Description: The Ocean Model Intercomparison Project (OMIP) focuses on the physics and biogeochemistry of the ocean component of Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). OMIP aims to provide standard protocols and diagnostics for ocean models, while offering a forum to promote their common assessment and improvement. It also offers to compare solutions of the same ocean models when forced with reanalysis data (OMIP simulations) vs. when integrated within fully coupled Earth system models (CMIP6). Here we detail simulation protocols and diagnostics for OMIP's biogeochemical and inert chemical tracers. These passive-tracer simulations will be coupled to ocean circulation models, initialized with observational data or output from a model spin-up, and forced by repeating the 1948–2009 surface fluxes of heat, fresh water, and momentum. These so-called OMIP-BGC simulations include three inert chemical tracers (CFC-11, CFC-12, SF6) and biogeochemical tracers (e.g., dissolved inorganic carbon, carbon isotopes, alkalinity, nutrients, and oxygen). Modelers will use their preferred prognostic BGC model but should follow common guidelines for gas exchange and carbonate chemistry. Simulations include both natural and total carbon tracers. The required forced simulation (omip1) will be initialized with gridded observational climatologies. An optional forced simulation (omip1-spunup) will be initialized instead with BGC fields from a long model spin-up, preferably for 2000 years or more, and forced by repeating the same 62-year meteorological forcing. That optional run will also include abiotic tracers of total dissolved inorganic carbon and radiocarbon, CTabio and 14CTabio, to assess deep-ocean ventilation and distinguish the role of physics vs. biology. These simulations will be forced by observed atmospheric histories of the three inert gases and CO2 as well as carbon isotope ratios of CO2. OMIP-BGC simulation protocols are founded on those from previous phases of the Ocean Carbon-Cycle Model Intercomparison Project. They have been merged and updated to reflect improvements concerning gas exchange, carbonate chemistry, and new data for initial conditions and atmospheric gas histories. Code is provided to facilitate their implementation.

Description: The fate of the organic matter (OM) produced by marine life controls the major biogeochemical cycles of the Earth's system. The OM produced through photosynthesis is either preserved, exported towards sediments or degraded through remineralisation in the water column. The productive eastern boundary upwelling systems (EBUSs) associated with oxygen minimum zones (OMZs) would be expected to foster OM preservation due to low O2 conditions. But their intense and diverse microbial activity should enhance OM degradation. To investigate this contradiction, sediment traps were deployed near the oxycline and in the OMZ core on an instrumented moored line off Peru. Data provided high-temporal-resolution O2 series characterising two seasonal steady states at the upper trap: suboxic ([O2] 〈 25µmolkg−1) and hypoxic–oxic (15 〈 [O2] 〈 160µmolkg−1) in austral summer and winter–spring, respectively. The OMZ vertical transfer efficiency of particulate organic carbon (POC) between traps (Teff) can be classified into three main ranges (high, intermediate, low). These different Teff ranges suggest that both predominant preservation (high Teff 〉 50%) and remineralisation (intermediate Teff 20 〈 50% or low Teff 〈 6%) configurations can occur. An efficient OMZ vertical transfer (Teff 〉 50%) has been reported in summer and winter associated with extreme limitation in O2 concentrations or OM quantity for OM degradation. However, higher levels of O2 or OM, or less refractory OM, at the oxycline, even in a co-limitation context, can decrease the OMZ transfer efficiency to below 50%. This is especially true in summer during intraseasonal wind-driven oxygenation events. In late winter and early spring, high oxygenation conditions together with high fluxes of sinking particles trigger a shutdown of the OMZ transfer (Teff 〈 6%). Transfer efficiency of chemical elements composing the majority of the flux (nitrogen, phosphorus, silica, calcium carbonate) follows the same trend as for carbon, with the lowest transfer level being in late winter and early spring. Regarding particulate isotopes, vertical transfer of δ15N suggests a complex pattern of 15N impoverishment or enrichment according to Teff modulation. This sensitivity of OM to O2 fluctuations and particle concentration calls for further investigation into OM and O2-driven remineralisation processes. This should include consideration of the intermittent behaviour of OMZ towards OM demonstrated in past studies and climate projections.

Description: Biogeochemical models, capturing the major feedbacks of the pelagic ecosystem of the world ocean, are today often embedded into Earth system models which are increasingly used for decision making regarding climate policies. These models contain poorly constrained parameters (e.g., maximum phytoplankton growth rate), which are typically adjusted until the model shows reasonable behavior. Systematic approaches determine these parameters by minimizing the misfit between the model and observational data. In most common model approaches, however, the underlying functions mimicking the biogeochemical processes are nonlinear and non-convex. Thus, systematic optimization algorithms are likely to get trapped in local minima and might lead to non-optimal results. To judge the quality of an obtained parameter estimate, we propose determining a preferably large lower bound for the global optimum that is relatively easy to obtain and that will help to assess the quality of an optimum, generated by an optimization algorithm. Due to the unavoidable noise component in all observations, such a lower bound is typically larger than zero. We suggest deriving such lower bounds based on typical properties of biogeochemical models (e.g., a limited number of extremes and a bounded time derivative). We illustrate the applicability of the method with two real-world examples. The first example uses real-world observations of the Baltic Sea in a box model setup. The second example considers a three-dimensional coupled ocean circulation model in combination with satellite chlorophyll a.

Description: This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom–up approach which combines expert judgment and statistical information to systematically select transparent, nonredundant indicators for a comprehensive assessment of the state of the Earth system. The methods consists of two basic steps: (1) the calculation of a correlation matrix among variables relevant for a given research question and (2) the systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators. Optional further analysis steps include (3) the interpretation of the identified clusters, enabling a learning effect from the selection of indicators, (4) testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, (5) enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, and (6) the inclusion of expert judgment, for example, to prescribe indicators, to allow for considerations other than statistical consistency. The example application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of reevaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate–high, as well as a business-as-usual, climate change scenario simulation. This necessity arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

Description: Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many (≈10–≈100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long time scales. The framework combines an offline approach for transport of biogeochemical tracers with an Estimation of Distribution Algorithm (Covariance Matrix Adaption Evolution Strategy, CMAES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent, to which different setups of the optimization influence model's fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter time scales, are more difficult to determine. In particular the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.

Description: The Apparent Oxygen Utilisation (AOU) is a classical measure of the amount of oxygen respired in the ocean's interior. We show that AOU systematically overestimates True Oxygen Utilisation (TOU) in 6 coupled circulation-biogeochemical ocean models. This is due to atmosphere–ocean oxygen disequilibria in the subduction regions, consistent with previous work. We develop a simple, new, observationally-based approach which we call Evaluated Oxygen Utilisation (EOU). In this approach, we take into account the impact of the upper ocean oxygen disequilibria into the interior, considering that transport takes place predominantly along isopycnal surfaces. The EOU approximates the TOU with less than half of the bias of AOU in all 6 models despite large differences in the physical and biological components of the models. Applying the EOU approach to a global observational dataset yields an oxygen consumption rate 25% lower than that derived from AOU-based estimates, for a given ventilation rate.

Description: The natural abundance of 14C in total CO2 dissolved in seawater is a property applied to evaluate the water age structure and circulation in the ocean and in ocean models. In this study we use three different representations of the global ocean circulation augmented with a suite of idealised tracers to study the potential and limitations of using natural 14C to determine water age, the time elapsed since a body of water had contact with the atmosphere. We find that, globally, bulk 14C-age is dominated by two equally important components, one associated with aging, i.e. the time component of circulation and one associated with a "preformed 14C-age". This latter quantity exists because of the slow and incomplete atmosphere/ocean equilibration of 14C in particular in high latitudes where many water masses form. The relative contribution of the preformed component to bulk 14C-age varies regionally within a given model, but also between models. Regional variability, e.g. in the Atlantic Ocean is associated with the mixing of waters with very different end members of preformed 14C-age. In the Atlantic, variations in the preformed component over space and time mask the circulation component to an extent that its patterns are not detectable from bulk 14C-age alone. Between models the variability of age can also be considerable (factor of 2), related to the combinations of physical model parameters, which influence circulation dynamics, and gas exchange in the models. The preformed component was found to be very sensitive to gas exchange and moderately sensitive to ice cover. In our model evaluation exercise, the choice of the gas exchange constant from within the current range of uncertainty had such a strong influence on preformed and bulk 14C-age that if model evaluation would be based on bulk 14C-age it could easily impair the evaluation and tuning of a models circulation on global and regional scales. Based on the results of this study, we propose that considering preformed 14C-age is critical for a correct assessment of circulation in ocean models.

Description: This paper presents the application of the Linear Quadratic Optimal Control (LQOC) method to a parameter optimization problem for a one-dimensional marine ecosystem model of NPZD (N for dissolved inorganic nitrogen, P for phytoplankton, Z for zooplankton and D for detritus) type. This ecosystem model, developed by Oschlies and Garcon, simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. The LQOC method is used to introduce annually periodic model parameters in a linearized version of the model. We show that the obtained version of the model gives a significant reduction of the model-data misfit, compared to the one obtained for the original model with optimized constant parameters. The found inner-annual variability of the optimized parameters provides hints for improvement of the original model. We use the obtained optimal periodic parameters also in validation and prediction experiments with the original non-linear version of the model. In both cases, the results are significantly better than those obtained with optimized constant parameters.

Description: Marine biological production as well as the associated biotic uptake of carbon in many ocean regions depends on the availability of nutrients in the euphotic zone. While large areas are limited by nitrogen and/or phosphorus, the micronutrient iron is considered the main limiting nutrient in the North Pacific, equatorial Pacific and Southern Ocean. Changes in iron availability via changes in atmospheric dust input are discussed to play an important role in glacial–interglacial cycles via climate feedbacks caused by changes in biological ocean carbon sequestration. Although many aspects of the iron cycle remain unknown, its incorporation into marine biogeochemical models is needed to test our current understanding and better constrain its role in the Earth system. In the University of Victoria Earth System Climate Model (UVic) iron limitation in the ocean was, until now, simulated pragmatically with an iron concentration masking scheme that did not allow a consistent interactive response to perturbations of ocean biogeochemistry or iron cycling sensitivity studies. Here, we replace the iron masking scheme with a dynamic iron cycle and compare the results to available observations and the previous marine biogeochemical model. Sensitivity studies are also conducted with the new model to test the sensitivity of the model to parameterized iron ligand concentrations, the importance of considering the variable solubility of iron in dust deposition, the importance of considering high-resolution bathymetry for the sediment release of iron, the effect of scaling the sedimentary iron release with temperature and the sensitivity of the iron cycle to a climate change scenario.

Description: The Eastern Tropical Pacific (ETP) is believed to be one of the largest marine sources of the greenhouse gas nitrous oxide N2O). Future N2Oemissions from the ETP are highly uncertain because oxygen minimum zones are expected to expand, affecting both regional production and consumption of N2O. Here we assess three primary uncertainties in how N2O may respond to changing O2 levels: (1) the relationship between N2O production and O2 (is it linear or exponential at low O2 concentrations?), (2) the cutoff point at which net N2O production switches to net N2O consumption (uncertainties in this parameterization can lead to differences in model ETP N2O concentrations of more than 20%), and (3) the rate of net N2O consumption at low O2. Based on the MEMENTO database, which is the largest N2O dataset currently available, we find that N2O production in the ETP increases linearly rather than exponentially with decreasing O2. Additionally, net N2O consumption switches to net N2O production at ~ 10 μM O2, a value in line with recent studies that suggest consumption occurs on a larger scale than previously thought. N2O consumption is on the order of 0.129 mmol N2O m−3 yr−1 in the Peru–Chile Undercurrent. Based on these findings, it appears that recent studies substantially overestimated N2O production in the ETP. In light of expected deoxygenation, future N2O production is still uncertain, but due to higher-than-expected consumption levels, it is possible that N2Oconcentrations may decrease rather than increase as oxygen minimum zones expand.