Abstract:

The Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO) model system is an open source code that simulates oil plume dynamics and oil surface drift as well as biodegradation, sedimentation, and surface weathering processes. This is a coupled modeling system embedded within the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model. The system is based on the following components: three-dimensional oil transport and surface weathering, biogeochemistry (microbial biodegradation), and sedimentation (flocculation). A central component in CSOMIO coupled system is a newly developed algorithm of two-way mapping between the Eulerian and Lagrangian frameworks that allows coupling between the oil model and biogeochemical and sediment components. A full description can be found in the associated journal article by Dukhovskoy et al., 2021. The dataset includes CSOMIO code, an example of the user-defined input files (modified for specific experiments), initial fields, and boundary forcing fields. This dataset supports the publication: Dukhovskoy, Dmitry S., Steven L Morey, Eric P Chassignet, Xu Chen, Victoria Janet Coles, Linlin Cui, Courtney K Harris, Robert Hetland, Tian-Jian Hsu, Andrew James Manning, Michael R Stukel, Kristen Thyng, and Jiaze Wang (2021). Development of the CSOMIO coupled ocean-oil-sediment-biology model. Frontiers in Marine Science, 8, 194. doi: 10.3389/fmars.2021.629299

Suggested Citation:

Dukhovskoy, Dmitry, Courtney Harris, Linlin Cui, Victoria Coles, Jiaze Wang, Xu Chen, Steven Morey, Eric Chassignet, Kristen Thyng and Rob Hetland. 2021. Dataset for: Development of the CSOMIO Coupled Ocean-Oil-Sediment- Biology Model. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/JYQJVN6N

Publications:

Dukhovskoy, D. S., Morey, S. L., Chassignet, E. P., Chen, X., Coles, V. J., Cui, L., … Wang, J. (2021). Development of the CSOMIO Coupled Ocean-Oil-Sediment- Biology Model. Frontiers in Marine Science, 8. doi:10.3389/fmars.2021.629299

Purpose:

The Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO) model has been developed to simulate and analyze the fate of oil in the subsurface ocean layers and to understand the role of biodegradation and sedimentation processes during oil spills.

Data Parameters and Units:

Data parameters and units are documented in the source code and input files. Most codes use SI units except for the oil concentration that is differently specified in the biological, sediment, and oil components (documented in the source code).

Methods:

The source code is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport model (Warner et al., 2010) employing Lagrangian floats module with added "oil particle behavior" to simulate dynamics of the oil particles in the water column and ocean surface. The oil plume model calculates ascending velocity of the oil particles as a function of water density, oil density and oil particle size (based on Zheng and Yapa, 2000). Each float represents a number of individual oil particles characterized by the mean (within the float) density and size. The number of particles within the float depends on the specified flow rate of the oil at the spill source, the number of floats released per time step, and characteristics of the oil particles specified by the user in the oil input file. The structure of the oil is approximated by 3 components: saturates, aromatics, asphaltines+resins (SAR+A). The density of oil particles is defined as a function of the weight fraction of oil components and specified densities of oil components (specified in the oil floats input file). Oil characteristics (oil size and oil component fraction) are randomly assigned for each float based on parameters specified in the oil float input file. Two approaches calculating the ascending velocity of oil particles are coded: the two-equation and integrated method. On the surface, the wind drift effect is added (can be turned on/off by setting parameters in the oil floats input file). Oil weathering at the ocean surface is parameterized as multi-component evaporation. In the water column, biodegradation and sedimentation processes are simulated in the biogeochemistry and sediment components. The interaction between the oil module (operating in the Lagrangian framework) and sediment and biogeochemistry modules (both operating in the Eulerian framework) is facilitated by the two-way Eulerian-Lagrangian mapping. The oil transport component: Oil plume dynamics is simulated using Lagrangian elements (LEs or “particles”) seeded at spill location or initialized with some distribution; Oil LEs are advected in the hydrodynamic flow fields adjusted for additional wind and wave effect at the surface; Two algorithms for the oil ascent rate are based on Zheng and Yapa, 2000; Oil weathering at the surface is simulated as a multi-component weathering; Oil structure is approximated with k components (k=3: saturates, aromatics, and resins+asphaltines); Every time step, oil fields are mapped from Lagrangian → Eulerian (for bio/sediments), and updated oil fields are mapped back (Eulerian → Lagrangian). Biogeochemistry component: Based on the GENOME model (Coles, et al., 2017); Simulates emergent communities adapted to the local environment; Organisms are randomly assigned a size (metabolism) and genes associated with functions; Organisms respond to their environment based on environmental conditions (from an ocean circulation model); Genes alter the organism to bring new capabilities or specialized functions. Sediment component: Modification of the Community Sediment Transport Modeling System (CSTMS – coupled in ROMS-COAWST); Flocculation based on the model of Verney et al (2011); Simulates multiple size classes of sediments representing mineral aggregates (flocs); New sediment classes added to represent Oil-Mineral Aggregates (OMAs).

Instruments:

The Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO) model is coded using Fortran 90 and has been tested on x86_64 GNU/Linux compiled with GNU Fortran and Intel Fortran compilers with OpenMPI and NetCDF4 libraries. CSOMIO is based on the COAWST version in October 2018. All changes/additions to the COAWST codes are limited to the ROMS directory (COAWST/trunk/ROMS). Thus it is expected that users have COAWST code installed on their machines. The easiest way to install CSOMIO code is to untar CSOMIO_code.tar in COAWST/trunk directory. This will create a new directory roms_oil_sedbio. Then create a soft link "ln -s roms_oil_sedbio ROMS". If directory ROMS exists, it has to be renamed before creating a soft link to the CSOMIO code. All codes of the oil module are in the directory ROMS/Nonlinear/Oil. All changes in the ROMS codes made during the development of the CSOMIO model are indicated with comments "! DDMITRY ". Sediment code implementing oil-sediment interaction and formation of oil-mineral aggregates is in ROMS/Nonlinear/Sediment/sed_opa.F. CSOMIO biogeochemistry model is in several codes in ROMS/Nonlinear/Biology/genome_*. The code can be run as COAWST (in oil_sedbio.h undefine FLOAT_OIL, BIO_CISOMIO, OIL_SEDIMENT), oil only (define FLOATS, FLOAT_OIL), uncoupled oil/biology/sediment when all modules run during the model integration but do not interact with each other (define FLOATS, FLOAT_OIL, SEDIMENT, BIOLOGY, undef OIL_EULR), any coupled/uncoupled combination of oil-sediment-biology, or fully coupled oil-sediment-biology (define FLOATS, FLOAT_OIL, BIO_CSOMIO, OIL_SEDIMENT).

Error Analysis:

The presented version of CSOMIO has been tested for errors for a limited set of parameters and options. The main focus of the code development was to make the model compile and run-producing physically realistic results. Although output fields from the test simulations have been analyzed and compared to available observed fields, the model parameters have not been fine-tuned, adjusted or tested for a real-case hindcast scenario to accurately match observed characteristics of the oil spill or oil trajectories.

Provenance and Historical References:

Coles, V. J., Stukel, M. R., Brooks, M. T., Burd, A., Crump, B. C., Moran, M. A., Paul, J.H., Satinsky, B.M., Yager, P.L., Zielinski, B.L., and Hood, R. R. (2017). Ocean biogeochemistry modeled with emergent trait-based genomics. Science, 358(6367), 1149–1154. doi:10.1126/science.aan5712 Verney, R., Lafite, R., Claude Brun-Cottan, J., and Le Hir, P. (2011). Behaviour of a floc population during a tidal cycle: Laboratory experiments and numerical modelling. Continental Shelf Research, 31(10), S64–S83. doi:10.1016/j.csr.2010.02.005 Warner, J. C., Sherwood, C. R., Signell, R. P., Harris, C. K., and Arango, H. G. (2008). Development of a three-dimensional, regional, coupled wave, current, and sediment-transport model. Computers & Geosciences, 34(10), 1284–1306. doi:10.1016/j.cageo.2008.02.012 Zheng, L., and Yapa, P. D. (2000). Buoyant Velocity of Spherical and Nonspherical Bubbles/Droplets. Journal of Hydraulic Engineering, 126(11), 852–854. doi:10.1061/(asce)0733-9429(2000)126:11(852)

Individual Files: