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

On the reliability of the Autosub autonomous underwater vehicle

As autonomous underwater vehicles (AUVs) enter operational service an assessment of their reliability is timely. Using the Autosub AUV as an example, several design issues affecting reliability are discussed, followed by an analysis of recorded faults. Perhaps contrary to expectations, failures rarely involved the autonomous nature of the vehicle. Rather, faults were typical of those that occur with any complex item of marine electromechanical equipment. A statistical analysis showed that the failure rate decreased with distance travelled- an indicator that an AUV underway, submerged, is at less risk of a fault developing than during other phases of a mission. 1

On the joint estimation of model and satellite sea surface height anomaly errors

We describe a technique to estimate the error ®eld in the sea surface height (SSH) anomaly ®eld of an ocean model through the joint use of SSH anomaly ®elds measured from two satellites, Topex/Poseidon (T/P) and ERS-2. The joint error maps for the model, T/P and ERS-2 show distributions distinctly di erent from one another and globally inhomogeneous. Both sampling errors and instrument errors are represented in the mapped ®elds. Additionally, we compare the joint error estimation method to a technique using the model and only one satellite, and show the importance of the cross covariance between the measured SSH and the true SSH ®eld in the estimation of the error ®eld. Finally, we look at the distribution of the error versus the variance of the SSH at a location. This logged distribution suggests that the model errors are generally proportional to the model's variance (regression coe cient of 0.99, globally) while the satellites' errors do not exhibit this linear relationship (regression coe cients on the average of 0.60). The comparison of the two satellite distributions implies that ERS-2 has a lower sampling error than the T/P instrument except in the tropical region

Altimeter Measurements of the Volume Transport Through the Drake Passage

From in situ measurements it has been inferred that the variability of flow through the Drake Passage down to 250&n does not depend upon depth (barotropic flow). By making this assumption it is possible to calculate the variability of the volume transport from the surface slope as measured by a radar altimeter. It is shown that it is not possible to use data from individual passes as there is too much noise on the observations. However by averaging all the data in a ten day period in boxes north and south of the Passage an estimate of the variability comparable to the in situ observations is obtained. Using an additional two boxes in the centre of the Passage shows that most of the variability is in the northern half. The variability in the centre is negatively correlated with both the north and south

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