The Impact of Coastal Phytoplankton Blooms on Ocean-Atmosphere Thermal Energy Exchange: Evidence From a Two-Way Coupled Numerical Modeling System

A set of sensitivity experiments are performed with a two-way coupled and nested ocean-atmosphere forecasting system in order to deconvolve how dense phytoplankton stocks in a coastal embayment may impact thermal energy exchange processes. Monterey Bay simulations parameterizing solar shortwave transparency in the surface ocean as an invariant oligotrophic oceanic water type estimate consistently colder sea surface temperature (SST) than simulations utilizing more realistic, spatially varying shortwave attenuation terms based on satellite estimates of surface algal pigment concentration. These SST differences lead to an similar to 88% increase in the cumulative turbulent thermal energy transfer from the ocean to the atmosphere over the three month simulation period. The result is a warmer simulated atmospheric boundary layer with respective local air temperature differences approaching similar to 2 degrees C. This study suggests that the retention of shortwave solar flux by ocean flora may directly impact even short-term forecasts of coastal meteorological variables. Citation: Jolliff, J. K., T. A. Smith, C. N. Barron, S. deRada, S. C. Anderson, R. W. Gould, and R. A. Arnone (2012), The impact of coastal phytoplankton blooms on ocean-atmosphere thermal energy exchange: Evidence from a two-way coupled numerical modeling system, Geophys. Res. Lett., 39, L24607, doi:10.1029/2012GL053634

High-Resolution Sampling of a Broad Marine Life Size Spectrum Reveals Differing Size- and Composition-Based Associations With Physical Oceanographic Structure

Observing multiple size classes of organisms, along with oceanographic properties and water mass origins, can improve our understanding of the drivers of aggregations, yet acquiring these measurements remains a fundamental challenge in biological oceanography. By deploying multiple biological sampling systems, from conventional bottle and net sampling to in situ imaging and acoustics, we describe the spatial patterns of different size classes of marine organisms (several microns to ∼10 cm) in relation to local and regional (m to km) physical oceanographic conditions on the Delaware continental shelf. The imaging and acoustic systems deployed included (in ascending order of target organism size) an imaging flow cytometer (CytoSense), a digital holographic imaging system (HOLOCAM), an In Situ Ichthyoplankton Imaging System (ISIIS, 2 cameras with different pixel resolutions), and multi-frequency acoustics (SIMRAD, 18 and 38 kHz). Spatial patterns generated by the different systems showed size-dependent aggregations and differing connections to horizontal and vertical salinity and temperature gradients that would not have been detected with traditional station-based sampling (∼9-km resolution). A direct comparison of the two ISIIS cameras showed composition and spatial patchiness changes that depended on the organism size, morphology, and camera pixel resolution. Large zooplankton near the surface, primarily composed of appendicularians and gelatinous organisms, tended to be more abundant offshore near the shelf break. This region was also associated with high phytoplankton biomass and higher overall organism abundances in the ISIIS, acoustics, and targeted net sampling. In contrast, the inshore region was dominated by hard-bodied zooplankton and had relatively low acoustic backscatter. The nets showed a community dominated by copepods, but they also showed high relative abundances of soft-bodied organisms in the offshore region where these organisms were quantified by the ISIIS. The HOLOCAM detected dense patches of ciliates that were too small to be captured in the nets or ISIIS imagery. This near-simultaneous deployment of different systems enables the description of the spatial patterns of different organism size classes, their spatial relation to potential prey and predators, and their association with specific oceanographic conditions. These datasets can also be used to evaluate the efficacy of sampling techniques, ultimately aiding in the design of efficient, hypothesis-driven sampling programs that incorporate these complementary technologies

The Application of Novel Research Technologies by the Deep Pelagic Nekton Dynamics of the Gulf of Mexico (DEEPEND) Consortium

The deep waters of the open ocean represent a major frontier in exploration and scientific understanding. However, modern technological and computational tools are making the deep ocean more accessible than ever before by facilitating increasingly sophisticated studies of deep ocean ecosystems. Here, we describe some of the cutting-edge technologies that have been employed by the Deep Pelagic Nekton Dynamics of the Gulf of Mexico (DEEPEND; www.deependconsortium.org) Consortium to study the biodiverse fauna and dynamic physical-chemical environment of the offshore Gulf of Mexico (GoM) from 0 to 1,500 m

A Multidisciplinary Approach to Investigate Deep-Pelagic Ecosystem Dynamics in the Gulf of Mexico Following Deepwater Horizon

The pelagic Gulf of Mexico (GoM) is a complex system of dynamic physical oceanography (western boundary current, mesoscale eddies), high biological diversity, and community integration via diel vertical migration and lateral advection. Humans also heavily utilize this system, including its deep-sea components, for resource extraction, shipping, tourism, and other commercial activity. This utilization has had impacts, some with disastrous consequences. The Deepwater Horizon oil spill (DWHOS) occurred at a depth of ∼1500 m (Macondo wellhead), creating a persistent and toxic mixture of hydrocarbons and dispersant in the deep-pelagic (water column below 200 m depth) habitat. In order to assess the impacts of the DWHOS on this habitat, two large-scale research programs, described herein, were designed and executed. These programs, ONSAP and DEEPEND, aimed to quantitatively characterize the oceanic ecosystem of the northern GoM and to establish a time-series with which natural and anthropogenic changes could be detected. The approach was multi-disciplinary in nature and included in situ sampling, acoustic sensing, water column profiling and sampling, satellite remote sensing, AUV sensing, numerical modeling, genetic sequencing, and biogeochemical analyses. The synergy of these methodologies has provided new and unprecedented perspectives of an oceanic ecosystem with respect to composition, connectivity, drivers, and variability

Numerical simulation of the Gulf of Mexico (GOM) using the HYbrid Coordinate Ocean Model (HYCOM), cruises DP03 and DP04 from January 2016 - December 2016

Physical circulation fields from a 1/25-degree Gulf of Mexico HYbrid Coordinate Ocean Model (HYCOM) simulation spanning January to December 2016. The circulation model output includes temperature, salinity, velocity (currents), sea surface elevation in the Gulf of Mexico. The native vertical structure is a 32-layer hybrid time-variant sigma/z/rho. Post processing interpolates it to a time invariant 50-level z vertical structure for end-user dissemination. The ocean model is forced by the Navy Global Environmental Model (NAVGEM, version 1.3/1.4) at 0.28-degree resolution, includes tides (TPXO), and 3D Variational data-assimilation (Navy Coupled Ocean Data Assimilation- NCODA). Tidal boundary conditions for water level and barotropic velocity are provided by the Oregon State University global Ocean Tide Inverse Solution (OTIS). Rivers are implemented as a \u27precipitation bogus\u27 at the river source specified by a monthly climatological global database, that includes the mass-transports and other pertinent demographics for each river

Numerical simulation of the Gulf of Mexico (GOM) using the HYbrid Coordinate Ocean Model (HYCOM), cruises DP01 and DP02 from January 2015 - December 2015

Physical circulation fields from 1/25-degree Gulf of Mexico HYbrid Coordinate Ocean Model (HYCOM) simulation spanning May-December 2015. Ocean model is forced by 1/2-degree atmospheric forcing, includes tides, and 3D Variational data-assimilation

A High-Resolution Contouring Algorithm (HRCA)

This investigation encompasses the review of known interpolating and contouring schema appropriate for contour map generation. The research leads to the development of a robust contouring algorithm suitable for Naval research

Coupling Ocean Models and Satellite Derived Optical Fields To Estimate LIDAR Penetration and Detection Performance

A global-scale climatological assessment of the temporal and spatial relationships between physical and optical ocean layers was previously performed to determine LIDAR efficiency for measuring the 3D Ocean. That effort provided estimates of laser sensor penetration depth (PD) in the global oceans and identified critical coupling between Mixed Layer Depth (MLD) and Optical Depth (OD) based on potential laser power and ensuing attenuation. We make use of a Bio-Physical ocean model configured for the Gulf of Mexico (GOM) along with remotely sensed satellite measurements to examine LIDAR performance in the Gulf of Mexico coastal regions. The 4Km GOM ocean model runs in near-realtime and produces physical and bio-optical fields which are coupled to in-house derived satellite bio-optical products such as the Diffuse Attenuation Coefficient at 490 nm (Kd490). PD and MLD are coupled to determine laser power efficiency rates across multiple attenuation lengths. The results illustrate the potential utilization of space-borne oceanographic LIDAR to penetrate through the water column, elucidating its applicability for a variety of scientific (characterization of the ocean subsurface layers) and applied (target detection) objectives. © 2012 SPIE

DEEPEND: A Tool for Classification of Mesoscale Watermass Structure for Pelagic Community Analyses

Gulf of Mexico (GOM) pelagic waters are dominated by mesoscale oceanic features such as anti- and cyclonic eddies and the swift Loop Current. These GOM features may structure faunal communities in the deep pelagial and influence trophic linkages from surface waters down. Classifying pelagic habitat structure based on mesoscale watermass features therefore may facilitate quantitative evaluation of pelagic community assemblages. In this study, we developed a tool to classify deep pelagic habitat in the GOM using the deviation of sea surface height (SSH) from mean SSH for the entire GOM and water temperature at 300 m water depth, founded on ocean condition data from the 1/25 ° GOM HYbrid Coordinate Ocean Model (HYCOM) for broad application. Pelagic habitats were segregated into anticyclonic, mixed boundaries, and common water units – all of which likely produce varying levels of forage for deep-sea fauna and may be trophic drivers. Next we contrasted these classifications to classifications based on water column temperature and salinity at depth, as measured by CTD casts during cruises by the Deep Pelagic Nekton Dynamics of the Gulf of Mexico (DEEPEND) consortium over the years 2015-2016. The classification scheme was further cross-validated by comparing the model classifications to classifications based on microbial communities found within the same water masses. We found high levels of agreement between all three methods. Going forward, this tool will be used to aid pelagic community analyses of fauna collected by DEEPEND cruises in the GOM spanning the years 2010-2017

DEEPEND: Characterizing Pelagic Habitats in the Gulf of Mexico Using Model, Empirical, and Remotely-Sensed Data

Pelagic waters of the Gulf of Mexico (GOM) are dominated by mesoscale features such as cyclonic and anticyclonic eddies and the strongly flowing Loop Current. These GOM features may be important drivers of population structure and trophic linkages within the water column. It is important, therefore, to classify water bodies associated with these features to allow quantitative evaluation of community assemblages. We first used an algorithm that integrated sea surface height anomaly and water velocity gradients to classify GOM surface waters between the years 2011-2016, founded on ocean condition data from the 1/25 ° GOM HYbrid Coordinate Ocean Model (HYCOM). The water bodies were segregated into anti-cyclonic, cyclonic, anti-cyclonic boundary, cyclonic boundary, and common water units. Next we compared these classifications to empirically derived ocean conditions as measured by CTD casts within each unit that were collected during the same period on cruises by the Deep Pelagic Nekton Dynamics of the Gulf of Mexico (DEEPEND) consortium. The classification scheme was further cross-validated by comparing the identified water bodies to the depths of the 20° and 22° isotherms, microbial community assemblages within each unit, and chlorophyll concentrations derived from satellite measurements. We found good agreement of the classification scheme between model (i.e., HYCOM), empirical (i.e., CTD and microbial assemblages), and remotely sensed (i.e., chlorophyll) data. Going forward, the classification scheme will be used to characterize assemblages of pelagic fauna that were collected by DEEPEND cruises in the GOM between 2010-2017