Summer monsoon rainfall over Bangladesh in relation to Multivariate ENSO Index

The relationship between monthly rainfall over Bangladesh during monsoon season and bi-monthly Multivariate ENSO Index (MEI) pertaining to the period from first week of previous month to first week of the month under consideration, has been investigated. The MEI is calculated as the first Principal Component (PC) of six variables over the tropical Pacific, viz., sea surface temperature, sea level pressure, zonal and meridional components of the surface wind, surface air temperature and total cloudiness fraction of the sky. The MEI values for prognostic purposes are available by the first week of every month. MEI is better for monitoring ENSO than other indices like Southern Oscillation Index (SOI) or various SST indices as it integrates complete information on ENSO and reflects the nature of complete ocean atmosphere system. Positive values of MEI indicate warm ENSO phase (EI-Nino) and negative ones represent cold phase (La-Nina). The results of the present study show that June rainfall of Bangladesh is adversely affected by the ENSO. But interestingly Bangladesh seems to receive more than normal rainfall during August of ENSO years. ENSO does not seem to have any significant adverse impact on July and September rainfall of Bangladesh. The results of the study may find applications in foreshadowing monsoon rainfall over Bangladesh on a monthly scale.

The El Niño–Southern Oscillation and the Transatlantic Slave Trade

The relationship between the El Niño-Southern Oscillation (ENSO) and the Transatlantic Slave Trade (TAST) is examined using the Slave Voyages dataset and a reconstructed ENSO index. The ENSO index is used as a proxy for West African rainfall and temperature. In the Sahel, the El Niño (warm) phase of ENSO is associated with less rainfall and warmer temperatures, whereas the La Niña (cold) phase of ENSO is associated with more rainfall and cooler temperatures. The association between ENSO and the TAST is weak but statistically significant at a two-year lag. In this case, El Niño (drier and warmer) years are associated with a decrease in the export of enslaved Africans. The response of the TAST to El Niño is explained in terms of the societal response to agricultural stresses brought on by less rainfall and warmer temperatures. ENSO-induced changes to the TAST are briefly discussed in light of climate-induced movements of peoples in centuries past, and in the drought-induced movement of peoples in the Middle East today.

Investigating links between rainfall variations in the Ogooué River basin and ENSO in the Pacific Ocean over the period 1940–1999

This paper investigates links between rainfall variability in the Ogooué River Basin (ORB) and El Niño Southern Oscillation (ENSO) in the Pacific Ocean. Recent hydroclimatology studies of the ORB and surrounding areas resulting in contrasting conclusions about links between rainfall variability and ENSO. Thus, to make the issue clearer, this study investigates the links between ENSO and rainfall in the ORB over the period 1940–1999. The principal component analysis of monthly rainfall in the ORB was done. The temporal mode of the first component corresponds to the interannual variations of rainfall on the ORB. Also, the pattern of the spatial mode of the first component shows that the ORB is a homogeneous hydroclimatic zone. However, no leading mode is significantly correlated to the ENSO index. A cross-wavelet analysis of the time series of basin-scale rainfall and the ENSO index was therefore carried out. The result is a set of periodogram structures corresponding to some ENSO episodes recorded over the study period. And wavelet coherence analysis of both time series confirms that there are significant links between ENSO and rainfall in the ORB.

Functional Data Visualization and Outlier Detection on the Anomaly of El Niño Southern Oscillation

The El Niño Southern Oscillation (ENSO) is a well-known cause of year-to-year climatic variations on Earth. Floods, droughts, and other natural disasters have been linked to the ENSO in various parts of the world. Hence, modeling the ENSO’s effects and the anomaly of the ENSO phenomenon has become a main research interest. Statistical methods, including linear and nonlinear models, have intensively been used in modeling the ENSO index. However, these models are unable to capture sufficient information on ENSO index variability, particularly on its temporal aspects. Hence, this study adopted functional data analysis theory by representing a multivariate ENSO index (MEI) as functional data in climate applications. This study included the functional principal component, which is purposefully designed to find new functions that reveal the most important type of variation in the MEI curve. Simultaneously, graphical methods were also used to visualize functional data and capture outliers that may not have been apparent from the original data plot. The findings suggest that the outliers obtained from the functional plot are then related to the El Niño and La Niña phenomena. In conclusion, the functional framework was found to be more flexible in representing the climate phenomenon as a whole.

Bicovariance and Bispectrum of ENSO index and its impact in nonlinear predictability

<p>El Niño Southern Oscillation (ENSO) index has been shown as a non-Gaussian and nonlinear stochastic process. Here we assess the statistical significance of non-Gaussianity and non-linearity through the analysis of third-order statistics of El Niño 3.4 index in the period 1870–2018, namely the bicovariance (lagged third-order moments) and bispectrum (its 2D Fourier transform). The analysis of bicovariance reveals a tendency for extreme (weak) ENSO signal in the Boreal Spring to be followed by la Niñas (El Niños) in the forthcoming Boreal Winter, thus contributing for a nonlinear attenuation of the ENSO Spring Predictability Barrier. The bispectrum provides a spectral decomposition of skewness in a similar way of the spectral decomposition of variance.  Positive and negative real bispectrum values identify triadic phase synchronizations (at frequencies f1, f2 and f1+f2, mostly in the period range 2–6 years) contributing respectively to extreme El Niños and La Niñas. The known positive ENSO skewness and the main features of the ENSO bicovariance and bispectrum are shown to be well reproduced by fitting a bilinear stochastic model where the influence of non-observed variables is simulated by a delayed multiplicative noise, being able to generate non-Gaussianity and non-linearity. The model shows improved forecasts, with respect to benchmark linear models, up to four trimesters ahead, especially of the amplitude of extreme El Niños. The authors would like to acknowledge MISU (Meteorological Institute at Stockholm University) and the financial support FCT through project   UIDB/50019/2020 – IDL and project JPIOCEANS/0001/2019 (ROADMAP: ’The Role of ocean dynamics and Ocean–Atmosphere interactions in Driving cliMAte variations and future Projections of impact–relevant extreme events’).</p>

Vertical crustal deformations and climate variability through PCA and SVD

<p>We investigated a large network of permanent GPS stations to identify and analyse common patterns in the series of the GPS height, environmental parameters, and climate indexes.</p><p>The study is confined to Europe, the Mediterranean, and the North-eastern Atlantic area, where 114 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive. The GPS time series were selected on the basis of the completeness and the length of the series.</p><p>In addition to the GPS height, the parameters analysed in this study are the atmospheric surface pressure (SP), the terrestrial water storage (TWS), and a few climate indexes, such as MEI (Multivariate ENSO Index). The Principal Component Analysis (PCA) is the methodology adopted to extract the main patterns of space/time variability of the parameters.</p><p>Moreover, the coupled modes of space/time interannual variability between pairs of variables was investigated. The methodology adopted is the Singular Value Decomposition (SVD).</p><p>Over the study area, main modes of variability in the time series of the GPS height, SP and TWS were identified. For each parameter, the main modes of variability are the first four. In particular, the first mode explains about 30% of the variance for GPS height and TWS and about 46% for SP. The relevant spatial patterns are coherent over the entire study area in all three cases.</p><p>The SVD analysis of coupled parameters, namely H-AP and H-TWS, shows that most of the common variability is explained by the first 3 modes, which account for almost 80% and 45% of the covariance, respectively.</p><p>Finally, we investigated the relation between the GPS height and a few climate indexes. Significant correlations, up to 50%, were found between the MEI (Multivariate Enso Index) and about half of the stations in the network.</p>

The Impact Study of El Niño-Southern Oscillation to the Wind and Solar Data in Malaysia Using the Wavelet Analysis

Malaysia has launched initiatives for utilizing renewable energy (RE) as a source of electricity since 2011 by establishing renewable energy-related laws and policies. Malaysia's geographical location and climate have led to a limited amount of intermittent RE resources. Thus, a more thorough study of the various factors affecting the RE-based electricity generation is needed for energy output optimization. This article aims to understand the impact of El Niño-Southern Oscillation (ENSO) events on wind and solar reanalysis datasets using the Wavelet Transform. The thirty-year ERA5 solar and wind datasets were used in the study, together with the multivariate ENSO Index (MEI). As a result, the selected sites experienced an increase in solar irradiation during moderate to very strong El Niño and a decrease during the La Niña period. The wind speed increases during La Niña and decreases during El Niño, with the exception of the high wind speed during the Northeast monsoon season. Also, there was a significant coherence relationship between the wind and solar datasets with the ENSO index at a specified period. Therefore, the ENSO is essential as an input factor for future development plans for wind and solar power, energy predictions, and risk assessment.