Mesoscale eddies are a dominant source of spatial variability in the surface ocean and play a major role in the biological marine carbon cycle. Satellite altimetry is often used to locate and track eddies, but this approach is rarely validated against in situ observations. Here we compare measurements of a small (under 25 km radius) mode water anticyclonic eddy over the Procupine Abyssal Plain using CTD and ADCP measurements from 3 ships, 2 gliders, 2 profiling floats, and one Lagrangian float with those derived from sea level anomaly. In situ estimates of the eddy center were estimated from maps of the thickness of its central isopycnal layer, from ADCP velocities at a reference layer, and from the trajectory of the Lagrangian float. These were compared to three methods using altimetric SLA: one based on maximizing geostrophic rotation, one based on a constant SLA contour, and one which maximizes geostrophic velocity speed along the eddy boundary. All algorithms were used to select CTD profiles that were within the eddy. The in-situ metrics agreed to 97\%. The altimetry metrics showed only a small loss of accuracy, giving $>90$\% agreement with the in situ results. This suggests that current satellite altimetry is adequate for understanding the spatial representation of even relatively small mesoscale eddies.

Elisa Romanelli 1, Julia Sweet 2,3, Sarah Lou Carolin Giering 4, David A. Siegel1, Uta Passow 2,51 Earth Research Institute, University of California, Santa Barbara, CA, United Stateselisa.romanelli@lifesci.ucsb.edu, david.siegel@ucsb.edu2 Marine Science Institute, University of California, Santa Barbara, CA, United States3 University of Louisiana at Lafayette, LA, United Statesjulia.sweet1@louisiana.edu4 National Oceanography Centre, Southampton, United Kingdoms.giering@noc.ac.uk5 Memorial University, St John’s, Canadauta.passow@mun.ca

The Underwater Vision Profiler (UVP) provides abundant in situ data of the marine particle size distribution (PSD) on global scales and has been used for a diversity of applications, but the uncertainty associated with its measurements has not been quantified. Here we use a global compilation of UVP (version 5) observations of the PSD to assess the sampling uncertainty associated with the UVP’s sampling characteristics. We model UVP sampling uncertainty using Bayesian Poisson statistics and provide formulae for the uncertainty associated with a given sampling volume and observed particle count. We also model PSD observations using a power law with an exponential cutoff to better match the low concentration associated with rare large particles as seen by the UVP. We use the two shape parameters from this statistical model to describe changes in the PSD shape across latitude band, season, and depth. The UVP sampling uncertainty propagates into an uncertainty for modeled carbon flux exceeding 50%. The statistical model is used to extend the size interval used in a PSD-derived carbon flux model, revealing a high sensitivity of the PSD-derived flux model to the inclusion of small particles (80-128 microns). We close with recommendations on how to revise the carbon flux model, and we provide avenues to address additional uncertainties associated with UVP-derived carbon flux calculations.

High performance liquid chromatography (HPLC) remains one of the most widely applied methods currently available for estimation of phytoplankton taxonomy from ocean samples. This method measures the concentrations of phytoplankton pigments, some of which are useful chemotaxonomic markers that can be used to diagnose the relative abundance of phytoplankton groups. Here, we use HPLC phytoplankton pigment concentrations measured on surface water samples from 38 field surveys for a total of over 3,000 distinct samples that cover every major ocean basin and represent a wide range of ecological regimes. The data compilation has been quality controlled to remove measurements below pigment detection limits and outliers from the linear regression of total chlorophyll-a concentration with total accessory pigment concentrations and only samples from labs that have participated in round-robin quality assurance experiments (e.g. NASA SeaHARRE) have been included. We assess the environmental and spatial drivers controlling the global distribution and co-variability of individual phytoplankton pigments. Preliminary results of hierarchical clustering show strong differentiation in phytoplankton pigments following known relationships between phytoplankton size class and relative pigment concentration, partitioning their contributions by micro-, nano-, and pico-phytoplankton size classes. However, the exact clusters relationships change when the data are divided by ocean basin or latitude. We also use statistical techniques, including EOFs and network-based exploration, to examine the associations between groups of pigments over a range of environmental conditions on local to global scales and diagnose the main controls on these associations.