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Sciences in Cold and Arid Regions ›› 2021, Vol. 13 ›› Issue (3): 220-233.doi: 10.3724/SP.J.1226.2021.19059.

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Temporal changes in seasonal precipitation over the Sahara Desert from 1979 to 2016

Sindikubwabo Celestin1,2,Qi Feng1(),RuoLin Li1,3,WenJu Cheng1,2,Jian Ma4,Habiyakare Telesphore2,Nzabarinda Vincent2   

  1. 1.Key Laboratory of Eco-hydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou, Gansu 730000, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.Qilian Mountains Eco environment Research Center, Lanzhou, Gansu 730000, China
    4.Academy of Water Resources Conservation Forests in Qilian Mountains, Zhangye, Gansu 734000, China
  • Received:2020-06-24 Accepted:2021-02-02 Online:2021-06-30 Published:2021-07-05
  • Contact: Qi Feng E-mail:qifeng@lzb.ac.cn
  • Supported by:
    the National Key R&D Program of China(2017YFC0404305);National Natural Science Foundation of China(41801015);the Foundation for Excellent Young Scholars of Northwest Institute of Eco-Environment and Resources NIEER Chinese Academy of Sciences, CAS(51Y851D61);the Major Program of the Natural Science Foundation of Gansu province, China(18JR4RA002);Science and Technology Program of Gansu Province, China(18JR2RA026);the Chinese Academy of Sciences-The World Academy of Sciences (CAS-TWAS) President's Fellowship programme

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Abstract:

Rainfall variability dominates livelihoods in all countries of Saharan Africa. To better understand the processes involved in Sahara precipitation changes, we used the Global Precipitation Climatology Center (GPCC) dataset to examine dry and wet seasonal trends in the Sahara region from 1979 to 2016. We also used the European Centre for Medium-Range Weather Forecasts (ECMWF) to evaluate the general atmospheric circulation associated with seasonal change of Sahara precipitation. The Mann-Kendall test and Theil sens' slope estimator methods were adopted to test and estimate the significance and weight of precipitation trend, respectively. The results revealed that Sahara precipitation has increased significantly. The seasonal evaluation shows a positive trend of 0.42 mm/decade and 1.43 mm/decade in JAS (June, August, and September) seasons for the northern and southern Saharan Desert, respectively. Moreover, the JFMA (January, February, March, and April) period shows a negative trend but not statistically significant. An examination of the general circulation and moisture transport changes suggested an increase of rainfall in southern Sahara. The wet period is also driven by northward penetration of moisture originating from the Sahel region, African Easterly Jet (AEJ), and weakening in the upper tropospheric zonal wind. Summer rainfall has also been likely associated with positive anomalies of sea surface temperature (SST) in the North Tropical Atlantic (NTA) and the Mediterranean Sea.

Key words: Sahara, precipitation, variability, arid, Africa, climate

Figure 1

(a) Depicts the annual mean total precipitation estimated from GPCC dataset and (b) indicates the annual mean maximum temperature calculated from the ERA5 datasets. The dashed line divides the northern and southern parts of the Sahara Desert (box)"

Figure 2

The monthly mean precipitation averaged between 1979 and 2016 in northern (green) and southern (blue) parts of the Sahara Desert"

Figure 3

Spatial distributions of seasonal mean precipitation estimated from GPCC. The hatch areas indicate regions with seasonal mean precipitation of less than 1 mm"

Figure 4

Spatial mean seasonal precipitation anomalies averaged from temporal seasonal anomaly calculated with respect to the 30-year climatological mean (1981-2000)"

Figure 5

The seasonal precipitation anomalies for the northern (orange) and southern (blue) Sahara Desert. The anomalies are calculated in respect to the 30-year climatological mean (1981-2000)"

Table 1

Results summary of the MK-test and Theil Sen's slope computed from seasonal precipitation over the Sahara Desert. Note: the positive (negative) values of Z score, S and Slope indicate an upward (downward) trend, while the Z score ≥ 1.96 and P-value ≤0.05 indicate trend significance"

RegionSeasonTrendP valueZ scoreSSlope
NorthJASincreasing0.00282.99212390.0416
JFMAno trend0.6328-0.4777-39-0.0118
MJno trend0.13141.50861210.0172
ONDno trend0.78210.2766230.0053
SouthJASincreasing0.00023.69612950.1427
JFMAno trend0.4507-0.7543-61-0.0078
MJno trend0.74380.3269270.0074
ONDno trend0.43570.7795630.0101

Figure 6

(a) Correlation between the southern Sahara precipitation and SST anomalies (b) Variation of NTA SST anomalies (bar) and southern Sahara precipitation anomalies during the JAS period 1979-2016 (dash line). The contour values and dots indicate the spatial and significant estimated from SST anomalies of the JAS period 1979-2016"

Figure 7

Comparison of seasonal changes in general circulation components. Contour lines indicate the seasonal mean of specific humidity (a, d) at 1000 hPa, zonal wind at 200 hPa (b, e) and zonal wind at 600 hPa (c, f). Colors in (a, d) indicate correlation between southern Sahara precipitation and Specific humidity at 950 hPa for (a) winter and (d) summer, and arrows in (a, d) depicts seasonal moisture flux calculated at 950 hPa"

Figure 8

Composite analysis for (a) moisture and (b) zonal components performed between the wet and dry period of the JAS season. Color and arrows in (a) indicated changes in moisture contents (g/kg) and moisture flux components, respectively. Colors and contours in (b) show changes in zonal wind components at 600 hPa and 200 hPa, respectively"

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[2] AiHong Xie, ShiMeng Wang, YiCheng Wang, ChuanJin Li. Comparison of temperature extremes between Zhongshan Station and Great Wall Station in Antarctica[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 369 -378 .
[3] YanZai Wang, YongQiu Wu, MeiHui Pan, RuiJie Lu. Comparison of two classification methods to identify grain size fractions of aeolian sediment[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 413 -420 .
[4] YinHuan Ao, ShiHua Lyu, ZhaoGuo Li, LiJuan Wen, Lin Zhao. Numerical simulation of the climate effect of high-altitude lakes on the Tibetan Plateau[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 379 -391 .
[5] Zhuo Ga, Za Dui, Duodian Luozhu, Jun Du. Comparison of precipitation products to observations in Tibet during the rainy season[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 392 -403 .
[6] Rong Yang, JunQia Kong, ZeYu Du, YongZhong Su. Altitude pattern of carbon stocks in desert grasslands of an arid land region[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 404 -412 .
[7] Yang Qiu, ZhongKui Xie, XinPing Wang, YaJun Wang, YuBao Zhang, YuHui He, WenMei Li, WenCong Lv. Effect of slow-release iron fertilizer on iron-deficiency chlorosis, yield and quality of Lilium davidii var. unicolor in a two-year field experiment[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 421 -427 .
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[9] YuMing Wei, XiaoFei Ma, PengShan Zhao. Transcriptomic comparison to identify rapidly evolving genes in Braya humilis[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 428 -435 .
[10] Yong Chen, Tao Wang, LiHua Zhou, Rui Wang. Industrialization model of enterprises participating in ecological management and suggestions: A case study of the Hobq Model in Inner Mongolia[J]. Sciences in Cold and Arid Regions, 2018, 10(4): 286 -292 .