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        <datestamp>2026-09-30T22:38:12Z</datestamp>
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          <dc:title>&lt;b&gt;Reproducibility package for &lt;/b&gt;&lt;b&gt;Synoptic Wind-Dependent Sampling Asymmetry Biases Satellite Sea Surface Chlorophyll-a&lt;/b&gt;&lt;b&gt; &lt;/b&gt;&lt;b&gt;in the Mid-to-High-Latitude North Pacific and North Atlantic&lt;/b&gt;</dc:title>
          <dc:creator>Qixiang Wang (24209868)</dc:creator>
          <dc:creator>Zhaohui Chen (22747115)</dc:creator>
          <dc:subject>Physical oceanography</dc:subject>
          <dc:subject>Biological oceanography</dc:subject>
          <dc:subject>remote sensing</dc:subject>
          <dc:subject>sea surface chlorophyll</dc:subject>
          <dc:subject>synoptic-scale wind</dc:subject>
          <dc:subject>sampling asymmetry</dc:subject>
          <dc:subject>subpolar gyres</dc:subject>
          <dc:subject>sampling bias</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;Satellite observations of sea surface chlorophyll-a concentration (SChl) provide a basin-scale record for interpreting marine productivity and upper ocean biogeochemical variability. However, clouds and other observational limits create gaps in SChl retrieval, which can bias the SChl record, especially in cloud-prone subpolar gyres. Here, we use satellite SChl data from 2010–2019 to quantify sampling bias associated with synoptic-scale wind speed conditions. Compared with low wind speed conditions, high wind speed conditions are often cloudy, yielding fewer valid retrievals of SChl as well as lower observed SChl in subpolar gyres of North Atlantic and North Pacific. The wind-speed-dependent sampling asymmetry biases mean SChl upward; for instance, the largest SChl bias occurs in winter, with local values reaching over 10%. The resulting SChl bias also propagates into satellite-based estimates of ocean productivity.&lt;/p&gt;&lt;p dir="ltr"&gt;This archive provides the reproducibility package associated with the manuscript. It includes the public analysis scripts, MATLAB builders for plot-ready NetCDF/CSV products, Python plotting scripts, and the plot-ready products used to generate the submitted main-text and supporting-information figures. Large original datasets are not included in this archive. They can be downloaded from the original public data providers listed in the manuscript Open Research statement.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-30T22:38:12Z</dc:date>
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          <dc:identifier>10.6084/m9.figshare.32687367.v1</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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