ALBERT

Description: The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

Description: During phytoplankton growth a fraction of dissolved inorganic carbon (DIC) assimilated by phytoplankton is exuded in the form of dissolved organic carbon (DOC), which can be transformed into extracellular particulate organic carbon (POC). A major fraction of extracellular POC is associated with carbon of transparent exopolymer particles (TEP; carbon content = TEPC) that form from dissolved polysaccharides (PCHO). The exudation of PCHO is linked to an excessive uptake of DIC that is not directly quantifiable from utilisation of dissolved inorganic nitrogen (DIN), called carbon overconsumption. Given these conditions, the concept of assuming a constant stoichiometric carbon-to-nitrogen (C:N) ratio for estimating new production of POC from DIN uptake becomes inappropriate. Here, a model of carbon overconsumption is analysed, combining phytoplankton growth with TEPC formation. The model describes two modes of carbon overconsumption. The first mode is associated with DOC exudation during phytoplankton biomass accumulation. The second mode is decoupled from algal growth, but leads to a continuous rise in POC while particulate organic nitrogen (PON) remains constant. While including PCHO coagulation, the model goes beyond a purely physiological explanation of building up carbon rich particulate organic matter (POM). The model is validated against observations from a mesocosm study. Maximum likelihood estimates of model parameters, such as nitrogen- and carbon loss rates of phytoplankton, are determined. The optimisation yields results with higher rates for carbon exudation than for the loss of organic nitrogen. It also suggests that the PCHO fraction of exuded DOC was 63±20% during the mesocosm experiment. Optimal estimates are obtained for coagulation kernels for PCHO transformation into TEPC. Model state estimates are consistent with observations, where 30% of the POC increase was attributed to TEPC formation. The proposed model is of low complexity and is applicable for large-scale biogeochemical simulations.

Description: To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling. We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time- and space-dependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation

Description: The influence of seawater carbon dioxide (CO2) concentration on the size distribution of suspended particles (2–60 μm) and on phytoplankton abundance was investigated during a mesocosm experiment at the large scale facility (LFS) in Bergen, Norway, in the frame of the Pelagic Ecosystem CO2 Enrichment study (PeECE II). In nine outdoor enclosures the partial pressure of CO2 in seawater was modified by an aeration system to simulate past (~190 μatm CO2), present (~370 μatm CO2) and future (~700 μatm CO2) CO2 conditions in triplicates. Due to the initial addition of inorganic nutrients, phytoplankton blooms developed in all mesocosms and were monitored over a period of 19 days. Seawater samples were collected daily for analysing the abundance of suspended particles and phytoplankton with the Coulter Counter and with Flow Cytometry, respectively. During the bloom period, the abundance of small particles (〈4 μm) significantly increased at past, and decreased at future CO2 levels. At that time, a direct relationship between the total-surface-to-total-volume ratio of suspended particles and DIC concentration was determined for all mesocosms. Significant changes with respect to the CO2 treatment were also observed in the phytoplankton community structure. While some populations such as diatoms seemed to be insensitive to the CO2 treatment, others like Micromonas spp. increased with CO2, or showed maximum abundance at present day CO2 (i.e. Emiliania huxleyi). The strongest response to CO2 was observed in the abundance of small autotrophic nano-plankton that strongly increased during the bloom in the past CO2 mesocosms. Together, changes in particle size distribution and phytoplankton community indicate a complex interplay between the ability of the cells to physiologically respond to changes in CO2 and size selection. Size of cells is of general importance for a variety of processes in marine systems such as diffusion-limited uptake of substrates, resource allocation, predator-prey interaction, and gravitational settling. The observed changes in particle size distribution are therefore discussed with respect to biogeochemical cycling and ecosystem functioning.

Description: Mesocosm experiments on phytoplankton dynamics under high CO2 concentrations mimic the response of marine primary producers to future ocean acidification. However, potential acidification effects can be hindered by the high standard deviation typically found in the replicates of the same CO2 treatment level. In experiments with multiple unresolved factors and a sub-optimal number of replicates, post-processing statistical inference tools might fail to detect an effect that is present. We propose that in such cases, data-based model analyses might be suitable tools to unearth potential responses to the treatment and identify the uncertainties that could produce the observed variability. As test cases, we used data from two independent mesocosm experiments. Both experiments showed high standard deviations and, according to statistical inference tools, biomass appeared insensitive to changing CO2 conditions. Conversely, our simulations showed earlier and more intense phytoplankton blooms in modeled replicates at high CO2 concentrations and suggested that uncertainties in average cell size, phytoplankton biomass losses, and initial nutrient concentration potentially outweigh acidification effects by triggering strong variability during the bloom phase. We also estimated the thresholds below which uncertainties do not escalate to high variability. This information might help in designing future mesocosm experiments and interpreting controversial results on the effect of acidification or other pressures on ecosystem functions

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 analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3−, PO43−, O2, and surface chlorophyll a concentrations. According to our metric the optimal model solutions comprise low rates of global N2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the “best” model solutions we therefore also consider the model’s ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3− inventory. Global O2 varies by a factor of two and NO3− by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (QN0,phy) and zooplankton maximum specific ingestion rate. QN0,phy is revealed as a major determinant of the oceanic NO3− pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via QN0,phy, is a prerequisite for understanding the marine nitrogen inventory.

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: Marine particulate organic carbon-13 stable isotope ratios (δ13CPOC) provide insights in understanding carbon cycling through the atmosphere, ocean, and biosphere. They have been used to trace the input of anthropogenic carbon in the marine ecosystem due to the distinct isotopically light signature of anthropogenic emissions. However, δ13CPOC is also significantly altered during photosynthesis by phytoplankton, which complicates its interpretation. For such purposes, robust spatio-temporal coverage of δ13CP OC observations is essential. We collected all such available data sets, merged and homogenized them to provide the largest available marine δ13CPOC data set (Verwega et al., 2021). The data set consists of 4732 data points covering all major ocean basins beginning in the 1960s. We describe the compiled raw data, compare different observational methods, and provide key insights in the temporal and spatial distribution that is consistent with previously observed patterns. The main different sample collection methods (bottle, intake, net, trap) are generally consistent with each other when comparing within regions. An analysis of 1990s mean δ13CP OC values in an meridional section accross the Atlantic Ocean shows relatively high values (≥ −22 ‰) in the low latitudes (〈 30°) trending towards lower values in the Arctic Ocean (∼ −24 ‰) and Southern Ocean (≤ −28 ‰). The temporal trend since the 1960s shows a decrease of mean δ13CPOC by more than 3 ‰ in all basins except for the Southern Ocean which shows a weaker trend but contains relatively poor multi-decadal coverage.

Description: Probability density functions (PDFs) provide information about the probability of a random variable taking on a specific value. In geoscience, data distributions are often expressed by a parametric estimation of their PDF, such as, for example, a Gaussian distribution. At present there is growing attention towards the analysis of non-parametric estimation of PDFs, where no prior assumptions about the type of PDF are required. A common tool for such non-parametric estimation is a kernel density estimator (KDE). Existing KDEs are valuable but problematic because of the difficulty of objectively specifying optimal bandwidths for the individual kernels. In this study, we designed and developed a new implementation of a diffusion-based KDE as an open source Python tool to make diffusion-based KDE accessible for general use. Our new diffusion-based KDE provides (1) consistency at the boundaries, (2) better resolution of multimodal data, and (3) a family of KDEs with different smoothing intensities. We demonstrate our tool on artificial data with multiple and boundary-close modes and on real marine biogeochemical data, and compare our results against other popular KDE methods. We also provide an example for how our approach can be efficiently utilized for the derivation of plankton size spectra in ecological research. Our estimator is able to detect relevant multiple modes and it resolves modes that are located closely to a boundary of the observed data interval. Furthermore, our approach produces a smooth graph that is robust to noise and outliers. The convergence rate is comparable to that of the Gaussian estimator, but with a generally smaller error. This is most notable for small data sets with up to around 5000 data points. We discuss the general applicability and advantages of such KDEs for data–model comparison in geoscience.