Tag eof [28 articles]

Abstract

An important question in assessing 20th century climate change is to what extent have ENSO-related variations contributed to the observed trends. Isolating such contributions is challenging for several reasons, including ambiguities arising from how ENSO itself is defined. In particular, defining ENSO in terms of a single index and ENSO-related variations in terms of regressions on that index, as done in many previous studies, can lead to wrong conclusions. This paper argues that ENSO is best viewed not as a number ...

Abstract

The predictability of stochastically forced linear systems is investigated under the condition that an ensemble of forecasts are each initialized at the true state but driven by different realizations of white noise. Some important issues of predictability are brought out by analytically investigating a stochastically driven, damped inertial oscillator. These issues are then studied in a generic context without reference to any specific linear stochastic system. The predictability is measured primarily by the mean-square forecast error normalized by the mean climatological ...

Abstract

Abstract  Climate fluctuations in the North Atlantic Ocean have wide-spread implications for Europe, Africa, and the Americas. This study assesses the relative contribution of the long-term trend and variability of North Atlantic warming using EOF analysis of deep-ocean and near-surface observations. Our analysis demonstrates that the recent warming over the North Atlantic is linked to both long-term (including anthropogenic and natural) climate change and multidecadal variability (MDV, ~50–80 years). Our results suggest a general warming trend of 0.031 ± 0.006°C/decade in the upper 2,000 m North ...

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The multi-scale nature and climate noise properties of teleconnection indices are examined by using the Empirical Mode Decomposition (EMD) procedure. The EMD procedure allows for the analysis of non-stationary time series to extract physically meaningful intrinsic mode functions (IMF) and nonlinear trends. The climatologically relevant monthly mean teleconnection indices of the North Atlantic Oscillation (NAO), the North Pacific index (NP) and the Southern Annular Mode (SAM) are analyzed. <br><br> The significance of IMFs and trends are tested against the null hypothesis ...

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A general method is described for constructing simple dynamical models to approximate complex dynamical systems with many degrees of freedom. The technique can be applied to interpret sets of observed time series or numerical simulations with high-resolution models, or to relate ...

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The Independent Component Analysis (ICA) is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components—a stronger constraint that uses higher-order statistics—instead of the classical decorrelation (in the sense of “no correlation”), which is a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e., exploratory approach). The authors demonstrate ...

Abstract

Empirical orthogonal function (EOF) analysis is a powerful tool for data compression and dimensionality reduction used broadly in meteorology and oceanography. Often in the literature, EOF modes are interpreted individually, independent of other modes. In fact, it can be shown that no such attribution can generally be made. This review demonstrates that in general individual EOF modes (i) will not correspond to individual dynamical modes, (ii) will not correspond to individual kinematic degrees of freedom, (iii) will not be statistically independent ...

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Summary  A new time series analysis technique, Empirical Mode Decomposition (EMD), which has been successfully applied to nonlinear and nonstationary data, is used to examine paleoclimate cycles in the Pleistocene (1 Ma bp–20 Ka bp). The purpose of this study is to improve knowledge of the climatic significance of solar insolation. The results show that the eccentricity band signal is much larger than previously estimated, having an amplitude of about 1% of solar irradiance which is comparable to the amplitude of the precession and obliquity band ...

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Data analysis has been one of the core activities in scientific research, but limited by the availability of analysis methods in the past, data analysis was often relegated to data processing. To accommodate the variety of data generated by nonlinear and ...

Abstract

The polar regions have long been expected to warm strongly as a result of anthropogenic climate change, because of the positive feedbacks associated with melting ice and snow1, 2. Several studies have noted a rise in Arctic temperatures over recent decades2, 3, 4, but have not formally attributed the changes to human influence, owing to sparse observations and large natural variability5, 6. Both warming and cooling trends have been observed in Antarctica7, which the Intergovernmental Panel on Climate Change Fourth Assessment ...

Abstract

The complexity inherent in climate data makes it necessary to introduce more than one statistical tool to the researcher to gain insight into the climate system. Empirical orthogonal function (EOF) analysis is one of the most widely used methods to analyze weather/climate modes of variability and to reduce the dimensionality of the system. Simple structure rotation of EOFs can enhance interpretability of the obtained patterns but cannot provide anything more than temporal uncorrelatedness. In this paper, an alternative rotation method based ...

Abstract

Preferred structures in the surface pressure variability are investigated in and compared between two 100-year simulations of the Hadley Centre climate model HadCM3. In the first (control) simulation, the model is forced with pre-industrial carbon dioxide concentration (1 × CO2) and in the second simulation the model is forced with doubled CO2 concentration (2 × CO2). Daily winter (December-January-February) surface pressures over the Northern Hemisphere are analysed. The identification of preferred patterns is addressed using multivariate mixture models. For the control ...

Abstract

A recent paper used a simple artificial example to illustrate the shortcomings of EOFs and rotated EOFs in identifying underlying physical modes. The example raises at least as many questions as it answers. This note addresses some of these questions and clarifies some of the behavior of EOFs and related techniques. ...

Abstract

Empirical orthogonal function (EOF) analyses (rotated or not) are widely used in climate research. In recent years there have been several studies in which EOF analyses were used to highlight potential physical mechanisms associated with climate variability. For example, several SST modes were identified such as the “Tropical Atlantic Dipole,” the “Tropical Indian Ocean Dipole,” and different SLP modes in the Northern Hemisphere winter. In this note it is emphasized that caution should be used when trying to interpret these statistically ...

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A Singular Value Decomposition analysis is applied to climatological data to determine the modes of variability of monthly mean Sea Surface Temperature (SST) in the tropics coupled with the southern hemisphere stratospheric polar vortex intensity for the 1958 to 2006 period ...

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10.1073/pnas.0900173106 The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables. The use of a few Empirical Orthogonal Functions (EOFs) (also known as Principal Component Analysis, Karhunen–Loéve and Proper Orthogonal Decomposition) depending on observational data to span the low-frequency subspace ...

Abstract

This study considers the theory of a general three-dimensional (space and time) statistical prediction/extrapolation algorithm. The predictor is in the form of a linear data filter. The prediction kernel is based on the minimization of prediction error and its construction requires the covariance statistics of a predictand field. The algorithm is formulated in terms of the spatiotemporal EOFs of the predictand field. This EOF representation facilitates the selection of useful physical modes for prediction. Limited tests have been conducted concerning the ...

Abstract

Multilag singular value decomposition (MLSVD) analysis is developed and applied to diagnosing the impact of interannual variations of outgoing longwave radiation (OLR) on tropical stratospheric temperature changes. MLSVD is designed to analyze simultaneously variations at multiple levels and for a large number of temporal lags and leads. The two dominant MLSVDs are strongly related to El Niño–Southern Oscillation (ENSO). The associated patterns of tropical OLR are similar to the canonical ENSO SST patterns with strong negative sign regions stretching along the ...

Abstract

A new spectral-based approach is presented to find orthogonal patterns from gridded weather/climate data. The method is based on optimizing the interpolation error variance. The optimally interpolated patterns (OIP) are then given by the eigenvectors of the interpolation error covariance matrix, obtained using the cross-spectral matrix. The formulation of the approach is presented, and the application to low-dimension stochastic toy models and to various reanalyses datasets is performed. In particular, it is found that the lowest-frequency patterns correspond to largest eigenvalues, ...

Abstract

The equilibrium feedback assessment (EFA) is combined with the singular value decomposition (SVD) to assess the large-scale feedback modes from a lower boundary variability field onto an atmospheric field. The leading EFA-SVD modes are the optimal feedback modes, with the lower boundary forcing patterns corresponding to those that generate the largest atmospheric responses, and therefore provide upper bounds of the feedback response. The application of EFA-SVD to an idealized coupled ocean–atmosphere model demonstrates that EFA-SVD is able to extract the leading ...

Abstract

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field ...

Abstract

Climate and weather constitute a typical example where high dimensional and complex phenomena meet. The atmospheric system is the result of highly complex interactions between many degrees of freedom or modes. In order to gain insight in understanding the dynamical/physical behaviour involved it is useful to attempt to understand their interactions in terms of a much smaller number of prominent modes of variability. This has led to the development by atmospheric researchers of methods that give a space display and a ...

Abstract

Trends are very important in climate research and are ubiquitous in the climate system. Trends are usually estimated using simple linear regression. Given the complexity of the system, trends are expected to have various features such as global and local characters. It is therefore important to develop methods that permit a systematic decomposition of climate data into different trend patterns and remaining no-trend patterns. Empirical orthogonal functions and closely related methods, widely used in atmospheric science, are unable in general to ...

Abstract

Recent research has pointed to a number of inherent disadvantages of unrotated principal components and empirical orthogonal functions when these techniques are used to depict individual modes of variation of data matrices in exploratory analyses. The various pitfalls are outlined and illustrated with an alternative method introduced to minimize these problems via available linear transformations known as simple structure rotations. The rationale and theory behind simple structure rotation and Procrustes target rotation is examined in the context of meteorological/climatological applications. This ...

Abstract

We present a method for simultaneous dimension reduction, model fitting, and metastability analysis of high-dimensional time series. The approach is based on the combination of hidden Markov models (HMMs) with localized principal component analysis (PCA) (which is used to identify the essential dimensions in the form of empirical orthogonal functions (EOFs) for each of the hidden states) and fitting of multidimensional stochastic differential equations (SDEs). This means that the analyzed data is clustered according to differences in essential dimensions and SDE ...

Abstract

Abstract  A linear analysis is applied to a multi-thousand member “perturbed physics" GCM ensemble to identify the dominant physical processes responsible for variation in climate sensitivity across the ensemble. Model simulations are provided by the distributed computing project, climate prediction.net . A principal component analysis of model radiative response reveals two dominant independent feedback processes, each largely controlled by a single parameter change. The leading EOF was well correlated with the value of the entrainment coefficient—a parameter in the model’s atmospheric convection ...

Abstract

Abstract  In this paper it is suggested that a stochastic isotropic diffusive process, representing a spatial first order auto regressive process (AR(1)-process), can be used as a null hypothesis for the spatial structure of climate variability. By comparing the leading empirical orthogonal functions (EOFs) of a fitted null hypothesis with EOF modes of an observed data set, inferences about the nature of the observed modes can be made. The concept and procedure of fitting the null hypothesis to the observed EOFs is ...