scholarly journals On the interconnections among major climate modes and their common driving factors

2020 ◽  
Vol 11 (2) ◽  
pp. 525-535
Author(s):  
Xinnong Pan ◽  
Geli Wang ◽  
Peicai Yang ◽  
Jun Wang ◽  
Anastasios A. Tsonis
Keyword(s):  
Climate Variability ◽  
Driving Force ◽  
Driving Forces ◽  
Regional Climate ◽  
Feature Analysis ◽  
Climate Indices ◽  
Quasi Biennial Oscillation ◽  
Climate Modes ◽  
Significant Peak ◽  
Slow Feature Analysis

Abstract. The variations in oceanic and atmospheric modes on various timescales play important roles in generating global and regional climate variability. Many efforts have been devoted to identifying the relationships between the variations in climate modes and regional climate variability, but these have rarely explored the interconnections among these climate modes. Here we use climate indices to represent the variations in major climate modes and examine the harmonic relationship among the driving forces of climate modes using slow feature analysis (SFA) and wavelet analysis. We find that all of the significant peak periods of driving-force signals in the climate indices can be represented as harmonics of four base periods: 2.32, 3.90, 6.55, and 11.02 years. We infer that the period of 2.32 years is associated with the signal of the quasi-biennial oscillation (QBO). The periods of 3.90 and 6.55 years are linked to the intrinsic variability of the El Niño–Southern Oscillation (ENSO), and the period of 11.02 years arises from the sunspot cycle. Results suggest that the base periods and their harmonic oscillations related to QBO, ENSO, and solar activities act as key connections among the climatic modes with synchronous behaviors, highlighting the important roles of these three oscillations in the variability of the Earth's climate. Highlights. i. The harmonic relationship among the driving forces of climate modes was investigated by using slow feature analysis and wavelet analysis.ii. All of the significant peak periods of driving-force signals in climate indices can be represented as the harmonics of four base periods.iii. The four base periods related to QBO, ENSO, and solar activities act as the key linkages among different climatic modes with synchronous behaviors.

scholarly journals On the interconnections among major climate modes and their common driving factors

2019 ◽  
Author(s):  
Xinnong Pan ◽  
Geli Wang ◽  
Peicai Yang ◽  
Jun Wang ◽  
Anastasios A. Tsonis
Keyword(s):  
Climate Variability ◽  
Driving Forces ◽  
Southern Oscillation ◽  
Global Climate ◽  
Regional Climate ◽  
Sunspot Cycle ◽  
Climate Indices ◽  
Intrinsic Variability ◽  
Quasi Biennial Oscillation ◽  
Climate Modes

Abstract. The variations in oceanic and atmospheric modes on various timescales play important roles in generating regional and global climate variability. Many efforts have been devoted to identify the relationships between the variations in climate modes and regional climate variability, but rarely explored the interconnections among these climate modes. Here we use climate indices to represent the variations in major climate modes and we examine the harmonic relationship among the driving forces of climate modes by the combination of Slow Feature Analysis (SFA) and wavelet analysis. We find that all of the significant peak-periods of driving-force signals in the climate indices can be represented as the harmonics of four base periods: 2.32 yr, 3.90 yr, 6.55 yr and 11.02 yr. We infer that the period of 2.32 yr is associated with the signal of Quasi Biennial Oscillation (QBO). The periods of 3.90 yr and 6.55 yr are connected with the intrinsic variability of El Niño-Southern Oscillation (ENSO), and the period of 11.02 yr arises from the sunspot cycle. Results suggest that the base periods and their harmonic oscillations linked to QBO, ENSO and solar activities act as the key connections among the climatic modes with synchronous behaviors, highlighting the important roles of these three oscillations in the variability of current climate.

scholarly journals Nonstationary time series prediction combined with slow feature analysis

2015 ◽  
Vol 2 (1) ◽  
pp. 97-114
Author(s):  
G. Wang ◽  
X. Chen
Keyword(s):  
Time Series ◽  
Driving Force ◽  
Driving Forces ◽  
Time Series Prediction ◽  
Main Idea ◽  
Stationary Time Series ◽  
Feature Analysis ◽  
State Variables ◽  
Slow Feature Analysis ◽  
External Driving

Abstract. Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. 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 non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill.

Extracting the driving force from ozone data using slow feature analysis

2015 ◽  
Vol 124 (3-4) ◽  
pp. 985-989 ◽  
Author(s):  
Geli Wang ◽  
Peicai Yang ◽  
Xiuji Zhou
Keyword(s):  
Driving Force ◽  
Feature Analysis ◽  
Slow Feature Analysis ◽  
Ozone Data

scholarly journals Nonstationary time series prediction combined with slow feature analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 377-382 ◽  
Author(s):  
G. Wang ◽  
X. Chen
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Driving Forces ◽  
Time Series Prediction ◽  
Nonstationary Time Series ◽  
Feature Analysis ◽  
State Variables ◽  
Slow Feature Analysis ◽  
Almost All ◽  
External Driving

Abstract. Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

scholarly journals Extracting the driving force signal from hierarchy system based on slow feature analysis

2017 ◽  
Vol 66 (8) ◽  
pp. 080501
Author(s):  
Pan Xin-Nong ◽  
Wang Ge-Li ◽  
Yang Pei-Cai
Keyword(s):  
Driving Force ◽  
Feature Analysis ◽  
Slow Feature Analysis ◽  
Force Signal

scholarly journals Causality of the Drought in the Southwestern United States Based on Observations

2017 ◽  
Vol 30 (13) ◽  
pp. 4891-4896 ◽  
Author(s):  
Feng Zhang ◽  
Yadong Lei ◽  
Qiu-Run Yu ◽  
Klaus Fraedrich ◽  
Hironobu Iwabuchi
Keyword(s):  
United States ◽  
Solar Activity ◽  
Driving Forces ◽  
First Century ◽  
Feature Analysis ◽  
Southwestern United States ◽  
The Past ◽  
Fundamental Frequencies ◽  
Slow Feature Analysis ◽  
The Pacific

Slow feature analysis is used to extract driving forces from the monthly mean anomaly time series of the precipitation in the southwestern United States (1895–2015). Four major spectral scales pass the 95% confidence test after wavelet analysis of the derived driving forces. Further harmonic analysis indicates that only two fundamental frequencies are dominant in the spectral domain. The frequencies represent the influence of the Pacific decadal oscillation (PDO) and solar activity on the precipitation from the southwestern United States. In addition, solar activity has exerted a greater effect than the PDO on the precipitation in the southwestern United States over the past 120 years. By comparing the trend of droughts with the two fundamental frequencies, it is found that both the droughts in the 1900s and in the twenty-first century were affected by the PDO and solar activity, whereas the droughts from the 1950s to the 1970s were mainly affected by solar activity.

scholarly journals Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

2021 ◽  
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner ◽  
John P. O’Brien ◽  
Christina M. Patricola ◽  
Travis A. O’Brien ◽  
...  
Keyword(s):  
Climate Variability ◽  
Extreme Precipitation ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
In Situ Measurements ◽  
Climate Indices ◽  
Natural Climate Variability ◽  
Modes Of Variability ◽  
Natural Climate

AbstractWhile various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.

scholarly journals An application of slow feature analysis to the genetic sequences of coronaviruses and influenza viruses

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Anastasios A. Tsonis ◽  
Geli Wang ◽  
Lvyi Zhang ◽  
Wenxu Lu ◽  
Aristotle Kayafas ◽  
...  
Keyword(s):  
Mathematical Method ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Transmission Rate ◽  
Complex Structure ◽  
Mortality Rates ◽  
Influenza Viruses ◽  
Feature Analysis ◽  
Slow Feature Analysis ◽  
Genetic Sequences

Abstract Background Mathematical approaches have been for decades used to probe the structure of DNA sequences. This has led to the development of Bioinformatics. In this exploratory work, a novel mathematical method is applied to probe the DNA structure of two related viral families: those of coronaviruses and those of influenza viruses. The coronaviruses are SARS-CoV-2, SARS-CoV-1, and MERS. The influenza viruses include H1N1-1918, H1N1-2009, H2N2-1957, and H3N2-1968. Methods The mathematical method used is the slow feature analysis (SFA), a rather new but promising method to delineate complex structure in DNA sequences. Results The analysis indicates that the DNA sequences exhibit an elaborate and convoluted structure akin to complex networks. We define a measure of complexity and show that each DNA sequence exhibits a certain degree of complexity within itself, while at the same time there exists complex inter-relationships between the sequences within a family and between the two families. From these relationships, we find evidence, especially for the coronavirus family, that increasing complexity in a sequence is associated with higher transmission rate but with lower mortality. Conclusions The complexity measure defined here may hold a promise and could become a useful tool in the prediction of transmission and mortality rates in future new viral strains.

Impact of local and regional climate variability on fungi production from Pinus sylvestris forests in Soria, Spain

2021 ◽  
Author(s):  
Elena García‐Bustamante ◽  
J. Fidel Fidel González‐Rouco ◽  
Elena García‐Lozano ◽  
Fernando Martinez‐Peña ◽  
Jorge Navarro
Keyword(s):  
Climate Variability ◽  
Pinus Sylvestris ◽  
Regional Climate
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