ALBERT

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: Conventional integration of Earth system and ocean models can accrue considerable computational expenses, particularly for marine biogeochemical applications. "Offline" numerical schemes in which only the biogeochemical tracers are time stepped and transported using a pre-computed circulation field can substantially reduce the burden and are thus an attractive alternative. One such scheme is the "transport matrix method" (TMM), which represents tracer transport as a sequence of sparse matrix–vector products that can be performed efficiently on distributed-memory computers. While the TMM has been used for a variety of geochemical and biogeochemical studies, to date the resulting solutions have not been comprehensively assessed against their "online" counterparts. Here, we present a detailed comparison of the two. It is based on simulations of the state-of-the-art biogeochemical sub-model embedded within the widely used coarse-resolution University of Victoria Earth System Climate Model (UVic ESCM). The default, non-linear advection scheme was first replaced with a linear, third-order upwind-biased advection scheme to satisfy the linearity requirement of the TMM. Transport matrices were extracted from an equilibrium run of the physical model and subsequently used to integrate the biogeochemical model offline to equilibrium. The identical biogeochemical model was also run online. Our simulations show that offline integration introduces some bias to biogeochemical quantities through the omission of the polar filtering used in UVic ESCM and in the offline application of time-dependent forcing fields, with high latitudes showing the largest differences with respect to the online model. Differences in other regions and in the seasonality of nutrients and phytoplankton distributions are found to be relatively minor, giving confidence that the TMM is a reliable tool for offline integration of complex biogeochemical models. Moreover, while UVic ESCM is a serial code, the TMM can be run on a parallel machine with no change to the underlying biogeochemical code, thus providing orders of magnitude speed-up over the online model.

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: 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: In this study we look beyond the previously studied effects of oceanic CO2 injections on atmospheric and oceanic reservoirs and also account for carbon cycle and climate feedbacks between the atmosphere and the terrestrial biosphere. Considering these additional feedbacks is important since backfluxes from the terrestrial biosphere to the atmosphere in response to reducing atmospheric CO2 can further offset the targeted reduction. To quantify these dynamics we use an Earth system model of intermediate complexity to simulate direct injection of CO2 into the deep ocean as a means of emissions mitigation during a high CO2 emission scenario. In three sets of experiments with different injection depths, we simulate a 100-year injection period of a total of 70 GtC and follow global carbon cycle dynamics over another 900 years. In additional parameter perturbation runs, we varied the default terrestrial photosynthesis CO2 fertilization parameterization by ±50 % in order to test the sensitivity of this uncertain carbon cycle feedback to the targeted atmospheric carbon reduction through direct CO2 injections. Simulated seawater chemistry changes and marine carbon storage effectiveness are similar to previous studies. As expected, by the end of the injection period avoided emissions fall short of the targeted 70 GtC by 16–30 % as a result of carbon cycle feedbacks and backfluxes in both land and ocean reservoirs. The target emissions reduction in the parameter perturbation simulations is about 0.2 and 2 % more at the end of the injection period and about 9 % less to 1 % more at the end of the simulations when compared to the unperturbed injection runs. An unexpected feature is the effect of the model's internal variability of deep-water formation in the Southern Ocean, which, in some model runs, causes additional oceanic carbon uptake after injection termination relative to a control run without injection and therefore with slightly different atmospheric CO2 and climate. These results of a model that has very low internal climate variability illustrate that the attribution of carbon fluxes and accounting for injected CO2 may be very challenging in the real climate system with its much larger internal variability.

Description: The subtropical Indian Ocean along 32° S was for the first time simultaneously sampled in 2002 for inorganic carbon and transient tracers. The vertical distribution and inventory of anthropogenic carbon (CANT) from five different methods: four data-base methods (ΔC*, TrOCA, TTD and IPSL) and a simulation from the OCCAM model are compared and discussed along with the observed CFC-12 and CCl4 distributions. In the surface layer, where carbon-based methods are uncertain, TTD and OCCAM yield the same result (7±0.2 molC m−2), helping to specify the surface CANT inventory. Below the mixed-layer, the comparison suggests that CANT penetrates deeper and more uniformly into the Antarctic Intermediate Water layer limit than estimated from the much utilized ΔC* method. Significant CFC-12 and CCl4 values are detected in bottom waters, associated with Antarctic Bottom Water. In this layer, except for ΔC* and OCCAM, the other methods detect significant CANT values. Consequently, the lowest inventory is calculated using the ΔC* method (24±2 molC m−2) or OCCAM (24.4±2.8 molC m−2) while TrOCA, TTD, and IPSL lead to higher inventories (28.1±2.2, 28.9±2.3 and 30.8±2.5 molC m−2 respectively). Overall and despite the uncertainties each method is evaluated using its relationship with tracers and the knowledge about water masses in the subtropical Indian Ocean. Along 32° S our best estimate for the mean CANT specific inventory is 28±2 molC m−2. Comparison exercises for data-based CANT methods along with time-series or repeat sections analysis should help to identify strengths and caveats in the CANT methods and to better constrain model simulations.

Description: Global models of the oceanic nitrogen cycle are subject to many uncertainties, among them type and form of biogeochemical processes involved in the fixed nitrogen cycle, and the spatial and temporal scales, on which the global nitrogen budget is regulated. We investigate these aspects using a global model of ocean biogeochemistry, that explicitly considers phosphorus and nitrogen, including pelagic denitrification and nitrogen fixation as sink and source terms of fixed nitrogen, respectively. The model explores different parameterizations of organic matter sinking speed, oxidant affinity of oxic and suboxic remineralization, and regulation of nitrogen fixation by temperature and different stoichiometric ratios. Examination of the initial transient behaviour of different model setups initialized from observed tracer distributions reveal changes in simulated nitrogen inventories and fluxes particularly during the first centuries. Millennial timescales have to be resolved in order to bring all biogeochemical and physical processes into a dynamically consistent steady state, for which global patterns of biogeochemical tracers and fluxes are reproduced quite well. Analysis of global properties suggests that particularly particle sinking speed, but also the parameterization of denitrification determines the extent of oxygen minimum zones, global nitrogen fluxes, and hence the oceanic nitrogen inventory. However, the ways and directions, in which different parameterizations of particle sinking, nitrogen fixation and denitrification affect the global diagnostics, are different, suggesting that these may, in principle, be constrained independently from each other. Analysis of the model misfit suggests a particle flux profile close to the one suggested by Martin et al. (1987). Simulated pelagic denitrification best agrees with the lower values between 59 and 84 Tg N yr−1 recently estimated by other authors.

Description: The size of the bio-available (i.e. "fixed") nitrogen inventory in the ocean influences global marine productivity and the biological carbon pump. Despite its importance, the pre-industrial rates for the major source and sink terms of the oceanic fixed nitrogen budget, N2 fixation and denitrification, respectively, are not well known. However, these processes leave distinguishable imprints on the ratio of stable nitrogen isotopes, δ15N, which can therefore help to infer their patterns and rates. Here we use δ15N observations from the water column and a new database of seafloor measurements to constrain rates of N2 fixation and denitrification predicted by a global three-dimensional Model of Ocean Biogeochemistry and Isotopes (MOBI). Sensitivity experiments were performed to quantify uncertainties associated with the isotope effect of denitrification in the water column and sediments. They show that the level of nitrate utilization in suboxic zones, that is the balance between nitrate consumption by denitrification and nitrate replenishment by mixing (dilution effect), significantly affects the isotope effect of water column denitrification and thus global mean δ15NO3−. Experiments with lower levels of nitrate utilization within the suboxic zone (i.e. higher residual water column nitrate concentrations, ranging from 20–32 μM) require higher ratios of benthic to water column denitrification (BD:WCD = 0.75–1.4, respectively), to satisfy the global mean NO3− and δ15NO3− constraints in the modern ocean. This suggests that nitrate utilization in suboxic zones play an important role in global nitrogen isotope cycling. Increasing the net fractionation factor for benthic denitrification (ϵBD = 0–4‰) requires even higher ratios of benthic to water column denitrification (BD:WCD = 1.4–3.5, respectively). The model experiments that best reproduce observed seafloor δ15N support the middle to high-end estimates for the net fractionation factor of benthic denitrification (ϵBD = 2–4‰). Assuming a balanced fixed nitrogen budget, we estimate that pre-industrial rates of N2 fixation, water column denitrification, and benthic denitrification were approximately 195–345, 65–75, and 130–270 Tg N yr−1, respectively. Although uncertainties still exist, these results suggest that previous estimates of N2 fixation have been significantly underestimated and the residence time for oceanic fixed nitrogen is between ~ 1500–3000 yr.

Description: Observations and model runs indicate trends in dissolved oxygen (DO) associated with current and ongoing global warming. However, a large-scale observation-to-model comparison has been missing and is presented here. This study presents a first global compilation of DO measurements covering the last 50 yr. It shows declining upper-ocean DO levels in many regions, especially the tropical oceans, whereas areas with increasing trends are found in the subtropics and in some subpolar regions. For the Atlantic Ocean south of 20° N, the DO history could even be extended back to about 70 yr, showing decreasing DO in the subtropical South Atlantic. The global mean DO trend between 50° S and 50° N at 300 dbar for the period 1960 to 2010 is –0.066 μmol kg−1 yr−1. Results of a numerical biogeochemical Earth system model reveal that the magnitude of the observed change is consistent with CO2-induced climate change. However, the pattern correlation between simulated and observed patterns of past DO change is negative, indicating that the model does not correctly reproduce the processes responsible for observed regional oxygen changes in the past 50 yr. A negative pattern correlation is also obtained for model configurations with particularly low and particularly high diapycnal mixing, for a configuration that assumes a CO2-induced enhancement of the C : N ratios of exported organic matter and irrespective of whether climatological or realistic winds from reanalysis products are used to force the model. Depending on the model configuration the 300 dbar DO trend between 50° S and 50° N is −0.027 to –0.047 μmol kg−1 yr−1 for climatological wind forcing, with a much larger range of –0.083 to +0.027 μmol kg−1 yr−1 for different initializations of sensitivity runs with reanalysis wind forcing. Although numerical models reproduce the overall sign and, to some extent, magnitude of observed ocean deoxygenation, this degree of realism does not necessarily apply to simulated regional patterns and the representation of processes involved in their generation. Further analysis of the processes that can explain the discrepancies between observed and modeled DO trends is required to better understand the climate sensitivity of oceanic oxygen fields and predict potential DO changes in the future.

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.