Show simple item record

dc.contributor.authorLiu, Lei  Concept link
dc.contributor.authorPeng, Shiqiu  Concept link
dc.contributor.authorHuang, Rui Xin  Concept link
dc.date.accessioned2017-04-18T18:10:40Z
dc.date.available2017-08-10T08:36:20Z
dc.date.issued2017-02-10
dc.identifier.citationJournal of Geophysical Research: Oceans 122 (2017): 1042–1056en_US
dc.identifier.urihttps://hdl.handle.net/1912/8918
dc.descriptionAuthor Posting. © American Geophysical Union, 2017. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 122 (2017): 1042–1056, doi:10.1002/2016JC011927.en_US
dc.description.abstractObservational surface data are used to reconstruct the ocean's interior through the “interior + surface quasigeostrophic” (isQG) method. The input data include the satellite-derived sea surface height, satellite-derived sea surface temperature, satellite-derived or Argo-based sea surface salinity, and an estimated stratification of the region. The results show that the isQG retrieval of subsurface density anomalies is quite promising compared to Argo profile data. At ∼1000 m depth, the directions of retrieved velocity anomalies are comparable to those derived from Argo float trajectories. The reconstruction using surface density input field approximated only by SST (with constant SSS) performs less satisfactorily than that taking into account the contribution of SSS perturbations, suggesting that the observed SSS information is important for the application of the isQG method. Better reconstruction is obtained in the warm season than in the cold season, which is probably due to the stronger stratification in the warm season that confines the influence of the biases in the surface input data (especially SSS) in a shallow layer. The comparison between the performance of isQG with Argo-based SSS input and that with satellite-derived SSS input suggests that the biases in the SSS products could be a major factor that influences the isQG performance. With reduced biases in satellite-derived SSS in the future, the measurement-based isQG method is expected to achieve better reconstruction of ocean interior and thus is promising in practical application.en_US
dc.description.sponsorshipMOST of China Grant Number: 2014CB953904; China Special Fund for Meteorological Research in the Public Interest Grant Number: GYHY201406008; Strategic Priority Research Program of the Chinese Academy of Sciences Grant Number: XDA11010304; National Natural Science Foundation of China Grant Number: 41376021 and 41306013en_US
dc.language.isoen_USen_US
dc.publisherJohn Wiley & Sonsen_US
dc.relation.urihttps://doi.org/10.1002/2016JC011927
dc.subjectIsQG methoden_US
dc.subjectReconstructionen_US
dc.subjectSea surfaceen_US
dc.subjectOcean interioren_US
dc.subjectSMOS SSSen_US
dc.subjectArgo floatsen_US
dc.titleReconstruction of ocean's interior from observed sea surface informationen_US
dc.typeArticleen_US
dc.description.embargo2017-08-10en_US
dc.identifier.doi10.1002/2016JC011927


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record