Parameterizing the microbial loop: an experiment in reducing model complexity

The structure of the plankton food web in the upper mixed layer has important implications for the export of biogenic material from the euphotic zone. While the action of the microbial loop causes material to be recycled near the surface, activity of the larger zooplankton leads to a significant downward flux of material. The balance between these pathways must be properly represented in climate models to predict carbon export. However, the number of biogeochemical compartments available to represent the food web is limited by the need to couple biogeochemical models with general circulation models. A structurally simple model is therefore sought, with a number of free parameters, which can be constrained by available observations to produce reliable estimates of export.A step towards addressing this aim is described: an attempt is made to emulate the behavior of an 11 compartment model with an explicit microbial loop, using a 4 compartment model. The latter, incorporating a basic microbial loop parameterization, is derived directly from the 'true' model. The results are compared with equivalent results for a 4 compartment model with no representation of the microbial loop. These non-identical twin experiments suggest that export estimates from 4 compartment models are prone to serious biases in regions where the action of the microbial loop is significant. The basic parameterization shows some promise in addressing the problem but a more sophisticated parameterization would be needed to produce reliable estimates. Some recommendations are made for future research

Traceability of performance between two ocean biogeochemistry models of differing complexity

A diverse range of candidate ocean biogeochemistry models exists for addressing scientific questions of societal importance in an Earth system context. Limitations imposed by computer resources favour the use of simpler models. However, there are recognized benefits of supporting different levels of complexity, not least because the appropriate level of complexity for a given application is an open research question. An important aim is to ensure that when simplifications are made there is a traceable link between models so that the implications are understood. A pilot study in traceability of model performance is presented in which the ability of a simple surrogate model, based on HadOCC, to emulate the behaviour of the intermediate complexity MEDUSA model is investigated. Adjustable HadOCC parameter values are optimized to fit MEDUSA output for an array of sites representing a range of oceanic conditions

Quantitative modelling of spatial variability in the north Atlantic spring phytoplankton bloom

The effects of variability in the physical environment on the development of the spring phytoplankton bloom are investigated using a physically forced model of the annual plankton cycle in the ocean mixed layer. The model is optimised to fit survey data from the eastern North Atlantic, collected over a 1500 x 1500 km area between 39N and 54N, from April-June 1991, establishing the feasibility of using spatially distributed point-in-time data in model calibration. Measurements made below the seasonal pycnocline show the existence of an empirical relationship between preformed nitrate and salinity in this area, allowing salinity-based estimates of pre-bloom mixed layer nitrate concentration to be made. These estimates provide important additional constraints for the model. The observed spatio-temporal patterns, at scales between 36 km and 1500 km, in nutrients, chlorophyll and measures of bloom progression derived from these data with reference to pre-bloom nitrate are discussed, together with the corresponding patterns in seasonal stratification. During the spring bloom, when biogeochemical concentrations vary rapidly in response to the developing stratification, absence of data defining its history limits the value ofcomparison between point-in-time observations and model results. Predictions of variationin stratification at the seasonal time-scale from general circulation models (GCMs) can beused in place of observational data to force ecosystem models. However, the degree to which observations are used to constrain the model solutions should allow for both model error in stratification and misrepresentation of the seasonal development of stratification by the observations. The latter occurs due to sampling error associated with short-term fluctuations. It can be corrected for if a suitable contemporary sea surface temperature dataset is available to define the variation of mixed layer temperature at the seasonal time-scale. It is shown that the accuracy of the GCM predictions can be improved by the application of meteorology specific to the year of observation. It is also shown that the sensitivity of the ecosystem model predictions to error in the physical forcing can be reduced by matching model and observations by a stratification measure, rather than by time, when comparing fields. The survey data show an important contribution to the stratification arising from the 'tilting' action of vertical shear on pre-existing horizontal buoyancy gradients in the winter¬time mixed layer. This effect was severely underestimated by the GCM. The discrepancy can be accounted for by the absence of density fronts and mesoscale dynamics in the model. Ecosystem model results suggest that spatial variance in Zooplankton grazing, due to the effect of differences in the depth and duration of winter-time mixing on the over-wintering success of plankton populations, is one of the major factors controlling the spatial and temporal characteristics of the phytoplankton bloom

A material balancing scheme for ocean colour data assimilation

A material balancing scheme for assimilation of ocean colour data in the FOAM-HadOCC model (Forecasting Ocean Assimilation Model with Hadley Centre Ocean Carbon Cycle Model biogeochemistry) has been developed with the aim of exploiting satellite data to improve air-sea CO2 flux estimates. The balancing scheme uses surface chlorophyll increments to determine increments for the biogeochemical tracers: nutrient, phytoplankton, zooplankton, detritus, dissolved inorganic carbon (DIC) and alkalinity. The analysis conserves carbon at each grid point and nitrogen at grid points where sufficient nitrogen is available, on the assumption that the modelling of processes transferring material between biogeochemical compartments is the main source of error. Phytoplankton increments are calculated using the model nitrogen:chlorophyll ratio. Increments to the other nitrogen pools (nutrient, zooplankton and detritus) are determined by a balancing model that responds to changes in the plankton dynamics. The nutrient balancing factor, the fraction of the phytoplankton increment to be balanced by the nutrient increment, varies according to the relative contributions of growth and loss rate errors to the phytoplankton error, as estimated from a probability model. Preliminary balancing factor values are adjusted to satisfy state-dependent restrictions on the size of the increments. Increments derived in this way are applied down to the depth of the mixed layer. Further increments are applied where necessary to avoid the creation of unrealistic sub-surface nutrient minima. Increments to DIC balance the implied carbon changes in the organic compartments and alkalinity increments are inferred from those for nutrient. An off-line evaluation of the scheme is carried out in a 1-D test-bed in which HadOCC biogeochemistry is forced by physical data for a range of latitudes in the eastern North Atlantic. Evaluation is by twin experiments for which synthetic system trajectories are generated by perturbing model parameters during integration to provide a range of plausible truths. Assimilation of daily chlorophyll observations, with or without simulated observation error, gives major improvements in pCO2 at the high latitudes but less improvement at low latitudes where it has a detrimental effect on summer and early autumn pCO2 due to errors in the model nitrogen:chlorophyll ratio. Beneficial effects of nitrogen balancing are demonstrated by comparison with experiments in which only phytoplankton and DIC are updated. The sub-surface nutrient correction increments are shown to reduce, but not remove, undesirable effects of assimilation on the nutrient and phytoplankton profiles. <br/

Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha)

A wide variety of different plankton system models have been coupled with ocean circulation models, with the aim of understanding and predicting aspects of environmental change. However, an ability to make reliable inferences about real-world processes from the model behaviour demands a quantitative understanding of model error that remains elusive. Assessment of coupled model output is inhibited by relatively limited observing system coverage of biogeochemical components. Any direct assessment of the plankton model is further inhibited by uncertainty in the physical state. Furthermore, comparative evaluation of plankton models on the basis of their design is inhibited by the sensitivity of their dynamics to many adjustable parameters. Parameter uncertainty has been widely addressed by calibrating models at data-rich ocean sites. However, relatively little attention has been given to quantifying uncertainty in the physical fields required by the plankton models at these sites, and tendencies in the biogeochemical properties due to the effects of horizontal processes are often neglected. Here we use model twin experiments, in which synthetic data are assimilated to estimate a system's known "true" parameters, to investigate the impact of error in a plankton model's environmental input data. The experiments are supported by a new software tool, the Marine Model Optimization Testbed, designed for rigorous analysis of plankton models in a multi-site 1-D framework. Simulated errors are derived from statistical characterizations of the mixed layer depth, the horizontal flux divergence tendencies of the biogeochemical tracers and the initial state. Plausible patterns of uncertainty in these data are shown to produce strong temporal and spatial variability in the expected simulation error variance over an annual cycle, indicating variation in the significance attributable to individual model-data differences. An inverse scheme using ensemble-based estimates of the simulation error variance to allow for this environment error performs well compared with weighting schemes used in previous calibration studies, giving improved estimates of the known parameters. The efficacy of the new scheme in real-world applications will depend on the quality of statistical characterizations of the input data. Practical approaches towards developing reliable characterizations are discussed

Mechanistic site-based emulation of a global ocean biogeochemical model (MEDUSA 1.0) for parametric analysis and calibration: an application of the Marine Model Optimization Testbed (MarMOT 1.1)

Biogeochemical ocean circulation models used to investigate the role of plankton ecosystems in global change rely on adjustable parameters to compensate for missing biological complexity. In principle, optimal parameter values can be estimated by fitting models to observational data, including satellite ocean colour products such as chlorophyll that achieve good spatial and temporal coverage of the surface ocean. However, comprehensive parametric analyses require large ensemble experiments that are computationally infeasible with global 3-D simulations. Site-based simulations provide an efficient alternative but can only be used to make reliable inferences about global model performance if robust quantitative descriptions of their relationships with the corresponding 3-D simulations can be established

Ocean color data assimilation with material conservation for improving model estimates of air-sea CO2 flux

A nitrogen balancing scheme for ocean color data assimilation in general circulation models is described and its potential for improving air-seaCO2 flux estimates is demonstrated. Given increments for phytoplankton, obtainable from a univariate surface chlorophyll analysis, the scheme determines mixed layer concentration increments for the other nitrogen pools: zooplankton, detritus and dissolved inorganic nitrogen (DIN). The fraction of the phytoplankton increment to be balanced by changing DIN varies dynamically with the likely contributions of phytoplankton growth and loss errors to the error in the background state. Further increments are applied below the mixed layer wherever positive DIN increments in shallower layers would otherwise cause the creation of unrealistic sub-surface minima. Total nitrogen at each grid point is conserved where possible. The scheme is evaluated by 1-D twin experiments for two contrasting locations in the North Atlantic, in which synthetic chlorophyll observations are assimilated in an attempt to recover known system trajectories generated by perturbing model parameters. Dissolved inorganic carbon (DIC) and alkalinity tracers, controlled by the nitrogen dynamics, determine the biological modification of sea-water pCO2 at the ocean surface. Assimilation affects DIC and alkalinity directly, the increments being inferred from the nitrogen increments, as well as having a post-analysis effect via the dynamics. It gives major improvements in surface pCO2 at 50N but less improvement at 30N where errors in the phytoplankton nitrogen:chlorophyll ratio cause it to have a detrimental effect in summer. Beneficial effects of nitrogen balancing are demonstrated by comparison with experiments in which only phytoplankton and DIC are updated in the analysis

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