3.1 Evaluation of CORDEX ensemble dataset with Change Detection Test and Trend Analysis
Performance evaluation of the ensemble mean CORDEX datasets over the three agro-climatic zones of Nigeria was performed and summarized in Table 2&3. The simulation datasets covers the historical period 1961–2000 and the evaluation was performed with CRU observation datasets. The performance evaluation help us to have better understanding of the capability of ensemble mean of RCMs in replicating seasonal distribution of temperature and precipitation. The ensemble mean of the models captured temperature and precipitation very well over the three climatic zones when compared to CRU observation datasets. Nigeria was divided into three agro-climatic zones the Guinea coast, Savannah and Sahel. Evaluation of precipitation through Nash-Sutcliffe Efficiency (NSE) parameter shows very close estimated value for all the three agro-climatic zones with the least value of 0.84 in the Guinea coast and the highest value of 0.89 in the Sahel. The Mean Bias Error and Mean Gross Error shows the same precipitation trend with the highest estimated value of 23.9 and 31.73 in the Guinea coast respectively while the least values varies between Guinea coast and the Sahel region. The ensemble mean shows highest precipitation disparity in the Guinea coat when compared to observation. However, the correlation coefficient shows an overall significant value for the three agro-climatic zones but with the least value of 0.89 in the Guinea coast. Thus, ensemble mean of the seven selected simulated datasets performed relatively well in all the three regions when compared to observation. In the same vein, Table 3 presents statistical performance evaluation of ensemble mean temperature over the three agro-climatic zones of Nigeria. The NSE shows similar values for all the three zones with the least value of 0.79 in Savannah. The ensemble mean reproduced precipitation and temperature pattern over the three climatic zones but overestimates in some parts of the region. The reason for overestimation of the ensemble mean can be associated to different parameterization schemes used in the various RCMs that made up the ensemble mean (Afiesimama et al. 2006; Diallo et al. 2012).
However, change detection analysis was applied on the annual mean temperature and precipitation average over the Guinea Coast, Savannah, and the Sahel with historical datasets period of 1961 to 2000 at 5% significant level. Three statistical tests with null homogenous (H0) hypothesis are presented in Tables 4&5 for rainfall and temperature. Figure 4, 5, and 6 represents the graphical form of ensemble-mean temperature with a change point over Nigeria. For identification of change point in rainfall and temperature the following three conditions have to be met which was earlier used and proven by Winingaard et al. (2003) and Jaiswal et al. (2015) for chamge point detection.
i. The first condition is when the change-point is homogeneous (HGN) or not occur. This is an indication that temperature and precipitation may be regarded as homogeneous when one or none of the test rejected the null (Ho) hypothesis at a significant level of 5%.
ii. The second condition is when the change-point occurs or heterogeneous (CPO).This is an indication that precipitation and temperature may have shifted or heterogeneous when two or the three tests reject the null (Ho) hypothesis at a significant level of 5%.
iii. The third condition is when the change point is Doubtful (DBF). This is an indication that the precipitation and temperature is regarded doubtful if at a significant level of 5%. It can also be affirmed if further evaluation of the three tests failed to agree on when the change point occurred.
However, Table 4 shows statistical test for Centre for Research (CRU) observation datasets and ensemble mean. The test revealed homogeneity nature of precipitation over Guinea Coast, Savannah and Sahel with a significant change point as both the Petite’s, SNHT and Buishand’s test agreed that precipitation over the three climatic zones are homogeneous except for ensemble mean which shows inhomogeneity change point for the climatic zones. Over the Guinea Coast, the change point occurred in 1984 while the Savannah and Sahel is 1983. Although, the precipitation in the Sahel is known to be homogeneous in nature but the three test were able to record the inhomogeneity of the precipitation for the change point in 1983 and 1984.
Likewise it is observed from the ensemble mean that there were no significant change point occurred in precipitation for all the three climatic zones except for the Buishand's test which shows significant change for the three climatic zones while precipitation in Sahel region also demonstrates homogeneous characteristic. The nature of the precipitation in the Savanna is doubtful as the three statistical tests could not agree on the change point. The reason for all these could be linked to Buishand’s and Pettitt’s test which are very sensitive to identify changes on the trends whereas SNHT is well known to locate change point at the beginning and end of a datasets as affirmed by (Winingaard et al. 2003; Jaiswal and Lohani 2015). The change point noticed from the CRU and ensemble mean agreed to have occurred in the early 1980s, a decade that is characterized with drought in Nigeria (Le Barbé et al. 2002; Lebel and Ali 2009; Nicholson 2013) A condition that resulted into significant low precipitation amount which has become a source of water stress in the region. The decade would likely been characterized with frequent sand storm and buildup of atmospheric dust (Ekpoh and Nsa 2011) which in turn might have contributed to precipitation anomaly (Adeniyi and Oladiran 2000) and as well weakened circulation of global monsoon (Pant 2003). These reflected in the change point observed over the rainfall over the three zones. Table 5 also presents change point detection in temperature over the three climatic zones of Nigeria. It shows a significant shift in the mean temperature which is in relation with the change point that occurred between 1976 and 1983 on different climatic zones with varying period but within the same decades of 1970 and 1980s. This was a period well known for prevailing droughts in the zone. The primary causes of the drought is associated with human activities that leads to increase in emission of Green House Gases (Charney et al. 1977; Sylla et al. 2016), The results of external forcing of GHGs due to change point in temperature could be responsible for global climate shift identified by Baines (2006).
Table 8 presents Mann–Kendall trend test at 5% significant level performed over the three climatic zones of Nigeria for precipitation and temperature. CRU datasets precipitation trend analysis over Guinea Coast and the Sahel indicated a significant decrease in total annual precipitation at 5% significant level and a non-significant decreasing trend over the Savannah. An insignificant decrease in rainfall trend from the ensemble-mean was observed over the three climatic zones of Nigeria which shows a critical point in precipitation variability. This is also noted by Nicholson (2013) and Emmanuel et al. (2019). However ensemble mean and CRU temperature shows a significant increase in temperature trend at a significant level of 5% with a Sen.’s slope 0.0078 to be the lowest in the Guinea Coast and 0.0255 to be the highest in the Sahel region under RCP4.5 while there is a significant trend 0.057 and 0.063 under RCP8.5 over the three climatic zones of Nigeria. High frequency in precipitation and temperature variabilities have been linked to global warming due to human activities such as burning of fossil fuel which plays a major role in decreasing and increasing trend of rainfall over Nigeria (IPCC 2013; Sylla et al. 2016). These results are also similar to the findings of Nelson et al. (2010), N’Tcha M’Po et al. (2017) and Lawin et al. (2018) but with a projected increase in annual mean temperature between 0.59 ℃ and 1.30 ℃. This further confirms that West Africaregion includong Nigeria will continue to be warmer than before.
Tables 9 and 10 shows a similar result for trend test at 5% significant level performed over the three climatic zones of Nigeria with respect to precipitation and temperature variability for the near and far future cases. It was deduced that precipitation in the near future replicates the same pattern with the reference period with insignificant change in precipitation trend but with decrease precipitation trend in the Sahel and with improvement in the Savannah for both CRU and ensemble mean datasets. The precipitation was more pronounced in the ensemble mean than the CRU datasets. There are also possibilities of increase in air temperature in the near future with the highest in the Sahel and Savannah. Far future for both CRU and ensemble mean shows insignificant change in precipitation and temperature in both RCP4.5 and RCP8.5 scenario. All these could be linked to the present variability in climate variables and changes in climatic trends while there could be limitation of global warming bellow 1.5 in far future.
Table 4 Results of temperature change point detection from the three tests for the historical period (1961–2000) at 5% significant level
3.2 Near‑Future Rainfall and Temperature Pattern in Nigeria
3.2.1 Change Detection and Trend Analysis
Table 6 presents the results of change-point detection performed using SNHT, Pettitt’s, and Buishand’s test for the ensemble mean of precipitation and air temperature and as well as for Mann–Kendall test under RCP4.5 and RCP8.5 for the near-future (2020–2059). The change-point detection (HGN) is observed for both RCP4.5 and 8.5. HGN is an indication that no change occur in the precipitation time serie for all the three climatic zones as observed in Table 6. However, for the temperature series under RCP4.5, a change point expected to occur over the Guinea Coast and the Sahel by the year 2038 and 2036 and this may be as a result of the projected increase in the annual mean of temperature within the range of 0.56–0.91 ℃ in the near future as observed in Table 6. There is posibility of positive shift in the mean annual temperature which is likely to take place in Guinea coast and Savannah climatic zones by the year 2044 within the range of 1.26℃ and 1.16℃ in the near-future under RCP8.5 emission scenario. Table 7 presents far future point change detection for both precipitation and temperature respectivelly. Precipitation time series remain homogenous in all the three climatic zones and in both RCP4.5 &8.5 while the temperature shows a drastic increase in temperature and changes for the year 2081with temperature of 27.91o C to 31.84 o C in Guinea coast and Sahel respectively and with difference of 1.15-1.58oC in RCP 8.5 scenario.
Figure 2 presents projected temperature anomaly with sgnificant values of change detection in five years interval and with decadal scale variation. In addition Fig. 2a shows a projected increase in temperature meas\ value 25.87oC before and 26.48 after change detection occurs for Pettitt’s test on five years interval. Similarly Fig. 6b shows the mean temperature values before change detection is 25.87 and 26.43 after change detection occurred for the SNHT test. In the same vein, Buishand’s test shows a similar change detection values in the same years interval. In all, the tests performed showed a similar change detection values for the near future projected years. Figure 3 shows annual mean rainfall trend analysis result over Nigeria from 2020–2059. The near-future precipitation trends under RCP4.5 indicate that there are indication of significant precipitation trends nearly in all three climatic zones of Nigeria excepts for the Guinea coast where there are insignificant trends of precipitation and this is due to homogeneity of precipitation pattern in the region but for the projected precipitation under RCP8.5 there are indication of more insignificant precipitation trends seen especially in the Savannah and Sahel region of the country. This means there is likely more drought events in the near future in the Savannah and Sahel region of Nigeria in relation to (IPCC, 2013) hypothesis that some regions will become warmer in sub-Sahara Africa. More so, far-future precipitation trends under RCP4.5 shows more than 40% of significant precipitation trends in the Savannah and Sahel while others under RCP 8.5 show a greater percentage of insignificant precipitation trends across all the three climatic zones. The stipple on the plot also indicates grid points with a statistically significant trend at 95% confidence interval.
However, mean temperature under RCP4.5 shows very significant trends in the Savannah and Sahel region of Nigeria than the Guinea coast region with an annual average temperature change 0.034oC but less varying degree in the Coastal region while the mean temperature under RCP8.5 shows higher significant change from Coastal area to the Sahel with varying degree 0.038oC in the Sahel region. This means that mean temperature will still be under the base line of 1.5oC under RCP4.5 in the near future and it is likely to exceed the target of 1.5oC in far future under RCP8.5 scenario.
3.3. Standardized Precipitation and Temperature Anomaly Index near-future under RCP4.5 and RCP8.5
Figure 5 and table 11 shows an average number of years of precipitation based on different degree of wetness and dryness in the near-future. The relative change in decadal rainfall in Guinea coast under RCP4.5 in Fig. 5a projects moderately wet and dry years in the first decade 2020–2030 and as well as moderate wet years with no significant dry years in the second decades 2030–2040. There are projected extreme wet and dry years in 2040–2050 and mild dry years in 2050–2060 decade. In Savannah however, 2030–2040 decades projects moderately wet years while the last two decades 2040 − 260 projects mild and severe dry years. The Sahel region projects decadal moderately wet years with extreme dry years in 2020–2060. However, Standardized Rainfall Anomaly Index (SRAI) under RCP8.5 for the near-future projects moderate decadal dry years 2030 − 250 in the Guinea coast and with extreme wet years in the first and last decade 2050–2060. In the same vein, there are projected dry years for two decades 2030–2050 in the Savannah (Fig. 5b) with moderate and with severe drought in the year 2020–2030 and 2050–2060. The decadal precipitation pattern in the Sahel region are similar to the Savannah except the high intensity of dryness in the year 2030–2050 with moderate precipitation in the first and the middle of last decade. In all, there are projected dry years in the near future with high degree of decadal precipitation variation across the three climatic zones of Nigeria. Figure 6a and table 12 shows extreme dry years and Standardized Rainfall Anomaly Index (SRAI) in the first decades 2060–2070 and as well as 2090–2100. Also from mild to moderate dry years in the second and third decade 2070–2090 with moderately wet years from 2070–2100. Extreme wet years are projected toward the end of the third decade 2080–2090 and as well as severe dry years in the first decade of 2060–2070 in the Savannah under RCP 4.5 for the far future (Fig. 6c). However, Sahel in Fig. 6e also shows similar trends of precipitation anomaly with extreme wet years toward the end of the third decade 2080–22090. For precipitation anomaly index under RCP8.5 was projected in Guinea coast with extreme wet years in the last decade 2090–2100 (Fig. 6b) and as well as moderate dry years in the middle of 2080–2090 and 2090–2100. Figure 6d shows Savannah precipitation anomaly index with extreme wet year in the fourth decade 2090–2100 and with moderate dry years in the third decade 2080–2090 while Fig. 6f shows precipitation anomaly index for Sahel under RCP8.5 with extreme wet years in the first decade of 2060–2070 and also with moderate wet years from 2080–2100 and also with moderate dry years in the third decade 2080–2090. In all, precipitation anomaly index shows more dry decades with few extreme years of precipitation in the near future and as well as with extreme wet years in the far future in all the three agro-climatic zones of Nigeria. The reasons for this is linked to continuous burning of fossil fuel and cutting of trees as an important factors in bio-geophysical feedback mechanism which have been suspected together with a shift in global climate system (Farmer and Wigley 1985; Charney et al. 1977; Baines 2006) to be responsible for the projected anomalies in rainfall and temperature.
Figure 7 shows projection of higher temperature values when compare to historical period under RCP4.5 and RCP8.5. Also, the rate of increase in temperature relative to historical mean was noticed to be higher under RCP8.5 when compared to lower emission scenarios (RCP4.5). Since the warmer atmosphere can hold larger moisture, the projected increase in precipitation relative to the historical mean over the Guinea Coast and Savannah may be associated with the expected increase in temperature (Lenderink and Meijgaard 2010)