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NEMO

Version 5.0

  • Add PISCES-Simple (ln_p2z), the NPZD version of PISCES-Operational (ln_p4z). It models 9 prognostic tracers with one generic group of phytoplankton and zooplankton and includes the Fe cycle for a better representation of primary production in iron-limited regions. Dedicated namelist parameters for the TOP module, namelist_top_cfg_p2z, can be found in the ORCA2_OFF_PISCES reference configuration.
  • PISCES-Research improvements (ln_p5z). The multi-prey parameterization applied to zooplankton grazing is modified for a more mechanistic setting. Tuning of different parameters of this version.
  • Phasing with RK3 time stepping.
  • Reduction of PISCES memory footprint to reduce memory access.
  • Reduction of the number of MPI communications.
  • Improved performance: vectorization, numerical calculation, 1/day call of the POC lability calculation instead of a time-step call of the biogeochemical model.
  • The parameterization of phytoplankton size has been revised with a local temporal evolution equation (not transported).
  • Sediment metamodel burial parameters added to the PISCES namelist.
  • Calculation of diazotrophy removed from p4zsed.F90 and done in the p4zdiaz.F90.
  • Add debugging options by biogeochemical process (namelist + code).
  • Switch from newprod to oldprod scheme for phytoplankton growth rate.
  • New semi-lagrangian sinking scheme in TOP.

Version 4.2.3

  • Sediment module : switch to Rosenbrock's implicit time scheme (orders 3 and 4) involving a major rewrite of the diagenetic code. Also significant developments both in terms of cpu efficiency (twice as fast), and physics with an improved parameterization of sulfur and iron cycles in sediment. Tunings of different parameters.
  • External inputs are now managed in TOP (trcbc.F90), the remaining inputs in PISCES (Iron from sediment and ice) are managed in p4zbc.F90.
  • Add the parameterization of diurnal vertical migration (DVM) of mesozooplankton (ln_dvm_meso, Gorgues et al., 20191).
  • Reformulation of phytoplankton size and its effect on ½ saturation constants and grazing by microzooplankton.
  • Phytoplankton N/P ratio reformulated in PISCES-Research (full quota version of PISCES).
  • Reformulation of calcite dissolution according to Naviaux et al., 20192.
  • Alkalinity damping control separated from nutrient damping control.
  • Source of Fe from Antarctic continental ice (Person et al., 20193).

Version 4.0

  • Major update of the sediment module (ln_sediment).
  • Variable composition of POC (Aumont et al., 20174).
  • Outsourcing of particle sinking management to TOP.
  • Removal of ln_oldprod scheme, ln_newprod becomes the standard and only scheme (see Aumont et al., 20155)
  • Changing impact of mixing layer on primary productivity. The effect of photoperiod (day length, ln_p4z_dcyc) and the mixed-layer depth is merged into a single parameterization based on Shatwell et al., 20126
  • Add PISCES-Research, the full quota version of PISCES (ln_p5z, Kwiatkowski et al., 20187)
  • Diurnal cycle management added in TOP (ln_trcdm2dc, daily mean to diurnal cycle on short wave).
  • Removal of complex Fe chemistry, disappearance of ln_fechem (Tagliabue et Völker, 20118)
  • Add prognostic ligands, ln_ligand (Völker and Tagliabue, 20159).

Version 3.6

Version of Aumont et al. (20155) with the addition of :

  • Ocean carbonate system changed to Mocsy 2.0 standards (Orr et Epitalon, 201510).
  • Bug correction of particles accumulation in anoxic and nitrate-limited areas (nitrfac2 in p4zrem.F90)
  • Elimination of mass conservation based on adjustment of sediment losses to compensate exactly for sediment inputs

  1. Thomas Gorgues, Olivier Aumont, and Laurent Memery. Simulated Changes in the Particulate Carbon Export Efficiency due to Diel Vertical Migration of Zooplankton in the North Atlantic. Geophysical Research Letters, 46(10):5387–5395, 2019. doi:10.1029/2018GL081748. ↩

  2. John D Naviaux, Adam V Subhas, Nick E Rollins, Sijia Dong, William M Berelson, and Jess F Adkins. Temperature dependence of calcite dissolution kinetics in seawater. Geochimica et Cosmochimica Acta, 246:363–384, 2019. doi:10.1016/j.gca.2018.11.037. ↩

  3. Renaud Person, Olivier Aumont, Gurvan Madec, Martin Vancoppenolle, Laurent Bopp, and Nacho Merino. Sensitivity of ocean biogeochemistry to the iron supply from the Antarctic Ice Sheet explored with a biogeochemical model. Biogeosciences, 16(18):3583–3603, September 2019. URL: https://www.biogeosciences.net/16/3583/2019/ (visited on 2019-11-06), doi:10.5194/bg-16-3583-2019. ↩

  4. Olivier Aumont, Marco van Hulten, Matthieu Roy-Barman, Jean-Claude Dutay, Christian Éthé, and Marion Gehlen. Variable reactivity of particulate organic matter in a global ocean biogeochemical model. Biogeosciences, 14(9):2321–2341, 2017. doi:10.5194/bg-14-2321-2017. ↩

  5. Olivier Aumont, Christian Ethé, Alessandro Tagliabue, Laurent Bopp, and Marion Gehlen. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geoscientific Model Development, 8:2465–2513, 2015. doi:10.5194/gmd-8-2465-2015. ↩↩

  6. Tom Shatwell, Andreas Nicklisch, and Jan Köhler. Temperature and photoperiod effects on phytoplankton growing under simulated mixed layer light fluctuations. Limnology and Oceanography, 57(2):541–553, 2012. doi:10.4319/lo.2012.57.2.0541. ↩

  7. Lester Kwiatkowski, Olivier Aumont, Laurent Bopp, and Philippe Ciais. The Impact of Variable Phytoplankton Stoichiometry on Projections of Primary Production, Food Quality, and Carbon Uptake in the Global Ocean. Global Biogeochemical Cycles, 32(4):516–528, 2018. doi:10.1002/2017GB005799. ↩

  8. Alessandro Tagliabue and Christoph Voelker. Towards accounting for dissolved iron speciation in global ocean models. Biogeosciences, 8(10):3025–3039, 2011. doi:10.5194/bg-8-3025-2011. ↩

  9. Christoph Völker and Alessandro Tagliabue. Modeling organic iron-binding ligands in a three-dimensional biogeochemical ocean model. Marine Chemistry, 173:67–77, 2015. doi:10.1016/j.marchem.2014.11.008. ↩

  10. JC Orr and J-M Epitalon. Improved routines to model the ocean carbonate system: mocsy 2.0. Geoscientific Model Development, 8(3):485–499, 2015. doi:10.5194/gmd-8-485-2015. ↩