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\title{Analysis of Rainfall and Temperature Variability Over Nigeria}
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             \author[1]{Akinsanola Akintomide  Afolayan}

             \affil[1]{  Federal University of Technology, Akure. Nigeria}

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\date{\small \em Received: 9 December 2013 Accepted: 31 December 2013 Published: 15 January 2014}

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\begin{abstract}
        


This study investigates rainfall and temperature variabilities in Nigeria using observations of air temperature (oC) and rainfall (mm) from 25 synoptic stations from 1971-2000 (30years). The data were analyzed for the occurrences of abrupt changes in temperature and rainfall values over Nigeria while temporal and spatial trends were also investigated. Statistical approach was deployed to determine the confidence levels, coefficients of kurtosis, skewness and coefficient of variations. Analysis of air temperature indicated that in the first decade of 1971-1980 anomalies between -0.2 and -1.6 were predominant, in the second decade of 1981-1990, only five stations (Lokoja, Kaduna, Bida, Bauchi and Warri) shows positive anomaly while greater portion of the country were normal with evidence of warming in the third decade of 1991-2000. Results further indicated that there have been statistically significant increases in precipitation and air temperature in vast majority of the country. Analyses of long time trends and decadal trends in the time series further suggest a sequence of alternately decreasing and increasing trends in mean annual precipitation and air temperature in Nigeria during the study period.

\end{abstract}


\keywords{rainfall, temperature, coefficient of skewness, kurtosis and variations.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
he knowledge of climate variability over the period of instrumental records and beyond on different temporal and spatial scale is important to understand the nature of different climate systems and their impact on the environment and society \hyperref[b35]{(Oguntunde et al. 2012}). Most of the observational and numerical simulation studies on climate are based on the instrumental records of about a century which are aimed at the understanding of the natural variability of climate system and to identify processes and forcings that contribute to this variability. This is essential if we are to predict global and regional climate variations, determine the extent of human influence on the climate and make sound projections of human induced climate change. The climate of a location can be understood most easily in terms of annual or seasonal averages of temperature and precipitation.\par
The global climate has changed rapidly with the global mean temperature increasing by 0.7 o C within the last century \hyperref[b20]{(IPCC 2007)}. However, the rates of change are significantly different among regions \hyperref[b20]{(IPCC 2007)}. This is primarily due to the varied types of land surfaces with different surface albedo, evapotranspiration and carbon cycle affecting the climate in different ways \hyperref[b24]{(Meissner et al. 2003;}\hyperref[b43]{Snyder et al. 2004}). Several studies have been carried out at different temporal scales and in different part of the globe. For example, \hyperref[b15]{Hasanean (2001)} examined trends and periodicity of air temperature from eight meteorological stations in the east Mediterranean and observed positive significant trends in Malta and Tripoli, and negative trend in Amman. \hyperref[b44]{Turkes et al. (2002)} evaluated mean, maximum and minimum air temperature data in Turkey during the period 1929-1999. Their analysis revealed spatiotemporal patterns of long-term trends, change points, and significant warming and cooling periods. \hyperref[b10]{Easterling 1997}\hyperref[b13]{, Fan et al. 2010} reported separately that diurnal temperature range (DTR) has been on the decrease in most region of the world. \hyperref[b21]{Karl et al. (1993)} analyzed temperature data from 37\% of global land mass and found high increment in the minimum compared to the maximum temperature. Studies on the spatio-temporal variability and trend in temperature are very limited in Africa.\par
Increasing flood risk is now being recognized as the most important sectoral threat from climate change in most parts of the region which has prompted public debate on the apparent increased frequency of extreme, and in particular, on perceived increase in rainfall intensities \hyperref[b42]{(Oriola, 1994)}. Several studies have adduced extreme rainfall to be the major cause of flood worldwide. Such studies include \hyperref[b7]{Bunting et al. (1976)}, \hyperref[b14]{Folland et al. (1986)}, \hyperref[b30]{Odekunle (2001)}, and \hyperref[b38]{Ologunorisa (2004)}. Other studies have identified the characteristics of extreme rainfall that are associated with flood frequency to include duration, intensity, frequency, seasonality, variability, trend and fluctuation \hyperref[b36]{(Olaniran, 1983}\hyperref[b37]{, Ologunorisa, 2001)}. \hyperref[b2]{Adefolalu (1986)}    
\section[{Study Area, Data and Methods}]{Study Area, Data and Methods}\par
Nigeria which lies between 4 0 and 14 0 N latitude and longitude 4 0 to 14 0 E, it is bounded on the north by the Republic of Niger, east by Cameroon and west by Benin Republic while the southern boundary is Gulf of Guinea which is an arm of the Atlantic ocean (see Figure  {\ref 1}). The Nigerian climate is characterized mainly by the interplay between the dry north-easterly and the moist south-westerly winds. The main ecological zones are the tropical rainforest along the coast, savannah in the middle belt and semi-arid zones in the northern fringes. Quality-controlled monthly rainfall and temperature (maximum and minimum) data over twenty five meteorological stations in Nigeria were extracted from the archive of the Nigerian Meteorological Agency (NIMET), which spans for a period of thirty years each . The stations selected have less than 10\% of the daily values were missing in each year. The annual rainfall and temperature values were computed for each station from the monthly rainfall amount using equations 1 \& 2.?? ?? = 1 12 ? ?? ?? 12 ??=1 ?? ?? = 1 12 ? ????\textbf{12}??=1\par
Where R is the monthly rainfall amount at each station, T is the monthly temperature amount for each station, i is the months of the year, and A R is the annual rainfall amount at that station, A T is the annual temperature amount at that station.\par
The mean monthly rainfall and temperature amount for the period of thirty years were computed for each station using equation 3 \& 4 respectively.RR j ????? = ? R j 30 j=1 30 ð?"ð?"ð?"ð?" ?? ????? = ? ð?"ð?" ?? ???? ??=?? 
\section[{????}]{????}\par
Where ???? ?? ????? and TT j ????? represents the mean monthly rainfall and temperature amount respectively for each station over the 30 -year period, while j is the period of thirty years.\par
The standardized values were calculated for all the years from the use of the long-term mean, yearly mean and the standard deviation using equation 5.? = x ? x ? ?\par
Where ? represents the standardized departure, x is the actual value of each parameters (air temperature and rainfall), x ? is the long term mean value of each parameters (air temperature and rainfall), ? is the standard deviation.\par
Confidence test was performed on the dataset used and it was verified using 95\% confidence interval. Coefficients of skewness, kurtosis and variation were also investigated. 
\section[{III.}]{III.} 
\section[{Results and Discussion}]{Results and Discussion} 
\section[{a) Variability in Temperature}]{a) Variability in Temperature}\par
The results of statistical analysis performed on air temperature dataset over the selected stations are shown in Table \hyperref[tab_1]{2}. Generally peaked distribution occurred in most cases (positive coefficient of skewness) with most stations having a distribution with an asymmetric tail extending towards more negative values as evident in the negative coefficient of kurtosis. Air temperature were observed to be significant at 95\% or 99\% confidence level in most part of the stations.\par
Monthly mean air temperature over Nigeria from 1971-2000 for the months of January to June are shown in Figures 2 while Figure \hyperref[fig_5]{3} illustrates the air temperature pattern for the months of July to December. Temperature is observed to increase southward during the months of January to March with temperature ranging from 21.1 O C to 30 O C. However there is a little variation in air temperature in the month of April with corresponding increase northward in May and June only. Also generally observed is a northward increase in temperature extending from July to September before a reverse in trend in the month of October (i.e. decreasing southward). It was observed that air temperature values are generally lower in the Northern part of Nigeria during dry season when compared with the wet season. This implies that temperature variation is higher over northern part of the country than over the southern part. This can be attributed to the equator ward incursion of midlatitude systems (with alternating cool and warm air masses) which has greater influence on temperature variation over the northern part than over the southern part of Nigeria \hyperref[b0]{(Adefolalu, 2007)}. Secondly, the influence of the tropical maritime air mass from Gulf of Guinea moderates temperature fluctuations along the coastal region \hyperref[b14]{(Folland, et al. 1986}., \hyperref[b8]{Charney, 1975}., \hyperref[b0]{Adefolalu, 2007)}.\par
Analyses of standardized decadal anomalies of air temperature over Nigeria are clearly shown in Figure \hyperref[fig_6]{4a-c}    {\ref 2012}) which reported separately that spatial and temporal variations in temperatures were noticed in Nigeria where air temperature has been on the increase gradually since 1901 and with significant increase from 1970. Figure \hyperref[fig_8]{6} shows the air temperature standardized anomaly over different climatic zones in Nigeria. In coastal region of Nigeria (Figure \hyperref[fig_8]{6a}), it is observed that between 1971-1987, negative anomaly of air temperature were more prominent than positive anomaly but a change was noted from 1998 when temperature began to change to positive anomaly and these prolong well into 1990s. Result further shows that the changes are significant at 95\% and 99\% confidence level. In the tropical rainforest (Figure \hyperref[fig_8]{6b}), there are more years of negative temperature anomalies within the periods of study. This explains that temperature as been on the decrease in this zone while in the guinea savannah (Figure \hyperref[fig_8]{6c}), between 1971-1982 temperature was on the decrease. However starting from 1983, it was observed that there was more positive anomaly with only few years of negative anomalies within the same period in the guinea savannah. This observed pattern is similar to that of coastal areas which shows that temperature has been on the increase since 80's. Temperature anomaly was observed to be on the decrease in both Sudan and Sahel savannah (Figure \hyperref[fig_8]{6d} \& 6e) of Nigeria from 1971-1982, but changes suddenly to increasing temperature anomalies from 1983-2000 with about three years of negative anomalies period occurring within this period. The changes are significant at 95\% and 99\% confidence level. \hyperref[b31]{Odjugo and Ikhuoria (2003)}, \hyperref[b0]{Adefolalu (2007)}, reported that the increasing temperature in the semi-arid region of Sokoto, Katsina, Kano, Nguru and Maiduguri may be attributed to increasing evapotranspiration, drought and desertification in Nigeria. 
\section[{b) Variability in Rainfall}]{b) Variability in Rainfall}\par
Figure \hyperref[fig_9]{7} and 8 shows the mean monthly rainfall pattern over Nigeria from 1971-2000 for January-June and July-December respectively. It was observed that rainfall decreases from the coast (Warri, Calabar) to the Sahel (Nguru, Katsina, Kano, Maiduguri) at all seasons. This result is in line with the work of Nicholson, (1993), who reported that rainfall in West Africa generally decreases with latitude with essentially zonal isohyets. Rainfall in the lower latitude almost doubled that of the higher latitude in each of the months from January to December. It was observed also that rainfall pattern below latitude 10oN is bimodal having a primary peak in June-July, and another secondary peak in September with little dry season in August as a result of absence of the Africa Easterly Jet \hyperref[b40]{(Omotosho, 2007)}.\par
Decadal anomaly of rainfall in Nigeria is shown in Figure \hyperref[fig_11]{9a-c}. In the first decade of 1971-2000, there is an increase in the rainfall amount in cities like Jos, Enugu, Kaduna, Minna, Nguru and Katsina. A decline in rain amount was noted in larger part of south West and North eastern Nigeria. In the second decade only few stations in south west Nigeria (e.g. Osogbo, Ikeja and Ondo) were having wet years while the whole country exhibits dryness throughout the entire during of analysis. In the third decade, Jos and Katsina were the only stations with dry tendencies while most part of the country is having abundant rainfall amount. This gradual reduction in rainfall amount may be attributed to variation in local factors such as orography, boundary layer forcing and moisture build up.\par
Figure \hyperref[fig_12]{10} shows the decadal trend of rainfall over Nigeria for 1971-2000. It is observed that in the first decade  {\ref (1971)} {\ref (1972)} {\ref (1973)} {\ref (1974)} {\ref (1975)} {\ref (1976)} {\ref (1977)} {\ref (1978)} {\ref (1979)} {\ref (1980)} that rainfall is on the increase in almost all parts of Nigeria with exception in Bida and Minna with decreasing trends in rainfall. Cities like Yola, Bauchi, Jos, Kaduna, Enugu and Benin were having normal rainfall. During the second decade  {\ref (1981)} {\ref (1982)} {\ref (1983)} {\ref (1984)} {\ref (1985)} {\ref (1986)} {\ref (1987)} {\ref (1988)} {\ref (1989)} {\ref (1990)}, only Nguru, Minna and Jos experience decreasing rainfall throughout Nigeria while Sokoto, Bauchi, Kaduna, Zaria, Benin, Yelwa and Gausau were normal and others part of the country shows positive trends. Decreasing amount of rainfall was observed in larger part of Nigeria in the third decade for locations such as Bauchi, Gusau, Bida, Minna, Osogbo, Ondo, Benin, Enugu and Warri. Looking at the trend of the whole dataset, rainfall has been on the decrease in Jos and Katsina while areas around longitude 30E-90E are on the increase with the remaining part of the country having their normal rainfall. The increasing rainfall in the coastal cities may be partly responsible for the increase in flood events devastating the coastal cities of Warri, Lagos, Port Harcourt and Calabar as observed by \hyperref[b33]{Ogundebi, 2004;}\hyperref[b17]{Ikhile 2007;}\hyperref[b29]{Nwafor, 2007;}\hyperref[b45]{Umoh, 2007;}\hyperref[b32]{Odjugo, 2010.} The SPI calculation used in Table \hyperref[tab_2]{3} was based on the long-term precipitation record for a desired period. This long-term record is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero \hyperref[b11]{(Edwards and McKee, 1997)}. Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation. Because the SPI is normalized, wetter and drier climates can be represented in the same way, and wet periods can also be monitored using the SPI.\par
Figure \hyperref[fig_13]{11} shows the standardized rainfall anomaly over different climatic zones in Nigeria from1971-2000. In the coastal, tropical rainforest, guinea and Sudan savannah areas it was observed that there are more wet years than dry years (see Table \hyperref[tab_3]{4}). But for the Sahel savannah, the dry years were more than the wet years during the 30years study period. The result corresponds to IPCC projection stating that the coastal areas are prone to more wet years leading to the occurrence of flooding while region around the Sahel will experience more of drought as a result of reduction in the total precipitation. 
\section[{IV.}]{IV.} 
\section[{Conclusion}]{Conclusion}\par
This study provides valuable insight on the spatial and temporal patterns of temperature and rainfall in Nigeria. The results revealed that there is significant increase (positive trend) in temperature in the country at 95\% confidence level. Also, rainfall has been on the increase within the year of consideration. The rainfall anomaly over all the stations revealed that there was a composite nature in which some of dry years were mixed with wet years and vice versa and this occurred in all seasons in all stations. The decrease in rainfall may be due to failure of rain-producing mechanism such as ITD, AEJ, TEJ, to organise thunderstorm, squall line that are responsible for over 70\% of the total annual precipitation.           \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 5}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-5.png}
\caption{\label{fig_4}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-7.png}
\caption{\label{fig_6}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-8.png}
\caption{\label{fig_7}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-9.png}
\caption{\label{fig_8}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-10.png}
\caption{\label{fig_9}Figure 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8}\includegraphics[]{image-11.png}
\caption{\label{fig_10}Figure 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9}\includegraphics[]{image-12.png}
\caption{\label{fig_11}Figure 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10}\includegraphics[]{image-13.png}
\caption{\label{fig_12}Figure 10 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11}\includegraphics[]{image-14.png}
\caption{\label{fig_13}Figure 11 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.19918793503480278\textwidth}P{0.21496519721577725\textwidth}P{0.2701856148491879\textwidth}P{0.165661252900232\textwidth}}
Longitude (°E)\tabcellsep Latitude (°N)\tabcellsep Stations\tabcellsep Abbreviation\\
8.35\tabcellsep 4.97\tabcellsep CALABAR\tabcellsep CAL\\
5.73\tabcellsep 5.52\tabcellsep WARRI\tabcellsep WAR\\
5.6\tabcellsep 6.3\tabcellsep BENIN\tabcellsep BEN\\
3.5\tabcellsep 6.5\tabcellsep lagos\tabcellsep OSD\\
7\tabcellsep 6.5\tabcellsep enugu\tabcellsep ENU\\
3.33\tabcellsep 6.58\tabcellsep ikeja\tabcellsep IKE\\
4.83\tabcellsep 7.1\tabcellsep ondo\tabcellsep OND\\
3.9\tabcellsep 7.43\tabcellsep ibadan\tabcellsep IBA\\
4.5\tabcellsep 7.82\tabcellsep oshogbo\tabcellsep OSG\\
6.73\tabcellsep 7.8\tabcellsep lokoja\tabcellsep LOK\\
6\tabcellsep 9.8\tabcellsep bida\tabcellsep BID\\
12.47\tabcellsep 9.23\tabcellsep yola\tabcellsep YOL\\
6.54\tabcellsep 9.56\tabcellsep minna\tabcellsep MIN\\
8.85\tabcellsep 9.63\tabcellsep jos\tabcellsep JOS\\
9.82\tabcellsep 10.28\tabcellsep bauchi\tabcellsep BAU\\
7.45\tabcellsep 10.6\tabcellsep kaduna\tabcellsep KAD\\
4.5\tabcellsep 11\tabcellsep yelwa\tabcellsep YEL\\
7.75\tabcellsep 11.07\tabcellsep zaria\tabcellsep ZAR\\
13.08\tabcellsep 11.85\tabcellsep maiduguri\tabcellsep MAI\\
8.53\tabcellsep 12.05\tabcellsep kano\tabcellsep KAN\\
6.77\tabcellsep 12.17\tabcellsep gusau\tabcellsep GUS\\
10.47\tabcellsep 12.88\tabcellsep nguru\tabcellsep NGU\\
5.2\tabcellsep 12.92\tabcellsep sokoto\tabcellsep SOK\\
7.68\tabcellsep 13.02\tabcellsep katsina\tabcellsep KAT\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.13567110036275695\textwidth}P{0.12128174123337364\textwidth}P{0.1757557436517533\textwidth}P{0.13978234582829505\textwidth}P{0.13875453446191052\textwidth}P{0.13875453446191052\textwidth}}
\tabcellsep \tabcellsep Coefficient\tabcellsep \tabcellsep 95\% Confidence\tabcellsep 99\% Confidence\\
Stations\tabcellsep C.V\tabcellsep \multicolumn{2}{l}{of Skewness Coefficient of Kurtosis}\tabcellsep level\tabcellsep level\\
calabar\tabcellsep 0.042\tabcellsep 0.189\tabcellsep -0.996\tabcellsep 0.709\tabcellsep 1.001\\
warri\tabcellsep 0.042\tabcellsep -0.156\tabcellsep -1.233\tabcellsep 0.721\tabcellsep 1.018\\
lagos\tabcellsep 0.084\tabcellsep 0.423\tabcellsep -0.813\tabcellsep 1.530\tabcellsep 2.159\\
enugu\tabcellsep 0.096\tabcellsep 0.937\tabcellsep 0.937\tabcellsep 1.753\tabcellsep 2.474\\
ikeja\tabcellsep 0.040\tabcellsep -0.391\tabcellsep -0.830\tabcellsep 0.701\tabcellsep 0.989\\
ondo\tabcellsep 0.054\tabcellsep -0.034\tabcellsep -0.922\tabcellsep 0.903\tabcellsep 1.275\\
ibadan\tabcellsep 0.057\tabcellsep 0.289\tabcellsep -0.363\tabcellsep 0.973\tabcellsep 1.373\\
oshogbo\tabcellsep 0.050\tabcellsep 0.483\tabcellsep -0.748\tabcellsep 0.832\tabcellsep 1.174\\
lokoja\tabcellsep 0.062\tabcellsep -0.265\tabcellsep -1.380\tabcellsep 1.148\tabcellsep 1.620\\
bida\tabcellsep 0.085\tabcellsep 1.214\tabcellsep 1.055\tabcellsep 1.569\tabcellsep 2.214\\
yola\tabcellsep 0.122\tabcellsep 0.089\tabcellsep -1.036\tabcellsep 2.341\tabcellsep 3.304\\
minna\tabcellsep 0.069\tabcellsep 0.720\tabcellsep -0.493\tabcellsep 1.221\tabcellsep 1.723\\
jos\tabcellsep 0.079\tabcellsep 0.040\tabcellsep -1.647\tabcellsep 1.111\tabcellsep 1.568\\
bauchi\tabcellsep 0.142\tabcellsep -0.361\tabcellsep -0.760\tabcellsep 2.406\tabcellsep 3.395\\
kaduna\tabcellsep 0.086\tabcellsep 0.769\tabcellsep -0.614\tabcellsep 1.395\tabcellsep 1.969\\
yelwa\tabcellsep 0.079\tabcellsep 0.783\tabcellsep -0.317\tabcellsep 1.409\tabcellsep 1.988\\
zaria\tabcellsep 0.110\tabcellsep 0.068\tabcellsep -1.110\tabcellsep 1.816\tabcellsep 2.563\\
maiduguri\tabcellsep 0.134\tabcellsep -0.384\tabcellsep -1.035\tabcellsep 2.383\tabcellsep 3.363\\
kano\tabcellsep 0.160\tabcellsep -0.307\tabcellsep -1.326\tabcellsep 2.814\tabcellsep 3.970\\
gusau\tabcellsep 0.104\tabcellsep 0.449\tabcellsep -0.580\tabcellsep 1.756\tabcellsep 2.478\\
nguru\tabcellsep 0.173\tabcellsep -0.109\tabcellsep -1.595\tabcellsep 2.976\tabcellsep 4.199\\
sokoto\tabcellsep 0.103\tabcellsep -0.059\tabcellsep -0.919\tabcellsep 1.886\tabcellsep 2.662\\
katsina\tabcellsep 0.131\tabcellsep -0.431\tabcellsep -0.666\tabcellsep 2.237\tabcellsep 3.156\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.3955974842767296\textwidth}P{0.4544025157232704\textwidth}}
2.0+\tabcellsep extremely wet\\
1.5 to 1.99\tabcellsep very wet\\
1.0 to 1.49\tabcellsep moderately wet\\
-.99 to .99\tabcellsep near normal\\
-1.0 to -1.49\tabcellsep moderately dry\\
-1.5 to -1.99\tabcellsep severely dry\\
-2 and less\tabcellsep extremely dry\end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.33730158730158727\textwidth}P{0.029682539682539682\textwidth}P{0.002698412698412698\textwidth}P{0.032380952380952385\textwidth}P{0.03507936507936508\textwidth}P{0.11603174603174601\textwidth}P{0.04317460317460317\textwidth}P{0.03777777777777778\textwidth}P{0.021587301587301586\textwidth}P{0.19428571428571428\textwidth}}
\tabcellsep 14\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{SOK}\tabcellsep KAT\tabcellsep NGU\tabcellsep \\
\tabcellsep 12\tabcellsep \tabcellsep \tabcellsep \tabcellsep GUS\tabcellsep KAN\tabcellsep \tabcellsep MAI\tabcellsep Sahel Savannah\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep YEL\tabcellsep \tabcellsep ZAR\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{KAD}\tabcellsep \tabcellsep \\
Latitude (°N)\tabcellsep 8 10\tabcellsep \tabcellsep OSD IKE IBA\tabcellsep OND OSG\tabcellsep BEN BID MIN ENU LOK\tabcellsep JOS\tabcellsep BAU\tabcellsep YOL\tabcellsep Guinea Savannah Sudan Savannah\\
\tabcellsep 6\tabcellsep \tabcellsep \tabcellsep \tabcellsep WAR\tabcellsep \tabcellsep \tabcellsep \tabcellsep Tropical Rainforest\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep CAL\tabcellsep \tabcellsep \\
\tabcellsep 4\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Coastal\\
\tabcellsep 2\tabcellsep 2\tabcellsep 4\tabcellsep \tabcellsep 6\tabcellsep 8\tabcellsep 10\tabcellsep 12\tabcellsep 14\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Longitude (°E)}\tabcellsep \\
\multicolumn{10}{l}{Figure 1 : Map of Nigeria Showing Selected Meteorological Stations in Each Climatic Zone. Adapted from Adejuwon,}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep (2004)\tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
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\end{document}
