Effects of Oceanic Mesoscale and Submesoscale Frontal Processes on the Vertical Transport of Phytoplankton

Abstract

Oceanic fronts are dynamically active regions of the global ocean that support upwelling and downwelling with significant implications for phytoplankton production and export. However (on time scales ≳ the inertial time scale), the vertical velocity is 103–104 times weaker than the horizontal velocity and is difficult to observe directly. Using intensive field observations in conjunction with a process study ocean model, we examine vertical motion and its effect on phytoplankton fluxes at multiple spatial horizontal scales in an oligotrophic region in the Western Mediterranean Sea. The mesoscale ageostrophic vertical velocity (∼10 m/day) inferred from our observations shapes the large‐scale phytoplankton distribution but does not explain the narrow (1–10 km wide) features of high chlorophyll content extending 40–60 m downward from the deep chlorophyll maximum. Using modeling, we show that downwelling submesoscale features concentrate 80% of the downward vertical flux of phytoplankton within just 15% of the horizontal area. These submesoscale spatial structures serve as conduits between the surface mixed layer and pycnocline and can contribute to exporting carbon from the sunlit surface layers to the ocean interior.

Frontal Convergence and Vertical Velocity Measured by Drifters in the Alboran Sea

“…These density fronts become unstable and generate an intense ageostrophic secondary circulation associated with surface convergence and strong vertical velocity (Lévy et al, 2001;Mahadevan & Archer, 2000;Mahadevan et al, 2016;McWilliams, 2016;McWilliams et al, 2019). Evidence for these vertical motions in the Alboran Basin has previously been observed by other authors (Allen et al, 2001;Flexas et al, 2006;Oguz et al, 2014;Pascual et al, 2017;Tintoré et al, 1991) who found strong signals of subduction in physical and biogeochemical properties (Ruiz et al, 2009(Ruiz et al, , 2019.…”

EcoCTD for Profiling Oceanic Physical–Biological Properties from an Underway Ship

“…The main objective of the field campaign was to unravel the three-dimensional coherent pathways by which water parcels are transported from the surface ocean to depths below the mixed layer. The EcoCTD was deployed to profile the water column repeatedly at a horizontal spacing of 1-2 km across a sharp density front, with the aim of characterizing water mass subduction from the ocean mixed layer to ocean interior as was previously observed in this region by an ocean glider (Ruiz et al 2009). a. Sensor processing…”

“…The EcoCTD was deployed to collect biophysical observations of subducted water masses at submesoscales (,10 km) along the Almeria-Oran front (Ruiz et al 2019). The hypotheses are that 1) subducted filaments are easily detectable in bio-optical variables and should correspond to the hydrographic signals, and 2) bio-optical variables can be used as a passive tracer to infer the pathway and subduction rate of the subducted water.…”

Design of the Observing System Simulation Experiments with multi-platform in situ data and impact on fine- scale structures

“…Optimal interpolation (OI) will be used for this analysis, as this method has been widely used to reconstruct in situ observations (e.g. Bretherton, et al, 1976;Davis, 1985;Le Traon, 1990, Gomis et al, 2001Pascual et al, 2004Pascual et al, , 2017Ruiz et al, 2019, Barceló-Llull et al, 2017. This analysis will be done in each region of study and using all models to provide robustness to the results.…”

“…Optimal Interpolation is a powerful method of data analysis that has been widely used by oceanographers and meteorologists to estimate values of geophysical variables on a regular grid from irregularly sampled observations (e.g. Bretherton, et al, 1976;Davis, 1985;Le Traon, 1990, Gomis et al, 2001Pascual et al, 2004Pascual et al, , 2017Melnichenko et al, 2014, Barceló-Llull et al, 2017Ruiz et al, 2019). This technique determines a point-wise estimate of the interpolated field with minimum ensemble mean-square error, considering information about the variances and correlation functions of the estimated field and the observational data.…”