Modelling climate variables

  • O.M. Olayiwola
  • A.A. Amalare
  • O.R. Olaoye
Keywords: Climatic Change, Principal Component, Eigenvalues, Eigenvectors, Anthropogenic Causes.

Abstract

Climate change is a change in the statistical distribution of weather patterns that lasts for an extended period of time. It is widely recognized as one of the most important issues facing humanity. Most scientists agree that emissions of anthropogenic greenhouse gases are responsible for the observed increases in global air temperature. This study was carried out to examine the effects of both anthropogenic and natural causes of climatic change and their possible solutions. The monthly weather data on solar radiation, maximum temperature, minimum temperature, rain, wind, dew point, 2-metre temperature and relative humidity were obtained from the Nigerian Meteorological Agency Weather Station. The eigenvalues and eigenvectors for the data set were obtained to determine the principal components. The bar and scree plots were used to select the required principal components. The correlation matrix was determined for the selected principal components and the atmospheric condition variables. The first principal component is strongly correlated with maximum temperature, minimum temperature, wind, dew point and relative humidity. It can be
said that the first principal component is primarily a measure of humidity as it correlates mostly with dew point and relative humidity with correlation values of 0.933 and 0.932 respectively. The second principal component is strongly correlated with solar radiation, maximum temperature, minimum temperature, wind, dew point and 2-metre temperature but it can be said to be primarily a measure of solar radiation as it correlates with it at 0.942. The third principal component correlates strongly with solar radiation, maximum temperature, wind and 2-metre temperature. In this component, as solar radiation decreases; maximum temperature, wind and 2-metre temperature increases. This component is primarily a measure of temperature as it has a correlation value of 0.909 with 2-metre temperature. A glimpse into the projection of the climatic pattern shows a steady increase in monthly average temperature which may pose significant risks to human health and activities, as high temperature is associated with risk of injury, illnesses and death from the resulting heat waves; wildfires, intense storms and floods rises.

Published
2021-05-03
Section
Articles

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eISSN: 1116-4336