The El Niño Southern Oscillation (ENSO) is the Earth's strongest climate fluctuation on inter-annual time-scales and has global impacts although originating in the tropical Pacific. Many point indices have been developed to describe ENSO but the Multivariate ENSO Index (MEI) is considered the most representative since it links six different meteorological parameters measured over the tropical Pacific. Extreme values of MEI are correlated to the extreme values of atmospheric CO 2 concentration rate variations and negatively correlated to equivalent scale extreme values of the length of day (LOD) rate variation. We evaluate a first order conversion function between MEI and the other two indexes using their annual rate of variation. The quantification of the strength of the coupling herein evaluated provides a quantitative measure to test the accuracy of theoretical model predictions. Our results further confirm the idea that the major local and global Earth-atmosphere system mechanisms are significantly coupled and synchronized to each other at multiple scales.

The El Niño phenomenon is the Earth’s strongest climatic fluctuation on an inter-annual time-scale and has a quasi-global impact although originating in the tropical Pacific Ocean. A very strong El Niño is recognized to cause extreme dryness and wetness in different parts of the world. We show that all the eight well documented influenza pandemics, starting from the first certain one documented in AD 1580, originated in China and in Russia, a few years after the occurrence of a very strong or after a prolonged strong/moderate El Niño event. At present, the next El Niño will probably occur at the beginning of 2013 (Mazzarella et al., 2010) and this forecast may suggest to be well prepared to take appropriate precautionary epidemiological measures

Many point indices have been developed to describe El Niño/Southern Oscillation but the Multivariate El Niño Southern Oscillation (ENSO) Index (MEI) is considered the most representative since it links six different meteorological parameters measured over the tropical Pacific. Spectral analysis with appropriate data reduction techniques of monthly values of MEI (1950-2008) has allowed the identification of a large 60-month cycle, statistically confident at a level larger than 99%. The highest values of MEI (typical of El Niño events) and the lowest values of MEI (typical of La Niña events) are concordant with respective maxima and minima values of the identified 60-month cycle

The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War. The academic literature indicates that climatic features, specifically temperature and absolute humidity, are very important factors affecting infectious pulmonary disease epidemics - such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS); however, the influence of climatic parameters on COVID-19 remains extremely controversial. The goal of this study is to individuate relationships between several climate parameters (temperature, relative humidity, accumulated precipitation, solar radiation, evaporation, and wind direction and intensity), local morphological parameters, and new daily positive swabs for COVID-19, which represents the only parameter that can be statistically used to quantify the pandemic. The daily deaths parameter was not considered, because it is not reliable, due to frequent administrative errors. Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy Region (Northern Italy) from 1 March, 2020 to 20 April, 2020. This region exhibited the largest rate of official deaths in the world, with a value of approximately 1700 per million on 30 June 2020. Moreover, the apparent lethality was approximately 17% in this area, mainly due to the considerable housing density and the extensive presence of industrial and craft areas. Both the Mann–Kendall test and multivariate statistical analysis showed that none of the considered climatic variables exhibited statistically significant relationships with the epidemiological evolution of COVID-19, at least during spring months in temperate subcontinental climate areas, with the exception of solar radiation, which was directly related and showed an otherwise low explained variability of approximately 20%. Furthermore, the average temperatures of two highly representative meteorological stations of Molise and Lucania (Southern Italy), the most weakly affected by the pandemic, were approximately 1.5 °C lower than those in Bergamo and Brescia (Lombardy), again confirming that a significant relationship between the increase in temperature and decrease in virulence from COVID-19 is not evident, at least in Italy.