This version of the model was runs with the gud package (git://gud.mit.edu/gud1). The model is based on Dutkiewicz et al (2015) and resolves the cycling of carbon, phosphorus, nitrogen silica, iron, and oxygen through inorganic, living, dissolved and particulate organic phases (including CDOM). The biogeochemical and biological tracers are transported and mixed by the MIT general circulation model (MITgcm, Marshall et al., 1997). The three dimensional configuration used here has nominally 18km horizontal resolution and 50 levels ranging from 10m in the surface to 500m at depth (the ECCO2 CS510 physical simulation, Menemenlis et al 2008).

We resolve 35 phytoplankton types, covering several functional groups (picophytoplankton, coccolithophores, diazotrophs, diatoms, mixotrophic dinoflagellates), 16 size classes (from 0.6 to 228um equivalent spherical diameter). There are 16 grazer size classes from 6.6 to 2280um equivalent spherical diameter. The phytoplankton types differ in the types of nutrients they require (e.g. diatoms require silica), maximum growth rate, nutrient half saturation constants, sinking rates, and palatability to grazers. Many parameters are linked to size following the allometric scaling used in Dutkiewicz et al (2019). The phytoplankton also differ in their spectral absorption and scattering (see Figure 1 in Dutkiewicz et al., 2015) and maximum Chl-a:C. The different scattering and absorption spectra for each size class incorporate the packaging effect. The phytoplankton have dynamic Chl-a:C ratios that change with light availability, temperature and nutrient stress following Geider et al (1998). This model also explicitly includes radiative transfer of spectral irradiance in 25nm bands between 400 and 700nm.

Output from this model have been used in several papers (Kuhn et al, in press, Benoiston et al, 2018; McParland and Levine, 2018; Tregruer al 2018).

Model output for 3-day means over 24 years (1992-2016) are available from and opendap server at MIT: http://engaging-opendap.mit.edu:8080/las/UI.vm (Click “Data Set” in the top left corner and select “Darwin v0.2 cs510”).

Given size constraints only a limited amount of the output are provided here.

References for papers using this model output:
Benoiston, A.-S., F.M. Ibarbalz, L. Bittner, L. Guidi, O. Jahn, S. Dutkiewicz and C. Bowler, 2017: The evolution of diatoms and their biogeochemical functions. Philosophical Transcations of the Royal Society B, 372: 20160397, doi: 10.1098/rstb.2016.0397
Kuhn, A.M., S. Dutkiewicz, O. Jahn, S. Clayton, T. Rynearson, M. Mazzloff, and A. Barton. Phytoplankton community temporal and spatial scales of decorrelation. In press for Journal of Geophysical Research
McParland, EL and NM Levine (2018), The role of differential DMSP production and community composition in predicting variability of global surface DMSP concentrations, Limnology and Oceanography, doi:10.1002/lno.11076
Tréguer, P., C. Bowler, B. Moriceau, S. Dutkiewicz, M. Gehlen, K. Leblanc, O. Aumont, L. Bittner, R. Dugdale, Z. Finkel, D. Iudicone, O. Jahn, L. Guidi, M. Lasbleiz, M. Levy, and P. Pondaven, 2017: Influence of diatoms on the ocean biological pump. Nature Geoscience, doi:10.1038/s41561-017-0028-x.

Other References:
Dutkiewicz, S., A.E. Hickman, O. Jahn, W.W. Watson, C. Mouw, and M.J. Follows, 2015: Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model. Biogeoscience, 12, 4447-4481, doi:10.5194/bg-12-4447-201
Dutkiewicz, S., Cermeno, P., Jahn, O., Follows, M. J., Hickman, A. E., Taniguchi, D. A. A., and Ward, B. A.: Dimensions of Marine Phytoplankton Diversity, Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-311, in review, 2019.
Geider, R., Macintyre, H.L., and Kana, T.M.: A Dynamic Regulatory Model of Phytoplanktonic Acclimation to Light, Nutrients, and Temperature. Limnol. Oceanogr, 43, 679–94, 1998.
Marshall, J., Adcroft, A., Hill, C. N., Perelman, L., and Heisey, C: A finite-volume, incompressible Navier–Stokes model for studies of the ocean on parallel computers, J. Geophys. Res., 102, 5753–5766, 1997.
Menemenlis, D., Campin, J.-M., Heimbach, P., Hill, C., Lee, T., Nguyen, A., et al. (2008). ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newslwtter, (31), 13–21.