Abstract
The interannual variability of summertime eddy-induced heat transport (EHT) in the western South China Sea (WSCS) is investigated based on the downgradient eddy diffusivity method and explored its formation mechanism. Estimations of long-term mean EHT and its monthly evolution reveal that the largest EHT in the SCS occurs in the WSCS region during the summer. In the WSCS, enhanced EHT and eddy kinetic energy (EKE) levels are simultaneously observed in 1994, 1999, 2002, 2006, 2008, 2009, 2012, 2014 whereas the lower EHT and EKE levels are observed in 1995, 2000, 2001, 2003, 2004, 2007, 2010, 2015, 2017 during JAS (July, August and September) months. Analysis of the Simple Ocean Data Assimilation, version 3.3.1 (SODAv3) data along 110.75° E reveals a strong surface intensification of the summertime eastward jet (SEJ) in the EHT-strong years than the EHT-weak years. Linear stability analysis conducted by adopting a 2 ½-layer reduced gravity model shows that the increased EHT in EHT-strong years is due to the enhanced baroclinic instability caused by the strong vertical shear developed through the surface-intensification of SEJ. The cause for the interannually varying vertical shear can be sought in the interannually varying meridional temperature gradient which is influenced by the combined forcing of the meridional Ekman flux convergence, meridional geostrophic flux convergence, and convergence of the latitudinally dependent surface heat flux forcing. It is also found that the interannual variations of EHT in the SCS are partially influenced by the local wind stress curl and remote forcing from the eastern boundary.
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Acknowledgements
We thank two anonymous reviewers for helpful comments on an earlier version of this manuscript. This research is supported by Innovative Research Group of National Natural Science Foundation of China 41521005, 2017YFB0502700, the Strategic Priority Research Program of Chinese Academy of Sciences XDB42000000, NSFC 41822602, NSFC 41976016, NSFC 41676010, NSFC 42076021,XDA15020901, XDA 20060502, the International Partnership Program of the Chinese Academy of Sciences (Grant no. 131551KYSB20160002), China-Sri Lanka Joint Center for Education and Research (CSL-CER), University of Ruhuna, Sri Lanka, Youth Innovation Promotion Association CAS (2017397), the Pearl River S&T Nova Program of Guangzhou (201806010105), NHXX2018WL0101, LTOZZ2002, Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Grants GML2019ZD0306 and GML2019ZD0302) and Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311020004). The SSHA data are produced by European Copernicus Marine Environment Monitoring Service (CMEMS) and downloaded online from ftp://ftp.sltac.cls.fr/Core/SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047/dataset-duacs-rep-global-merged-allsat-phy-l4-v3/. World Ocean Atlas 2013 version 2 data are obtained at the National Centers for Environmental Information website (https://www.nodc.noaa.gov/). GODAS and SODAv3 data are obtained from https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html and http://apdrc.soest.hawaii.edu/datadoc/soda_3.3.1.php. BOA-Argo data are downloaded from ftp://data.argo.org.cn/pub/ARGO/BOA_Argo/. CCMP satellite ocean surface wind vector data Version 2 were taken from the Remote Sensing Systems website (ftp://ftp2.remss.com/ccmp/v02.0/). Time series of the climate indices (Niño3.4 and DMI) are obtained from the Global Climate Observing System (https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/).
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Appendix
Appendix
According to Chen et al. (2012), the quasi-geostrophic equations governing the perturbation potential vorticity \({q}_{n}\) are
in which \({\phi }_{n}\) is the perturbation stream function, \({U}_{n}\) is the zonal mean velocity, and \({\Pi }_{n}\) is the mean potential vorticity in each layer (\(n=1\) and \(n=2\) refer to the surface and lower layers, respectively). Assuming \({U}_{n}\) is meridionally uniform, \({q}_{n}\) and the meridional gradient of \({\Pi }_{n}\) can be expressed as
and
Here,
where \({H}_{n}\) is the mean thickness of layer \(n\), \({\rho }_{n}\) is the density of layer \(n\), \({f}_{0}\) is the Coriolis parameter at the reference latitude, \(\beta\) is the meridional gradient of the Coriolis parameter, \({\rho }_{0}\) is the reference density, and \({\rho }_{3}\) is the density of the motionless layer. The stability of the system can be analyzed by substituting the normal mode solution \({\phi }_{n}=\mathrm{Re}\left[{A}_{n}\mathrm{exp}i\left(kx+ly-kct\right)\right]\) into Eq. (8). Requiring nontrivial solutions for \({A}_{n}\) leads to the dispersion relationship
where
and \({K}^{2 }={k}^{2}+ {l}^{2}\) is the total wavenumber. For a system with \({U}_{1}-{U}_{2}>0\), it can be shown that the necessary and sufficient condition for instability is \({\left({U}_{1}-{U}_{2}\right)}_{min}>{\gamma }_{2}{\lambda }^{2}\beta +{\gamma }_{2}{U}_{2}\) (Qiu 1999).
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Gonaduwage, L.P., Chen, G., Priyadarshana, T. et al. Interannual variability of summertime eddy-induced heat transport in the Western South China Sea and its formation mechanism. Clim Dyn 57, 451–468 (2021). https://doi.org/10.1007/s00382-021-05719-7
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DOI: https://doi.org/10.1007/s00382-021-05719-7