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  <front>
    <journal-meta><journal-id journal-id-type="publisher">OS</journal-id><journal-title-group>
    <journal-title>Ocean Science</journal-title>
    <abbrev-journal-title abbrev-type="publisher">OS</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Ocean Sci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1812-0792</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/os-22-329-2026</article-id><title-group><article-title>Coastal-to-offshore submesoscale horizontal stirring enhances wintertime phytoplankton blooms in the ultra-oligotrophic  Eastern Mediterranean Sea</article-title><alt-title>Coastal-to-offshore submesoscale horizontal stirring enhances phytoplankton blooms</alt-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Fadida</surname><given-names>Yotam</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0947-7289</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Verma</surname><given-names>Vicky</given-names></name>
          
        <ext-link>https://orcid.org/0009-0006-1936-2492</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3 aff4">
          <name><surname>Barkan</surname><given-names>Roy</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Biton</surname><given-names>Eli</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Solodoch</surname><given-names>Aviv</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0701-5937</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Lehahn</surname><given-names>Yoav</given-names></name>
          <email>ylehahn@univ.haifa.ac.il</email>
        </contrib>
        <aff id="aff1"><label>1</label><institution>Department of Marine Geosciences, Charney School of Marine Science, University of Haifa, Haifa, Israel</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Israel Oceanographic and Limnological Research, Haifa, Israel</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv-Yafo, Israel</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Institute of Earth Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Yoav Lehahn (ylehahn@univ.haifa.ac.il)</corresp></author-notes><pub-date><day>2</day><month>February</month><year>2026</year></pub-date>
      
      <volume>22</volume>
      <issue>1</issue>
      <fpage>329</fpage><lpage>343</lpage>
      <history>
        <date date-type="received"><day>8</day><month>July</month><year>2025</year></date>
           <date date-type="rev-request"><day>27</day><month>August</month><year>2025</year></date>
           <date date-type="rev-recd"><day>16</day><month>January</month><year>2026</year></date>
           <date date-type="accepted"><day>19</day><month>January</month><year>2026</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2026 Yotam Fadida 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://os.copernicus.org/articles/22/329/2026/os-22-329-2026.html">This article is available from https://os.copernicus.org/articles/22/329/2026/os-22-329-2026.html</self-uri><self-uri xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026.pdf">The full text article is available as a PDF file from https://os.copernicus.org/articles/22/329/2026/os-22-329-2026.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d2e155">The large seasonal increases in marine photosynthetic organisms – i.e., phytoplankton blooms – are a ubiquitous oceanic phenomenon that contributes to the removal of carbon dioxide from the atmosphere and supports the growth of larger marine organisms. The underlying mechanisms controlling the intensity and timing of these blooms have been proposed to be dominated by vertical transport and mixing processes that are enhanced at fine-scale frontal and filamental circulations, commonly known as submesoscale currents. Here we show that the winter blooms characteristic of the ultra-oligotrophic waters of the Eastern Mediterranean Sea, which manifest as a seasonal increase in satellite-derived levels of surface chlorophyll, are intensified by enhanced horizontal stirring induced by the submesoscale currents. Using ocean color remote sensing data and high-resolution numerical simulations, we demonstrate that the intensification of submesoscale currents in winter efficiently connect the coastal waters and the ultra-oligotrophic open-sea waters, thereby enriching the latter with chlorophyll-rich waters. A climatological chlorophyll time series comparison between two different regions equidistant to the Nile River Delta indicates that this submesoscale horizontal stirring mechanism accounts for the <inline-formula><mml:math id="M1" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 24.8 % larger wintertime increase in surface chlorophyll observed downstream of the Nile Delta. These results shed new light on the processes governing phytoplankton bloom intensity and emphasize the important role of submesoscale horizontal stirring in modulating the marine ecosystem.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>Israel Science Foundation</funding-source>
<award-id>1266/23</award-id>
<award-id>2054/23</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

      
<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d2e176">Seasonal phytoplankton blooms occur worldwide, playing a critical role in the removal of carbon dioxide from the atmosphere <xref ref-type="bibr" rid="bib1.bibx2 bib1.bibx36" id="paren.1"/>, and in supporting the growth and development of organisms throughout the marine ecosystem <xref ref-type="bibr" rid="bib1.bibx28" id="paren.2"/>. Over the past two decades, systematic study of phytoplankton blooms at the scale of ocean basins has become possible through satellite imaging of surface chlorophyll <inline-formula><mml:math id="M2" display="inline"><mml:mi>a</mml:mi></mml:math></inline-formula> (Chl), a proxy for phytoplankton biomass <xref ref-type="bibr" rid="bib1.bibx33" id="paren.3"/>. Providing an invaluable synoptic view of Chl distribution patterns, satellite Chl time series have revealed distinct geographical variability in bloom phenology, with contrasting responses to seasonal variations in vertical mixing <xref ref-type="bibr" rid="bib1.bibx75 bib1.bibx56 bib1.bibx8 bib1.bibx62" id="paren.4"/>. In the biologically productive mid- and high latitudes, Chl exhibits an abrupt increase during the springtime re-stratification of the water column <xref ref-type="bibr" rid="bib1.bibx67" id="paren.5"/>, whereas in the nutrient-depleted subtropics Chl shows a more moderate enhancement driven by intensified winter mixing <xref ref-type="bibr" rid="bib1.bibx63 bib1.bibx6" id="paren.6"/>.</p>
      <p id="d2e205">Although these seasonal cycles are basin-scale in nature, they may be modulated by oceanic submesoscale currents–rapid, front- and filament-dominated flows with horizontal scales of 0.1–10 km and energetic vertical velocities <xref ref-type="bibr" rid="bib1.bibx45 bib1.bibx48" id="paren.7"/>. Submesoscale motions can inject nutrients into the euphotic layer <xref ref-type="bibr" rid="bib1.bibx45 bib1.bibx43 bib1.bibx44 bib1.bibx40 bib1.bibx35" id="paren.8"/> and reorganize planktonic communities through lateral stirring and mixing <xref ref-type="bibr" rid="bib1.bibx19 bib1.bibx41 bib1.bibx43 bib1.bibx51 bib1.bibx42" id="paren.9"/>. Importantly, mixed-layer observations show that submesoscale flows themselves undergo a pronounced seasonal cycle, with markedly stronger activity in winter than in summer <xref ref-type="bibr" rid="bib1.bibx14" id="paren.10"/>. Despite this emerging understanding, the relative contributions of vertical transport and horizontal stirring to shaping regional Chl distributions remain insufficiently quantified, particularly in oligotrophic environments.</p>
      <p id="d2e220">The Eastern Mediterranean Sea (EMS) provides a natural setting to address this question. It is an ultra-oligotrophic basin with chronically low nutrient concentrations in the photic layer <xref ref-type="bibr" rid="bib1.bibx73 bib1.bibx30 bib1.bibx32 bib1.bibx37 bib1.bibx38" id="paren.11"/>, where winter mixed-layer deepening is traditionally considered the dominant driver of phytoplankton seasonality <xref ref-type="bibr" rid="bib1.bibx31 bib1.bibx58" id="paren.12"/>. Satellite Chl time series show a pronounced winter bloom <xref ref-type="bibr" rid="bib1.bibx6 bib1.bibx60" id="paren.13"/> and strong coastal-to-open sea gradients <xref ref-type="bibr" rid="bib1.bibx57" id="paren.14"/>, providing the spatial heterogeneity required for horizontal stirring to influence phytoplankton distributions. High-resolution simulations further indicate a strong seasonal modulation of submesoscale activity across the basin, with enhanced wintertime intensification <xref ref-type="bibr" rid="bib1.bibx64 bib1.bibx70" id="paren.15"/>, consistent with the observational evidence from other subtropical regions <xref ref-type="bibr" rid="bib1.bibx66" id="paren.16"/>.</p>
      <p id="d2e242">Here, we study the role of submesoscale horizontal stirring in modulating the observed intensity of seasonal phytoplankton blooms in the EMS (Fig. <xref ref-type="fig" rid="F1"/>A). Satellite datasets provide critical coverage of surface Chl and circulation in the region <xref ref-type="bibr" rid="bib1.bibx4 bib1.bibx16 bib1.bibx47 bib1.bibx1" id="paren.17"/>, yet their <inline-formula><mml:math id="M3" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 1 km gridded resolution remains coarser than the smallest dynamical scales relevant to submesoscale transport (<inline-formula><mml:math id="M4" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 0.1–10 km). Although newer high-resolution sensors such as Landsat and Sentinel-2 offer spatial resolutions of 10–60 m <xref ref-type="bibr" rid="bib1.bibx27" id="paren.18"/>, their demonstrated applications for Chl retrieval have thus far been limited to coastal and estuarine environments <xref ref-type="bibr" rid="bib1.bibx74 bib1.bibx53 bib1.bibx55 bib1.bibx12" id="paren.19"/>, with little validation in the open sea. Satellite altimetry likewise cannot resolve the submesoscale dynamics relevant here: the effective spatial resolution of conventional altimeter products in the Eastern Mediterranean exceeds <inline-formula><mml:math id="M5" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 50 km <xref ref-type="bibr" rid="bib1.bibx5" id="paren.20"/>, rendering them unsuitable for directly capturing the rapidly evolving kilometer- and sub-kilometer-scale motions that modulate surface chlorophyll. To bridge this gap, we combine multi-year satellite imagery of Chl concentration (1 km gridded) with a nested high-resolution numerical circulation model (3 km, 1 km, and 300 m horizontal resolution) that resolves the three-dimensional velocities and boundary layer turbulence needed to characterize the submesoscale motions shaping the satellite-observed Chl patterns.</p>

      <fig id="F1" specific-use="star"><label>Figure 1</label><caption><p id="d2e284"><bold>(A)</bold> Long-term summer (July–September) mean satellite-derived surface Chl (mg m<sup>−3</sup>) over the EMS, with geostrophic surface currents overlaid in white. Blue and orange boxes denote the eastern (32.5–33.5° N, 33–34° E) and western (32.5–33.5° N, 28.5–29.5° E) pelagic regions used for statistical comparisons. The black dashed line marks the coast-to-open-sea transect used to compute the chlorophyll enrichment factor. <bold>(B)</bold> Long-term winter (January–March) mean Chl and modeled surface currents for the same region. <bold>(C)</bold> Monthly climatology of Chl in the eastern (blue) and western (orange) regions; shaded areas denote 95 % confidence intervals. Satellite data and modeled currents span 2010–2019.</p></caption>
        <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f01.png"/>

      </fig>

</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Data and Methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Data</title>
<sec id="Ch1.S2.SS1.SSS1">
  <label>2.1.1</label><title>Satellite data</title>
      <p id="d2e335">This study is conducted using E.U. Copernicus Marine Service Information; DOI: <ext-link xlink:href="https://doi.org/10.48670/moi-00300" ext-link-type="DOI">10.48670/moi-00300</ext-link> <xref ref-type="bibr" rid="bib1.bibx22" id="paren.21"/>. The product used for estimating the mass concentration of Chl in seawater was the gridded, level 4 ocean-color daily 1 km data set for the years 2010–2020. This product combines data from several satellite missions (SeaWiFS, MODIS, MERIS, VIIRS-SNPP and JPSS1, OLCI-S3A) providing interpolated gap-free <xref ref-type="bibr" rid="bib1.bibx71" id="paren.22"/> phytoplankton Chl concentration calculated using region-specific algorithms (Case-1 waters, <xref ref-type="bibr" rid="bib1.bibx72" id="altparen.23"/>, with new coefficients; and Case-2 waters, <xref ref-type="bibr" rid="bib1.bibx9" id="altparen.24"/>).</p>
      <p id="d2e353">Geostrophic currents were estimated from the Copernicus Level-4 gridded sea level anomaly (SLA) product, computed relative to a 20-year mean (1993–2012; DOI: <ext-link xlink:href="https://doi.org/10.48670/moi-00142" ext-link-type="DOI">10.48670/moi-00142</ext-link>; <xref ref-type="bibr" rid="bib1.bibx23" id="altparen.25"/>), at a spatial resolution of 0.0625°.</p>
</sec>
<sec id="Ch1.S2.SS1.SSS2">
  <label>2.1.2</label><title>Biogeochemical model data</title>
      <p id="d2e370">To create the area-averaged nutriclines, we used the Copernicus global ocean biogeochemical hindcast product which uses the PISCES biogeochemical model. The product provides 3D biogeochemical fields at a <inline-formula><mml:math id="M7" display="inline"><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>/</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula>° and on 75 vertical levels. DOI: <ext-link xlink:href="https://doi.org/10.48670/moi-00019" ext-link-type="DOI">10.48670/moi-00019</ext-link> <xref ref-type="bibr" rid="bib1.bibx24" id="paren.26"/>.</p>
</sec>
<sec id="Ch1.S2.SS1.SSS3">
  <label>2.1.3</label><title>Haifa Section Cruise data</title>
      <p id="d2e399">Haifa Section Cruises have been conducted by the Israeli Oceanographic and Limnological Research Institute twice a year since 2002 (53 cruises to date). During the cruises physical, chemical, and biological data are collected along a transect of 8 stations, commencing in Haifa Bay and leading into the open sea perpendicular to the Israeli shelf. The cruise data has been used for the purpose of validating satellite and model data (see Figs. <xref ref-type="fig" rid="FA1"/>, <xref ref-type="fig" rid="FA3"/> in the Appendix). For in-depth cruise sampling and lab analysis methods see <xref ref-type="bibr" rid="bib1.bibx54" id="text.27"/>.</p>
</sec>
<sec id="Ch1.S2.SS1.SSS4">
  <label>2.1.4</label><title>Numerical Circulation Model</title>
      <p id="d2e417">Coastal and Regional Ocean COmmunity (CROCO) model <xref ref-type="bibr" rid="bib1.bibx17 bib1.bibx3" id="paren.28"/>, a version of the Regional Oceanic Modeling System (ROMS) <xref ref-type="bibr" rid="bib1.bibx61" id="paren.29"/>, has been used to simulate the EMS. A brief overview of the model is given here and we refer the reader to <xref ref-type="bibr" rid="bib1.bibx64" id="text.30"/> for a more comprehensive description.</p>
      <p id="d2e429">The ocean model solves the free-surface, hydrostatic, Boussinesq primitive equations in a terrain-following vertical coordinate system. Furthermore, one-way grid nesting is used to generate three high-resolution, realistic regional simulations in the EMS with 3 km, 1 km, and 300 m horizontal grid resolutions, employing 80, 120, and 150 terrain-following (<inline-formula><mml:math id="M8" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula>) vertical levels, respectively (see Fig. 1 in <xref ref-type="bibr" rid="bib1.bibx64" id="altparen.31"/> for visualizing the nested-domain boundaries).</p>
      <p id="d2e442">Atmospheric forcing is computed via the bulk formulae described in <xref ref-type="bibr" rid="bib1.bibx25" id="text.32"/>, and the atmospheric state is prescribed based on hourly output from the Integrated Forecasting System <xref ref-type="bibr" rid="bib1.bibx34" id="paren.33"/>, which is a high horizontal resolution (<inline-formula><mml:math id="M9" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 9 km) numerical weather prediction model. The turbulent mixing in the top and bottom boundary layers as well as the interior is parameterized using the K-profile parameterization <xref ref-type="bibr" rid="bib1.bibx39" id="paren.34"><named-content content-type="pre">KPP;</named-content></xref>. The initial and open boundary conditions for the child solutions (1 km and 300 m) are obtained from the respective parent solutions (3 km and 1 km), following the methodology of Mason et al. (2010). For the 3 km simulation, the initial and boundary data are interpolated from the Copernicus Marine Environment Monitoring Service (CMEMS) Mediterranean Sea reanalysis <inline-formula><mml:math id="M10" display="inline"><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>/</mml:mo><mml:mn mathvariant="normal">24</mml:mn></mml:mrow></mml:math></inline-formula>° model output <xref ref-type="bibr" rid="bib1.bibx20" id="paren.35"/>.</p>
      <p id="d2e479">The model configuration and validation are described in detail in <xref ref-type="bibr" rid="bib1.bibx64" id="text.36"/>. Briefly, the simulated mean surface circulation reproduces the observed basin-scale structure diagnosed from satellite altimetry, including the cyclonic boundary current and recurrent mesoscale eddies such as the Cyprus and Ierapetra eddies (their Fig. 1a–f). In terms of mesoscale variability, the domain-mean surface eddy kinetic energy (EKE) in the model differs from the altimetry-derived estimate by about 0.002 m<sup>2</sup> s<sup>−2</sup> and from the CMEMS reanalysis estimate by about 0.005 m<sup>2</sup> s<sup>−2</sup>; applying spatial smoothing to account for the effective resolution of the observational products improves the agreement of the EKE patterns. The domain-mean mixed layer depth was almost identical to the reanalysis. The modeled sea-surface temperature and salinity differences relative to satellite and reanalysis products fall within the statistical uncertainty of those datasets. Finally, the high-frequency dynamics of the 300 m solution are evaluated against in situ measurements: the 300 m modeled velocity frequency spectra are in accord with observations from the DeepLev mooring in the EMS and also capture the wintertime submesoscale energization in the 1–5 d band above 500 m depth. Mean monthly velocities were in agreement with in-situ observations from a shelf-break mooring <xref ref-type="bibr" rid="bib1.bibx59" id="paren.37"/> at 500 and 120 m bathymetric depths, both located in the EMS. This research utilizes all three nested solutions. The 3 km resolution model is run from January 2017 to December 2019, and the 1 km resolution model from February 2017 to December 2018. The 300 m resolution model ran for 53 d in the winter of 2018 (16 January to 9 March) and 69 d during the summer of 2018 (16 June to 23 August).</p>
</sec>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Methods</title>
<sec id="Ch1.S2.SS2.SSS1">
  <label>2.2.1</label><title>Lagrangian particle tracking</title>
      <p id="d2e547">For the Lagrangian particle tracking, the particles are modeled as material points that move with the local fluid velocity. The advection of the virtual particles is carried out over a 2D horizontal plane near the surface (2 m depth) using Parcels <xref ref-type="bibr" rid="bib1.bibx18" id="paren.38"/>. The software uses a bilinear spatial interpolation of the surface model velocity to the particle location, and the time-stepping is performed with an explicit RK4 scheme. The particle trajectories are computed during both winter and summer seasons, utilizing hourly velocity fields from the 300 m models. A total of 40 000 tracer particles were released arranged in a <inline-formula><mml:math id="M15" display="inline"><mml:mrow><mml:mn mathvariant="normal">200</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">200</mml:mn></mml:mrow></mml:math></inline-formula> grid pattern over a patch (31.5–32.0° N and 32.75–33.25° E) in the vicinity of the Nile Delta on 23 January 2018 and 19 July 2018 for the wintertime and the summertime analyses, respectively, and advected for 40 d during winter and 33 d during summer, until the end of the flow simulation. The particle advection during the summer is selected to coincide with a period of boundary current instabilities and spiral formation.</p>
      <p id="d2e565">Along the particle trajectories, we also monitor flow properties such as relative vorticity, <inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:mi mathvariant="italic">ζ</mml:mi><mml:mo>=</mml:mo><mml:mo>(</mml:mo><mml:mo>∂</mml:mo><mml:mi>v</mml:mi><mml:mo>/</mml:mo><mml:mo>∂</mml:mo><mml:mi>x</mml:mi><mml:mo>-</mml:mo><mml:mo>∂</mml:mo><mml:mi>u</mml:mi><mml:mo>/</mml:mo><mml:mo>∂</mml:mo><mml:mi>y</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, and horizontal velocity divergence, <inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:mi mathvariant="italic">δ</mml:mi><mml:mo>=</mml:mo><mml:mo>(</mml:mo><mml:mo>∂</mml:mo><mml:mi>u</mml:mi><mml:mo>/</mml:mo><mml:mo>∂</mml:mo><mml:mi>x</mml:mi><mml:mo>+</mml:mo><mml:mo>∂</mml:mo><mml:mi>v</mml:mi><mml:mo>/</mml:mo><mml:mo>∂</mml:mo><mml:mi>y</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, where <inline-formula><mml:math id="M18" display="inline"><mml:mi>u</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M19" display="inline"><mml:mi>v</mml:mi></mml:math></inline-formula> are horizontal velocity components along zonal <inline-formula><mml:math id="M20" display="inline"><mml:mi>x</mml:mi></mml:math></inline-formula> and meridional <inline-formula><mml:math id="M21" display="inline"><mml:mi>y</mml:mi></mml:math></inline-formula> directions. The relative vorticity and horizontal divergence are first calculated with the model velocity and then linearly interpolated to the particle position.</p>
</sec>
<sec id="Ch1.S2.SS2.SSS2">
  <label>2.2.2</label><title>Mean offshore particle distance</title>
      <p id="d2e679">To quantify the extent of offshore horizontal transport in the particle-tracking experiments, we computed a mean offshore distance <inline-formula><mml:math id="M22" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> at each particle advection. Following the procedure illustrated in Fig. A4, we divided the domain between 32.2 and 34.5° N into zonally oriented strips of fixed width. For each strip, we identified the particle located farthest offshore and recorded its cross-shore distance from the EMS coastline. The mean offshore distance <inline-formula><mml:math id="M23" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> was then defined as the average of these farthest-offshore distances across all strips. The strip width was two grid cells (approximately 6 km) in the 3 km simulation and seven grid cells (approximately 2.1 km) in the 300 m simulations.</p>
</sec>
<sec id="Ch1.S2.SS2.SSS3">
  <label>2.2.3</label><title>Chlorophyll Climatologies</title>
      <p id="d2e718">Monthly Chl climatologies were computed from the merged daily CMEMS 1 km product (2010–2019), and 95 % confidence intervals were estimated as <inline-formula><mml:math id="M24" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 1.96 <inline-formula><mml:math id="M25" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> SEM (Standard Error of the Mean) using the Student's <inline-formula><mml:math id="M26" display="inline"><mml:mi>t</mml:mi></mml:math></inline-formula>-distribution. Chl concentration time series are calculated from daily data averaged spatially within the study region.  The data is smoothed with a rolling average (30 d window).</p>
</sec>
<sec id="Ch1.S2.SS2.SSSx1" specific-use="unnumbered">
  <title>Probability density functions</title>
      <p id="d2e748">Horizontal Chl gradients were computed from the daily gridded satellite fields by first estimating the zonal and meridional spatial derivatives using finite-difference approximations on the native grid. The horizontal gradient magnitude was then calculated as

              <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M27" display="block"><mml:mrow><mml:mo>|</mml:mo><mml:mi mathvariant="normal">∇</mml:mi><mml:mi mathvariant="normal">CHL</mml:mi><mml:mo>|</mml:mo><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:msup><mml:mfenced open="(" close=")"><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mo>∂</mml:mo><mml:mi mathvariant="normal">CHL</mml:mi></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:mfrac></mml:mstyle></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:msup><mml:mfenced open="(" close=")"><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mo>∂</mml:mo><mml:mi mathvariant="normal">CHL</mml:mi></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfrac></mml:mstyle></mml:mfenced><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:msqrt><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula>

            Gradient values were spatially averaged within each analysis box (East and West) to produce daily domain-mean time series. Each seasonal subset (summer and winter) was converted into an empirical probability density function (PDF) by normalizing a 50-bin histogram to unit area. Statistical differences between regions were assessed using a two-sample Kolmogorov–Smirnov test applied separately to the summer and winter distributions.</p>
      <p id="d2e805">To examine the dynamical forcing associated with submesoscale activity, we additionally computed PDFs of near-surface relative vorticity (<inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:mi mathvariant="italic">ζ</mml:mi><mml:mo>/</mml:mo><mml:mi>f</mml:mi></mml:mrow></mml:math></inline-formula>) from the 1 km numerical simulation, using the seasonally averaged values provided in the model output. Vertical velocity (<inline-formula><mml:math id="M29" display="inline"><mml:mi>w</mml:mi></mml:math></inline-formula>) at 20 m depth and the parameterized vertical mixing coefficient <inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi mathvariant="normal">Kv</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (from the K-Profile Parameterization scheme; <xref ref-type="bibr" rid="bib1.bibx39" id="altparen.39"/>) were extracted along the same regional boxes, seasonally averaged, and converted to PDFs using the same normalization procedure.</p>
</sec>
<sec id="Ch1.S2.SS2.SSSx2" specific-use="unnumbered">
  <title>Chlorophyll enrichment factor</title>
      <p id="d2e848">To quantify the offshore decay of Chl concentration downstream of the Nile Delta, we extracted a one-dimensional transect of Chl from the merged daily CMEMS 1 km product. The transect (dashed line in Fig. <xref ref-type="fig" rid="F1"/>A, B) follows a southeast–northwest diagonal between (31.6° N, 34° E) and (33.5° N, 32.5° E), chosen because it provides the most monotonic and linearly increasing distance from the EMS coast; shifting the transect farther north or south would shorten the distance to another coastline and violate the desired single-coast geometry. Chl values were interpolated along this line and averaged by month, after which seasonal means were constructed for winter (January–March) and summer (July–September).</p>
      <p id="d2e853">To compare seasonal enrichment independently of background concentrations, each transect was normalized by its far-offshore value. The enrichment factor was defined as:

              <disp-formula id="Ch1.Ex1"><mml:math id="M31" display="block"><mml:mrow><mml:mi>E</mml:mi><mml:mo>(</mml:mo><mml:mi>x</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mi mathvariant="normal">CHL</mml:mi><mml:mo>(</mml:mo><mml:mi>x</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi mathvariant="normal">CHL</mml:mi><mml:mi mathvariant="normal">offshore</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula>

            For consistency across seasons, <inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="normal">CHL</mml:mi><mml:mi mathvariant="normal">offshore</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> was set to the minimum seasonal Chl concentration along the transect, which was 0.027 mg m<sup>−3</sup> in summer and 0.062 mg m<sup>−3</sup> in winter. Distances along the line were converted to kilometers using great-circle spacing. A 95 % confidence interval was estimated at each position using the standard error and the Student's <inline-formula><mml:math id="M35" display="inline"><mml:mi>t</mml:mi></mml:math></inline-formula>-distribution, providing uncertainty bounds for both winter and summer enrichment profiles.</p>
</sec>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Seasonality and spatial characteristics of satellite-derived Chl and modeled vorticity</title>
      <p id="d2e948">Focusing on the vicinity of the Nile River Delta, at the southeastern part of the basin, which appears to be the largest Chl source in the area, we distinguish between two regions: the region to the east of the Delta, which is characterized by strong coast-to-open sea Chl gradient (Fig. <xref ref-type="fig" rid="FA1"/>), and the region to the west of the Delta, where such gradients are not observed (Fig. <xref ref-type="fig" rid="F1"/>A, B). This difference is attributed to the combined effect of nutrient enrichment from the Nile River Delta by agricultural seepage  <xref ref-type="bibr" rid="bib1.bibx52" id="paren.40"/>, and the characteristic along-slope cyclonic circulation through the EMS boundary current, which transports the Chl-rich waters along the coast <xref ref-type="bibr" rid="bib1.bibx64 bib1.bibx70 bib1.bibx21 bib1.bibx29" id="paren.41"/>.</p>
      <p id="d2e961">Comparing the 2010–2019 chlorophyll time series from two pelagic regions equidistant from the Nile Delta (East and West; Fig. <xref ref-type="fig" rid="F1"/>C) reveals a pronounced seasonal asymmetry in surface Chl dynamics. For each region, Chl values represent spatial averages over the respective boxes and temporal averages over the indicated seasons. During summer (July–September), mean Chl concentrations in the two regions are nearly identical, differing by only 0.1 % (both <inline-formula><mml:math id="M36" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 0.028 mg m<sup>−3</sup>). In contrast, during winter (January–March), the eastern region consistently exhibits higher biomass, with mean Chl concentrations 11.6 % higher than in the western region (0.067 vs. 0.060 mg m<sup>−3</sup>). To compare the seasonal amplitude between regions, winter Chl concentrations were referenced to the common summer baseline defined by the mean summer Chl concentration. Relative to this baseline, the wintertime increase in the East is on average <inline-formula><mml:math id="M39" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 24.8 % larger than in the West, indicating a stronger seasonal rebound east of the Nile Delta. Marked interannual variability is observed, with the winter East–West contrast spanning approximately 3 %–27 % across the decade (mean 11.5 <inline-formula><mml:math id="M40" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 8.9 %) and the enhanced eastern seasonal increase ranging from <inline-formula><mml:math id="M41" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 5 %–65 % (mean 25.0 <inline-formula><mml:math id="M42" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 22.5 %). To evaluate whether the choice of domain size influenced the east–west comparison, we repeated the analysis using EN and WN boxes expanded by 50 % in area while keeping their centers fixed. The resulting winter and summer Chl statistics, as well as the interannual variability, changed only marginally (e.g., winter East–West contrast: 11.6 % vs. 10.9 %; extra East winter rise: 24.8 % vs. 22.7 %). This demonstrates that the observed east–west differences are robust to reasonable variations in domain size and are not an artifact of the exact box boundaries.</p>

      <fig id="F2" specific-use="star"><label>Figure 2</label><caption><p id="d2e1028">Representative spatial distribution patterns in snapshots of observed and modeled fields during summer and winter. <bold>(A)</bold> Summer surface Chl from satellite observations (30 July 2020). <bold>(B)</bold> Summer modeled surface relative vorticity (29 July 2018). <bold>(C)</bold> Winter surface Chl from satellite observations (25 February 2020). <bold>(D)</bold> Winter modeled surface relative vorticity (15 February 2018). The vertical black line in <bold>(B)</bold> and <bold>(D)</bold> marks the boundary between the 1 km and 300 m nested model grids.</p></caption>
          <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f02.png"/>

        </fig>

      <p id="d2e1057">In search of possible explanations for the winter-time difference between the two regions, daily images of satellite ocean color data are examined and compared to the modeled surface vorticity. The spatial characteristics of Chl display patterns that are qualitatively consistent with the modeled surface vorticity field (Fig. <xref ref-type="fig" rid="F2"/>). Focusing on the area downstream of the Nile Delta, during summer (Fig. <xref ref-type="fig" rid="F2"/>A, B), both fields display a distinct separation between the coastal region and the open sea, consistently generating a series of mesoscale (horizontal scales of about 10–100 km that last for days to weeks) meanders with relatively high Chl concentrations and high levels of vorticity that are constrained to the near-shore area. In contrast, during winter, the distinction between the coastal and open sea regions is substantially less pronounced and we observe small-scale variability in Chl concentrations and modeled vorticity throughout the entire EMS (Fig. <xref ref-type="fig" rid="F2"/>C, D). However, while the Chl concentrations decay with increasing distance from the coast (Figs. <xref ref-type="fig" rid="F5"/>, <xref ref-type="fig" rid="FA1"/>), the surface vorticity field is almost uniform throughout the basin. The similarities between the organization of the chl concentration and vorticity structures in the model solution further emphasize that the model captures the important seasonal circulation features in the region.</p>
      <p id="d2e1070">The seasonal change in the spatial characteristics of Chl concentrations and vorticity fields indicates a shift in the dynamical processes that shape the surface Chl distribution. The sharp contrast between the near-shore and open sea waters during summer indicates the presence of a transport barrier, which restricts the Chl-rich coastal waters from mixing horizontally with the open sea. This barrier likely results from the strong alongshore boundary current in the region <xref ref-type="bibr" rid="bib1.bibx59" id="paren.42"/>. In this scenario, the dominant mode of offshore transport is via a chain of mesoscale eddies meandering along the coast, and the subsequent formation of Chl-rich submesoscale filaments at the eddy peripheries <xref ref-type="bibr" rid="bib1.bibx70" id="paren.43"/>. In contrast, the more uniform distribution of surface Chl during winter suggests that the transport barrier weakens substantially. Here we test the hypothesis that this is due to an increase in submesoscale activity, which induces horizontal transport of Chl-rich water from the coastal region to the open sea.</p>

      <fig id="F3" specific-use="star"><label>Figure 3</label><caption><p id="d2e1081">Probability density functions (PDFs) of <bold>(A)</bold> satellite-derived Chl-gradient magnitudes (2010–2019), <bold>(B)</bold> modeled surface relative vorticity, <bold>(C)</bold> vertical velocity <inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:mo>|</mml:mo><mml:mi>w</mml:mi><mml:mo>|</mml:mo></mml:mrow></mml:math></inline-formula> at 20 m depth, and <bold>(D)</bold> KPP-derived boundary layer turbulence. Statistics are shown for the eastern and western pelagic regions north of the Nile Delta during summer and winter. Modeled fields are computed from the 1 km solution in both regions for consistency.</p></caption>
          <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f03.png"/>

        </fig>

      <p id="d2e1114">Submesoscale currents are known to generate strong vertical motions that can enhance nutrient transport and, in some settings, stimulate phytoplankton growth <xref ref-type="bibr" rid="bib1.bibx45 bib1.bibx43" id="paren.44"/>. To evaluate whether such vertical processes could explain the downstream enhancement in wintertime open-sea Chl gradients, we compare the probability density functions (PDFs) of observed Chl-gradient magnitudes (Fig. <xref ref-type="fig" rid="F3"/>A) with modeled indicators of submesoscale dynamics (Fig. <xref ref-type="fig" rid="F3"/>B–D) in the western and eastern regions upstream and downstream of the Nile Delta. The Chl-gradient PDFs exhibit a clear seasonal and spatial asymmetry. During summer (warm colors in Fig. <xref ref-type="fig" rid="F3"/>A), the East and West show similar distributions (KS <inline-formula><mml:math id="M44" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.242, <inline-formula><mml:math id="M45" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.009</mml:mn></mml:mrow></mml:math></inline-formula>), consistent with the uniformly oligotrophic and relatively stable surface conditions characteristic of the stratified season. In winter (cool colors in Fig. <xref ref-type="fig" rid="F3"/>A), the distributions diverge sharply (KS <inline-formula><mml:math id="M46" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.678, <inline-formula><mml:math id="M47" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">6</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">20</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>), revealing that the eastern region experiences far more frequent and intense high-gradient events than the western region. To determine whether this winter asymmetry reflects differences in submesoscale dynamics, we examine modeled PDFs of relative vorticity, vertical velocity, and vertical mixing (Fig. <xref ref-type="fig" rid="F3"/>B–D). Relative vorticity PDFs broaden markedly in winter, with standard deviation increasing by approximately fourfold and upper-tail values – corresponding to rare, high-magnitude vorticity events – rising by 300 %–525 %, consistent with the known winter intensification of submesoscale currents <xref ref-type="bibr" rid="bib1.bibx7" id="paren.45"/>. This seasonal broadening occurs in both the eastern and western regions, indicating a basin-wide strengthening of submesoscale activity. The modeled vertical velocity and vertical mixing fields provide an important check on whether vertical processes could explain the observed east–west asymmetry in Chl gradients. The winter vertical-velocity PDFs (Fig. <xref ref-type="fig" rid="F3"/>C) show negatively skewed distributions in both regions, with <inline-formula><mml:math id="M48" display="inline"><mml:mrow><mml:mo>|</mml:mo><mml:mi>w</mml:mi><mml:mo>|</mml:mo><mml:mo>&gt;</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m s<sup>−1</sup>, a signature of intermittent submesoscale upwelling/downwelling <xref ref-type="bibr" rid="bib1.bibx46" id="paren.46"/>, actually occurring more frequently west of the Nile Delta. Similarly, the KPP-derived vertical mixing PDFs (Fig. <xref ref-type="fig" rid="F3"/>D) indicate slightly stronger turbulent mixing in the western region during both seasons. Taken together, these modeled fields do not exhibit the eastward enhancement that would be expected if vertical transport or mixing were responsible for the <inline-formula><mml:math id="M50" display="inline"><mml:mrow><mml:mo>≈</mml:mo><mml:mn mathvariant="normal">11.6</mml:mn></mml:mrow></mml:math></inline-formula> % higher winter Chl concentrations and steeper gradients observed downstream of the Delta. To verify that the east–west asymmetry does not arise from differences in the mixed-layer nutricline structure, we compared modeled (PISCES) and observed nutrient profiles in both regions (Fig. <xref ref-type="fig" rid="FA3"/>), confirming that nutricline depth and nutrient availability, which are similar in both regions, cannot explain the observed Chl-gradient contrast. Taken together, these results demonstrate that neither enhanced vertical motions nor intensified mixing downstream of the Delta can account for the observed east–west contrast in wintertime Chl gradients. This suggests that vertical processes alone cannot account for the spatial structure of the winter Chl field. Instead, as we show in the next section, the results are consistent with submesoscale horizontal stirring as the primary mechanism enhancing Chl-gradient magnitudes in the downstream (East) region. Accordingly, the westward decrease in Chl-gradient amplitudes can be understood as a dilution effect with increasing distance from the Chl-rich coastal waters discharged from the Nile Delta.</p>

      <fig id="F4" specific-use="star"><label>Figure 4</label><caption><p id="d2e1239">Lagrangian particle trajectories from the numerical simulations. <bold>(A–C)</bold> Summer simulation at 300 m horizontal resolution. <bold>(D–F)</bold> Winter simulation at 300 m horizontal resolution. <bold>(G–I)</bold> Winter simulation at 3 km horizontal resolution. Particles are initialized within the coastal region adjacent to the Nile Delta (31.5–32° N, 32.75–33.25° E), shown by the red square in <bold>(A)</bold>, <bold>(D)</bold>, and <bold>(G)</bold>. Snapshots are shown at days 5, 19, and 33.</p></caption>
          <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f04.png"/>

        </fig>

      <fig id="F5"><label>Figure 5</label><caption><p id="d2e1270"><bold>(A)</bold> Mean offshore distance <inline-formula><mml:math id="M51" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> of particles in the summer 300 m, winter 300 m, and winter 3 km simulations. Offshore distance is computed over latitudes 32.2–34.5° N using the envelope definition described in Fig. A4. <bold>(B)</bold> Chl enrichment factor (CEF) along the transect shown in Fig. 1A, B for winter and summer. Shaded regions denote 95 % confidence intervals. CEF is computed from 10 years of satellite-derived Chl (2010–2019).</p></caption>
          <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f05.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Lagrangian quantification of submesoscale horizontal stirring</title>
      <p id="d2e1306">The seasonal change in submesoscale horizontal transport and its impact on connectivity between the near-shore and open sea waters is emphasized by three Lagrangian particle-tracking experiments, one in summer and two in winter, initially seeded in the coastal region adjacent to the Nile Delta (red box in Fig. <xref ref-type="fig" rid="F4"/>A, D, G). During summer the particles are confined to the coastal region, flowing through the EMS boundary current and the boundary current instability generated mesoscale eddies (Fig. <xref ref-type="fig" rid="F4"/>A–C). Conversely, winter is characterized by distinctly different trajectories, with particles being shed from the coastal region as early as five days after release (Fig. <xref ref-type="fig" rid="F4"/>D–F). While the general direction of flow is still alongshore, a substantial amount of particles make their way to the open sea through horizontal stirring by submesoscale motions that prevent the formation of an effective transport barrier by the alongshore current. The 3 km simulation (Fig. <xref ref-type="fig" rid="F4"/>G–I) does not adequately resolve submesoscale features; therefore, it functions as a mesoscale-only control, enabling us to isolate the specific contribution of resolved submesoscale motions present in the 300 m winter run. Indeed, in the 3 km solution, the lateral transport is dictated mainly by the anticyclonic boundary current eddies, very similar to what we observe in the 300 m summer simulation. However, the offshore particle distribution differs compared to the summer season because of the bigger size of the mesoscale wintertime spirals, the further offshore position of the boundary current in winter <xref ref-type="bibr" rid="bib1.bibx70" id="paren.47"/>, and the presence of partially resolved submesoscale filaments in the 3 km winter simulation. The difference in the particle distribution between the 3 km and 300 m winter simulations is striking, with the strong impact of submesoscale stirring on the lateral transport clearly visible in the 300 m solution as the particles reach farther offshore than the extent of the anticyclonic spirals.</p>
      <p id="d2e1320">The extent of the offshore horizontal transport in the three model solutions is quantified by computing the mean offshore distance (<inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) over an envelope encompassing the offshore particles between latitudes 32.2–34.5° N (Figs. <xref ref-type="fig" rid="F5"/>A, <xref ref-type="fig" rid="FA4"/>). The important contribution of submesoscale currents to the offshore horizontal transport during winter is emphasized by the fact that <inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> increases continuously until more than 220 km in the 300 m winter solution, while constrained between 50–130 km and between 20–70 km in the 3 km winter solution and 300 km summer solution, respectively. The fluctuations in <inline-formula><mml:math id="M54" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> in the latter two are due to the strong influence of the spirals.  The larger <inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> in the winter 3 km solution compared with the summer 300 m solution (green and orange dots in Fig. <xref ref-type="fig" rid="F5"/>A) may also be attributed to the generally further offshore location of the boundary current in winter <xref ref-type="bibr" rid="bib1.bibx70" id="paren.48"/> (see also Fig. <xref ref-type="fig" rid="FA2"/>). To further estimate the regional impact of variations in submesoscale horizontal stirring, we examined the Chl enrichment factor (CEF) along the transect shown in Fig. <xref ref-type="fig" rid="F1"/>A. The CEF transect (Fig. <xref ref-type="fig" rid="F5"/>B) displays a pronounced seasonal difference. During summer, CEF values drop steeply within the first <inline-formula><mml:math id="M56" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">60</mml:mn></mml:mrow></mml:math></inline-formula> km from shore, after which they approach offshore background levels. In winter, CEF remains elevated over a much broader region and shows little decline until nearly <inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">200</mml:mn></mml:mrow></mml:math></inline-formula> km offshore. This seasonal contrast highlights a substantially wider downstream footprint of coastal Chl during winter compared to summer. The <inline-formula><mml:math id="M58" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 60 km sharp decrease in CEF during summer is consistent with the boundary current width during this season <xref ref-type="bibr" rid="bib1.bibx70" id="paren.49"/>.</p>
</sec>
</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <label>4</label><title>Discussion and Conclusions</title>
      <p id="d2e1436">Combining high-resolution numerical modeling and ocean-color satellite observations, we elucidate the role of submesoscale horizontal stirring in shaping the phenology and spatial structure of phytoplankton blooms in the oligotrophic waters of the EMS. The statistical analysis of a decade of satellite-derived Chl fields reveals a robust seasonal asymmetry east and west of the Nile Delta, with wintertime Chl concentrations and gradient magnitudes consistently elevated downstream of the coastal nutrient source. Notably, the magnitude of this enrichment exhibits substantial interannual variability, with wintertime East–West differences ranging from only a few percent to more than 25 %, indicating that the strength of submesoscale-driven connectivity varies considerably from year to year. By contrasting these patterns with modeled vorticity, vertical velocities, and mixing fields, we show that the observed winter enhancement cannot be explained by local vertical processes, regional differences in nutricline structure, or mesoscale currents alone. Instead, the results point to strengthened submesoscale horizontal stirring during winter as the primary mechanism that increases coastal–open sea connectivity and supplies Chl-poor pelagic waters with Chl-rich coastal waters.</p>
      <p id="d2e1439">Our findings indicate that this seasonal intensification in horizontal stirring drives a basin-scale regime shift–from a relatively isolated coastal region during summer, bounded by a strong transport barrier, to a well-connected system during winter in which submesoscale filaments and fronts efficiently redistribute materials offshore. Because this enhancement coincides with the basin-wide winter phytoplankton bloom, the increased stirring amplifies the bloom's magnitude through purely physical pathways, independent of biological production. Submesoscale currents can affect phytoplankton blooms through several physical mechanisms, including enhanced vertical transport and changes in turbulent mixing that influence phytoplankton residence within the euphotic zone <xref ref-type="bibr" rid="bib1.bibx45 bib1.bibx43" id="paren.50"/>. Here, we highlight an additional pathway in which submesoscale horizontal stirring contributes to bloom enhancement by laterally redistributing chlorophyll-rich waters across strong coastal–offshore gradients. Such a mechanism is expected to be most relevant in oligotrophic regions adjacent to nutrient sources. As shown in previous works, phytoplankton blooms and the processes underlying them in the EMS are representative of those in oligotrophic subtropical gyres <xref ref-type="bibr" rid="bib1.bibx68 bib1.bibx10" id="paren.51"/>. Moreover, the Chl seasonality characterizing these regions, with elevated Chl levels associated with a relatively deep mixed layer during winter, is intrinsically linked to the seasonality of submesoscale activity worldwide <xref ref-type="bibr" rid="bib1.bibx14 bib1.bibx50 bib1.bibx13 bib1.bibx66 bib1.bibx76" id="paren.52"/>. In winter, when the mixed layer is deep and stores a great deal of available potential energy, mixed layer instabilities <xref ref-type="bibr" rid="bib1.bibx11 bib1.bibx26 bib1.bibx65" id="paren.53"/> and frontogenesis events <xref ref-type="bibr" rid="bib1.bibx15 bib1.bibx49 bib1.bibx7 bib1.bibx69" id="paren.54"/> are frequent, leading to the formation of an energetic submesoscale current field that drives intense lateral stirring. In contrast, the shallow mixed layer during summer suppresses these processes, reducing the prevalence and strength of submesoscale currents. Therefore, submesoscale horizontal stirring is likely to enhance the intensity of winter phytoplankton blooms in locations where nutrient-poor waters exist in close proximity to nutrient-rich waters, such as the periphery of subtropical gyres.</p>
      <p id="d2e1457">Looking forward, the emerging SWOT mission will provide unprecedented spatial resolution of sea-surface height and may help identify submesoscale features and validate high-resolution model output. However, its low temporal sampling – with revisits on the order of two weeks – limits its ability to capture the rapid evolution or seasonal progression of submesoscale dynamics. SWOT is therefore expected to serve as a complementary, event-focused tool rather than a basis for continuous phenological analysis.</p>
      <p id="d2e1460">These findings underscore the importance of submesoscale horizontal stirring as a climate-sensitive driver of bloom dynamics in oligotrophic regions and highlight the need for integrated observing–modeling approaches to unravel fine-scale physical–biological coupling in the ocean.</p>
</sec>

      
      </body>
    <back><app-group>

<app id="App1.Ch1.S1">
  <label>Appendix A</label><title>Additional figures</title>

      <fig id="FA1"><label>Figure A1</label><caption><p id="d2e1476">Comparison of Chl (mg m<sup>−3</sup>) concentrations measured during the Haifa Section Cruises (averaged 2002–2020) and satellite data from Copernicus (averaged 2010–2019) at 9 stations with increasing distances from the coast. Both in-situ and satellite measurements show the same general trend with higher concentrations by the coast that gradually drop with distance from the coast. The decrease rate during the winter slows down at approximately 55 km from the coast and levels out while during the summer the CHL values continue to decline. The error bars demonstrate how there is substantially more variability in the coastal measurements compared to the open sea.</p></caption>
        
        <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f06.png"/>

      </fig>

      <fig id="FA2"><label>Figure A2</label><caption><p id="d2e1501">PDFs of vorticity and horizontal velocity divergence associated with the offshore particles, defined as those that are more than 100 km away from the coast. The offshore particles are sampled from day 19–day 40. The vorticity (horizontal divergence) PDF from 300 m simulation clearly shows positive (negative) skewness, suggesting that the offshore particles are associated with the submesoscale convergent cyclonic filaments. These features are also observed from the 3 km solution, as the vorticity PDF is positively skewed (skewness <inline-formula><mml:math id="M60" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.55) and the horizontal divergence negatively (skewness <inline-formula><mml:math id="M61" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> <inline-formula><mml:math id="M62" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.22</mml:mn></mml:mrow></mml:math></inline-formula>). Nevertheless, the skewness magnitudes in the 3 km simulation is significantly smaller than the 300 m solution.</p></caption>
        
        <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f07.png"/>

      </fig>

<fig id="FA3"><label>Figure A3</label><caption><p id="d2e1540"><bold>(A–C)</bold> Comparisons of nutriclines (NO<sub>3</sub>, PO<sub>4</sub>, Si) between the PISCES biogeochemical model averaged over the two study regions (2002–2019) and Haifa Section cruise data collected between 2002–2019 within the eastern region. The modeled nutrient profiles reproduced the observed nutricline structure with high fidelity (NO<sub>3</sub>: <inline-formula><mml:math id="M66" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.96</mml:mn></mml:mrow></mml:math></inline-formula>; PO<sub>4</sub>: <inline-formula><mml:math id="M68" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.79</mml:mn></mml:mrow></mml:math></inline-formula>; Si: <inline-formula><mml:math id="M69" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.97</mml:mn></mml:mrow></mml:math></inline-formula>), indicating that the vertical shape and depth of nutrient gradients are realistically represented. However, Kolmogorov–Smirnov tests (<inline-formula><mml:math id="M70" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>≪</mml:mo><mml:mn mathvariant="normal">0.001</mml:mn></mml:mrow></mml:math></inline-formula>) reveal systematic offsets in concentrations, with the model tending to overestimate NO<sub>3</sub> and PO<sub>4</sub> and slightly underestimate Si. Comparison between the eastern and western model domains shows that the profiles are nearly identical in shape (<inline-formula><mml:math id="M73" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>≥</mml:mo><mml:mn mathvariant="normal">0.998</mml:mn></mml:mrow></mml:math></inline-formula>), with only small magnitude differences for NO<sub>3</sub> and PO<sub>4</sub>, and a somewhat larger shift for Si. Overall, these results demonstrate that the modeled vertical nutrient structure is robust across the basin and broadly consistent with observations, with remaining discrepancies arising primarily from concentration biases rather than differences in vertical structure.</p></caption>
        
        <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f08.png"/>

      </fig>

      <fig id="FA4"><label>Figure A4</label><caption><p id="d2e1689">Illustration of the computation of the mean offshore distance <inline-formula><mml:math id="M76" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> using the 3 km particle experiment. Shading indicates the zonally oriented strips between 32.2 and 34.5° N from which the farthest-offshore particle in each strip is identified. The definition of <inline-formula><mml:math id="M77" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>L</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mi>y</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and the strip widths used in each model configuration are provided in the Methods section.</p></caption>
        
        <graphic xlink:href="https://os.copernicus.org/articles/22/329/2026/os-22-329-2026-f09.png"/>

      </fig>


</app>
  </app-group><notes notes-type="codedataavailability"><title>Code and data availability</title>

      <p id="d2e1734">This study has been conducted using E.U. Copernicus Marine Service; <list list-type="bullet"><list-item>
      <p id="d2e1739">DOI: <ext-link xlink:href="https://doi.org/10.48670/moi-00300" ext-link-type="DOI">10.48670/moi-00300</ext-link> (product name: <italic>OCEANCOLOUR_MED_BGC_L4_MY</italic>; <xref ref-type="bibr" rid="bib1.bibx22" id="altparen.55"/>).</p></list-item><list-item>
      <p id="d2e1752">DOI: <ext-link xlink:href="https://doi.org/10.48670/moi-00142" ext-link-type="DOI">10.48670/moi-00142</ext-link> (product name: <italic>SEALEVEL_EUR_PHY_L4_NRT_008_060</italic>; <xref ref-type="bibr" rid="bib1.bibx23" id="altparen.56"/>).</p></list-item><list-item>
      <p id="d2e1765">DOI: <ext-link xlink:href="https://doi.org/10.48670/moi-00019" ext-link-type="DOI">10.48670/moi-00019</ext-link> (product name: <italic>GLOBAL_MULTIYEAR_BGC_001_029</italic>; <xref ref-type="bibr" rid="bib1.bibx24" id="altparen.57"/>).</p></list-item></list> CROCO is provided by <uri>https://www.croco-ocean.org</uri> (last access: March 2023) and is available at <uri>https://doi.org/10.5281/zenodo.17642275</uri> <xref ref-type="bibr" rid="bib1.bibx3" id="paren.58"/>.</p>
  </notes><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d2e1790">Y.F. compiled, processed, and analyzed the satellite data. R.B., A.S. and V.V. developed the numerical model. V.V. analyzed the model output. Y.L. and R.B. oversaw the research. Y.F., Y.L, V.V., A.V., R.B. and E.B. interpreted the results. Y.F. led the writing of the paper with contribution from all coauthors.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d2e1796">The contact author has declared that none of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d2e1802">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.</p>
  </notes><ack><title>Acknowledgements</title><p id="d2e1808">We would like to acknowledge support from the Helmholtz International Laboratory: The Eastern Mediterranean Sea Centre – An Early-Warning Model-System for our Future Oceans: EMS Future Ocean REsearch (EMS-FORE), and from the Israel Science Foundation. RB and VV would like to acknowledge support from the Israeli Ministry of Energy and from the Israel-US Binational Industrial Research and Development (BIRD) foundation. We would like to acknowledge the IOLR Chemistry Department for kindly providing us with the Haifa Section Cruise data.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d2e1813">This research has been supported by the Israel Science Foundation (grant nos. 1266/23 and 2054/23).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d2e1819">This paper was edited by Damian Leonardo Arévalo-Martínez and reviewed by Alexandra Jones-Kellett and one anonymous referee.</p>
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