Statistical properties of the temperature, relative humidity, and ... - UiO

Theor Appl Climatol (2010) 101:397–409 DOI 10.1007/s00704-009-0225-7

ORIGINAL PAPER

Statistical properties of the temperature, relative humidity, and net solar radiation in the Blue Nile-eastern Sudan region Chong-Yu Xu & Qiang Zhang & M. El Hag El Tahir & Zengxin Zhang

Received: 11 May 2009 / Accepted: 1 October 2009 / Published online: 25 October 2009 # Springer-Verlag 2009

Abstract This paper presents the results of the first stage of an ongoing project of evaluating the spatial and temporal variability of soil water as fundamental factors for vegetation regeneration in the arid ecosystems in the Blue Nile-eastern Sudan. The specific aim of the present study is to understand the temporal and spatial variations of the major climate variables in the region and discuss its relevance to regional climate variability and changes. In this case, we systematically analyze the major climate variables (maximum and minimum air temperature, relative humidity, and net solar radiation). To evaluate the different characteristics of the climate variables, Mann–Kendall method, two-phase regression scheme, and wavelet transform technique are used; each method has its own strength and weakness, and the results of the three methods complement each other. The results show that the annual and seasonal maximum temperatures are increasing significantly. The annual minimum temperaC.-Y. Xu (*) : M. El Hag El Tahir Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway e-mail: [email protected] Q. Zhang Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China C.-Y. Xu Department of Earth Sciences, Uppsala University, 75236 Uppsala, Sweden Z. Zhang Jiangsu Key Laboratory of Forestry Ecological Engineering, Nanjing Forestry University, Nanjing 210037, China

ture and minimum temperature in dry seasons are decreasing. The minimum temperature in rainy season is increasing with a smaller rate as compared with the increase of maximum temperature in the season. The difference between maximum and minimum temperature is increasing in all the seasons. Net solar radiation in the region shows a significant increasing trend in all seasons, which corresponds well with the changes of maximum temperature. Besides, significant decreasing trends can be identified for relative humidity in all the seasons.

1 Introduction Sudan is characterized by tropical climate. Within the Sudan territory, the climate shifts from arid in the north to tropical wet and dry in the far southwest. Generally, the humidity conditions are controlled by two air flows: dry northeasterly winds from the Arabian Peninsula and moist southwesterly winds from the Congo River basin. The dry northeasterly winds predominate in Sudan from January to March, when there is practically no rainfall across the entire country except some regions in the northwestern Sudan. From early April, the moist southwesterly winds prevail in Sudan, bringing rains and thunderstorms, and in August, the southwesterly flows extend to the northern limits around Abu Hamad (http://countrystudies.us/sudan/33. htm). Delayed arrival of the southwesterly flow and associated rainfall often inflict great loss of human lives and economy and causes disastrous results. Sudan is drained mainly by the Nile River and its two main tributaries, i.e., the Blue Nile and the White Nile (Fig. 1). The Nile River is the longest river in the world, flowing for 6,737 km from its farthest headwaters in central Africa to the Mediterranean and being the lifeline for

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Fig. 1 Map of Sudan and the study region

Sudan. The Blue Nile flows out of the Ethiopian highlands to meet the White Nile at Khartoum. In the south of Khartoum, the British built the Jabal al Auliya Dam in 1937 to store the water of the White Nile with the aim to alleviate the negative effects of slackening flow from the Blue Nile in the fall. Actually, much water from the reservoir is used for irrigation or lost by evaporation. Therefore, although the drainage area is large, evaporation takes most of the water from the rivers in this region and thus, the discharge of the Bahr al Ghazal into the White Nile is small (http://www. mongabay.com/reference/country_studies/sudan/all.html). Sudan is suffering from rainfall deficit and water shortage. The currently well-evidenced global warming is expected to alter the hydrological cycle, and previous studies tend to provide more observed evidences for this viewpoint (e.g., Beniston and Stephenson 2004; Brutsaert 2006; Zhang et al. 2008a). Therefore, exploring the changing characteristics of extreme temperature, relative humidity, and net solar radiation of the region is a prerequisite for a good assessment of the impacts of climatic changes on regional ecological environment and agricultural development. Actually, there are a lot of reports concerning extreme climatic events and hydrological responses to climate changes and so on (e.g., Zhang et al. 2009). Zhang et al. (2008b) indicated that the warming processes in the Yellow River basin in China are characterized by a significant increase of extreme low temperature. The extreme high temperature has no significant change trend. Xu et al. (2006) observed a significant decreasing trend in both the reference evapotranspiration and the pan evaporation and attributed this decreasing trend

to a significant decrease in net solar radiation and to a less extent, to a significant decrease in wind speed over the Yangtze River basin. Manton et al. (2001) found significant increases in hot days and warm nights and decreases in cool days and cold nights since 1961 across Southern Asia and the South Pacific region. As for climate changes in Sudan, little work has been done as compared to the rich studies carried out elsewhere in the world. Elagib and Mansell (2000) analyzed mean temperature changes during 1941–1996. However, study of changes of temperature extremes such as maximum and minimum temperature could be more helpful for better understanding of the regional responses of Sudan to global warming. Besides, some scientific problems are still kept unanswered such as: What are the changing properties of the major climate variables in eastern Sudan as revealed from the historical records? What could be the implications of the changing properties of climate variables for evaporation changes and regional water resource management? The answers of these scientific questions will be of great importance for better understanding of the implications of land-use change on soil degradation in the region. As the first stage of an ongoing project of evaluating the spatial and temporal variability of soil water as fundamental factors for vegetation regeneration in the arid ecosystems in the Blue Nile-Eastern Sudan, the objectives of this study are: (1) to understand the trends of climate variables such as temperature, relative humidity, and net solar radiation in the region; (2) to explore abrupt behaviors and periodicity properties of the regional climate variables; and (3) to discuss the differences of climate changes of eastern Sudan with other places of the world.

Statistical properties of the temperature, relative humidity, and net solar radiation in the Blue Nile-eastern Sudan region

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2 Study area and data The study area lies between latitudes 10° to 16°N and longitudes 30° to 35°E (Fig. 2). The area is in the belt of regular oscillation of the intertropical convergence zone with a tropical continental climate. Ninety percent of the annual rainfall is collected during the rainy season (April– October). The annual rainfall has also large interannual variability as well as spatial variability. It ranges between 75 mm in the north and above 600 mm in the south (Elhag 2006). The lowest daily mean temperature is 13°C and is measured in December. The highest mean daily temperature is 43.8°C and is measured in May. The region is characterized by low relative humidity especially in winter, reaching its minimum in March and its maximum in August, varying between 16% and 77% (Elhag 2006). The open nature of the area and free movement of the air accelerates evaporation, whether from the surface or subsoil. The land use types include agriculture, forests, and range areas. It exhibits high land cover variability. The area is bounded by Sennar in the north and Ad’ Damzzeen in the south and traversed by the Blue Nile. The data from ten stations in the region (Table 1, Fig. 2) are extracted from the dataset produced by the National Climatic Data Center in Asheville, NC. The data are available via: http://www.cdc.noaa.gov/data/gridded/data. ncep.reanalysis.surface.html and http://www.cdc.noaa.gov/ data/gridded/data.ncep.reanalysis.surfaceflux.html. The data used in this study are daily maximum and minimum temperature, relative humidity, and net solar radiation (difference between downward solar radiation flux and upward solar radiation flux) covering January 1, 1948 to December 31, 2006. The downloaded data were checked for two kinds of potential errors, i.e., outliers and consistency. The outliers (unusual values might be caused by misplacement of decimal point, etc.) are checked by using some threshold values. In this study, outliers were defined as all those values outside the mean±3 standard deviation (x 3s). If such a value exists, a close exam on Table 1 Information of the meteorological stations used in the study

WMO World Meteorological Organization, m a.s.l meters above sea level

Station

WMO code

KHARTOUM EDUEIM WAD MEDANI GEDAREF SENNAR ELOBEID ABUNAAMA RENK RASHAD DAMAZINE

62721 62750 62751 62752 62762 62771 62795 62801 62803 62805

Fig. 2 Study region and the location of the meteorological stations used in this study

the value is needed by comparing with other stations for the same day. Stations having sequences of several years of low values or high values might be caused by change of instrument and/or change of measurement place, etc. In this study, double mass plots of one station data against data at nearby stations were constructed and used to check the homogeneity and consistency for all the stations one by one. In the above checks, no remarkable errors were found.

3 Methodology The methods used in the study include the Mann–Kendall, wavelet transform, and the two-phase regression scheme. Each method has its own strength and weakness; the results of the three methods complement each other as will be shown in the results section. The Mann–Kendall trend test (MK; Mann 1945; Kendall 1975) is used to analyze the trends of the climate data. In Latitude ºN

Longitude ºE

15.60 14.00 14.40 14.03 13.55 13.17 12.73 11.75 11.87 11.78

32.55 32.33 33.48 35.40 33.62 30.23 34.13 32.78 31.05 34.38

Abbreviation kha dum wdm gdf snr obd abu rnk rsh dzm

Elevation (m a.s.l) 380 380 405 600 420 570 445 380 885 470

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this study, the influence of serial correlation in the time series on the results of MK test has been eliminated by prewhitening (e.g., Yue et al. 2002; Yue and Wang 2002). “Prewhitening” is one of the methods used to prevent false indication of trend, where autocorrelation is removed from the data by assuming a certain correlation model, usually a Markovian one (e.g., Von Storch 1995). The 95% confidence interval was used as a threshold to classify the significance of positive and negative MK trends. The continuous wavelet transform technique (Farge 1992; Torrence and Compo 1998; Grinsted et al. 2004), as a tool for analyzing localized variations of power within a time series, is applied in the current study. More recently, this method has been applied in studying annual maximum streamflow series of the Yangtze River basin by the authors of the present paper (Zhang et al. 2007). The concept and procedure of the wavelet method were thoroughly explained and discussed by Torrence and Compo (1998), and the wavelet software can be found at http://paos. colorado.edu/research/wavelets/. Besides, we also use the simple two-phase regression scheme (Solow 1987; Easterling and Peterson 1995; Vincent 1998; Lund and Reeves 2002) to explore the abrupt changes and the trend in the subseries. The model is formulated as: m1 þ a 1 t1 þ "t Xt ¼ ð1Þ m2 þ a 2 t2 þ "t where t1 =[j-n, j-1], t2 =[j, j+n-1], and n may be n=2, 3,..., Fth, then we can say that the change point is statistically significant. This method is used to detect significant change points and linear trends between the change points in the time series.

Statistical properties of the temperature, relative humidity, and net solar radiation in the Blue Nile-eastern Sudan region Maximum temperature (oC)

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Minimum temperature (oC)

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Fig. 3 Long-term monthly mean maximum and minimum temperature and the difference between monthly mean maximum and minimum temperature at the Khartoum station, Sudan. “+” denotes outlier

4 Results The study region is dominated by the northeast trade winds between October and May, which causes the prevailing aridity. Generally, three seasons can be distinguished: (1)

the cool and dry winter from December to February; (2) the warm and dry period from March to June; and (3) the warm and rainy period from July to November. Therefore, we will anatomize the changing properties of climate variables in these three seasons.

Table 2 Mann–Kendall trends of temperature variables of the meteorological stations of Sudan Stations

KHARTOUM ED_DUEIM WAD_MEDANI GEDAREF SENNAR EL_OBEID ABU_NAAMA RENK RASHAD DAMAZINE

Annual max. temperature

Annual min. temperature

Difference between max. and min. temperature

Annual mean max. temperature

Annual mean min. temperature

3.93 3.58 2.47 3.41 3.19 3.62 3.36 4.34 3.51 4.18

−2.70 −2.48 −3.42 −2.67 −3.64 −2.36 −3.41 −3.69 −2.24 −3.84

5.06 4.76 4.25 4.76 4.25 5.95 4.13 5.08 5.89 4.17

5.62 5.81 5.25 5.51 5.42 6.23 5.46 6.34 6.55 5.57

−1.59 2.98 2.99 2.07 2.45 −1.43 1.81 2.11 0.58 1.79

Values larger than 1.96 or smaller than −1.96 are significant at >95% confidence level. Annual max./min. temperature is defined as the highest/lowest daily temperature of the year. Annual mean max./min. temperature is defined as the mean daily max./min. temperature on the annual basis

Values larger than 1.96 or smaller than −1.96 are significant at >95% confidence level. Warm rainy season is from July to November, cool dry season is from December to February, and warm dry season is from March to June

5.88 6.53 6.44 6.35 −0.57 0.32 −1.96 0.95 3.90 4.68 6.13 4.01 7.41 8.23 8.48 7.62 ABU_NAAMA RENK RASHAD DAMAZINE

4.53 4.73 4.85 3.09

3.09 4.69 5.44 3.30

4.99 6.60 7.16 5.11

−2.71 −2.28 −2.99 −2.52

6.53 6.96 6.70 6.82

−3.71 −1.73 −1.90 −0.11 −1.43 −4.03 4.86 5.28 5.26 6.55 5.88 5.88 4.65 4.77 4.28 4.49 4.20 5.80 7.07 7.62 7.11 7.30 7.45 7.96 KHARTOUM ED_DUEIM WAD_MEDANI GEDAREF SENNAR EL_OBEID

3.77 5.06 4.95 4.25 5.24 4.57

3.23 2.79 2.82 3.25 2.75 3.93

3.89 5.80 5.26 5.57 5.43 5.83

−2.69 −1.96 −2.01 −2.31 −2.31 −3.89

Seasonal mean max. temperature in warm dry season Difference between seasonal mean max. and min. temperature in cool dry season Seasonal mean min. temperature in cool dry season Seasonal mean max. temperature in cool dry season Difference between seasonal mean max. and min. temperature in warm rainy season Seasonal mean min. temperature in warm rainy season Seasonal mean max. temperature in warm rainy season Stations

Table 3 Mann–Kendall trends of seasonal mean temperature variables of the meteorological stations of Sudan

For illustrative purpose, the Khartoum station is used to demonstrate the mean seasonal properties of the climate variables. Figure 3 is the boxplot of monthly mean maximum and minimum temperature and the associated difference between the maximum and minimum temperature at the Khartoum station. Comparing boxplot medians is like a visual hypothesis test, analogous to the t test used for means. Figure 3 illustrates that the higher monthly mean maximum temperature occurs in April, May, and June, and lower monthly mean maximum temperature can be observed in January, February, and December. Larger interquartile ranges of monthly mean maximum temperature can be detected in July, August, September, and October, showing larger maximum temperature variability in these months. Figure 3 also indicates a negative skew of monthly mean maximum temperature in June to October, showing a higher probability of higher maximum temperature in these months. Different changing properties are observed in terms of monthly mean minimum temperature (upper right panel of Fig. 3). Higher monthly mean minimum temperature is observed in May to October. Larger variability of monthly mean minimum temperature is found in January, February, and December and smaller variability in April to November, which is contrary to that of the monthly mean maximum temperature (upper left panel of Fig. 3). Lower panel of Fig. 3 shows monthly variability of the difference between maximum and minimum temperature. It can be seen that larger difference is identified in months from January to May and from September to December. Smaller difference is found in months from June to August. Besides, lower panel of Fig. 3 also shows larger variability of temperature difference in months from May to September. Thus, larger variability of monthly mean maximum temperature and larger temperature difference mainly occur in warm seasons. On the contrary, smaller variability is found mainly in the cool season. However, changing magnitude of monthly mean minimum temperature is larger in the cool season when compared to the warm seasons. Table 2 displays MK results of temperature variables. It can be seen from Table 2 that all stations in Sudan considered in this study show a significant increasing annual maximum temperature, but show a significant decreasing annual minimum temperature. Therefore, the difference between the annual maximum and minimum temperature is in significant increasing trend. Similarly, annual mean daily maximum temperature (column 5) is in significant increasing trends. However, annual mean daily minimum temperature (column 6) shows different changing characteristics. Eight out of ten stations show increasing trends in annual mean daily minimum temperature, of which five are significant at >95% confidence level. Two

Seasonal mean min. temperature in warm dry season

4.1 Temperature changes in Khartoum station

5.96 5.42 5.07 6.56 5.32 6.45

C.-Y. Xu et al. Difference between seasonal mean max. and min. temperature in warm dry season

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Maximum temperature 256 Time scale (months)

Fig. 4 Abrupt changes and linear trends estimation of areal average monthly mean maximum temperature variations of the study region by using the two-phase regression scheme method. The upper panel shows change points on different time scales; and lower panel shows linear trends of time intervals separated by change points. Dashed lines show decreasing trend after the change point, and solid lines indicate increasing trend after the change point. Thick solid and dashed lines denote significant change points

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maximum temperature shows a significant increasing trend, however, the seasonal mean minimum temperature in these two seasons is decreasing, and it is particularly the case in the cool dry season. All these facts cause the temperature differences between seasonal mean maximum and mini-

Period (years)

out of ten stations show decreasing trends but are not significant at >95% confidence level. Different changing properties are found in the seasonal changes of the temperature variables when compared to annual variations (Table 3). The seasonal mean maximum temperature in all seasons show significant increasing trends. However, the seasonal mean minimum temperature shows different changing characteristics in different seasons; significant increasing trends are found in the warm rainy season, and opposite are true in the cool dry season; both increase and decrease trends are found in the warm dry season, but most of them are not significant. In the warm rainy season, both seasonal mean maximum and minimum temperatures show a significant increasing trend, however, larger magnitude increases of seasonal mean maximum temperature than seasonal mean minimum temperature in the season cause a significant increase in temperature difference between seasonal mean maximum and minimum temperature. In the cool dry and warm dry seasons, the seasonal mean

0.25 0.5 1 2 4 8 16 1950

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1970 1980 Time (years)

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Fig. 5 Wavelet transform of areal average monthly mean maximum temperature of the study region. The thick black contour designates the 95% confidence level against red noise and the cone of influence (COI) where edge effects might distort the picture is shown as a U-shaped line

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Minimum temperature 256 Time scale (months)

Fig. 6 Abrupt changes and trends estimation of areal average monthly mean minimum temperature variations of the study region by using the two-phase regression scheme method. The upper panel shows change points on different time scales; and lower panel shows linear trends of time intervals separated by change points. Dashed lines show decreasing trend after the change point, and solid lines indicate increasing trend after the change point. Thick solid and dashed lines denote significant change points

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mum temperature in cool dry and warm dry seasons have significant increasing trends. 4.2 Changing properties of areal average temperature

wavelet transform method. The former method analyzes abrupt behaviors and linear trends of subseries. The wavelet transform method is used to implement the time–frequency analysis of the climate variable series and also the

The changing properties of areal average temperature are analyzed by using the two-phase regression scheme and the

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Relative humidity (%)

Period (years)

80

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1990

2000

Fig. 7 Wavelet transform of areal average monthly mean minimum temperature of Sudan. The thick black contour designates the 95% confidence level against red noise and the COI where edge effects might distort the picture is shown as a U-shaped line

70 60 50 40 30 20 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (months)

Fig. 8 Long-term monthly average relative humidity at the Khartoum station, Sudan. “+” denotes outlier


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Table 4 Mann–Kendall trends of seasonal mean relative humidity at the meteorological stations of Sudan Stations


Annual mean relative humidity

Seasonal mean relative humidity in cool dry season

Seasonal mean relative humidity in warm dry season

Seasonal mean relative humidity in warm rainy season

−7.97 −8.15 −7.92 −7.40 −7.91 −8.47 −7.47 −8.34 −8.61 −7.20

−5.71 −5.59 −5.34 −4.83 −5.25 −6.64 −5.08 −5.58 −6.43 −4.75

−6.59 −7.08 −7.02 −6.86 −7.21 −7.37 −7.12 −7.59 −7.84 −7.04

−6.98 −7.40 −7.04 −6.78 −7.16 −8.05 −6.87 −7.71 −8.39 −6.77


Relative humidity (%) Time scale (months)

Fig. 9 Abrupt changes and trends estimation of areal average relative humidity variations of the study region by using the two-phase regression scheme method. The upper panel shows change points on different time scales; and lower panel shows linear trends of time intervals separated by change points. Dashed lines show decreasing trend after the change point, and solid lines indicate increasing trend after the change point. Thick solid and dashed lines denote significant change points

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4.3 Changes of relative humidity in Khartoum station Figure 8 shows that higher relative humidity is observed mainly during July and October, and this period is also the warm rainy season in eastern Sudan. Boxplot of relative humidity during June and November shows a positive skew of relative humidity, indicating a higher probability of lower relative humidity of these months. The larger interquartile values of relative humidity during May and October show a larger variability of relative humidity changes in these months when compared to other months. More outlier values (denoted by +signals) also show larger changing magnitudes of relative humidity. Table 4 displays MK results of the seasonal relative humidity changes at the ten stations of Sudan. MK results indicate that the annual mean relative humidity and relative humidity in all the seasons are in significant decreasing trends at >95% confidence level. 4.4 Changing properties of areal average relative humidity

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Fig. 10 Wavelet transform of areal average relative humidity of the study region. The thick black contour designates the 95% confidence level against red noise and the COI where edge effects might distort the picture is shown as a U-shaped line

decrease dominates the changes of areal average relative humidity. On time scales of >64 months, the areal average relative humidity increases during 1980 and 1990. Besides, simpler changing patterns are also identified after 1980. Figure 10 indicates simple periodicity properties of areal average relative humidity of the study region. The 1-year period is significant and dominant throughout the time interval considered which implies no significant alterations within the periodicity of relative humidity of eastern Sudan. 4.5 Changes of net solar radiation Figure 11 shows the statistical properties of the mean monthly net solar radiation in Khartoum station. Obviously, higher net solar radiation is observed during February and May, and this period corresponds well with the warm dry season of Sudan. The lowest net solar radiation corresponds well to the warm rainy season (July–November) and the cool winter (December–January). Larger variability of net

260 240 Net shortwave radiation (W/m2)

periodicity properties. Figure 4 illustrates different abrupt behaviors at different time scales. Generally, the areal average monthly mean maximum temperature of the study region is dominated by increasing trends which are interrupted by short-term decreasing trends. Before 1960s, the areal average monthly mean maximum temperature is characterized mainly by decreasing trends and is featured by increasing trends after 1960s. This general increasing trend after 1960s is interrupted by short-term decreasing trend during mid-1990s. Upper panel of Fig. 4 shows that the decrease of areal average monthly mean maximum temperature occurs on the time scale of >64 months. As for the periods of variations of areal average monthly mean maximum temperature (Fig. 5), the periodicity is characterized by a 1-year period. It should be noted here that the 1-year band disappears from 1970 to 2000, showing a decreasing wavelet spectrum power. In this time interval, the half-year period component appears, showing higher frequency fluctuations. Figure 6 shows that the areal average monthly mean minimum temperature is dominated by an increasing trend except the time interval of 1960– 1965 which is characterized by decreasing areal average monthly mean minimum temperature. Lower panel of Fig. 6 also shows a smaller-magnitude increase of areal average monthly mean minimum temperature after 1990 when compared to that before 1990. Similarly, a 1-year period is dominant throughout the whole study period. Some significant bands distribute sporadically along the half- and quarter-year periods (Fig. 7).

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months

Distinctly remarkable changes can be observed in the areal average relative humidity of eastern Sudan (Fig. 9). A sharp

Fig. 11 Long-term monthly average net solar radiation at the Khartoum station, Sudan. “+” denotes outlier


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Table 5 Mann–Kendall trends of net solar radiation of the meteorological stations of Sudan Station name


Mean net solar radiation in rainy season

Mean net solar radiation in cool dry season

4.99 4.37 3.17 4.61 4.30 3.86 4.78 3.54 3.54 4.78

6.19 6.38 2.62 5.71 5.55 6.61 5.79 6.32 6.32 5.79

Mean net solar radiation in warm dry season 5.77 5.45 6.99 7.03 5.41 5.60 5.51 5.16 5.16 5.51


solar radiation occurs in April to August. MK results indicate that a significant increasing trend is found in all the seasons and stations (Table 5), which is consistent with the increasing trends found for the annual and seasonal maximum temperature (Table 3, columns 2, 5, and 8). 4.6 Changing properties of areal average net solar radiation Figure 12 shows different abrupt behaviors at different time scales. In general, the areal average net solar radiation is dominated by an increasing trend when the whole period from 1948 to 2005 is concerned. This general increasing trend is interrupted by short-term decreasing trends in 1950s and around 1990. The changes also show a large fluctuation in short periods with a jump in middle 1950s and a drop in earlier 1970s. Upper panel of Fig. 12 shows that the decrease of areal average monthly mean net solar radiation occurs on larger time scales of >64 months. The changes of net solar radiation show a clear similarity with the changes of maximum temperature (Fig. 4). Wavelet transform results indicate a significant 1-year period except in the period of earlier 1950s (Fig. 13).

5 Discussions The global temperature increase has exerted tremendous influences on hydrological cycle, human health, energy management, and agricultural production (e.g., Karl and Easterling 1999). Therefore, temperature changes have been drawing considerable concerns among scholars (e.g., Elagib and Mansell 2000). It should be noted here that the temperature changes are different from region to region. Bonsal et al. (2001) found great regional and seasonal differences in the spatial and temporal variability of

extreme temperature in Canada for the period of 1950– 1998, indicating significant warmer summer and spring periods and slight warmer winters (Founda et al. 2004). Manton et al. (2001) found significant increases in hot days and warm nights and decreases in cool days and cold nights since 1961 across the Southern Asia and South Pacific region. In China, Zhai and Pan (2003) showed that the number of hot days (over 35°C) displays a slight decreasing trend, while the number of frost days (below 0°C) exhibits a significant decreasing trend. Zhang et al. (2008b) studied the changing properties of extreme temperature of the Yellow River basin, China, indicating that the annual warming trend in the Yellow River basin mainly results from the increase in winter minimum temperature. The maximum temperature in the Yellow River basin is increasing, but is not significant at >95% confidence level. The current study reveals different changing characteristics of warming process of Sudan. The annual maximum temperature is in significant increasing trend; however, the annual minimum temperature is in decreasing trend, and it is particularly the case in the cool dry season. Thus, the difference between the annual maximum and minimum temperature is significantly increasing. Seasonal differences can be found in terms of minimum temperature changes. Significant decreasing minimum temperature mainly occurs in the cool dry season. Two out of ten stations show significant decreasing minimum temperature in the warm dry season. What is confirmed is the significant increasing difference between maximum and minimum temperature. The increasing magnitude of maximum temperature in the dry seasons is smaller than in the rainy season. These changing properties of temperature may be attributed to changes of net solar radiation. Our analysis results indicate that the net solar radiation is significantly increasing in all

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Time scales (months)

Fig. 12 Abrupt changes and trends estimation of areal average net solar radiation variations of the study region by using the two-phase regression scheme method. The upper panel shows change points on different time scales; and lower panel shows linear trends of time intervals separated by change points. Dashed lines show decreasing trend after the change point, and solid lines indicate increasing trend after the change point. Thick solid and dashed lines denote significant change points

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1960

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seasons and stations, which is consistent with the changing properties of maximum temperature in the region. Our results indicate a significant decreasing trend in relative humidity, particularly after mid-1960s. Elagib and Mansell (2000) draw the general conclusions based on more cited references (e.g., Perry 1986; Walsh et al. 1988; Elagib and Alvi 1996) that a marked progressive deterioration of 0.25 0.5 1 2 4 8 16 1950

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rainfall has occurred since mid-1960s with culmination in mid-1980s. The changing properties of precipitation reported by aforementioned scholars are in good agreement with those of relative humidity found in this study, which indicates that there exist good relations between precipitation and relative humidity in the study region.

6 Conclusions

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Fig. 13 Wavelet transform of areal average net solar radiation of the study region. The thick black contour designates the 95% confidence level against red noise and the COI where edge effects might distort the picture is shown as a U-shaped line

In this study, we systematically analyzed the major climate variables of maximum and minimum temperature, relative humidity, and net solar radiation with the aim to explore the statistical properties of these variables in eastern Sudan. We obtained the following conclusions: 1) In eastern Sudan, the annual and seasonal maximum temperatures are increasing significantly. The increasing magnitude of the maximum temperature in the rainy season is larger than that in the dry


seasons. Decreasing trends can be observed in the annual and dry seasons' minimum temperature. The minimum temperature in the rainy season is increasing but the rate of the increase is smaller than that of the maximum temperature in the same season. Consequently, the difference between annual maximum and minimum temperature is increasing in all the seasons. 2) There is a decreasing trend in relative humidity in eastern Sudan, particularly after mid-1960s, which is in agreement with precipitation changes as reported in earlier study. 3) The net solar radiation in the region is significantly increasing in all seasons and stations, which corresponds well with the changing properties of the maximum temperature. 4) Our analysis results also indicate a consistent 1-year period variation within these climate variables we studied. These results suggest that the climate regime variations of eastern Sudan, to a larger degree, are controlled by global climate signal.

Acknowledgments This work is financially supported by The Research Council of Norway with project number 171783 (FRIMUF) and by the “985 Project” (grant no. 37000-3171315).

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