Articles | Volume 23, issue 5
Research article 20 Sep 2016
Research article | 20 Sep 2016
Wavelet analysis of the singular spectral reconstructed time series to study the imprints of solar–ENSO–geomagnetic activity on Indian climate
Sri Lakshmi Sunkara and Rama Krishna Tiwari
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphereApplications of matrix factorization methods to climate dataBeyond univariate calibration: verifying spatial structure in ensembles of forecast fieldsImprovements to the use of the Trajectory-Adaptive Multilevel Sampling algorithm for the study of rare eventsEnsemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitationSimulation-based comparison of multivariate ensemble post-processing methodsDetecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysisVertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theoryRemember the past: a comparison of time-adaptive training schemes for non-homogeneous regressionOn fluctuating momentum exchange in idealised models of air–sea interactionA prototype stochastic parameterization of regime behaviour in the stably stratified atmospheric boundary layerStatistical post-processing of ensemble forecasts of the height of new snowUnravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approachStatistical hypothesis testing in wavelet analysis: theoretical developments and applications to Indian rainfallComparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere modelIdealized models of the joint probability distribution of wind speedsNonlinear analysis of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean SeaA general theory on frequency and time–frequency analysis of irregularly sampled time series based on projection methods – Part 1: Frequency analysisA general theory on frequency and time–frequency analysis of irregularly sampled time series based on projection methods – Part 2: Extension to time–frequency analysisTipping point analysis of ocean acoustic noiseOn the intrinsic timescales of temporal variability in measurements of the surface solar radiationOptimal heavy tail estimation – Part 1: Order selectionNetwork-based study of Lagrangian transport and mixingMulti-scale event synchronization analysis for unravelling climate processes: a wavelet-based approachFractional Brownian motion, the Matérn process, and stochastic modeling of turbulent dispersionA matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian AmazonParameterization of stochastic multiscale triadsCompound extremes in a changing climate – a Markov chain approachA new estimator of heat periods for decadal climate predictions – a complex network approachWavelet analysis for non-stationary, nonlinear time seriesBrief Communication: Breeding vectors in the phase space reconstructed from time series dataTransition process of abrupt climate change based on global sea surface temperature over the past centuryCumulative areawise testing in wavelet analysis and its application to geophysical time seriesA sequential Bayesian approach for the estimation of the age–depth relationship of the Dome Fuji ice coreArtificial neural networks and multiple linear regression model using principal components to estimate rainfall over South AmericaEfficient Bayesian inference for natural time series using ARFIMA processesReview: visual analytics of climate networksSystematic attribution of observed Southern Hemisphere circulation trends to external forcing and internal variabilityGlobal terrestrial water storage connectivity revealed using complex climate network analysesNonstationary time series prediction combined with slow feature analysisStatistical optimization for passive scalar transport: maximum entropy production versus maximum Kolmogorov–Sinai entropyGeometric and topological approaches to significance testing in wavelet analysisEvaluation of empirical mode decomposition for quantifying multi-decadal variations and acceleration in sea level recordsNon-Gaussian interaction information: estimation, optimization and diagnostic application of triadic wave resonanceFluctuations in a quasi-stationary shallow cumulus cloud ensembleOn the data-driven inference of modulatory networks in climate science: an application to West African rainfallCorrelations between climate network and relief dataFinding recurrence networks' threshold adaptively for a specific time seriesEstimation of sedimentary proxy records together with associated uncertaintyLogit-normal mixed model for Indian monsoon precipitationToward enhanced understanding and projections of climate extremes using physics-guided data mining techniques
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471,Short summary
Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.
Josh Jacobson, William Kleiber, Michael Scheuerer, and Joseph Bellier
Nonlin. Processes Geophys., 27, 411–427,Short summary
Most verification metrics for ensemble forecasts assess the representation of uncertainty at a particular location and time. We study a new diagnostic tool based on fractions of threshold exceedance (FTE) which evaluates an additional important attribute: the ability of ensemble forecast fields to reproduce the spatial structure of observed fields. The utility of this diagnostic tool is demonstrated through simulations and an application to ensemble precipitation forecasts.
Pascal Wang, Daniele Castellana, and Henk Dijkstra
Nonlin. Processes Geophys. Discuss.,
Revised manuscript accepted for NPGShort summary
This paper proposes two improvements to the use of the Trajectory-Adaptive Multilevel Sampling, a rare-event algorithm which computes noise-induced transition probabilities. The first improvement uses locally linearized dynamics in order to reduce the arbitrariness associated with defining what constitutes a transition. The second improvement uses empirical transition paths accumulated at high noise in order to formulate the score function which determines the performance of the algorithm.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys. Discuss.,
Revised manuscript accepted for NPGShort summary
An unprecedented amount of rainfall data is nowadays available, such as ensemble model output, weather radar estimates and in-situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is the precipitation reconstruction through the combination of numerical model output with observations from multiple data sources.
Sebastian Lerch, Sándor Baran, Annette Möller, Jürgen Groß, Roman Schefzik, Stephan Hemri, and Maximiliane Graeter
Nonlin. Processes Geophys., 27, 349–371,Short summary
Accurate models of spatial, temporal, and inter-variable dependencies are of crucial importance for many practical applications. We review and compare several methods for multivariate ensemble post-processing, where such dependencies are imposed via copula functions. Our investigations utilize simulation studies that mimic challenges occurring in practical applications and allow ready interpretation of the effects of different misspecifications of the numerical weather prediction ensemble.
Jaqueline Lekscha and Reik V. Donner
Nonlin. Processes Geophys., 27, 261–275,
Julian Steinheuer and Petra Friederichs
Nonlin. Processes Geophys., 27, 239–252,Short summary
Many applications require wind gust estimates at very different atmospheric altitudes, such as in the wind energy sector. However, numerical weather prediction models usually only derive estimates for gusts at 10 m above the land surface. We present a statistical model that gives the hourly peak wind speed. The model is trained based on a weather reanalysis and observations from the Hamburg Weather Mast. Reliable predictions are derived at up to 250 m, even at unobserved intermediate levels.
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34,Short summary
Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
Nonlin. Processes Geophys., 26, 457–477,Short summary
The conspicuous feature of the atmosphere–ocean system is the large difference in the masses of the two media. In this respect there is a strong analogy to Brownian motion, with light and fast molecules colliding with heavy and slow Brownian particles. I apply the tools of non-equilibrium statistical mechanics for studying Brownian motion to air–sea interaction.
Carsten Abraham, Amber M. Holdsworth, and Adam H. Monahan
Nonlin. Processes Geophys., 26, 401–427,Short summary
Atmospheric stably stratified boundary layers display transitions between regimes of sustained and intermittent turbulence. These transitions are not well represented in numerical weather prediction and climate models. A prototype explicitly stochastic turbulence parameterization simulating regime dynamics is presented and tested in an idealized model. Results demonstrate that the approach can improve the regime representation in models.
Jari-Pekka Nousu, Matthieu Lafaysse, Matthieu Vernay, Joseph Bellier, Guillaume Evin, and Bruno Joly
Nonlin. Processes Geophys., 26, 339–357,Short summary
Forecasting the height of new snow is crucial for avalanche hazard, road viability, ski resorts and tourism. The numerical models suffer from systematic and significant errors which are misleading for the final users. Here, we applied for the first time a state-of-the-art statistical method to correct ensemble numerical forecasts of the height of new snow from their statistical link with measurements in French Alps and Pyrenees. Thus the realism of automatic forecasts can be quickly improved.
Jürgen Kurths, Ankit Agarwal, Roopam Shukla, Norbert Marwan, Maheswaran Rathinasamy, Levke Caesar, Raghavan Krishnan, and Bruno Merz
Nonlin. Processes Geophys., 26, 251–266,Short summary
We examined the spatial diversity of Indian rainfall teleconnection at different timescales, first by identifying homogeneous communities and later by computing non-linear linkages between the identified communities (spatial regions) and dominant climatic patterns, represented by climatic indices such as El Nino–Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation, Pacific Decadal Oscillation and Atlantic Multi-Decadal Oscillation.
Justin A. Schulte
Nonlin. Processes Geophys., 26, 91–108,Short summary
Statistical hypothesis tests in wavelet analysis are used to asses the likelihood that time series features are noise. The choice of test will determine which features emerge as a signal. Tests based on area do poorly at distinguishing abrupt fluctuations from periodic behavior, unlike tests based on arclength that do better. The application of the tests suggests that there are features in Indian rainfall time series that emerge from background noise.
Jonathan Demaeyer and Stéphane Vannitsem
Nonlin. Processes Geophys., 25, 605–631,Short summary
We investigate the modeling of the effects of the unresolved scales on the large scales of the coupled ocean–atmosphere model MAOOAM. Two different physically based stochastic methods are considered and compared, in various configurations of the model. Both methods show remarkable performances and are able to model fundamental changes in the model dynamics. Ways to improve the parameterizations' implementation are also proposed.
Adam H. Monahan
Nonlin. Processes Geophys., 25, 335–353,Short summary
Bivariate probability density functions (pdfs) of wind speed characterize the relationship between speeds at two different locations or times. This study develops such pdfs of wind speed from distributions of the components, following a well-established approach for univariate distributions. The ability of these models to characterize example observed datasets is assessed. The mathematical complexity of these models suggests further extensions of this line of reasoning may not be practical.
Berenice Rojo-Garibaldi, David Alberto Salas-de-León, María Adela Monreal-Gómez, Norma Leticia Sánchez-Santillán, and David Salas-Monreal
Nonlin. Processes Geophys., 25, 291–300,Short summary
Hurricanes are complex systems that carry large amounts of energy. Its impact produces, most of the time, natural disasters involving the loss of human lives and of materials and infrastructure that is accounted for in billions of US dollars. Not everything is negative as hurricanes are the main source of rainwater for the regions where they develop. In this study we make a nonlinear analysis of the time series obtained from 1749 to 2012 of the hurricane occurrence in the Gulf of Mexico.
Guillaume Lenoir and Michel Crucifix
Nonlin. Processes Geophys., 25, 145–173,Short summary
We develop a general framework for the frequency analysis of irregularly sampled time series. We also design a test of significance against a general background noise which encompasses the Gaussian white or red noise. Our results generalize and unify methods developed in the fields of geosciences, engineering, astronomy and astrophysics. All the analysis tools presented in this paper are available to the reader in the Python package WAVEPAL.
Guillaume Lenoir and Michel Crucifix
Nonlin. Processes Geophys., 25, 175–200,Short summary
There is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular. Here we provide such a framework with the Morlet wavelet, based on the results of part I of this study. We also design a test of significance against a general background noise which encompasses the Gaussian white or red noise. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.
Valerie N. Livina, Albert Brouwer, Peter Harris, Lian Wang, Kostas Sotirakopoulos, and Stephen Robinson
Nonlin. Processes Geophys., 25, 89–97,Short summary
We have applied tipping point analysis to a large record of ocean acoustic data to identify the main components of the acoustic dynamical system: long-term and seasonal trends, system states and fluctuations. We reconstructed a one-dimensional stochastic model equation to approximate the acoustic dynamical system. We have found a signature of El Niño events in the deep ocean acoustic data near the southwest Australian coast, which proves the investigative power of the tipping point methodology.
Marc Bengulescu, Philippe Blanc, and Lucien Wald
Nonlin. Processes Geophys., 25, 19–37,Short summary
We employ the Hilbert–Huang transform to study the temporal variability in time series of daily means of the surface solar irradiance (SSI) at different locations around the world. The data have a significant spectral peak corresponding to the yearly variability cycle and feature quasi-stochastic high-frequency "weather noise", irrespective of the geographical location or of the local climate. Our findings can improve models for estimating SSI from satellite images or forecasts of the SSI.
Manfred Mudelsee and Miguel A. Bermejo
Nonlin. Processes Geophys., 24, 737–744,Short summary
Risk analysis of extremes has high socioeconomic relevance. Of crucial interest is the tail probability, P, of the distribution of a variable, which is the chance of observing a value equal to or greater than a certain threshold value, x. Many variables in geophysical systems (e.g. climate) show heavy tail behaviour, where P may be rather large. In particular, P decreases with x as a power law that is described by a parameter, α. We present an improved method to estimate α on data.
Kathrin Padberg-Gehle and Christiane Schneide
Nonlin. Processes Geophys., 24, 661–671,Short summary
Transport and mixing processes in fluid flows are crucially influenced by coherent structures, such as eddies, gyres, or jets in geophysical flows. We propose a very simple and computationally efficient approach for analyzing coherent behavior in fluid flows. The central object is a flow network constructed directly from particle trajectories. The network's local and spectral properties are shown to give a very good indication of coherent as well as mixing regions in the underlying flow.
Ankit Agarwal, Norbert Marwan, Maheswaran Rathinasamy, Bruno Merz, and Jürgen Kurths
Nonlin. Processes Geophys., 24, 599–611,Short summary
Extreme events such as floods and droughts result from synchronization of different natural processes working at multiple timescales. Investigation on an observation timescale will not reveal the inherent underlying dynamics triggering these events. This paper develops a new method based on wavelets and event synchronization to unravel the hidden dynamics responsible for such sudden events. This method is tested with synthetic and real-world cases and the results are promising.
Jonathan M. Lilly, Adam M. Sykulski, Jeffrey J. Early, and Sofia C. Olhede
Nonlin. Processes Geophys., 24, 481–514,Short summary
This work arose from a desire to understand the nature of particle motions in turbulence. We sought a simple conceptual model that could describe such motions, then realized that this model could be applicable to an array of other problems. The basic idea is to create a string of random numbers, called a stochastic process, that mimics the properties of particle trajectories. This model could be useful in making best use of data from freely drifting instruments tracking the ocean currents.
Finn Müller-Hansen, Manoel F. Cardoso, Eloi L. Dalla-Nora, Jonathan F. Donges, Jobst Heitzig, Jürgen Kurths, and Kirsten Thonicke
Nonlin. Processes Geophys., 24, 113–123,Short summary
Deforestation and subsequent land uses in the Brazilian Amazon have huge impacts on greenhouse gas emissions, local climate and biodiversity. To better understand these land-cover changes, we apply complex systems methods uncovering spatial patterns in regional transition probabilities between land-cover types, which we estimate using maps derived from satellite imagery. The results show clusters of similar land-cover dynamics and thus complement studies at the local scale.
Jeroen Wouters, Stamen Iankov Dolaptchiev, Valerio Lucarini, and Ulrich Achatz
Nonlin. Processes Geophys., 23, 435–445,
Katrin Sedlmeier, Sebastian Mieruch, Gerd Schädler, and Christoph Kottmeier
Nonlin. Processes Geophys., 23, 375–390,Short summary
Compound extreme events (e.g., simultaneous occurrence of hot and dry days) are likely to have a big impact on society. In our paper, we propose a new method to analyze the temporal succession of compound extreme events, an aspect that has been largely neglected so far. We analyze past and future changes and identify regions within Europe, which are probably susceptible to a future change in the succession of heavy precipitation and cold days in winter and hot and dry days in summer.
Michael Weimer, Sebastian Mieruch, Gerd Schädler, and Christoph Kottmeier
Nonlin. Processes Geophys., 23, 307–317,Short summary
This paper is the first time that a complex network approach has been used for analysis of decadal climate predictions. We have developed an alternative estimator of heat periods based on network statistics, which turns out to be superior for parts of Europe. This paper opens the perspective that network measures have the potential to improve decadal predictions.
Justin A. Schulte
Nonlin. Processes Geophys., 23, 257–267,
Erin Lynch, Daniel Kaufman, A. Surjalal Sharma, Eugenia Kalnay, and Kayo Ide
Nonlin. Processes Geophys., 23, 137–141,Short summary
In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the nearest-neighbor bred vectors are shown to be similar to bred vectors computed using evolution equations, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.
Pengcheng Yan, Wei Hou, and Guolin Feng
Nonlin. Processes Geophys., 23, 115–126,Short summary
In the previous work (Yan et al., 2015, NPG), we proposed a novel method to detect the transition process of climate change and exposed a new understanding of climate change. In this work, by using this method, we studied several climate changes of the sea surface temperature over the past century. The result shows that the system is bi-stable, and the persist time of transition process is shortened. Besides, a quantitative relation among the transition process parameters is obtained and verified.
Justin A. Schulte
Nonlin. Processes Geophys., 23, 45–57,Short summary
The paper presents a new method called cumulative areawise testing that allows scientists to better extract important signals from geophysical time series. The method was found to be able to distinguish aspects of time series that are random from those of potential physical importance better than existing methods in wavelet analysis.
Shin'ya Nakano, Kazue Suzuki, Kenji Kawamura, Frédéric Parrenin, and Tomoyuki Higuchi
Nonlin. Processes Geophys., 23, 31–44,Short summary
This paper proposes a technique for dating an ice core. The proposed technique employs a hybrid method combining the sequential Monte Carlo method and the Markov chain Monte Carlo method, which is referred to as the particle Markov chain Monte Carlo method. The sequential Monte Carlo method, which is also known as the particle filter, is widely used for nonlinear time-series analysis. This paper demonstrates the usefulness of the approach in time-series analysis for dating an ice core.
T. Soares dos Santos, D. Mendes, and R. Rodrigues Torres
Nonlin. Processes Geophys., 23, 13–20,Short summary
Statistical downscaling is widely used in large operational centers around the world, using exclusively linear relations (MLR); this study uses a statistical downscaling methodology using a nonlinear technique known as ANNs with CMIP5 project data. The artificial neural network can perform tasks that a linear program cannot. The main advantages of this are its temporal processing ability and its ability to incorporate several preceding predictor values as input without any additional effort.
T. Graves, R. B. Gramacy, C. L. E. Franzke, and N. W. Watkins
Nonlin. Processes Geophys., 22, 679–700,
T. Nocke, S. Buschmann, J. F. Donges, N. Marwan, H.-J. Schulz, and C. Tominski
Nonlin. Processes Geophys., 22, 545–570,Short summary
The paper reviews the available visualisation techniques and tools for the visual analysis of geo-physical climate networks. The results from a questionnaire with experts from non-linear physics are presented, and the paper surveys recent developments from information visualisation and cartography with respect to their applicability for visual climate network analytics. Several case studies based on own solutions illustrate the potentials of state-of-the-art network visualisation technology.
C. L. E. Franzke, T. J. O'Kane, D. P. Monselesan, J. S. Risbey, and I. Horenko
Nonlin. Processes Geophys., 22, 513–525,
A. Y. Sun, J. Chen, and J. Donges
Nonlin. Processes Geophys., 22, 433–446,Short summary
Terrestrial water storage (TWS) plays a key role in global water and energy cycles. This work applies complex climate networks to analyzing spatial patterns in TWS. A comparative analysis is conducted using a remotely sensed (GRACE) and a model-generated TWS data set. Our results reveal hotspots of TWS anomalies around the global land surfaces. Prospects are offered on using network connectivity as constraints to further improve current global land surface models.
G. Wang and X. Chen
Nonlin. Processes Geophys., 22, 377–382,Short summary
This paper presents a new technique of combining the driving force of a time series obtained using the slow feature analysis (SFA) approach, then introducing the driving force into a predictive model to predict nonstationary time series. It could be considered to be a data-driven attempt to make progress in predicting nonstationary climatic time series and in better understanding the climate causality research from observed climate data.
M. Mihelich, D. Faranda, B. Dubrulle, and D. Paillard
Nonlin. Processes Geophys., 22, 187–196,
J. A. Schulte, C. Duffy, and R. G. Najjar
Nonlin. Processes Geophys., 22, 139–156,
D. P. Chambers
Nonlin. Processes Geophys., 22, 157–166,Short summary
The ability of empirical mode decomposition (EMD) to extract multi-decadal variability from sea level records is tested using three simulations, based on sinusoidal oscillations and climate indices. In all cases, the longest-period modes are significantly distorted, with incorrect amplitudes and phases. This affects the estimated acceleration computed from the longest periodic IMF. Additionally, in all cases, extra low-frequency modes uncorrelated with the prescribed variability are found.
C. A. L. Pires and R. A. P. Perdigão
Nonlin. Processes Geophys., 22, 87–108,Short summary
Non-Gaussian joint PDFs and Shannon negentropies allow for nonlinear correlations and synergetic interaction information among random variables. Third-order cross-cumulants (triadic correlations -- TCs) under pair-wise (total or partial) independence are maximized on projections and orthogonal rotations of the full PDF. Fourier analysis allows decomposing TCs as wave resonant triads working as non-Gaussian sources of dynamical predictability. An illustration is given in a minimal fluid model.
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Nonlin. Processes Geophys., 22, 65–85,
D. L. González II, M. P. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, and N. F. Samatova
Nonlin. Processes Geophys., 22, 33–46,Short summary
We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that fall into two categories: well-known associations from prior knowledge, and putative links that invite further research.
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Nonlin. Processes Geophys., 21, 1127–1132,Short summary
In the past few years, complex networks have been extensively applied to climate sciences, yielding the new field of climate networks. Here, we generalize climate network analysis by investigating the influence of altitudes in network topology. More precisely, we verified that nodes group into different communities corresponding to geographical areas with similar relief properties. This new approach may contribute to obtaining more complete climate network models.
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Nonlin. Processes Geophys., 21, 1085–1092,
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Nonlin. Processes Geophys., 21, 1093–1111,Short summary
We present a new approach to estimating sedimentary proxy records along with the proxy uncertainty. We provide analytical expressions for the proxy record, while transparently propagating uncertainties from the ages to the proxy record. We represent proxies on an error-free, precise timescale. Our approach provides insight into the interrelations between proxy variability and the various uncertainties. We demonstrate our method with synthetic examples and proxy data from the Lonar lake in India.
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Nonlin. Processes Geophys., 21, 777–795,
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This paper presents a new spectral approach to identifying the periodic patterns from the published Indian temperature variability records. The wavelet analysis of the SSA reconstructed time series highlights the removal of noise in the data and identifies the existence of a high-amplitude, recurrent, multidecadal scale patterns in the Indian continent, confirming the possible influences of sunspot-geomagnetic activity-ENSO through teleconnection.
This paper presents a new spectral approach to identifying the periodic patterns from the...