ALBERT

Description: Here we provide optimised vertical eddy diffusivity estimates for the PeECE III and KOSMOS 2013 mesocosm experiment, obtained from a model-based reanalysis. These diffusivities are derived from the observed temperature and salinity profiles that have been published in Schulz et al., 2008. Furthermore, we make our model code available, providing an adjustable tool to simulate vertical mixing in any other pelagic mesocosm. We also provide the interpolated and regridded temperature and salinity profiles of the PeECE III experiment as well as the density profiles which we calculated from the temperature and salinity profiles using the R package seacarb (Lavigne et al., 2011). These data files are required as input to run simulations of the PeECE III experiment with the 1D mesocosm mixing model. The columns of the environmental files (required input files for the model) from left to right are: Experiment year, month, day, Julian day, photosynthetically active radiation (PAR) [W/m^2], temperature [C], salinity [PSU], CO2 concentration [ppm], wind speed [m/s]. The rows list the respective value of each hour of the experiment. Temperature and salinity in this table are hourly interpolated values of the daily measurements published by the PeECE III team (2005). PAR has been calculated from global radiation data of Bergen provided by Olseth et al., 2005. In the temperature, salinity and density files, the rows indicate the depth (0.5 m resolution, the first row is the surface, the last row is the bottom), whereas the columns indicate the experiment time at an hourly resolution.

Description: Effects of CO2 concentration on elemental composition of the coccolithophore Emiliania huxleyi were studied in phosphorus-limited, continuous cultures that were acclimated to experimental conditions for 30 d prior to the first sampling. We determined phytoplankton and bacterial cell numbers, nutrients, particulate components like organic carbon (POC), inorganic carbon (PIC), nitrogen (PN), organic phosphorus (POP), transparent exopolymer particles (TEP), as well as dissolved organic carbon (DOC) and nitrogen (DON), in addition to carbonate system parameters at CO2 levels of 180, 380 and 750 µatm. No significant difference between treatments was observed for any of the measured variables during repeated sampling over a 14 d period. We considered several factors that might lead to these results, i.e. light, nutrients, carbon overconsumption and transient versus steady-state growth. We suggest that the absence of a clear CO2 effect during this study does not necessarily imply the absence of an effect in nature. Instead, the sensitivity of the cell towards environmental stressors such as CO2 may vary depending on whether growth conditions are transient or sufficiently stable to allow for optimal allocation of energy and resources. We tested this idea on previously published data sets where PIC and POC divided by the corresponding cell abundance of E. huxleyi at various pCO2 levels and growth rates were available.

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: 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: 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: 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: 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 distribution of the replicates exposed to the same treatment. In experiments with multiple unresolved factors and a suboptimal number of replicates, post-processing statistical inference tools may fail to detect an effect. In such cases, model-based data analyses are suitable tools to unearth potential responses to the treatment and to identify which uncertainties may give rise to the observed divergences. As test cases, we use data showing high variability from two independent mesocosm experiments, where, according to statistical inference tools, biomass appeared insensitive to changing CO2 conditions. Our simulations, in stead, show earlier and more intense phytoplankton blooms in modeled replicates at high CO2 concentrations and suggest 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 estimate the thresholds below which uncertainties do not escalate into high variability. This information may help to interpret controversial results about acidification and to design future mesocosm experiments.

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. The simulations closest to the data with respect to our metric exhibit very low rates of global N2 fixation and denitrification, indicating that in order to achieve rates consistent with independent estimates, additional constraints have to be applied in the calibration process. For identifying the reference parameter sets, 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 2 and NO3- by more than a factor of 6 among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (Q0,phyN) and zooplankton maximum specific ingestion rate. Q0,phyN is revealed as a major determinant of the oceanic NO3- pool. This indicates that unravelling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q0,phyN, is a prerequisite for understanding the marine nitrogen inventory.

Description: A series of studies were conducted during the last two decades to investigate effects of ocean acidification (OA) on phytoplankton physiology, plankton ecology, and biogeochemical dynamics of marine ecosystems. Among those studies are experiments with tanks or bags called mesocosms, with some enclosed water volume that typically comprised a natural plankton community found in the surrounding environment. The Pelagic Ecosystem CO2 – Enrichment Study PeECE-I experiment is one such study, where mesocosms were perturbed and exposed to different carbon dioxide (CO2) concentrations to determine responses in growth dynamics of the coccolithophorid Emiliania huxleyi, a marine calcifying algae. The data from replicate mesocosms of PeECE-I show some natural variability and significant differences were revealed in the accumulation of particulate inorganic carbon (PIC) between mesocosms of similar CO2 treatments. In our study we reanalyse PeECE-I data and apply an optimality-based model approach to understand most of the variability observed, with major focus on total alkalinity (TA) and calcification. We explore how much of the observed variability in data can be explained by variations of initial conditions and by the effect of CO2 perturbations. According to our model approach, changes in cellular calcite formation are resolved at the organism-level in response to variations in CO2. With a data assimilation (DA) method we obtain three distinctive ensembles of model solutions, with low, medium and high calcification rates. Optimised values of initial conditions turned out to be correlated with estimates physiological model parameters. The spread of ensemble model solutions captures most of the observed variability, corresponding to the combinations of parameter estimates. Optimised model solutions of the high CO2 treatment are shown to systematically overestimate observed PIC production. Thus, the simulated CO2 effect on calcification is likely too weak. At the same time our model results yield large differences in optimal mass flux estimates of carbon and of nitrogen even between mesocosms exposed to similar CO2 conditions.

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 parameterisations 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 utilised 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 addressing issues of parameter identifiability. Aspects of model complexity will be covered and we describe how results from cross-validation studies provide much insight in this respect. Moreover, we elucidate inferences made in studies that allowed for variations in space and time of parameter values. The usage of dynamical and statistical emulator approaches will be briefly explained, discussing their advantage for parameter optimisations of large-scale biogeochemical models. Our survey extends to studies that approached parameter identification in global biogeochemical modelling. Parameter estimation results will exemplify some of the advantages and remaining problems in optimising 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.