Sub-seasonal variability of surface soil moisture over eastern China
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Sub‑seasonal variability of surface soil moisture over eastern China Yang Zhou1 · Xuan Dong1 · Haishan Chen1 · Lu Cao2 · Qing Shao3 · Shanlei Sun1 · Ben Yang4 · Jian Rao1 Received: 12 November 2019 / Accepted: 16 September 2020 © The Author(s) 2020
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 SSVSM, 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. Keywords Sub-seasonal · Soil moisture · Soil moisture memory · Eastern China
1 Introduction 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). On S2S time scales, the forecast period is too long for the atmosphere to memorize its initial state (Lorenz 1975) and too short for the atmosphere to acquire sufficient influences from slow * Yang Zhou [email protected] 1
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, School of Atmospheric Sciences, Nanjing University of Information Science and Technology, No. 219 Ningliu Road, Pukou District, Nanjing 210044, Jiangsu, China
2
Jiangsu Meteorological Observatory, Jiangsu Meteorological Bureau, Nanjing 210008, China
3
Zhejiang Institute of Meteorological Sciences, Zhejiang Meteorological Bureau, Hangzhou 310008, China
4
School of Atmospheric Sciences, Nanjing University, Nanjing 210032, China
evolving parts of the earth climate system (for example sea surface temperature; von Neumann 1955). Due to this, studying factors that possess longer climate memory than the atmosphere but evolve faster than the ocean is an important key to solve the S2S forecast issue. F
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