1.1 Background of Study
Climate is the average weather condition of a place over a long period of time, usually over a 30 year period (Planton, 2013). It is the statistical description in terms of the mean and variability of relevant quantities over a period ranging from months to thousands or millions of years (IPCC, 2001). These quantities mostly refer to surface variables of some weather parameters such as temperature, relative humidity, sunshine hours, rainfall, atmospheric pressure, precipitation, wind, e.t.c. The climate of any particular area is not static, it varies and this variation is referred to as a change in the statistical distribution of weather patterns when the change lasts for an extended period of time.
These changes in climate are observed through the weather parameters. The factors which are responsible for these variations have over the years been a topic of debate among scientists. These factors are called climate forcing agents or forcing climate mechanism (Smith, 2013). They can be classified as natural or anthropogenic in which case human activities alter the balance of the weather patterns over a long period of time for example in the case of the increased concentration of green house gases by various human activities (IPCC, 2001, 2007, 2013). Other factors are variations in solar radiation, variations in earth
s orbit, Earths reflectivity among others. The increase in the concentration of greenhouse gases due to industrialization is the most commonly known, however, some studies (Courtillot et al, 2006; Usoskin and Kovalstor, 2008) suggest that this could not have accounted for all the observed climate change but that some other natural factors such as solar and geomagnetic activities must be contributing to the observed changes.
Over Nigeria’s climate, not much has been done to relate solar and geomagnetic activities to climate change as observed in the variations of the trend of some weather parameters. This study intends to investigate following the recommendation of Okeke and Audu (2017) the relationship between solar activities (sunspot numbers), cosmic rays and geomagnetic activities on sunshine hours, relative humidity and wind speed.
1.2 Earth’s Magnetic Field.
The Earth’s magnetic field also referred to as geomagnetic field is the magnetic field that extends from the Earth’s interior out into space where it meets the solar wind (Finlay et al., 2010). It acts as a very strong protection to the earth from the streams of particles in the solar wind at the magnetosphere. Some authors (Usoskin and Kovaltsor, 2008, Dergachev et al., 2004) believe that geomagnetic field contribute to climate change. Geomagnetic activities are monitored using the geomagnetic indices such as the aa, kp, ap indices.
1.3 Solar Activities
Solar activity refers to the sum of all variable and short-lived disturbances on the sun, such as sunspot, prominences, Coronal Mass Ejections (CME), solar flares, e.t.c. This activity brings about changes in the levels of solar radiation and quantity of materials ejected from the sun. Changes in appearance such as in the number and size of sunspots, flares, CMEs are also observed. The period of this process (changes) from one minimum to another is called solar cycle or solar magnetic activity cycle and lasts for an average of 11 years. These changes on the sun have some effects in space, in the atmosphere and on our planet- the Earth. Related to solar activitites are cosmic rays which are high- energy radiation mainly originating from outside the solar system (Sharma, 2008). Svensmark et al. (2007) suggested that cosmic rays have an impact on climates variability.
1.4 Climate of Nigeria
It is important that we study the climate of Nigeria since our primary interest is to study the effects which solar and geomagnetic field activities have on it. Located in West Africa, Nigeria is bounded on the west by Benin republic while Chad and Cameroun are found on the East of the country with Niger republic on the North. Fig.1.1 is the map of Nigeria showing the various climate classifications.
Fig. 1.1 Nigeria’s climate classification (Ali, 2016)
Nigeria is found in the torrid zone, also known as the tropics or tropical zone having a climate that is seasonally damp and very humid. The country is affected by four climate types as one goes through its geographical segments. Table 1.1 shows the classification of Nigeria as used for the study.
Table 1.1: Meteorological Stations and Their Coordinates across Nigeria
ZONES STATIONS LATITUDE (ON) LONGITUDE(OE)
NORTH EAST AND NORTH WEST
[Region1 (R1)] YELWA 11.68 4.07
SOKOTO 13.01 5.25
KADUNA 10.52 7.43
KANO 12.01 8.59
BAUCHI 10.64 10.08
MAIDUGURI 11.83 13.15
YOLA 9.20 12.50
NORTH CENTRAL AND SOUTH WEST
[Region 2 (R2)] ILORIN 8.50 4.48
IKEJA 6.60 3.35
IBADAN 7.38 3.95
OSHOGBO 7.78 4.54
MAKURDI 7.73 8.54
LOKOJA 7.80 6.73
SOUTH-SOUTH AND SOUTH-EAST
[Region 3 (R3)] BENIN 6.23 5.60
WARRI 5.52 5.73
PORT HARCOURT 4.75 7.00
OWERRI 5.48 7.02
ENUGU 6.47 5.57
CALABAR 4.95 8.32
OGOJA 6.65 8.80
1.7 Purpose of Study
The purpose of this work is to investigate the effects of solar and geomagnetic field activities on Nigeria’s climate and the specific objectives include to:
i. determine the effects of solar activity on some climatic parameters using sunspot numbers.
ii. estimate the impacts of cosmic rays on climatic parameters (sunshine hours, relative humidity and wind speed)
iii. determine the effects of geomagnetic activity (aa-index) on the same climatic parameters.
Climate change was never attributed to geomagnetic activity until Courtillot et al., (2006) published a controversial paper linking geomagnetism to climate change. The paper was termed controversial because National Aeronautics and Space administration (NASA) attributes 97% of the causes of climate change to human activities (Kelly, 2016). Dergachev et al. (2012) supporting the unpopular findings of Courtillot suggested the possibility that geomagnetic field variations which modulate the cosmic ray flux could have played a major role on climate change in addition to previously induced solar radiation. Kitaba et al. (2017) also suggested a link between weakened geomagnetic field and climate cooling with geological evidence. These are few of the studies which have linked geomagnetic activities to climate change. However, the mechanism and extent of its contribution is yet to be established.
Some studies have suggested a connection between solar activity and climate change. Haigh (2007) reported that variations in solar activity at least as observed in number of sunspots have been apparent since ancient times but to what extent solar variability may affect global climate has been more controversial. This statement implies that the contribution solar activity to climate change is yet to gain a general scientific acceptance. On a multi-decadal time scale, Usoskin (2013) stated that solar variability can affect the Earth’s environment and climate in many ways. Chiodo et al. (2016) opined that the impact of solar activity on the projections of global mean temperature is negligible and so do not support the result of Usoskin.
Still on the contributions of solar activities indices, Svensmark and Friis-Christensen (1997) stated that cosmic rays play an important role in climate change through ionization of gases surrounding the Earth. Furthering their research, Svensmark et al. (2007) showed a relationship between cosmic rays and climate through cloud formation, their result suggest that when ionization by cosmic rays increases, the number of small aerosols increases as well. However, Sloan and Wolfendale (2013) using the changing cosmic ray as proxy for solar activity found that less than 14% of global warming since 1950’s comes from changes in solar activities, they further stated that the contribution of changing solar activity either through cosmic rays or otherwise could not have contributed more than 10% of the global warming seen in the twentieth century.
In contrast to the assertion of Sloan and Wolfendale (2013), Martin (2016) claims that cosmic rays contribute to global warming. Kitaba (2017) supporting this view stated that the effects of this global warming is seen more on continental climate than on oceanic climate. As earlier stated, these effects could only be studied through weather parameters such as sunshine hours, relative humidity, wind speed e.t.c.
Marin et al. (2014) observed a consistent increase in sunshine hours for a period of 52 years in Romania and concluded that this increase is an evidence of climate change. Agreeing with this result, Falayi and Rabiu (2015) stated that sunshine hour is a strong climate driver since it is proportional to solar radiation when other factors such as cloudiness are kept constant. In a study, Oyewole et al. (2014) found that the relative humidity of south-south region is the highest in Nigeria and it peaks between June and August. Continuing in the study on the role of relative humidity (RH) on climate change, Ajibola et al. (2014) found a strong positive correlation coefficient of 0.97 between shortwave radiation and RH at Ilorin, western Nigeria. Young et al. (2011) reported that as global warming increases, wind speed also increases.
Recently, Okeke and Audu (2017) studied the influence of solar and geomagnetic activity on climate change in Nigeria using temperature and rainfall as weather parameters; they reported that the correlation between solar indices and the climatic parameters used were statistically insignificant. They recommended that other weather parameters should be employed to study the same influence over Nigeria’s climate. This was the motivation behind this research.
SOURCES, THEORY AND METHOD OF DATA ANALYSIS
3.1 Sources of Data
The sets of data used for this study were obtained from the following sources:
• The cosmic ray data used was obtained from http://www.bartol.udel.edu/~neutron. They were supplied by the University of Delware research institute network of neutron monitors. The station used was Mcmurdo at 77.9oS and 166.6oE and the data spanned a period of 48 years (1965-2012).
• The world data centre (WDC) for sunspot index Royal Observatory of Belgium provided the daily smoothed sunspot numbers used for the analysis. The period analyzed spanned 48 years (1965-2012). It is available online at http://www.sidc.be/sunspot-data.
• The geomagnetic aa-index was obtained from two antipodal (opposite sides of the Earth) observatories: Hartland observatory in Europe and Canberra observatory in Australia. The data was provided by the National Oceanic and Atmospheric Administration (NOAA) and it is available online at http://www.ngdc.noaa.gov/stp/geomagneticdata/aastar.shtml. The period was for 46 years (1965-2010).
• The data for sunshine hours (SSH), relative humidity (RH) and wind speed (WS) from 20 stations across Nigeria were obtained from Nigeria Meteorological Agency (NIMET), Oshodi, Lagos. NIMET is under the Federal Ministry of science and technology. The data covered a period of 48 years (1965-2012).
3.2 Theory of Method of Data Analysis:
We employed different theories as they applied to the different data which we analyzed in the course of this research.
3.2.1 Analysis of Cosmic Rays
The following operations were carried out to obtain the needed values:
i. The daily cosmic ray mean ( ) was obtained from the hourly values ( i) as in equation 3.1
ii. The monthly mean ( ) was obtained from equation 3.2
Where n is the number of days in any given month.
iii. Equation 3.3 was employed to obtain the yearly mean ( )
3.2.2 Analysis for Sunspot Number:
i. The monthly mean of the sunspot was obtained from the daily mean as shown in equation 3.4
Where n is the number of days in the month.
ii. A yearly mean of sunspot number ( ) was obtained from equation 3.5
3.2.3 Analysis for Geomagnetic aa-Index:
aa-index is preferred to other geomagnetic indices such as K and Ap because the time series span further to about 1868 an up –to-date values are provided and made available on weekly basis. It is measured every 3 hours and hence produces 8 values in a 24-hour (daily) period. The following time series analysis was carried out:
i. The daily mean aa-index ( ) was obtained from equation 3.6
Where aai represents the 3-hourly values.
ii. Equation 3.7 was employed to obtain the monthly mean values ( )
Where n is the number of days in the month.
iii. Finally, the annual mean ( ) was obtained from equation 3.8
3.2.4 Analysis of Sunshine Hours:
Sunshine hours also called the sunshine duration measures the number of hours per day in which the direct solar irradiance exceeds a threshold of 120Wm-2.
i. Equation 3.9 was employed to obtain the monthly mean ( )
ii. The yearly mean ( ) was obtained from equation 3.10
Where SSdi and represent the daily and monthly values of sunshine hours respectively.
This analysis was carried out for 20 meteorological stations cutting across the climatic zones of the country.
3.2.5 Analysis of Relative Humidity;
The following time series analysis were carried out for the 20 stations across the country to obtain annual mean of relative humidity at 09hours for the different zones through the 48-year period under study:
i. The monthly mean ( ) was obtained using equation 3.11
Where is the daily value of relative humidity and n is the number of days in any particular month.
ii. To obtain the yearly mean ( ), the time-series analysis shown in equation 3.12 was used.
(RHmi is monthly values of relative humidity)
3.2.6 Analysis of Wind Speed:
The following operations were performed to obtain the yearly mean of wind speed for the 20 stations across the country:
i. To obtain the monthly mean ( ), we employed equation 3.13
Where is the daily value of the wind speed in ms-1 and n is the number of days in the month.
ii. The yearly mean ( ) was then obtained using equation 3.14
Where WSmi represents the monthly values of wind speed
3.3 Method of Data Analysis: Correlation analysis was used to ascertain the level and nature of the relationships which exist between the measured solar and geomagnetic activity with some of the weather parameters (sunshine hours, relative humidity and wind speed) being analyzed. The Pearson’s product moment correlation coefficient (r) shown in equation 3.15 was employed.
and respectively represent the ith values and mean of a set of variable. and also represent the ith values and mean value of the second variable being compared.
3.3.1 Method of Analysis of Cosmic Rays:
i. Equation 3.1, 3.2 and 3.3 were employed to calculate the daily, monthly and yearly mean respectively through the 48-year period.
ii. Correlation analysis was applied to reveal the relationship between the annual mean of cosmic rays and some weather parameters (sunshine hours, relative humidity and wind speed)
3.3.2 Method of Analysis of Sunspot Number:
i. Similarly, equations 3.4 and 3.5 were emploeyd respectively to obtain the monthly and yearly means of sunspot number.
ii. From equation 3.15, the pearson’s correlation coefficient was used to determine the relationship between yearly mean of sunspot number and the analyzed weather parameters.
3.3.3 Method of Analysis of aa-Index:
i. Applying equations 3.6, 3.7 and 3.8 the daily, monthly and yearly means of aa-index were respectively calculated.
ii. The obtained yearly average in (i) above was correlated with sunshine hours, relative humidity and wind speed.
RESULTS AND DISCUSSIONS
4.1 Variability of sunspot numbers with cosmic rays:
The solar and geomagnetic indices considered are strongly related. The result obtained shows that cosmic rays vary in nearly opposite direction with sunspot numbers. This is expressed in Fig.4.1. The two variables are strongly negatively correlated with the correlation coefficient of -0.84; this result is in agreement with the reports of many researchers (Okeke and Audu 2017; Tiwari et al., 2011).
Fig. 4.1: Yearly mean variability of cosmic rays and sunspot numbers (1965-2012)
4.2 Variability of Cosmic Rays and aa-Index. Fig. 4.2: Yearly mean variability of cosmic rays and aa-index (1965-2010)
It is observed from Fig. 4.2 that cosmic rays and aa-index also vary in an opposite direction with the correlation coefficient of -0.68 indicating a significant negative correlation between the two.
4.3 Variability of Sunspot number and aa-Index:
The relationship between the sunspot numbers and aa-index was computed. From Fig. 4.3 we can observe that the sunspot and aa-index were going in nearly the same direction. The correlation coefficient was found to be 0.42, indicating a moderate positive correlation.
Fig. 4.3: Yearly mean variability of cosmic rays and aa-index (1965-2010)
4.4 Relationship between Cosmic Rays and Sunshine Hours, Relative Humidity and Wind Speed
Fig. 4.4 (a): Yearly mean variability of cosmic rays and sunshine hours (1965-2012)
Figure 4.4 shows the relationship between cosmic rays and sunshine hours in region 1. Similar figures were also obtained for regions 2 and 3. Figures 4.4 (b) shows the relationship between cosmic rays and relative humidity which is another very important weather parameter for region 3, the same relationships were considered for regions 1 and 2.
Fig. 4.4(b): Yearly variability of cosmic rays and relative humidity (1965-2012)
From the results almost no relationship exists between cosmic rays and relative humidity in all the regions. The correlation coefficients obtained were 0.07, 0.15 and 0.19 for regions 1, 2 and 3 respectively, although they are positively correlated, the relationship is very weak and almost non-existent.
Another weather parameter considered was the wind speed. Figure 4.4(c) shows the pattern of the relationship between the two variables in region 2. The plots for regions 1 and 3 were also done and similar patterns were produced.
Fig. 4.4 (c): Yearly mean variability of cosmic rays and wind speed (1965-2012)
Cosmic rays was found to be very weakly correlated to wind speed in regions 1 and 2 although with positive values, however a negative correlation coefficient of -0.2 was obtained for region 3. In all, it is evident from the results obtained that cosmic rays have no significant effect on the wind speed measured across the states of Nigeria.
4.5 Relationship between Sunspot Numbers and Sunshine Hours, Relative Humidity and Wind Speed.
The relationship between sunspot numbers one of the major solar activity parameters and sunshine hours were considered for the three zones. Figures 4.5 (a) is a plot of sunspot numbers against sunshine hours for region 1. The results obtained in all the zones show that the two variables are almost perfectly independent especially for zones 2 and 3 where the correlation coefficient are -0.13 and -0.16 respectively.
Fig. 4.5 (a): Yearly mean variability of sunspot numbers with sunshine hours (1965-2012)
This suggests that sunspot numbers do not significantly influence the sunshine hours. However, for region 1, there is a moderate negative correlation of -0.31. The relationship here could be as a result of the extended hours of sunshine experienced in the northern part of Nigeria.
The next analysis was finding the relationship between the sunspot numbers and the relative humidity for the divisions made on the country. a plot of sunspot numbers with relative humidity for region 1 is shown by Figure 4.5 (b) where yearly means of the variables were plotted against the years. Similar plots were also made for regions 2 and 3. From the figures it is clear that they are independent of each other. The correlation coefficient also confirms this independence having obtained 0.07, 0.03 and 0.03 for regions 1, 2 and 3 respectively.
Figure 4.5 (b): Yearly mean variability of sunspot numbers with relative humidity (1965-2012)
It is evident therefore that sunspot numbers may not have an influence on the relative humidity in Nigeria.
Finally, the relationship of sunspot with wind speed in the three zones was considered. It was discovered also that sunspot had no significant influence on Nigeria’s climate as the correlation coefficients were rather very weak. Figure 4.5 (c) illustrates the relationship for region 3.
Fig. 4.5 (c): Yearly mean variability of sunspot numbers and windspeed (1965-2012)
4.6 Relationship between aa-index with Sunshine hours, Relative humidity and Wind speed
Geomagnetic activities were studied using aa-index. The relationships existing between the weather parameters (sunshine hours, relative humidity and wind speed) and aa-index were examined. Figure 4.6 (a) describes the form of the relationship between aa-index and sunshine hours for regions 2. In similar plots, the same relationship was found for regions 1 and 3.
Fig. 4.6 (a): Yearly mean variability of geomagnetic aa-index and sunshine hours (1965-2010)
There’s a moderate negative relationship between geomagnetic aa-index and sunshine hours in all the regions with region 1 being the highest correlated with nearly -0.5 correlation coefficient. The correlation coefficients for regions 2 and 3 were found to be -0.26 and -0.35 respectively. This shows that there could be some influence of geomagnetic activity on Nigeria’s climate when sunshine hour is considered.
Next, the effect of aa-index on the climate was examined using the relationship existing between the index and relative humidity in the three regions found in Nigeria based on our classification. From Figure 4.6 (b) we observe the relationship between the variables in region 1.
Fig. 4.6(b): Yearly mean variability of geomagnetic aa-index and relative humidity (1965-2010)
A very weak negative correlation (-0.08) exists between aa-index and relative humidity in region 1, while for regions 2 and 3 with very high values of relative humidity a moderate negative correlation coefficients of -0.34 and -0.43 were obtained respectively. This implies that geomagnetic activity may possibly be influencing Nigeria’s climate in relation to relative humidity.
Finally, wind speed another very important weather parameter was used to compare geomagnetic activity (aa-index). The yearly mean variability of aa-index and wind speed was plotted for regions 1, 2, and 3. Figure 4.6 (c) is a sample of the plot from region 3.
Fig. 4.6 (c): Yearly mean variability of aa-index and wind speed (1965-2010)
It is seen that geomagnetic activity moderately influences the climate of Nigeria when the wind speed is considered. Table 4.1 gives a summary of the correlation coefficients obtained.
Table 4.1 Summary of the Correlation Coefficients of the Analysed Weather Parameters
WEATHER PARAMETERS COSMIC RAYS SUNSPOT NUMBERS AA-INDEX
SUNSHINE HOURS 0.42 -0.31 -0.47
RELATIVE HUMIDITY 0.08 0.07 -0.08
WIND SPEED 0.20 -0.15 -0.36
SUNSHINE HOURS 0.16 -0.14 -0.26
RELATIVE HUMIDITY 0.15 0.03 -0.34
WIND SPEED 0.01 0.05 0.10
SUNSHINE HOURS 0.31 -0.16 -0.35
RELATIVE HUMIDITY 0.19 0.03 -0.43
WIND SPEED -0.26 0.21 0.28
CONCLUSION AND RECOMMENDATION
The results show that cosmic rays produced a weak correlation coefficient with all the weather parameters studied apart from sunshine hours which had a moderate value. We can deduce therefore that cosmic ray may not have a significant effect on Nigeria’s climate. similarly, the impacts of sunspot numbers on Nigeria’s climate during the period of study is statistically insignificant. This is in agreement with the results of Okeke and Audu (2017) who compared sunspot with temperature and rainfall.
We can deduce also from the results that geomagnetic activities may be a stronger climate driver over Nigeria than solar activities because it consistently had a moderate negative correlation coefficient with nearly all the parameters.
For further research in this area of interest, we make the following recommendations:
- That on Nigeria’s climate more solar activities like coronal mass ejections and solar flares should be considered.
- That together with the recommended solar activities more weather parameters should be analysed.
- That some other geomagnetic indices (Ap, Kp and K) should be used as well
- That the study should be expanded to cover the climate of Africa
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