scholarly journals El Niño Southern Oscillation, overseas arrivals and imported chikungunya cases in Australia: A time series analysis

2019 ◽  
Vol 13 (5) ◽  
pp. e0007376 ◽  
Author(s):  
Xiaodong Huang ◽  
Wenbiao Hu ◽  
Laith Yakob ◽  
Gregor J. Devine ◽  
Elizabeth A. McGraw ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Series Analysis

scholarly journals A skewed perspective of the Indian rainfall–El Niño–Southern Oscillation (ENSO) relationship

2020 ◽  
Vol 24 (11) ◽  
pp. 5473-5489 ◽  
Author(s):  
Justin Schulte ◽  
Frederick Policielli ◽  
Benjamin Zaitchik
Keyword(s):  
Time Series ◽  
Time Domain ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Wavelet Coherence ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
The Time Domain ◽  
Time Domain Correlation

Abstract. Wavelet coherence is a method that is commonly used in hydrology to extract scale-dependent, nonstationary relationships between time series. However, we show that the method cannot always determine why the time-domain correlation between two time series changes in time. We show that, even for stationary coherence, the time-domain correlation between two time series weakens if at least one of the time series has changing skewness. To overcome this drawback, a nonlinear coherence method is proposed to quantify the cross-correlation between nonlinear modes embedded in the time series. It is shown that nonlinear coherence and auto-bicoherence spectra can provide additional insight into changing time-domain correlations. The new method is applied to the El Niño–Southern Oscillation (ENSO) and all-India rainfall (AIR), which is intricately linked to hydrological processes across the Indian subcontinent. The nonlinear coherence analysis showed that the skewness of AIR is weakly correlated with that of two ENSO time series after the 1970s, indicating that increases in ENSO skewness after the 1970s at least partially contributed to the weakening ENSO–AIR relationship in recent decades. The implication of this result is that the intensity of skewed El Niño events is likely to overestimate India's drought severity, which was the case in the 1997 monsoon season, a time point when the nonlinear wavelet coherence between AIR and ENSO reached its lowest value in the 1871–2016 period. We determined that the association between the weakening ENSO–AIR relationship and ENSO nonlinearity could reflect the contribution of different nonlinear ENSO modes to ENSO diversity.

scholarly journals Self-Affine Analysis of ENSO in Solar Radiation

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4816
Author(s):  
Thiago B. Murari ◽  
Aloisio S. Nascimento Filho ◽  
Marcelo A. Moret ◽  
Sergio Pitombo ◽  
Alex A. B. Santos
Keyword(s):  
Time Series ◽  
Renewable Energy ◽  
Solar Radiation ◽  
South America ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Fluctuation Analysis

The major challenge we face today in the energy sector is to meet the growing demand for electricity with less impact on the environment. South America is an important player in the renewable energy resource. Brazil accelerated the growth of photovoltaic installed capacity in 2018. From April of 2017 to April of 2018, the capacity increased by 1351.5%. It is expected to reach the value of 2.4 GW until the end of the year. The new Chilean regulation requests that 20% of the total electricity production in 2025 must come from renewable energy sources. The aim of this paper is to establish time series behavior changes between El Niño Southern Oscillation and the solar radiation resource in South America. The results can be used to validate the measured data of energy production for new solar plants. The method used to verify the behavior of the time series was the Detrended Fluctuation Analysis. Solar radiation data were collected in twenty-five cities distributed inside the Brazilian solar belt, plus six cities in Chile, covering the continent from east to west, in a region with high potential of solar photovoltaic generation. The results show the impact of El Niño Southern Oscillation on the climatic behavior of the evaluated data. It is a factor that may lead to the wrong forecast of the long-term potential solar power generation for the region.

scholarly journals A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Roman Olson ◽  
Soon-Il An ◽  
Soong-Ki Kim ◽  
Yanan Fan
Keyword(s):  
Time Series ◽  
El Niño ◽  
Probabilistic Models ◽  
Time Series Data ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
Series Data ◽  
El Nino Southern Oscillation ◽  
Stochastic Dynamical System

AbstractStochastic differential equations (SDEs) are ubiquitous across disciplines, and uncovering SDEs driving observed time series data is a key scientific challenge. Most previous work on this topic has relied on restrictive assumptions, undermining the generality of these approaches. We present a novel technique to uncover driving probabilistic models that is based on kernel density estimation. The approach relies on few assumptions, does not restrict underlying functional forms, and can be used even on non-Markov systems. When applied to El Niño–Southern Oscillation (ENSO), the fitted empirical model simulations can almost perfectly capture key time series properties of ENSO. This confirms that ENSO could be represented as a two-variable stochastic dynamical system. Our experiments provide insights into ENSO dynamics and suggest that state-dependent noise does not play a major role in ENSO skewness. Our method is general and can be used across disciplines for inverse and forward modeling, to shed light on structure of system dynamics and noise, to evaluate system predictability, and to generate synthetic datasets with realistic properties.

scholarly journals Impact of El Niño-Southern oscillation on human leptospirosis in Colombia at different spatial scales

2019 ◽  
Vol 13 (12) ◽  
pp. 1108-1116
Author(s):  
Clara Arias-Monsalve ◽  
Alejandro Builes-Jaramillo
Keyword(s):  
Time Series ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
National Level ◽  
La Niña ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
La Nina

Introduction: Leptospirosis is a zoonotic disease caused by a bacteria of the genus Leptospira. Climate is key in order to understand leptospirosis dynamics. El Niño Southern Oscillation is the main modulator of climate in Colombia. Our goal was to analyze the changes that occurred in number of cases and incidence rate of leptospirosis during La Niña and El Niño episodes in Colombia at three spatial scales in the period between 2007 and 2015. Methodology: A cross-sectional retrospective study was performed. Data analysis: correlation and lagged cross correlation between time series of Oscillation Niño Index and time series of standardized number of leptospirosis cases; construction of annual cycle of leptospirosis; comparison of changes of number of cases between Neutral, periods with El Niño and Neutral periods with La Niña. Results: At the national level, monthly number of cases raised a 25% during La Niña and decreased of 17% during El Niño. At departmental level, increase of cases in both phases of ENSO, depending on the location in the country, was found. At the municipal level, 17 have a rise in the number of cases during La Niña months. Of those, seven presented also an increase of cases during El Niño months and eight have a significant negative correlation with ONI. Conclusions: In Colombia, there exists a relationship between leptospirosis and the excess and lack of rainfall related with ENSO. The contrasting results from each spatial scale, reinforce that leptospirosis is a multidimensional disease with high complex interactions among its determinants.

scholarly journals Time-Series Study of Associations between Rates of People Affected by Disasters and the El Niño Southern Oscillation (ENSO) Cycle

2019 ◽  
Vol 16 (17) ◽  
pp. 3146 ◽  
Author(s):  
Holly Ching Yu Lam ◽  
Andy Haines ◽  
Glenn McGregor ◽  
Emily Ying Yang Chan ◽  
Shakoor Hajat
Keyword(s):  
Time Series ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
The Philippines ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Enso Cycle ◽  
Time Series Regression

The El Niño Southern Oscillation (ENSO) is a major driver of climatic variability that can have far reaching consequences for public health globally. We explored whether global, regional and country-level rates of people affected by natural disasters (PAD) are linked to ENSO. Annual numbers of PAD between 1964–2017 recorded on the EM-DAT disaster database were combined with UN population data to create PAD rates. Time-series regression was used to assess de-trended associations between PAD and 2 ENSO indices: Oceanic Niño Index (ONI) and multivariate El Niño Index (MEI). Over 95% of PAD were caused by floods, droughts or storms, with over 75% of people affected by these three disasters residing in Asia. Globally, drought-related PAD rate increased sharply in El Niño years (versus neutral years). Flood events were the disaster type most strongly associated with El Niño regionally: in South Asia, flood-related PAD increased by 40.5% (95% CI 19.3% to 65.6%) for each boundary point increase in ONI (p = 0.002). India was found to be the country with the largest increase in flood-related PAD rates following an El Niño event, with the Philippines experiencing the largest increase following La Niña. Multivariate ENSO Index (MEI)-analyses showed consistent results. These findings can be used to inform disaster preparedness strategies.

scholarly journals Trends and variations in CO, C2H6, and HCN in the Southern Hemisphere point to the declining anthropogenic emissions of CO and C2H6

2012 ◽  
Vol 12 (16) ◽  
pp. 7543-7555 ◽  
Author(s):  
G. Zeng ◽  
S. W. Wood ◽  
O. Morgenstern ◽  
N. B. Jones ◽  
J. Robinson ◽  
...  
Keyword(s):  
Time Series ◽  
Southern Hemisphere ◽  
Biomass Burning ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Anthropogenic Sources ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Anthropogenic Emissions

Abstract. We analyse the carbon monoxide (CO), ethane (C2H6) and hydrogen cyanide (HCN) partial columns (from the ground to 12 km) derived from measurements by ground-based solar Fourier Transform Spectroscopy at Lauder, New Zealand (45° S, 170° E), and at Arrival Heights, Antarctica (78° S, 167° E), from 1997 to 2009. Significant negative trends are calculated for all species at both locations, based on the daily-mean observed time series, namely CO (−0.94 ± 0.47% yr−1), C2H6 (−2.37 ± 1.18% yr−1) and HCN (−0.93 ± 0.47% yr−1) at Lauder and CO (−0.92 ± 0.46% yr−1), C2H6 (−2.82 ± 1.37% yr−1) and HCN (−1.41 ± 0.71% yr−1) at Arrival Heights. The uncertainties reflect the 95% confidence limits. However, the magnitudes of the trends are influenced by the anomaly associated with the 1997–1998 El Niño Southern Oscillation event at the beginning of the time series reported. We calculate trends for each month from 1997 to 2009 and find negative trends for all months. The largest monthly trends of CO and C2H6 at Lauder, and to a lesser degree at Arrival Heights, occur during austral spring during the Southern Hemisphere tropical and subtropical biomass burning period. For HCN, the largest monthly trends occur in July and August at Lauder and around November at Arrival Heights. The correlations between CO and C2H6 and between CO and HCN at Lauder in September to November, when the biomass burning maximizes, are significantly larger that those in other seasons. A tropospheric chemistry-climate model is used to simulate CO, C2H6, and HCN partial columns for the period of 1997–2009, using interannually varying biomass burning emissions from GFED3 and annually periodic but seasonally varying emissions from both biogenic and anthropogenic sources. The model-simulated partial columns of these species compare well with the measured partial columns and the model accurately reproduces seasonal cycles of all three species at both locations. However, while the model satisfactorily captures both the seasonality and trends in HCN, it is not able to reproduce the negative trends in either C2H6 or CO. A further simulation assuming a 35% decline of C2H6 and a 26% decline of CO emissions from the industrial sources from 1997 to 2009 largely captures the observed trends of C2H6 and CO partial columns at both locations. Here we attribute trends in HCN exclusively to changes in biomass burning and thereby isolate the influence of anthropogenic emissions as responsible for the long-term decline in CO and C2H6. This analysis shows that biomass burning emissions are the main factors in controlling the interannual and seasonal variations of these species. We also demonstrate contributions of biomass burning emission from different southern tropical and sub-tropical regions to seasonal and interannual variations of CO at Lauder; it shows that long-range transport of biomass burning emissions from southern Africa and South America have consistently larger year-to-year contributions to the background seasonality of CO at Lauder than those from other regions (e.g. Australia and South-East Asia). However, large interannual anomalies are triggered by variations in biomass burning emissions associated with large-scale El Niño Southern Oscillation and prolonged biomass burning events, e.g. the Australian bush fires.

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