Adding Fadida paper

src/data/papers-citing-parcels.ts

@@ -2792,4 +2792,14 @@ export const papersCitingParcels: Paper[] = [
     abstract:
       'The large size of traditional drifters limits their ability to mimic the transport of buoyant objects at the ocean surface, which are subject to complex interactions among direct wind drag, fast-moving surface currents, and wave-induced transport. To better capture these dynamics, we track the trajectories of 12 novel, ultra-thin surface drifters deployed in the southern North Sea over 68 d. We adopt a data-driven approach to model drifter velocity using hydrodynamic and atmospheric data, applying both a linear leeway parameterisation and two machine learning models: random forest and support vector regression. Machine learning model-agnostic interpretation techniques reveal that tidal forcing predominantly drives zonal motion, whereas wind is the main driver in the meridional direction in this region. Notably, the wind exhibits a saturation effect, and its contribution to explaining the variance of the drifter velocity decreases at higher speeds. In trajectory prediction experiments, we find that machine learning models, particularly random forest, outperform linear models, with the latter achieving comparable accuracy only at short time scales. Using a hybrid approach and deriving a non-linear function of the wind from machine learning interpretable methods to include in the leeway parameterisation significantly improves the model prediction of the drifter trajectory. Finally, we test the generalisability of the North Sea-trained models using an independent drifter dataset from the Tyrrhenian Sea. Despite the differences in ocean dynamics between the regions, the machine learning models reproduce the observed trajectories with comparable accuracy to state-of-the-art studies, demonstrating robust explanatory skill and a low degree of overfitting in this instance.',
   },
+  {
+    title:
+      'Coastal-to-offshore submesoscale horizontal stirring enhances wintertime phytoplankton blooms in the ultra-oligotrophic Eastern Mediterranean Sea',
+    published_info: 'Ocean Science, 22, 329-343',
+    authors:
+      'Fadida, Y, V Verma, R Barkan, E Biton, A Solodoch, Y Lehahn (2026)',
+    doi: 'https://doi.org/10.5194/os-22-329-2026',
+    abstract:
+      '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 ∼ 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.',
+  },
 ]