Abstract
Various surface soil moisture (SM) data from station observations, the Soil Moisture Active Passive (SMAP) mission, three reanalyses (ERA-Interim, CFSR, and NCEP RII), and the Global Land Data Assimilation System (GLDAS) are used to explore the sub-seasonal variations of SM (SSV-SM) over eastern China. Based on the correlation with SM of SMAP, reanalyses, and GLDAS, it is found that the variations of SM observed by Liuhe and Chunan stations can generally represent the SM variations over eastern China. The correlation coefficients between the SMAP and station SM are around 0.7. The SMAP product can well capture the time variation of SM over eastern China. The spectral analysis suggests that periodic variations of SM are mainly and significantly over the 10–30-day period over eastern China in all the data. The significant spectra over the 10–30-day period basically occur during the rainy season over eastern China. For the spatial aspect of SSV-SM, precipitation is the main factor causing the spatial distribution of SSV-SM over eastern China. However, the spectra of the station precipitation are not consistent with those of the station SM, and there is less coherence between the precipitation and SM over the periods during which SM has significant spectra. This indicates that SSV-SM is also affected by other factors.
Highlights
Providing the useful climatic prediction on sub-seasonal to seasonal (S2S) time scales can be a great asset to government and business policymakers but is still a worldwide challenge for meteorologists (Zhang et al 2013; White et al 2017; Zhou et al 2019)
In West Africa, a significant interaction is found between soil moisture (SM) and the West African monsoon on sub-seasonal time scales, and the monsoon circulation can be adjusted by SM through surface energy fluxes (Taylor 2008)
Compared results from all those studies, spectral features of SM vary in different regions on different time scales, and less attention was paid on sub-seasonal features of SM over eastern China
Summary
Providing the useful climatic prediction on sub-seasonal to seasonal (S2S) time scales (usually 10–90 days) can be a great asset to government and business policymakers but is still a worldwide challenge for meteorologists (Zhang et al 2013; White et al 2017; Zhou et al 2019). SM is an important factor in the S2S climate forecast because it can significantly affect the atmosphere on subseasonal time scales. Can be significantly improved when accurate SM is considered in the model initial condition (Koster et al 2004, 2011; Boisserie and Cocke 2012; Hirsch et al 2014) This improvement probably relates to sub-seasonal features of SM, for example, SM memory and SSV-SM. Due to SM memory, variations of SM maybe different from those of precipitation on sub-seasonal time scales. Compared results from all those studies, spectral features of SM vary in different regions on different time scales, and less attention was paid on sub-seasonal features of SM over eastern China.
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