Preprints
https://doi.org/10.5194/egusphere-2025-4050
https://doi.org/10.5194/egusphere-2025-4050
21 Aug 2025
 | 21 Aug 2025
Status: this preprint is open for discussion and under review for Biogeosciences (BG).

Meta-analytical insights into organic matter enrichment in the surface microlayer

Amavi N. Silva, Surandokht Nikzad, Theresa Barthelmeß, Anja Engel, Hartmut Hermann, Manuela van Pinxteren, Kai Wirtz, Oliver Wurl, and Markus Schartau

Abstract. The surface microlayer (SML), the uppermost ~1 mm water layer at the air-water interface, plays a critical role in mediating Earth system processes, yet current knowledge of its composition and organic matter enrichment remains scattered across disciplines. Here, we present the first known meta-analysis of SML studies that quantitatively assesses the distributional characteristics of selected organic compounds, including organic carbon and nitrogen, amino acids, fatty acids, transparent exopolymer particles, carbohydrates, lipids and proteins, through probability density estimates, central tendency metrics and correlations analyses. Our results confirm a preferential enrichment of nitrogen-enriched, particulate organic matter in the SML, highlighting the significance of compound-specific accumulation and selective enrichment patterns. We also observe that the enrichment of a given compound may exhibit notable variability that depends on distinct internal and external conditions. Our evaluation of enrichment factors (EFs) of various measurable compounds provides updated estimates for their typical values and ranges. While delving into the ability of EFs to reflect the partitioning of organic matter within the SML, we also critically examine their limitation in capturing trophic conditions. Based on these findings, we propose that future SML research should incorporate both absolute concentration changes and enrichment capacities in the SML, alongside their relative changes (as denoted by EFs), to more accurately interpret ecological implications. Additionally, our meta-analysis demonstrates the value of logarithmic data transformations and robust central tendency estimates, as essential tools for improving the statistical reliability, comparability, and representation of SML enrichment patterns.

Competing interests: Authors A. S., T. B., A. E. and M. S. are affiliated with the same institution as H. B., who serves as an overseeing editor for the special issue “Biogeochemical processes and air-sea exchange in the sea-surface microlayer”. Authors A. S., T. B., A. E., H. H., M. P., O. W. and M. S. are collaborators with H. B. on an ongoing research project. These potential competing interests have been fully disclosed to the journal. The authors declare no other competing interests relevant to the submitted work.

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. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Amavi N. Silva, Surandokht Nikzad, Theresa Barthelmeß, Anja Engel, Hartmut Hermann, Manuela van Pinxteren, Kai Wirtz, Oliver Wurl, and Markus Schartau

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Amavi N. Silva, Surandokht Nikzad, Theresa Barthelmeß, Anja Engel, Hartmut Hermann, Manuela van Pinxteren, Kai Wirtz, Oliver Wurl, and Markus Schartau
Amavi N. Silva, Surandokht Nikzad, Theresa Barthelmeß, Anja Engel, Hartmut Hermann, Manuela van Pinxteren, Kai Wirtz, Oliver Wurl, and Markus Schartau

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Short summary
We conducted the first meta-analysis combining marine and freshwater studies to understand organic matter enrichment in the surface microlayer. Nitrogen-rich, particulate compounds are often enriched, with patterns varying by multiple factors. We recommend tracking both absolute concentrations and normalized enrichment patterns to better assess ecological conditions. Our study also introduces improved statistical methods for analyzing and comparing surface microlayer data.
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