Abstract:

This modeling dataset contains physical and biological variables that simulate the response of the idealized ocean in the Northern Hemisphere to an idealized translating tropical cyclone. The data was generated by the Regional Ocean Modeling System (ROMS), coupled with a biological model, and the variable values are recorded every two hours for ten full days. The dataset contains the model output (e.i., currents, temperature, salinity, phytoplankton, Zooplankton, and nutrients), model set up configuration, idealized hurricane information, and bathymetric data for both study cases: Deep ocean (Case 1) and Continental Shelf Sea (Case 2). This dataset supports the publication: McGee, L., & He, R. (2018). Mesoscale and submesoscale mechanisms behind asymmetric cooling and phytoplankton blooms induced by hurricanes: a comparison between an open ocean case and a continental shelf sea case. Ocean Dynamics, 68(11), 1443–1456. doi:10.1007/s10236-018-1203-3

Suggested Citation:

McGee, Laura and He, Ruoying. 2019. Dataset for: Mesoscale and submesoscale mechanisms behind asymmetric cooling and phytoplankton blooms induced by hurricanes: a comparison between an open ocean case and a continental shelf sea case. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/n7-ggxh-qh48

Publications:

McGee, L., & He, R. (2018). Mesoscale and submesoscale mechanisms behind asymmetric cooling and phytoplankton blooms induced by hurricanes: a comparison between an open ocean case and a continental shelf sea case. Ocean Dynamics, 68(11), 1443–1456. doi:10.1007/s10236-018-1203-3

Purpose:

This dataset was created to study, in an idealized form, the right-side asymmetric physical and biological factors (such as mixing and phytoplankton blooms) that form in the Northern Hemisphere in the wake of a tropical cyclone.

Data Parameters and Units:

Ocean_time (averaged time since initialization, seconds since 1-07-01 00:00:00), zeta (time-averaged free-surface, m), u (u-momentum, m/s), v(v-momentum, m/s), w (w-momentum, m/s), temp (potential temperature, degrees C), salt (salinity, psu), NO3 (nitrate concentration, mmol m-3), phytoplankton (phytoplankton concentration (mmol N m-3), zooplankton (mmol N m-3), detritus (mmol N m-3), density anomaly (kg/m3), pvorticity (potential vorticity, s-1), rvorticity (relative vorticity, s-1), temperature vertical diffusion coefficient (m2/s), surface u-momentum wind stress (N/m2), surface v-momentum wind stress (N/m2), time-averaged potential vorticity (m-1 s-1), time-averaged relative vorticity (s-1), solar shortwave radiation (Watt/m2).

Methods:

The Regional Ocean Modeling System (ROMS), coupled with a biological model described in Powell et al. 2006, was used to create this data. The generic length scale (GLS) vertical mixing scheme was used with the Kantha and Clayson stability function (see Umlauf and Burchard 2003, Warner et al. 2005, and Kantha and Clayson 1994). Tropical cyclone wind forcing is provided using the symmetric Holland wind model (Holland 1980), and forcing is one-way from the atmosphere to the ocean. The wind stress scheme from Oey et al. 2006 was used to convert the Holland model’s wind speed into wind stress. A simple day-night cycle was added, following Huang and Oey 2015. The model domain is an idealized Cartesian basin, 3600 km by 2800 km, with a grid size of 8 km. Gradient boundary conditions were used on all sides except for the western boundary, which has a closed boundary condition. The basin uses an f-plane approximation of 15°N. Case 1 has a constant bathymetry of 2000 m, while Case 2’s bathymetry starts at 2000 m deep on the eastern side and transitions to 50 m on the western side, simulating a continental slope and shelf. Initial conditions for phytoplankton, nitrogen, and temperature are based on those used in Huang and Oey 2015. Zooplankton concentration is 1/10th the value of the phytoplankton concentration at each depth, and detritus concentration is twice the phytoplankton concentration at each depth. For more information, and for plots of the domain, the initial conditions, and the equations used, please see the associated publication mentioned in the abstract (McGee and He 2018).

Error Analysis:

As an idealized dataset, no error analysis was performed.

Provenance and Historical References:

Holland, G. J. (1980). An Analytic Model of the Wind and Pressure Profiles in Hurricanes. Monthly Weather Review, 108(8), 1212–1218. doi:10.1175/1520-0493(1980)108<1212:aamotw>2.0.co;2 Huang, S.-M., & Oey, L.-Y. (2015). Right-side cooling and phytoplankton bloom in the wake of a tropical cyclone. Journal of Geophysical Research: Oceans, 120(8), 5735–5748. doi:10.1002/2015jc010896 Kantha, L. H., & Clayson, C. A. (1994). An improved mixed layer model for geophysical applications. Journal of Geophysical Research, 99(C12), 25235. doi:10.1029/94jc02257 Oey, L.-Y., Ezer, T., Wang, D.-P., Fan, S.-J., & Yin, X.-Q. (2006). Loop Current warming by Hurricane Wilma. Geophysical Research Letters, 33(8). doi:10.1029/2006gl025873 Powell, T. M., Lewis, C. V. W., Curchitser, E. N., Haidvogel, D. B., Hermann, A. J., & Dobbins, E. L. (2006). Results from a three-dimensional, nested biological-physical model of the California Current System and comparisons with statistics from satellite imagery. Journal of Geophysical Research, 111(C7). doi:10.1029/2004jc002506 Umlauf, L., & Burchard, H. (2003). A generic length-scale equation for geophysical turbulence models. Journal of Marine Research, 61(2), 235–265. doi:10.1357/002224003322005087 Warner, J. C., Sherwood, C. R., Arango, H. G., & Signell, R. P. (2005). Performance of four turbulence closure models implemented using a generic length scale method. Ocean Modelling, 8(1-2), 81–113. doi:10.1016/j.ocemod.2003.12.003

Individual Files: