Description: We present a robust method for diagnosing total diapycnal diffusivities, i.e. explicitly applied plus numerically induced diffusivities, from tracer release experiments in numerical z-level models. To this extent, numerical experiments differing only in the advection scheme used (CTRD using 2nd order centred differences, UPWIND using the upwind/upstream advection scheme, QUICK using the quicker advection scheme after Farrow and Stevens (1995) and FCT after Gerdes et al. (1991)) are analysed and compared. To obtain regionally resolved estimates of diapycnal diffusivities, individual inert dye tracers are released in dynamically different regions of a North Atlantic model, namely (i) in the interior of the subtropical gyre and (ii) in the western boundary current. Diagnosed diffusivities are robust with respect to changes in temporal and spatial sampling of the simulated dye tracer for both advection schemes and for both regions. The numerically induced diffusivity is generally positive, but can become negative for centred differences advection numerics after several months of simulated tracer dispersion.

Description: Diapycnal diffusion is a key process in the ocean, responsible for water mass transformation and the conversion of kinetic energy into potential energy. Despite its widely assumed importance in controlling ocean dynamics, diapycnal diffusion is difficult to quantify both in the real ocean and in ocean models. Here we focus on z-level models, arguably the most common vertical grid scheme of current ocean general circulation models. We examine different methods to diagnose diapycnal diffusivities in z-level models. Different scenarios are investigated, including the impact of advection and vertical convergence or divergence of isopycnals. In all cases we find that the transformation from z-space to density space has to be performed very carefully in order to obtain reliable and robust estimates of diapycnal diffusivities (and the associated diapycnal fluxes). A method involving the tracer flux taken from the work of Griffies et al. (2000) seems to be most appropriate in this respect and is suggested as our method of choice for subsequent applications to 3-dimensional ocean circulation models

Description: Controlled manipulation of environmental conditions within large enclosures in the ocean, so-called pelagic mesocosms, has become a standard method to explore potential responses of marine plankton communities to anthropogenic change. Among the challenges of interpreting mesocosm data is the often uncertain role of vertical mixing, which usually is not observed directly. To account for mixing nonetheless, two pragmatic assumptions are common: either that the water column is homogeneously mixed or that it is divided into two water bodies with a horizontal barrier inhibiting turbulent exchange. In this study, we present a model-based reanalysis of vertical turbulent diffusion in the mesocosm experiments PeECE III and KOSMOS 2013. Our diffusivity estimates indicate intermittent mixing events along with stagnating periods and yield simulated temperature and salinity profiles that are consistent with the observations. Here, we provide the respective diffusivities as a comprehensive data product in the Network Common Data Format (NetCDF). This data product will help to guide forthcoming model studies that aim at deepening our understanding of biogeochemical processes in the PeECE III and KOSMOS 2013 mesocosms, such as the CO2-related changes in marine carbon export. In addition, we make our model code available, providing an adjustable tool to simulate vertical mixing in any other pelagic mesocosm. The data product and the model code are available at https://doi.org/10.1594/PANGAEA.905311 (Mathesius et al., 2019).

Description: We present a new near-global coupled biogeochemical ocean-circulation model configuration. The configuration features a horizontal discretization with a grid spacing of less than 11 km in the Southern Ocean and gradually coarsens in meridional direction to more than 200 km at 64∘ N, where the model is bounded by a solid wall. The underlying code framework is the Geophysical Fluid Dynamics Laboratory (GFDL)'s Modular Ocean Model coupled to the Biogeochemistry with Light, Iron, Nutrients and Gases (BLING) ecosystem model of Galbraith et al. (2010). The configuration is unique in that it features both a relatively equilibrated oceanic carbon inventory and an eddying ocean circulation based on a realistic model geometry/bathymetry – a combination that has been precluded by prohibitive computational cost in the past. Results from a simulation with climatological forcing and a sensitivity experiment with increasing winds suggest that the configuration is sufficiently equilibrated to explore Southern Ocean carbon uptake dynamics on decadal timescales. The configuration is dubbed MOMSO, a Modular Ocean Model Southern Ocean configuration.

Description: Earth system climate models generally underestimate dissolved oxygen concentrations in the deep eastern equatorial Pacific. This problem is associated with the "nutrient trapping" problem, described by Najjar et al. [1992], and is, at least partially, caused by a deficient representation of the Equatorial Intermediate Current System (EICS). Here we emulate the unresolved EICS in the UVic earth system climate model by locally increasing the zonal isopycnal diffusivity. An anisotropic diffusivity of ∼50,000 m 2 s-1 yields an improved global representation of temperature, salinity and oxygen. In addition, it (1) resolves most of the local "nutrient trapping" and associated oxygen deficit in the eastern equatorial Pacific and (2) reduces spurious zonal temperature gradients on isopycnals without affecting other physical metrics such as meridional overturning or air-sea heat fluxes. Finally, climate projections of low-oxygenated waters and associated denitrification change sign and apparently become more plausible

Description: Open-ocean oxygen minimum zones (OMZs) occur in regions with high biological productivity and weak ventilation. They restrict marine habitats and alter biogeochemical cycles. Global models generally show a large model–data misfit with regard to oxygen. Reliable statements about the future development of OMZs and the quantification of their interaction with climate change are currently not possible. One of the most intense OMZs worldwide is located in the Arabian Sea (AS). We give an overview of the main model deficiencies with a detailed comparison of the historical state of 10 climate models from the 5th Coupled Model Intercomparison Project (CMIP5) that present our present-day understanding of physical and biogeochemical processes. Most of the models show a general underestimation of the OMZ volume in the AS compared to observations that is caused by an overly shallow layer of oxygen-poor water in the models. The deviation of oxygen values in the deep AS is the result of oxygen levels that are too high simulated in the Southern Ocean formation regions of Indian Ocean Deep Water in the models compared to observations and uncertainties in the deepwater mass transport from the Southern Ocean northward into the AS. Differences in simulated water mass properties and ventilation rates of Red Sea Water and Persian Gulf Water cause different mixing in the AS and thus influence the intensity of the OMZ. These differences in ventilation rates also point towards variations in the parameterizations of the overflow from the marginal seas among the models. The results of this study are intended to foster future model improvements regarding the OMZ in the AS.

Description: Global biogeochemical ocean models help to investigate the present and potential future state of the ocean, its productivity and cascading effects on higher trophic levels such as fish. They are often subjectively tuned against data sets of inorganic tracers and surface chlorophyll and only very rarely against organic components such as particulate organic carbon or zooplankton. The resulting uncertainty in biogeochemical model parameters (and parameterisations) associated with these components can explain some of the large spread of global model solutions with regard to the cycling of organic matter and its impacts on biogeochemical tracer distributions, such as oxygen minimum zones (OMZs). A second source of uncertainty arises from differences in the model spin-up length as, so far, there seems to be no agreement on the required simulation time that should elapse before a global model is assessed against observations. We investigated these two sources of uncertainty by optimising a global biogeochemical ocean model against the root-mean-squared error (RMSE) of six different combinations of data sets and different spin-up times. Besides nutrients and oxygen, the observational data sets also included phyto- and zooplankton, as well as dissolved and particulate organic phosphorus (DOP and POP, respectively). We further analysed the optimised model performance with regard to global biogeochemical fluxes, oxygen inventory and OMZ volume. Following the optimisation procedure, we evaluated the RMSE for all tracers located in the upper 100 m (except for POP, for which we considered the entire vertical domain), regardless of their consideration during optimisation. For the different optimal model solutions, we find a narrow range of the RMSE, between 14 % of the average RMSE after 10 years and 24 % after 3000 years of simulation. Global biogeochemical fluxes, global oxygen bias and OMZ volume showed a much stronger divergence among the models and over time than RMSE, indicating that even models that are similar with regard to local surface tracer concentrations can perform very differently when assessed against the global diagnostics for oxygen. Considering organic tracers in the optimisation had a strong impact on the particle flux exponent (Martin b) and may reduce much of the uncertainty in this parameter and the resulting deep particle flux. Independent of the optimisation setup, the OMZ volume showed a particularly sensitive response with strong trends over time, even after 3000 years of simulation time (despite the constant physical forcing); a high sensitivity to simulation time; and the highest sensitivity to model parameters arising from the tuning strategy setup (variation of almost 80 % of the ensemble mean). In conclusion, calibration against observations of organic tracers can help to improve global biogeochemical models even after short spin-up times; here especially, observations of deep particle flux could provide a powerful constraint. However, a large uncertainty remains with regard to global OMZ volume and its evolution over time, which can show very dynamic behaviour during the model spin-up, which renders temporal extrapolation to a final equilibrium state difficult if not impossible. Given that the real ocean shows variations on many timescales, the assumption of observations representing a steady-state ocean may require some reconsideration.

Description: In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real-world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. Different optimization tools have been successfully applied to address this task of model calibration. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real-world processes which cannot be fully resolved, it might be preferable to consider associated model parameters as random parameters. In this paper, a random parameter is drawn from a wide probability distribution for every singe model simulation. We developed an optimization approach that allows us to declare certain parameters random while optimizing those that are assumed to take fixed values. We designed a corresponding variant of the well known Covariance Matrix Adaption Evolution Strategy (CMA-ES). The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA-ES, our new method converges to a solution that better suits the credible range of the corresponding random parameter with less computational effort.