spatial resolution.</p><p> </p>

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

2006 ◽  
Vol 57 (3) ◽  
pp. 273 ◽  
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.

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

2006 ◽  
Vol 63 (3) ◽  
pp. 840-860 ◽  
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.

scholarly journals Investigating the Impact of CO2 on Low-Frequency Variability of the AMOC in HadCM3

2017 ◽  
Vol 30 (19) ◽  
pp. 7863-7883 ◽  
Author(s):  
Edward Armstrong ◽  
Paul Valdes ◽  
Jo House ◽  
Joy Singarayer
Keyword(s):  
Climate Model ◽  
Singular Spectrum Analysis ◽  
Empirical Orthogonal Functions ◽  
Meridional Overturning Circulation ◽  
The Arctic ◽  
Positive Density ◽  
Quasi Equilibrium ◽  
Ekman Pumping ◽  
Orthogonal Functions ◽  
The Impact

Abstract This study investigates the impact of CO2 on the amplitude, frequency, and mechanisms of Atlantic meridional overturning circulation (AMOC) variability in millennial simulations of the HadCM3 coupled climate model. Multichannel singular spectrum analysis (MSSA) and empirical orthogonal functions (EOFs) are applied to the AMOC at four quasi-equilibrium CO2 forcings. The amount of variance explained by the first and second eigenmodes appears to be small (i.e., 11.19%); however, the results indicate that both AMOC strength and variability weaken at higher CO2 concentrations. This accompanies an apparent shift from a predominant 100–125-yr cycle at 350 ppm to 160 yr at 1400 ppm. Changes in amplitude are shown to feed back onto the atmosphere. Variability may be linked to salinity-driven density changes in the Greenland–Iceland–Norwegian Seas, fueled by advection of anomalies predominantly from the Arctic and Caribbean regions. A positive density anomaly accompanies a decrease in stratification and an increase in convection and Ekman pumping, generating a strong phase of the AMOC (and vice versa). Arctic anomalies may be generated via an internal ocean mode that may be key in driving variability and are shown to weaken at higher CO2, possibly driving the overall reduction in amplitude. Tropical anomalies may play a secondary role in modulating variability and are thought to be more influential at higher CO2, possibly due to an increased residence time in the subtropical gyre and/or increased surface runoff driven by simulated dieback of the Amazon rain forest. These results indicate that CO2 may not only weaken AMOC strength but also alter the mechanisms that drive variability, both of which have implications for climate change on multicentury time scales.

scholarly journals Comparison of stochastic parameterizations in the framework of a coupled ocean-atmosphere model

2018 ◽  
Author(s):  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Probability Distributions ◽  
Low Frequency ◽  
Markov Operator ◽  
Model Errors ◽  
Low Frequency Variability ◽  
Ocean Atmosphere ◽  
Physically Based ◽  
Intermediate Order ◽  
Atmosphere Model ◽  
The Impact

Abstract. A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of MAOOAM, a coupled ocean-atmosphere model of intermediate complexity. Two physically-based parameterizations are investigated, the first one based on the singular perturbation of Markov operator, also known as homogenization. The second one is a recently proposed parameterization based on the Ruelle's response theory. The two parameterization are implemented in a rigorous way, assuming however that the unresolved scale relevant statistics are Gaussian. They are extensively tested for a low-order version known to exhibit low-frequency variability, and some preliminary results are obtained for an intermediate-order version. Several different configurations of the resolved-unresolved scale separations are then considered. Both parameterizations show remarkable performances in correcting the impact of model errors, being even able to change the modality of the probability distributions. Their respective limitations are also discussed.

The Relationship between Statistically Linear and Nonlinear Feedbacks and Zonal-Mean Flow Variability in an Idealized Climate Model

2009 ◽  
Vol 66 (2) ◽  
pp. 353-372 ◽  
Author(s):  
Sergey Kravtsov ◽  
John E. Ten Hoeve ◽  
Steven B. Feldstein ◽  
Sukyoung Lee ◽  
Seok-Woo Son
Keyword(s):  
Phase Space ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
Zonal Flow ◽  
Empirical Orthogonal Functions ◽  
Nonlinear Flow ◽  
Mean Flow ◽  
Orthogonal Functions ◽  
Flow Variability

Abstract Simulations using an idealized, atmospheric general circulation model (GCM) subjected to various thermal forcings are analyzed via a combination of probability density function (PDF) estimation and spectral analysis techniques. Seven different GCM runs are examined, each model run being characterized by different values in the strength of the tropical heating and high-latitude cooling. For each model run, it is shown that a linear stochastic model constructed in the phase space of the ten leading empirical orthogonal functions (EOFs) of the zonal-mean zonal flow provides an excellent statistical approximation to the simulated zonal flow variability, which includes zonal index fluctuations, and quasi-oscillatory, poleward, zonal-mean flow anomaly propagation. Statistically significant deviations from the above linear stochastic null hypothesis arise in the form of a few anomalously persistent, or statistically nonlinear, flow patterns, which occupy particular regions of the model’s phase space. Some of these nonlinear regimes occur during certain phases of the poleward propagation; however, such an association is, in general, weak. This indicates that the regimes and oscillations in the model may be governed by distinct dynamical mechanisms.

scholarly journals Modes of the BiOS-driven Adriatic Sea thermohaline variability

2021 ◽  
Author(s):  
Cléa Lumina Denamiel ◽  
Iva Tojčić ◽  
Petra Pranić ◽  
Ivica Vilibić
Keyword(s):  
Time Series ◽  
Thermohaline Circulation ◽  
Climate Model ◽  
Adriatic Sea ◽  
Decadal Variability ◽  
Current Speed ◽  
Empirical Orthogonal Functions ◽  
Climate Simulation ◽  
Orthogonal Functions ◽  
The Impact

Abstract In this study the impact of the Adriatic-Ionian Bimodal Oscillating System (BiOS) on the interannual to decadal variability of the Adriatic Sea thermohaline circulation is quantified during the 1987-2017 period with the numerical results of the Adriatic Sea and Coast (AdriSC) historical kilometer-scale climate simulation. The time series associated with the first five Empirical Orthogonal Functions (EOFs) computed from the salinity, temperature and current speed monthly detrended anomalies at 1-km resolution are correlated to the BiOS signal. First, it is found that the AdriSC climate model is capable to reproduce the BiOS-driven phases derived from in-situ observations along a long-term monitoring transect in the middle Adriatic. Then, for the entire Adriatic basin, high correlations to the 2-year delayed BiOS signal are obtained for the salinity and current speed first two EOF time series at 100 m depth and the sea-bottom Finally, the physical interpretation of the EOF spatial patterns reveals that Adriatic bottom temperatures are more influenced by the dense water circulation than the BiOS. These findings confirmed and generalized the known dynamics derived previously from observations, and the AdriSC climate model can thus be used to better understand the past and future BiOS-driven physical processes in the Adriatic Sea.

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