Can Wavelet Analysis detect low frequency periodicities in climatic time series ? (original) (raw)
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Izvestiya, Atmospheric and Oceanic Physics, 2021
Slow climatic fluctuations of the World Ocean temperature in the Northern Hemisphere over the past century and a half as a response to the corresponding solar-activity modulations are identified and investigated. It is shown that, taking into account the nonstationary nature of climatic series, the most appropriate method for analysis is the calculation of wavelet spectra. It is found that the maxima of 11-year solar cycles obey longterm modulations with a wide range of quasi-periodicities, in which intervals of the 60-80 year oscillations, as well as the secular and two-century oscillations, are distinguished. These fluctuations are translated into the corresponding oscillations of the sea surface temperature (SST) of the ocean in the tropical zone. In the El Niño indices Niño 1,2 and Niño 4, responsible for SST anomalies in the water area near the coast of South America and in the central part of Pacific tropics, there is a different emphasis on the abovementioned long-term modulation. It can be assumed that 11-year fluctuations in solar activity are translated into short-period quasi-periodic (5-11 years) El Niño oscillations. In turn, long-term modulations, which we call, following previous researchers, the fundamental climatic oscillation (FCO), pass into SST oscillations in the Pacific and Atlantic Oceans, preserving the quasi-periodicity given by fluctuations in solar activity. In turn, the FCO is repeated in a series of the following climate indices: Pacific Decadal Oscillations (PDO) and Pacific North American (PNA) oscillation in the Pacific Ocean, as well as in the Atlantic Multidecadal Oscillation (AMO). The positive phase of AMO that began in the mid-1970s intensified the greenhouse effect on climate warming in the last quarter of the 20th century, but the stabilization of AMO values in the early 21st century led to a known "climate pause" in warming. It is shown that, for the shortened climatic series over the past half century, when the role of satellite measurements was dominant in the estimates of SST anomalies and the considered indices of the World Ocean, similar results were obtained for all wavelet spectra.
Detecting climate-induced patterns using wavelet analysis
Environmental Pollution, 1994
One of the difficulties encountered in the detection of ecosystem responses to climate change is distinguishing climate-induced patterns from those created by other sources. For example, changes in the trend of stream discharge records over time may reflect a composite response of changes in the climate (i.e. precipitation and temperature), land-use (e.g. timber harvesting and grazing), and local basin characteristics. Methods which quantify and relate information of temporal and spatial patterns across scales are critical to assess climatically induced changes in the forest and stream ecosystems. A methodology utilizing wavelet analysis is introduced for the purpose of identifying and isolating inferred climatic components of the hydrologic record Trends observed in stream discharge records from eastern Oregon, USA are identified and used to illustrate the utility of a new time series technique, wavelet analysis, as a complementary approach for discerning pattern. This methodology affords an informed procedure for choosing filter dimensions for the purpose of signal decomposition. The wavelet cross-covariance is applied to precipitation and discharge records to identify the climatic component in the discharge record. Reconstruction of these dominant frequencies is effected to isolate the climatic components. The discharge pattern shows two dominant scales of pattern coincident with the precipitation record A 3-year half-period pattern is found to be correlated with the Southern Oscillation Index at the same frequency.
30 and 43 months period cycles found in air temperature time series using the Morlet wavelet method
Climate Dynamics, 2009
A wavelet-based methodology is applied to relevant climatic indices and air temperature records and allow to detect the existence of unexpected cycles. The scale spectrum shows the presence of two cycles of about 30 and 43 months, respectively, in the air–temperature time series, in addition to the well-known cycles of 1 day and 1 year. The two cycles do not affect the globe uniformly: some regions seem to be more influenced by the period of 30 months (e.g. Europe), while other areas are affected by the period of 43 months (e.g. North-West of the USA). Similar cycles are found in the indices and the regions influenced by these indices: the NAO index and the Western Europe display a cycle of 30 months, while the cycle of 43 months can be found in the ENSO index and in regions where it is known to have an impact.
Using Wavelet to Analyze Periodicities in Hydrologic Variables
World Environmental and Water Resources Congress 2017, 2017
The trend and shift in the seasonal temperature, precipitation and streamflow time series across the Midwest have been analyzed, for the period 1960-2013, using the statistical analyses (Mann-Kendall test with and without considering short term persistence (MK2 and MK1, respectively) and Pettitt test). The paper also utilizes a relatively new approach, wavelet analysis, for testing the existence of trend and shift in the time series. The method has the ability to decompose a time series in to lower (trend) and higher frequency components (noise). Discrete wavelet transform (DWT) has been employed in the present study with an aim to find which periodicities are mainly responsible for trend in the original data. The combination of MK1, MK2 and DWT along with Pettitt test hasn't been extensively used up to this time, especially in detecting trend and shift in the Midwest. The analysis of climate division temperature and precipitation data and USGS naturalized streamflow data revealed the presence of periodicity in the time series data. All the incorporated time series data were seasonal to analyze the trends and shifts for four seasons-winter, spring, summer and fall independently. D3 component of DWT were observed to be influential in detecting real trend in Temperature, precipitation and streamflow data, however unlike temperature, precipitation and streamflow showed decreasing trend as well. Shift was relatively observed more than trend in the region with dominance of D3 component in the data. The result indicate the significant warming trend which agrees with the "increasing temperature" observations in the past two decades, however a clear explanation for precipitation and streamflow is not obvious.
Nonlinear Processes in Geophysics, 2010
Recently, new cycles, associated with periods of 30 and 43 months, respectively, have been observed by the authors in surface air temperature time series, using a wavelet-based methodology. Although many evidences attest the validity of this method applied to climatic data, no systematic study of its efficiency has been carried out. Here, we estimate confidence levels for this approach and show that the observed cycles are significant. Taking these cycles into consideration should prove helpful in increasing the accuracy of the climate model projections of climate change and weather forecast.
2006
Most traditional mathematical methods used to analyze time series, such as Fourier analysis, show periodicities in a frequency domain, but they don’t reveal whether they are stationary in time, intermittent or whether the time series have suffered a shift in the data. Wavelets are functions that permit to transform a time series into a bi-dimensional variable (frequency and time spaces). They are useful to study non-stationary processes occurring over finite spatial and temporal domains. Torrence and Compo (1998) presented a complete guide of wavelet analysis together with a free software that has been extensively used (see reference below) In the present study, we look for common climate signals in temperature extremes and Atlantic sea surface temperatures performing wavelet analysis In the literature, Sea surface temperature studies related with atmospheric conditions are led by ENSO events studies (Karoly, 1989, Trenberth and Carol, 2000 and reference there in). With respect to t...
A wavelet analysis to compare environmental time series
2012
Cities such as São Paulo, Tokyo, New York and Mexico City are on the list of the most polluted in the world. PM10 is a major component of air pollution that threatens both our health and our environment. In this paper, we are interested in comparing time series using wavelet analysis. The comparison is made using three statistical procedures, namely, the scalogram, the test given by the ratio of cumulative wavelet periodograms and the analysis of variance. These methods are applied to compare the rates of hourly PM10 in four different districts of the city of São Paulo, Brazil. For the analysis we use the discrete wavelet transformation (DWT) considering the Haar and Daubechies wavelets. In the analysis of variance for the wavelet coefficients, we tested the local effect considering the months from June to October as replications. Scalograms were constructed for each series and we note that for the two wavelet bases used they presented different behavior leading to the conclusion that the series of energies are different. The effect of location was significant in the analysis of variance considering levels 4 ≤ j for both bases Haar and Daubechies DWT. We also consider a statistical test at each level j given by the ratio of Thelma Sáfadi and Pedro A. Morettin 80 the cumulative wavelet periodograms of the series. Again we could find that the series are generated by different processes.
Earth Interactions, 2000
For the purpose of climate signal detection, we introduce a method for identifying significant episodes of large-scale oscillatory variability. The method is based on a multivariate wavelet algorithm that identifies coherent patterns of variation simultaneously within particular ranges of time and periodicity (or frequency) that may vary regionally in the timing and amplitude of the particular temperature oscillation. By using this methodology, an analysis is performed of the instrumental record of global temperatures spanning the past 140 years. The duration of an "episode" is chosen to correspond to 3-5 cycles at a specified oscillation period, which is useful for detecting signals associated with the global El Niño/Southern Oscillation (ENSO) phenomenon. To confirm the robustness of signals detected in the earliest, sparse data (only 111 5° longitude by 5° latitude grid points are available back to 1854), we performed multiple analyses overlapping in time, using increasingly dense subsets of the full (1570 grid point) temperature data. In every case, significant interannual episodes are centered in the 3-7 year period range corresponding to the conventional band of ENSO-related variance and describe intervals of quasi-oscillatory variability of decadal-scale duration. These episodes consist of a sequence of one or two warm and cold events with sea surface temperature fluctuations in the eastern tropical Pacific of amplitude ±0.6°-1.1°C. Each episode includes one or more historically prominent El Niño events. The signals are characterized as significant, however, by virtue of their global-scale pattern of temperature variations as well as their oscillatory pattern in time. The 1920-1940 interval of increasing global temperatures was bracketed by oscillatory 1 episodes with unusual global patterns of expression relative to the recent ENSO episodes of the 1970s and 1980s. The episodes that preceded the 1920-1940 and 1975-present intervals of rapid warming were associated with globally averaged temperature fluctuations of T GLB > 0.4°C, the largest among those identified. In contrast, the episode that concludes the 1920-1940 temperature rise exhibits a global-mean fluctuation T GLB = 0.05°C, smallest among the observed episodes. These observations motivate speculation about the possible relationship between ENSO variability and global warming, in particular, the relationship between ENSO and the transient storage of heat in the tropical upper ocean layer, and the relationship between secular climate change and the amplitude of interannual ENSO events.
Wavelet Analysis of Meteorological Data
Introduction The investigation of the properties of meteorological data is important for the analysis and classification of measured data, generation of synthetic data for simulation, and statistical description of solar systems. Since the temporal variations of meteorological data are nondeterministic, they are usually described by the conventional tools for stochastic functions like probability density function, auto- and cross-correlation functions, power density spectrum and correlation coefficients. An important feature of meteorological data that is not to be overlooked is that they contain information on different time scales. The most obvious examples are the seasonal and daily cycles with fixed and well known periods. Depending on the location, also variations on the scale of several days to several weeks occur. They do not possess sharply defined periods and may be present only at certain times of the year. As an example, temperature variations on this scale are impo
Wavelet analysis of ecological time series
2008
Wavelet analysis is a powerful tool that is already in use throughout science and engineering. The versatility and attractiveness of the wavelet approach lie in its decomposition properties, principally its time-scale localization. It is especially relevant to the analysis of nonstationary systems, i.e., systems with short-lived transient components, like those observed in ecological systems. Here, we review the basic properties of the wavelet approach for time-series analysis from an ecological perspective. Wavelet decomposition offers several advantages that are discussed in this paper and illustrated by appropriate synthetic and ecological examples. Wavelet analysis is notably free from the assumption of stationarity that makes most methods unsuitable for many ecological time series. Wavelet analysis also permits analysis of the relationships between two signals, and it is especially appropriate for following gradual change in forcing by exogenous variables.