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EN
This study focuses on investigating the chaotic and multifractal behavior of atmospheric time series of solar radiation (solar), maximum temperature (Tmax), and minimum temperature (Tmin) over Lagos State for a period of 24 years. Chaotic quantifiers such as sample entropy, Lyapunov exponent, and correlation dimension were employed to unveil the chaotic nature of the time series. Values of Lyapunov exponents obtained for the three parameters were in the range 0.251–0.261, which confirms chaos in the time series. The scaling properties of the time series were revealed by applying the Multifractal Detrended Fluctuation Analysis (MFDFA). Based on the multifractal strength, we infer that the dynamics of solar radiation (0.932) is different from that of maximum temperature (0.155) and minimum temperature (0.198). The time series have a long-range correlation and broad probability distribution. Results obtained showed that the time series is chaotic and exhibited a multifractal behavior. The results also show that chaotic and multifractal analyses are useful in unveiling the complex dynamics of the atmosphere.
EN
The impact of extreme climate such as drought and flooding on agriculture, tourism, migration and peace in Nigeria is immense. There is the need to study the trend and statistics for better planning, preparation and adaptation. In this study, the statistical and temporal variation of climatic indices Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) was computed for eighteen (18) stations covering four climatic zones (Sahel, Midland, Guinea Savannah and Coastal) of tropical Nigeria. Precipitation, minimum and maximum temperature from 1980 to 2010 obtained from the archives of the Nigerian Meteorological Services were used to compute both the SPI and SPEI indices at 1-, 3- 6- and 12-month timescales. The temporal variation of drought indices showed that droughts were more prominent at 6- and 12-month timescales. SPI and SPEI were found to be better correlated at longer timescales than short timescales. Predominant small, positive and significant trend across the region suggest an increasing trend due to climate change.
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