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.

Description: A growing population on a planet with limited resources demands finding new sources of protein. Hence, fisheries are turning their perspectives towards mesopelagic fish, which have, so far, remained relatively unexploited and poorly studied. Large uncertainties are associated with regards to their biomass, turn-over rates, susceptibility to environmental forcing and ecological and biogeochemical role. Models are useful to disentangle sources of uncertainties and to understand the impact of different processes on the biomass. In this study, we employed two food-web models – OSMOSE and the model by Anderson et al. (2019, or A2019) – coupled to a regional physical–biogeochemical model to simulate mesopelagic fish in the Eastern Tropical South Pacific ocean. The model by A2019 produced the largest biomass estimate, 26 to 130% higher than OSMOSE depending on the mortality parameters used. However, OSMOSE was calibrated to match observations in the coastal region off Peru and its temporal variability is affected by an explicit life cycle and food web. In contrast, the model by A2019 is more convenient to perform uncertainty analysis and it can be easily coupled to a biogeochemical model to estimate mesopelagic fish biomass. However, it is based on a flow analysis that had been previously applied to estimate global biomass of mesopelagic fish but has never been calibrated for the Eastern Tropical South Pacific. Furthermore, it assumes a steady-state in the energy transfer between primary production and mesopelagic fish, which may be an oversimplification for this highly dynamic system. OSMOSE is convenient to understand the interactions of the ecosystem and how including different life stages affects the model response. The combined strengths of both models allow us to study mesopelagic fish from a holistic perspective, taking into account energy fluxes and biomass uncertainties based on primary production, as well as complex ecological interactions.

Description: Observed oxygen minimum zones (OMZs) in the tropical Pacific Ocean are located above intermediate-depth waters (IDWs), defined here as the 500–1500 m water layer. Typical climate models do not represent IDW properties well and are characterized by OMZs that are too deep-reaching. We analyze the role of the IDW in the misrepresentation of oxygen levels in a heterogeneous subset of ocean models characterized by a horizontal resolution ranging from 0.1 to 2.8∘. First, we show that forcing the extratropical boundaries (30∘ S and N) to observed oxygen values results in a significant increase in oxygen levels in the intermediate eastern tropical region. Second, we highlight the fact that the Equatorial Intermediate Current System (EICS) is a key feature connecting the western and eastern part of the basin. Typical climate models lack in representing crucial aspects of this supply at intermediate depth, as the EICS is basically absent in models characterized by a resolution lower than 0.25∘. These two aspects add up to a “cascade of biases” that hampers the correct representation of oxygen levels at intermediate depth in the eastern tropical Pacific Ocean and potentially future OMZ projections.