One size fits all? – Calibrating an ocean biogeochemistry model for different circulations

Abstract

Abstract. Global biogeochemical ocean models are often tuned to match the observed distributions and fluxes of inorganic and organic quantities. This tuning is typically carried out by hand. However, this rather subjective approach might not yield the best fit to observations, is closely linked to the circulation employed, and thus influenced by its specific features and even its faults. We here investigate the effect of model tuning, via objective optimisation, of one biogeochemical model of intermediate complexity when simulated in five different offline circulations. For each circulation, three of six model parameters have adjusted to characteristic features of the respective circulation. The values of these three parameters – namely, the oxygen utilisation of remineralisation, the particle flux parameter and potential nitrogen fixation rate – correlate significantly with deep mixing and ideal age of NADW and the outcrop area of AAIW and SAMW in the Southern Ocean. The clear relationship between these parameters and circulation characteristics, which can be easily diagnosed from global models, can provide guidance when tuning global biogeochemistry within any new circulation model. The results from 20 global cross-validation experiments show that parameter sets optimised for a specific circulation can be transferred between similar circulations without losing too much of the model's fit to observed quantities. When compared to model intercomparisons of subjectively tuned, global coupled biogeochemistry-circulation models, each with different circulation and/or biogeochemistry, our results show a much lower range of oxygen inventory, OMZ volume and global biogeochemical fluxes. Export production depends to a large extent on the circulation applied, while deep particle flux is mostly determined by the particle flux parameter. Oxygen inventory, OMZ volume, primary production and fixed nitrogen turnover depend more or less equally on both factors, with OMZ volume showing the highest sensitivity, and residual variability. These results show a beneficial effect of optimisation, even when a biogeochemical model is first optimised in a relatively coarse circulation, and then transferred to a different, finer resolution circulation model.

The Flexible Ocean and Climate Infrastructure version 1 (FOCI1): mean state and variability

“…15a, b). This compares well with the observed winter-mean sea-ice thickness of the 1980s (3.64 m) (Kwok and Rothrock, 2009). The sea-ice extent is similarly well represented as can be seen by comparing modeled (magenta) and observed sea-ice edges (black).…”

Zooplankton-Mediated Fluxes in the Eastern Tropical North Atlantic

“…However, no model setup reflects the double OMZ observed at cOMZ and 5N. Even objective parameter optimization against global data sets of nutrients and oxygen (as carried out by Kriest et al, 2020) does not yield any significant improvement at the three locations analyzed here. At CVOO and cOMZ the optimized model and the experiment with relatively shallow remineralization (as represented by b = 1.0725) show a good match to the observed oxygen below 400 m. On the other hand, at 5N the best fit to observed oxygen is obtained with b = 0.858 or less.…”

“…Considering DVM-mediated fluxes at midwater depth might also improve the representation of OMZs in global biogeochemical models. We here compare model simulations by Kriest and Oschlies (2015) and Kriest et al (2020) described above (both without DVM-mediated processes; hereafter referred to as "MOPS"), as well as results of the NEMO/PISCES/APECOSM model by Aumont et al (2018) which does include DVMmediated processes. Modifications of the particle settling velocity in model MOPS show that the model run with more slowly settling particles (equivalent to shallow remineralization and a large attenuation coefficient b) matches observed deep oxygen at CVOO and cOMZ, but underestimates deep particle flux, while those model runs that match observed particle flux overestimate deep oxygen by ≈ 20-50 mmol m −3 .…”

“…Optimized b was estimated at 1.46. The other optimal parameters and further details of model performance can be found in Kriest et al (2020).…”

“…Thick red lines: b = 1.0725; medium red lines: b = 0.858; thin red lines: b = 0.6345. Magenta dashed lines show results of the same model optimized against observed nutrients and oxygen (Kriest et al, 2020). Cyan lines show results from Aumont et al (2018).…”