<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "https://jats.nlm.nih.gov/nlm-dtd/publishing/3.0/journalpublishing3.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="data-paper" specific-use="SMUR" dtd-version="3.0" xml:lang="en">
<front>
<journal-meta>
<journal-id journal-id-type="publisher">ESSDD</journal-id>
<journal-title-group>
<journal-title>Earth System Science Data Discussions</journal-title>
<abbrev-journal-title abbrev-type="publisher">ESSDD</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Earth Syst. Sci. Data Discuss.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1866-3591</issn>
<publisher><publisher-name></publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/essd-2026-440</article-id>
<title-group>
<article-title>A new High-Resolution daily Mean South China Sea Ocean Reanalysis dataset: SCSORA</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhang</surname>
<given-names>Xuri</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhu</surname>
<given-names>Xueming</given-names>
<ext-link>https://orcid.org/0000-0002-4087-8884</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zu</surname>
<given-names>Ziqing</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Lv</surname>
<given-names>Guokun</given-names>
<ext-link>https://orcid.org/0000-0003-0079-5352</ext-link>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Wang</surname>
<given-names>Hailong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Yan</surname>
<given-names>Yan</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Liu</surname>
<given-names>Jian</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Wang</surname>
<given-names>Hui</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Marine Sciences, Sun Yat–sen University, and Southern Marine Science and Engineering Guangdong Laboratory  (Zhuhai), Zhuhai, 519082, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>State Key Laboratory of Satellite Ocean Environment Dynamics, National Marine Environmental Forecasting Center, Beijing 100081, China</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>Key Laboratory for Polar Science and National Arctic and Antarctic Data Center (NADC), Polar Research Institute of China,  Ministry of Natural Resources, Shanghai, 200136, China</addr-line>
</aff>
<aff id="aff5">
<label>5</label>
<addr-line>93110 Troops, Beijing, 100871, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>22</day>
<month>07</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>32</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Xuri Zhang et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://essd.copernicus.org/preprints/essd-2026-440/">This article is available from https://essd.copernicus.org/preprints/essd-2026-440/</self-uri>
<self-uri xlink:href="https://essd.copernicus.org/preprints/essd-2026-440/essd-2026-440.pdf">The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-440/essd-2026-440.pdf</self-uri>
<abstract>
<p>High-resolution three-dimensional (3D) ocean reanalysis data are essential for investigating multiscale ocean dynamics in the regional ocean and their impacts on energy transport, marine ecosystems, and climate change. The South China Sea (SCS), as one of the most eddy-active marginal seas in the global ocean, is characterized by complex 3D dynamical processes and frequent extreme ocean events, imposing urgent data demands on the scientific community. This paper presents a new South China Sea Ocean Reanalysis (SCSORA) dataset, with high resolution (1/30&amp;deg;) covering the period 2001&amp;ndash;2024, which is generated by the second version of the South China Sea Operational Oceanography Forecast System (SCSOFSv2). SCSORA provides daily 3D fields of temperature, salinity, and current velocity, together with sea surface height (SSH).Three categories of observational data are assimilated into SCSOFSv2, including satellite-derived optimum interpolation sea surface temperature (OISST), along-track sea level anomaly (SLA) from AVISO, and &lt;em&gt;in&lt;/em&gt;-&lt;em&gt;situ&lt;/em&gt; temperature&amp;ndash;salinity Argo profiles. Systematic validations against multisource satellite retrievals, &lt;em&gt;in&lt;/em&gt;-&lt;em&gt;situ&lt;/em&gt; observations, and existing reanalysis products demonstrates that SCSORA achieves satisfactory accuracy and reliability in reproducing sea surface temperature (RMSE: 0.34 &amp;deg;C), SLA (RMSE: 5.9 cm), and subsurface thermohaline structure. Kinetic energy spectral analysis reveals that SCSORA is capable of resolving ocean dynamical processes spanning from mesoscale to part of the submesoscale range. Representative applications demonstrate the potential of SCSORA in characterizing the spatiotemporal features of marine heatwaves (MHWs) in the SCS and in examining the 3D structural modulation of MHWs by mesoscale eddies, revealing distinct modulation mechanisms of different eddy polarities on the vertical structure of MHWs. SCSORA provides a critical 3D data foundation for the physical oceanography and extreme event research communities focusing on the SCS. It is publicly available at &lt;a href=&quot;https://doi.org/10.12378/geodb.2026.2.005.V1&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;https://doi.org/10.12378/geodb.2026.2.005.V1&lt;/a&gt; (Zhu et al., 2026).</p>
</abstract>
<counts><page-count count="32"/></counts>
<funding-group>
<award-group id="gs1">
<funding-source>Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)</funding-source>
<award-id>SML2024SP023</award-id>
<award-id>SML2023SP202</award-id>
</award-group>
<award-group id="gs2">
<funding-source>National Natural Science Foundation of China</funding-source>
<award-id>42176029</award-id>
</award-group>
</funding-group>
</article-meta>
</front>
<body/>
<back>
</back>
</article>