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    Online Resource
    Online Resource
    Wiley ; 2017
    In:  International Journal of Climatology Vol. 37, No. S1 ( 2017-08), p. 157-170
    In: International Journal of Climatology, Wiley, Vol. 37, No. S1 ( 2017-08), p. 157-170
    Abstract: This study examines the temporal variations and spatial distributions of annual precipitation over Central Asia during the periods of 1901–2013, 1951–2013, and 1979–2013 using the latest version of Global Precipitation Climatology Centre (GPCC) full data reanalysis version 7 ( GPCC V7 ) data set. The linear trend and multiperiods of the precipitation over the entire region and plain and mountainous area separately are analysed by linear least square method and ensemble empirical mode decomposition method. An overall increasing trend [0.66 mm (10 years) −1 ] is found for the entire region during 1901–2013, which is smaller than that of 1951–2013. The regional annual precipitation exhibits multi‐decadal variations, with a sharp decline during 1901–1944, followed by an increase until 1980s, and a fluctuation thereafter. During 1979–2013, the mountainous area shows a greater increasing trend than the entire region. Furthermore, the regional annual precipitation has exhibited high‐frequency variations with 3‐year and 6‐year quasiperiods and a low‐frequency variation with 28‐year quasiperiods. In terms of the spatial distribution, increasing trend in the annual precipitation is found in Xinjiang and decreasing trends appear over the five countries of Central Asia during 1951–2013. Empirical orthogonal function results show that the mountainous area is the large variability centre of the annual precipitation. The dominant mode of interannual variability in Central Asia annual precipitation is related to El Niño‐Southern Oscillation, which explains about 17% of the interannual variance during 1951–2013. The results of this study describe the long‐term variation in the annual precipitation over Central Asia as well as its relationship with some key climate indices in great detail, which will benefit the understanding and the prediction of the climate variations in this region.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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