scholarly journals Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models

2015 ◽  
Vol 28 (5) ◽  
pp. 1962-1976 ◽  
Author(s):  
Dmitry Mukhin ◽  
Dmitri Kondrashov ◽  
Evgeny Loskutov ◽  
Andrey Gavrilov ◽  
Alexander Feigin ◽  
...  
Keyword(s):  
Time Series ◽  
Southern Oscillation ◽  
Singular Spectrum Analysis ◽  
Coupled Model ◽  
Empirical Orthogonal Functions ◽  
Empirical Modeling ◽  
Orthogonal Functions ◽  
Critical Transitions ◽  
Spatially Distributed ◽  
Learning Set

Abstract The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin–Neelin–Ghil (JNG) hybrid seasonally forced coupled ocean–atmosphere model of El Niño–Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample.

scholarly journals An Empirical Seasonal Rainfall Forecasting Model for the Northeast Region of Brazil

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1613
Author(s):  
Rodrigo Lins da Rocha Júnior ◽  
David Duarte Cavalcante Pinto ◽  
Fabrício Daniel dos Santos Silva ◽  
Heliofábio Barros Gomes ◽  
Helber Barros Gomes ◽  
...  
Keyword(s):  
Time Series ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Northern Region ◽  
Empirical Orthogonal Functions ◽  
Rainfall Forecasting ◽  
Orthogonal Functions ◽  
Northeast Region ◽  
Teleconnection Patterns ◽  
The North

The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve seasonal forecasting for the NEB, the achievement of a satisfactory accuracy often remains a challenge, and forecasting methods aimed at reducing uncertainties regarding future climate are needed. In this work, we implement and assess the performance of an empirical model (EmpM) based on a decomposition of historical data into dominant modes of precipitation and seasonal forecast applied to the NEB domain. We analyzed the model’s performance for the February-March-April quarter and compared its results with forecasts based on data from the North American Multi-model Ensemble (NMME) project for the same period. We found that the first three leading precipitation modes obtained by empirical orthogonal functions (EOF) explained most of the rainfall variability for the season of interest. Thereby, this study focuses on them for the forecast evaluations. A teleconnection analysis shows that most of the variability in precipitation comes from sea surface temperature (SST) anomalies in various areas of the Pacific and the tropical Atlantic. The modes exhibit different spatial patterns across the NEB, with the first being concentrated in the northern half of the region and presenting remarkable associations with the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), both linked to the latitudinal migration of the intertropical convergence zone (ITCZ). As for the second mode, the correlations with oceanic regions and its loading pattern point to the influence of the incursion of frontal systems in the southern NEB. The time series of the third mode implies the influence of a lower frequency mode of variability, probably related to the Interdecadal Pacific Oscillation (IPO). The teleconnection patterns found in the analysis allowed for a reliable forecast of the time series of each mode, which, combined, result in the final rainfall prediction outputted by the model. Overall, the EmpM outperformed the post-processed NMME for most of the NEB, except for some areas along the northern region, where the NMME showed superiority.

ADAPTIVE FILTERING AND PREDICTION OF NOISY MULTIVARIATE SIGNALS: AN APPLICATION TO SUBANNUAL VARIABILITY IN ATMOSPHERIC ANGULAR MOMENTUM

1993 ◽  
Vol 03 (03) ◽  
pp. 625-634 ◽  
Author(s):  
CHRISTIAN L. KEPPENNE ◽  
MICHAEL GHIL
Keyword(s):  
Time Series ◽  
Angular Momentum ◽  
Multivariate Time Series ◽  
Singular Spectrum Analysis ◽  
Predictive Ability ◽  
Principal Component ◽  
Entropy Method ◽  
Empirical Orthogonal Functions ◽  
Atmospheric Angular Momentum ◽  
Orthogonal Functions

Principal component analysis (PCA) in the space and time domains is applied to filter adaptively the dominant modes of subannual (SA) variability of a 12-year long multivariate time series of Northern Hemisphere atmospheric angular momentum (AAM); AAM is computed in 23 latitude bands of equal area from operational analyses of the U.S. National Meteorological Center. PCA isolates the leading empirical orthogonal functions (EOFs) of spatial dependence, while multivariate singular spectrum analysis (M-SSA) yields filtered time series that capture the dominant low-frequency modes of SA variability. The time series prefiltered by M-SSA lend themselves to prediction by the maximum entropy method (MEM). Whole-field predictions are made by combining the forecasts so obtained with the leading spatial EOFs obtained by PCA. The combination of M-SSA and MEM has predictive ability up to about a month. These methods are essentially linear but data-adaptive. They seem to perform well for short, noisy, multivariate time series, to which purely nonlinear, deterministically based methods are difficult to apply.

scholarly journals A Hierarchy of Data-Based ENSO Models

2005 ◽  
Vol 18 (21) ◽  
pp. 4425-4444 ◽  
Author(s):  
D. Kondrashov ◽  
S. Kravtsov ◽  
A. W. Robertson ◽  
M. Ghil
Keyword(s):  
El Niño ◽  
Additive Noise ◽  
Polynomial Regression ◽  
El Nino ◽  
Southern Oscillation ◽  
Model Performance ◽  
Empirical Orthogonal Functions ◽  
Quadratic Model ◽  
Orthogonal Functions ◽  
Optimizing Model

Abstract Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase space of the dataset’s leading empirical orthogonal functions. Multiple linear regression has been widely used to obtain inverse stochastic models; it is generalized here in two ways. First, the dynamics is allowed to be nonlinear by using polynomial regression. Second, a multilevel extension of classic regression allows the additive noise to be correlated in time; to do so, the residual stochastic forcing at a given level is modeled as a function of variables at this level and the preceding ones. The number of variables, as well as the order of nonlinearity, is determined by optimizing model performance. The two-level linear and quadratic models have a better El Niño–Southern Oscillation (ENSO) hindcast skill than their one-level counterparts. Estimates of skewness and kurtosis of the models’ simulated Niño-3 index reveal that the quadratic model reproduces better the observed asymmetry between the positive El Niño and negative La Niña events. The benefits of the quadratic model are less clear in terms of its overall, cross-validated hindcast skill; this model outperforms, however, the linear one in predicting the magnitude of extreme SST anomalies. Seasonal ENSO dependence is captured by incorporating additive, as well as multiplicative forcing with a 12-month period into the first level of each model. The quasi-quadrennial ENSO oscillatory mode is robustly simulated by all models. The “spring barrier” of ENSO forecast skill is explained by Floquet and singular vector analysis, which show that the leading ENSO mode becomes strongly damped in summer, while nonnormal optimum growth has a strong peak in December.

scholarly journals Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data

2018 ◽  
Vol 57 (10) ◽  
pp. 2217-2229
Author(s):  
Christopher Dupuis ◽  
Courtney Schumacher
Keyword(s):  
Time Series ◽  
Meteorological Data ◽  
Tropical Rainfall Measuring Mission ◽  
Empirical Orthogonal Functions ◽  
Eof Analysis ◽  
Complex Topography ◽  
Sampled Data ◽  
Orthogonal Functions ◽  
Hard Limit ◽  
Irregularly Sampled Data

AbstractThe Lomb–Scargle discrete Fourier transform (LSDFT) is a well-known technique for analyzing time series. In this study, a solution for empirical orthogonal functions (EOFs) based on irregularly sampled data is derived from the LSDFT. It is demonstrated that this particular algorithm has no hard limit on its accuracy and yields results comparable to those of complex Hilbert EOF analysis. Two LSDFT algorithms are compared in terms of their performance in evaluating EOFs for precipitation observations from the Tropical Rainfall Measuring Mission satellite. Both are shown to be able to capture the pattern of the diurnal cycle of rainfall over the complex topography and diverse land cover of South America, and both also show other consistent features in the 0–12-day frequency band.

scholarly journals The Influence of European Climate Variability Mechanism on Air Temperatures in Romania

2014 ◽  
Vol 8 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Nicoleta Ionac ◽  
Monica Matei
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Spatial And Temporal Variability ◽  
Orthogonal Functions ◽  
Mean Values ◽  
European Climate ◽  
Air Temperatures ◽  
And Shifts

Abstract The present paper investigates on the spatial and temporal variability of maximum and minimum air-temperatures in Romania and their connection to the European climate variability. The European climate variability is expressed by large scale parameters, which are roughly represented by the geopotential height at 500 hPa (H500) and air temperature at 850 hPa (T850). The Romanian data are represented by the time series at 22 weather stations, evenly distributed over the entire country’s territory. The period that was taken into account was 1961-2010, for the summer and winter seasons. The method of empirical orthogonal functions (EOF) has been used, in order to analyze the connection between the temperature variability in Romania and the same variability at a larger scale, by taking into consideration the atmosphere circulation. The time series associated to the first two EOF patterns of local temperatures and large-scale anomalies were considered with regard to trends and shifts in their mean values. The non- Mann-Kendall and Pettitt parametric tests were used in this respect. The results showed a strong correlation between T850 parameter and minimum and maximum air temperatures in Romania. Also, the ample variance expressed by the first EOF configurations suggests a connection between local and large scale climate variability.

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 A Comparison Study of EOF Techniques: Analysis of Nonstationary Data with Periodic Statistics

1999 ◽  
Vol 12 (1) ◽  
pp. 185-199 ◽  
Author(s):  
Kwang-Y. Kim ◽  
Qigang Wu
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Second Mode ◽  
Nonstationary Data ◽  
Cyclic Statistics ◽  
Surface Temperature Field ◽  
Climate Studies ◽  
Moving Patterns

Abstract Identification of independent physical/dynamical modes and corresponding principal component time series is an important aspect of climate studies for they serve as a tool for detecting and predicting climate changes. While there are a number of different eigen techniques their performance for identifying independent modes varies. Considered here are comparison tests of eight eigen techniques in identifying independent patterns from a dataset. A particular emphasis is given to cyclostationary processes such as deforming and moving patterns with cyclic statistics. Such processes are fairly common in climatology and geophysics. Two eigen techniques that are based on the cyclostationarity assumption—cyclostationary empirical orthogonal functions (EOFs) and periodically extended EOFs—perform better in identifying moving and deforming patterns than techniques based on the stationarity assumption. Application to a tropical Pacific surface temperature field indicates that the first dominant pattern and the corresponding principal component (PC) time series are consistent among different techniques. The second mode and the PC time series, however, are not very consistent from one another with hints of significant modal mixing and splitting in some of derived patterns. There also is a detailed difference of intraannual scale between PC time series of a stationary technique and those of a cyclostationary one. This may bear an important implication on the predictability of El Niño. Clearly there is a choice of eigen technique for improved predictability.

Sign in / Sign up

Export Citation Format

Share Document