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Advances in Atmospheric Sciences

, Volume 35, Issue 5, pp 550–566 | Cite as

The 30–60-day Intraseasonal Variability of Sea Surface Temperature in the South China Sea dur1ing May–September

  • Jiangyu Mao
  • Ming Wang
Original Paper
  • 56 Downloads

Abstract

This study investigates the structure and propagation of intraseasonal sea surface temperature (SST) variability in the South China Sea (SCS) on the 30–60-day timescale during boreal summer (May–September). TRMM-based SST, GODAS oceanic reanalysis and ERA-Interim atmospheric reanalysis datasets from 1998 to 2013 are used to examine quantitatively the atmospheric thermodynamic and oceanic dynamic mechanisms responsible for its formation. Power spectra show that the 30–60-day SST variability is predominant, accounting for 60% of the variance of the 10–90-day variability over most of the SCS. Composite analyses demonstrate that the 30–60-day SST variability is characterized by the alternate occurrence of basin-wide positive and negative SST anomalies in the SCS, with positive (negative) SST anomalies accompanied by anomalous northeasterlies (southwesterlies). The transition and expansion of SST anomalies are driven by the monsoonal trough–ridge seesaw pattern that migrates northward from the equator to the northern SCS. Quantitative diagnosis of the composite mixed-layer heat budgets shows that, within a strong 30–60-day cycle, the atmospheric thermal forcing is indeed a dominant factor, with the mixed-layer net heat flux (MNHF) contributing around 60% of the total SST tendency, while vertical entrainment contributes more than 30%. However, the entrainment-induced SST tendency is sometimes as large as the MNHF-induced component, implying that ocean processes are sometimes as important as surface fluxes in generating the 30–60-day SST variability in the SCS.

Keywords

sea surface temperature 30–60-day intraseasonal variability South China Sea vertical entrainment 

摘要

本文研究了夏季5-9月南海海表温度(SST)30-60天季节内变率的结构和传播特征. 基于1998-2013年TRMM的海表温度, GODAS和ERA-interim的海洋和大气再分析资料, 定量地分析了大气的热力强迫和海洋的动力过程在海表温度30-60天变率形成中的作用. 功率谱分析发现, 南海SST存在显著的 30-60天季节内振荡; 这种30-60天变率所占的10-90天季节内变率总方差在南海大部海域均超过60%. 较强的30-60天SST振荡事件的合成分析表明, 南海SST 30-60天变率的主要特征是海盆尺度的正、负SST异常的交替出现, 同时, 正(负)SST异常伴随着东北(西南)风异常, 并且SST异常的转换和扩张是受自赤道向南海北部移动的季风槽脊所驱动的. 通过混合层热力收支方程的定量诊断发现, 在较强的30-60天SST变率事件中, 混合层净的热通量对海表温度趋势变化的贡献超过60%, 而海洋的垂直夹卷对海表温度趋势变化的贡献也超过了30%. 尽管大气的热力强迫是影响SST异常变化的主要因子. 然而, 个例分析发现, 在某些30-60天SST振荡事件中, 海洋的垂直夹卷效应与混合层净热通量强迫对SST趋势的贡献是同样的. 这表明, 海洋动力过程和海表热通量对于30-60天SST异常变化的影响在某些情况下是同等重要的.

关键词

海表温度 30-60天季节内变率 南海 垂直夹卷 

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Notes

Acknowledgements

The TRMM-based TMI SST data are produced by Remote Sensing Systems and sponsored by the NASA Earth Sciences Program, available at http://www.remss.com/ missions/tmi. The ERA-Interim data can be downloaded from http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/. The GODAS data are available at http://cfs.ncep.noaa.gov/cfs/godas. The GPCP rainfall data can be downloaded from the databank at http://precip.gsfc.nasa.gov/pub/gpcp-v2/. TropFlux data are produced under a collaboration between Laboratoire d’Océanographie: Expérimentation et Approches Numériques (LOCEAN), from L’Institut Pierre Simon Laplace (IPSL, Paris, France), and the National Institute of Oceanography/CSIR (NIO, Goa, India), and supported by L’Institut de Recherche pour le Développement (IRD, France). TropFlux relies on data provided by ERA-Interim and ISCCP. This research was jointly supported by the SOA Program on Global Change and Air–Sea Interactions (Grant No. GASI-IPOVAI- 03), the National Basic Research Program of China (Grant No. 2014CB953902), the Natural Science Foundation of China (Grant Nos. 91537103 and 41375087), and the Priority Research Program of the Chinese Academy of Sciences (Grant Nos. QYZDY-SSWDQC018 and XDA11010402).

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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LASG, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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