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OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. MODELING BIOGEOCHEMICAL PROCESSES IN MARINE ECOSYSTEMS Grégoire, M. GHER, University of Liege, Sart-Tilman B5, B-4000, Liege, Belgium Oguz, T. METU, Institute of Marine Sciences, P.O. Box 28, Erdemli, 33731, Icel, Turkey Keywords: Marine biogeochemical cycles, ecosystem, mathematical modeling, food web, trophic levels, primary production, new and regenerated production, plankton, phytoplankton blooms, microbial loop, organic matter generation and decomposition, inverse modeling, data assimilation, general circulation, mesoscale eddies, upwelling, vertical mixing, entrainment. Contents 1. Introduction 2. General Classification of Biogeochemical Models 3. Models Complexity in terms of Food Web Structure 4. Physical Controls on Biogeochemical Models 5. Inverse Approaches in Biogeochemical Modeling 6. Regional Applications of Coupled Physical-biogeochemical Models Glossary Bibliography Biographical Sketches Summary Ecosystems are dynamic systems and the study of their functioning and evolution requires the development of dynamic biogeochemical models. A wide application and a pronounced development of this type of model has taken place during the last two decades due essentially, to the development of computer technology, which has permitted the handling of very complex mathematical systems, and also to the increasing need of understanding marine ecosystems and their dynamic response to environmental stresses from localized pollution to global climate changes. A review of the progresses realized in ecosystem modeling, starting from simple NPZtype models to more complex models, describing in parallel the biogeochemical cycles of different biogenic nutrients, is made in this paper. The evolution of the complexity introduced to the model's structure and in the parameterization of biochemical laws, is analyzed. The increasing coupling of ecosystem models with hydrodynamical models in order to assess the role of physical processes on the ecodynamics is discussed. The efforts made to develop ecosystem models more and more consistent with the available observations by, for instance, improving the calibration of the most sensitive model parameters and/or by determining the "optimal" model structure using data assimilation techniques are outlined. ©Encyclopedia of Life Support Systems (EOLSS) 261 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Finally, some regional applications of the coupled physical-biogeochemical models for various oceanic basins and coastal and shelf seas, as well as the recent attempts at incorporating them into the global carbon cycle models are described. 1. Introduction The oceans exhibit highly diverse and variable ecosystems governed by complicated sets of physical-biogeochemical interactions between the atmosphere, the surface ocean, and its interior, on a variety of spatial and temporal scales. The marine biogeochemical cycling involves organic matter generation by photosynthesis by primary producers constituting the first trophic level of the food web, its transfer to higher trophic levels by the feeding activities of animals, and its decomposition back to inorganic forms (Figure 1). Figure 1. Schematic simplified representation of the organic and inorganic matter cycling in the ocean. Particulate non-living organic material (the so-called detritus) is formed through the natural mortality of phytoplankton and zooplankton, or through the production of fecal pellets. Dissolved organic matter is formed by soluble organic materials released during excretion and exudation. These organic materials are then decomposed by microbial processes. The growth of primary producers is usually limited by the availability of one or several biogenic elements such as nitrogen (NO3+NH4+NO2), bioavailable iron (Fe), phosphate (PO4), and dissolved silicon (Si(OH)4). The silicon cycle is relatively simple, affects primarily diatoms and involves only inorganic forms. The phosphorus cycle is also relatively simple; organic phosphate is converted back to an inorganic form which then becomes available again for uptake by phytoplankton. It is a rapid process, and therefore phosphorus is generally not limiting in the marine environment. Recycling of nitrogen is a more complex process. Organic nitrogen is regenerated in the water column by bacterial activities and zooplankton excretion in the form of ammonium, which is then oxidized to nitrite and then to nitrate in the nitrification process occurring in the oxygenated part of the water column. In the anaerobic systems, mostly in sediments, nitrate is consumed as an oxidizer instead of oxygen. This process is referred to as the denitrification, and results in conversion of nitrate by bacteria, into first nitrite ©Encyclopedia of Life Support Systems (EOLSS) 262 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. and then to nitrogen gas, thus leading to the loss of nitrogen from the system. The atmospheric nitrogen gas may also be converted to organic nitrogen compounds by some phytoplankton species (e.g., the blue-green algae). The ocean is the largest reservoir of carbon with rapid exchanges with the atmosphere, and it is the largest net sink for anthropogenic atmospheric CO2. A fundamental process regulating the air-sea balance of CO2 and the amount of CO2 fixed in the ocean is the biological pump; namely the efficiency of photosynthesis, the foodweb structure and the amount of new nitrate entered into the euphotic zone, coupled with remineralisation at depth. In general, CO2 is converted from inorganic to organic carbon by the photosynthesis of the phytoplankton. This is then consumed by the higher trophic levels, and some CO2 is recycled as inorganic bicarbonate formed by interaction of free dissolved CO2 with water. Some losses may occur from the ocean surface in gaseous form. Respiration and remineralisation processes also contribute to CO2 production in the water column. (see Marine biogeochemical cycles: effects on climate and response to climate change). All the biogeochemical processes and interactions between living and non-living components of the ecosystems cannot possibly be explored through observations alone. The satellite-based observations, which are the only means of synoptic information on regional and global scales, are restricted to the upper few meters of the water column, and their correct interpretation requires additional knowledge on the physicalbiogeochemical processes taking place in the ocean interior. Comprehensive observational programs for basin-scale measurements are economically not yet feasible, and have to make compromises between temporal and spatial coverages as well as on the number of variables to be measured. The observations alone are therefore unable to provide a complete description of ecosystems. Observations generally provide distributions of concentrations and/or biomass, but hardly yield details on the spatial and temporal properties of the rates and processes controlling these distributions. Some mathematical tools are necessary to interpolate and extrapolate the available data to other parts of the region, and to complement missing data in a dynamically consistent way. The relative importance of different factors and their response to different conditions can be most efficiently analyzed using models, by varying individual factors independently. The ecosystem models may therefore be defined as mathematical tools that help to further understand, conceptualize, and predict marine environmental processes using a simplified representation of the real world in the form of a series of differential equations. Biogeochemical modeling has made considerable progress during the last two decades thanks essentially to the development of computer technology, and increasing public concern about environmental stresses spanning from localized pollution to global climate changes. In the following sections, highlights from some important modeling initiatives are presented in order to describe the progress made in marine biogeochemical modeling within the last ten years. The biogeochemical models are first classified according to their objectives. The structural complexity introduced into the food webs and nutrient cycles are then described. It is followed by a description of efforts on the coupling of ©Encyclopedia of Life Support Systems (EOLSS) 263 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. upper ocean physics and biogeochemistry, and of the role of physical controls on the biological production. The next section deals with inverse approaches used in the biogeochemical modeling for a more realistic estimation of the model parameters by means of assimilating the available data. The rest of the paper describes the regional applications of the coupled physical-biogeochemical models for oceanic basins and coastal and shelf seas, as well as the recent attempts of incorporating them into global carbon cycle models. 2. General Classification of Biogeochemical Models A wide spectrum of biogeochemical models is available, ranging from simple budget models, to more complicated process, heuristic, predictive and inverse type models. The budget models deal with estimation of the fluxes of water, nutrients and other materials in a region over annual or multi-annual time scales. Using the available data, these models attempt to make a budget of the system involving all major external and internal sources and sinks, as well as the dominant internal transformation processes of the variable in question (Figure 2). Figure 2. Cycling and transport processes that occur in the cycle of trace metals in the Adriatic Sea (above), and a simple budget model for the Adriatic Sea (below). ©Encyclopedia of Life Support Systems (EOLSS) 264 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. (Reprinted from Journal of Marine Systems, 25, Tankere S. P. C., Price N. B. and Statham P. J., pages 270 and 272, copyright 2000, with permission from Elsevier Science). They therefore require a substantial amount of data. Comparing fluxes through the system allows the budget models to identify key processes and to make tentative predictions about the consequences of environmental changes. Some models, called process models, are devoted to process oriented studies (Figure 3). They are designed to identify specific physical, chemical and biological processes, and to determine the relative influence of major environmental forcing functions. They are generally constructed on the basis of field or laboratory studies to test a hypothesis, to explore general principles on functioning of an ecosystem, and to predict the outcome of idealized scenarios. In most cases, they are developed in isolation from the total system with a limited number of variables and parameters, and by making use of a specific empirical dataset collected at a specific location. They constitute important building blocks of more sophisticated models. Figure 3. Diagrammatic representation of the internal metabolism of phytoplankton cells. Cellular constituents are divided according to their cellular function, into small metabolites S, reserve products R, and structural products F. (redrawn from Lancelot and Billen, 1989). Heuristic models are concerned with the behavior of whole ecosystems of a region by incorporating their physical and biogeochemical aspects. They are used to elucidate the dynamics underlying a particular set of observations. They integrate information on what are considered to be the most important variables and processes for the entire ecosystem. They have well defined spatial boundaries and temporal scales, and their behavior depends very much upon the scales selected. They contain a large number of physical, chemical and biological processes, variables and parameters, many of which are poorly understood. They are constrained, to some degree of accuracy, to reproduce time series of one or more measured state variables. These models are used to draw inferences about the dynamics that led to the observations. They can be either descriptive (e.g., flow or network analysis) or dynamic (e.g., simulation models). Most ©Encyclopedia of Life Support Systems (EOLSS) 265 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. of the available modeling studies fall into this group, for which some examples will be given in the following sections as we classify them below according to their structures. Heuristic models may also be designed to predict responses of the system to both natural variability and human activity (e.g., reduced freshwater runoff, increased nutrient input), and to understand how critical biogeochemical processes are influenced by environmental variables (Figure 4). This approach is particularly useful in extrapolating the present state of the ecosystem to the future, and allows prediction of the system's functioning under new conditions. The predictive capability of ecosystem models can be used as management tools for investigating alternative management plans and response scenarios to climatic and man-made environmental changes. Figure 4. Diagrammatic representation of a complex ecosystem model which has been used to hindcast the eutrophication of the North Sea during the last 40 years from 1955 to 1993. ©Encyclopedia of Life Support Systems (EOLSS) 266 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. This is a generic model that describes both the pelagic and benthic ecosystems and the coupling between them in terms of the significant bio-geo-chemical processes affecting the flow of carbon, nitrogen, phosphorus and silicon. It uses a 'functional group' approach to describe the ecosystem whereby biota are grouped together according to their trophic level, and sub-divided according to size and feeding method. The pelagic food web describes phytoplankton succession (in terms of diatoms, flagellates, picoplankton and inedible phytoplankton), the microbial loop (bacteria, heterotrophic flagellates and microzooplankton), mesozooplankton (omnivores and carnivores) and fish predation at a fixed grazing rate. The benthic sub-model contains a food web capable of describing nutrient and carbon cycling. The model contains about hundred parameters.The North Sea has been divided in 130 interior boxes interacting at their common boundaries via matter and energy exchanges. The hydrodynamical transports of matter by advection/diffusion are derived from a corresponding hydrodynamical simulation using a general circulation model. (Reprinted from Neth. Journal of Sea Research, 33, Baretta J.W., Ebenhoh W. and Ruardij P., copyright 1995, with permission from Elsevier Science). Early ecosystem models have generally been zero-dimensional, box models, integrated over some appropriately defined region of the sea. However, one now realizes that biological populations have complex spatial distributions, significantly affected by the hydrodynamic processes on similar space and time scales. Therefore, marine biogeochemical systems demand the time-dependent, three-dimensional models describing multiple interactions between the state variables. The success of ecosystem simulations are usually limited by our theoretical and experimental knowledge of the performance of all components of the ecosystem. Despite some limitations and problems of initialization, calibration and validation, when applied correctly, the 3D models provide useful insights into the internal processes of the system. The description of these processes can be used to predict the behavior of the ecosystem in relation to environmental change (Figure 5). The theoretical and experimental limitations of these models can be avoided to some extent by carrying out parallel studies on process models and more systematic measurements. a global entrainment by the mean flow, i.e., horizontal and vertical advection: ∂wy ∂z ∇ H .(uy ) + a slipping through the flow due to the diffusion of y, i.e., horizontal and vertical diffusion: λH ∇ 2H y + ∂ ∂y (ν ) ∂z ∂z or to its migration, i.e. ∂ ( wsy y ) ∂z . ©Encyclopedia of Life Support Systems (EOLSS) 267 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Figure 5. Schematic representation of a coupled 3D physical-biogeochemical/ecosystem model including its interactions with the atmosphere, the land and the sediments. The evolution in space and time of any 3D biogeochemical state variable y is described by equation 1. This equation illustrates the different ways according to which the hydrodynamics influences the ecodynamics. In particular, the transport of any biogeochemical variable results from the superposition of: Qy is the local production-destruction term resulting from biogeochemical interactions. A sound understanding of the oceans requires a combination of observations and models. Models are initialized by observations, solved by integrating forward in time, and then compared with a set of observations. If the simulations are successful, the models predictions and data have only minor discrepancies. The model results can then ©Encyclopedia of Life Support Systems (EOLSS) 268 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. be used to diagnose the processes governing the observed ecosystem. If the model simulations are inconsistent with the data, the food web structure and/or parameters of the model are then revised until the simulations are able to provide a desirable accuracy. Alternatively, as a recently growing discipline in marine ecosystem modeling, inverse methods offer a novel approach to determine the model inputs (e.g., parameters, forcing functions) directly from the observations. More precisely, ocean data assimilation refers to the estimation of marine fields of interest by melding data and dynamics in accord with their specific uncertainties (Figure 6). Data assimilation provides a powerful methodology for state and parameter estimation. Figure 6. General process of data assimilation. ©Encyclopedia of Life Support Systems (EOLSS) 269 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. 3. Models Complexity in Terms of Food Web Structure Models, in essence, reflect idealized, mathematical forms of natural ecosystems. A model is therefore never expected to contain all features of a real system, otherwise it would be the real system itself. The important point is to formulate the system as realistically as possible under given simplifications and approximations. Usually no practical, objective method exits to prefer a particular model; the choice is somewhat arbitrary, but it is dictated by the characteristics of the ecosystem under consideration, the degree of complexity one wishes to introduce, and the available data structure and observational knowledge. Therefore, several alternative models can be developed for the same environment, and it is always questionable to set up the right complexity. The simplicity versus complexity in representation of an ecosystem structure is also a matter of choice depending on the problem at hand, and the personal preferences of modelers. Some modelers are inclined to represent the ecosystem as simply and as idealized as possible, while some others tend to keep as much biological details as possible. It is a general assumption that a model may be made more realistic by adding more and more connections, up to a point. Addition of new state variables and new parameters beyond that point does not contribute further to improve simulations. Indeed, more parameters imply more uncertainty, because of the possible lack of information about all the processes. Given a certain amount of data, introducing additional new state variables or parameters beyond a certain model complexity does not give a further advantage, but only adds unaccounted uncertainty. Biologically more complex models may be thought of as providing potentially better representation of the system due to the great amount of state variables and process equations. They, however, have a major drawback: i.e., the need tospecify a larger number of unconstrained parameters. They are also more difficult to solve and understand, and often it is quite impractical to carry out detailed sensitivity analyses. Simpler models may be an oversimplified representation of the ecosystems, but they are easier to understand, contain a smaller number of parameters and are more amenable to detailed sensitivity studies. Simpler models may be preferred in the process-oriented studies designed to thoroughly elucidate the dynamics. On the contrary, those aimed at simulating observations, or to predicting a future state of an ecosystem as realistically as possible unavoidably require some degree of sophistication. Ideally, marine ecosystem models should comprise not only food web representations, but also biogeochemical cycles of major nutrients (i.e., nitrogen, phosphorus, carbon and silicon). However, most of the earlier models specified the entire foodweb structure in a very simple way by single phytoplankton and zooplankton groups, without providing details either at the species level or size fractionation. They were also supported by a grossly aggregated organic matter (detritus) compartment including both particulate and dissolved substances and a single inorganic nutrient (usually nitrogen) (i.e., NPZ-type models). In spite of the apparent simplicity in the mathematical description of the ecosystems, such grossly-aggregated models had some problems and difficulties in the representation of biochemical processes and in the specification of metabolism rates (e.g., growth, grazing, mortality). However, such simplified nitrogenlimited ecosystem models have been applied to numerous marine ecosystems around the world. ©Encyclopedia of Life Support Systems (EOLSS) 270 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. One of the simplest refinements of the NPZ-type grossly aggregated models is to divide the phytoplankton and/or the zooplankton compartments in several species groups or/and to consider several limiting nutrients (e.g., nitrogen, phosphorus, silicon). The phytoplankton compartment was often found necessary to be represented in the form of at least two species groups, as diatoms and flagellates, or two size groups as small and large phytoplankton. Such distinctions allow the simulation of the succession of blooms under different light, nutrient and zooplankton grazing conditions, and therefore gives rise to a more realistic representation of seasonal variations of the primary production and phytoplankton biomass. In some cases, this distinction was also accompanied by the introduction of silicon as another limiting nutrient, since the growth of diatoms depends on the efficiency of the silicate pump. The zooplankton compartment was customarily differentiated into two groups, as microzooplankton and mesozooplankton. In some cases, it was necessary to introduce more detailed structure in the form of several classes of microzooplankton, of mesozooplankton and higher predators. For instance, the ecosystem models applied to eutrophic regions have been refined to take into account opportunistic species, such as mixotrophic dinoflagellates and gelatinous carnivores. This is the case for the north-western shelf of the Black Sea and the European continental shelf of the North Sea. By the early 1980s, the importance of the microbial loop in controlling the organic matter recycling and plankton productivity became evident by measurements. Following these observational studies, a description of the microbial loop was introduced in ecosystem models and the NPZ-type models were extended to include bacteria, dissolved organic matter and microzooplankton. Some studies incorporated further sophistication by representing the refractory and labile fractions of DOM separately, and dividing the POM into small and large groups. Such fractionations led to a more realistic organic matter recycling. For instance, in the western Mediterranean, the small POM and the labile DOM are mostly regenerated, while the others are exported below the mixed layer. In environments with a limited ventilation, such as the Black Sea and the Arabian Sea deep waters, the degradation of the biological production leads to the formation of hydrogen sulfide, a toxic substance for all marine living resources. In the transition zone between the oxic and anoxic waters, the oxidization-reduction potential (redox potential) of the water decreases sharply due to oxygen deficiency and the appearance of hydrogen sulfide at its lower boundary. As a result, this layer contains a rich set of redox reactions involving oxygen, nitrogen, sulfur, carbon, manganese and iron, and so its lower and upper interfaces are characterized by important gradients of biological and chemical variables. Also, models describing the nitrogen and sulfur cycles (e.g., hydrogen sulfide, total elemental sulfur, thiosulfates, sulfates, total organic nitrogen, ammonium, nitrite, nitrate) coupled with oxygen dynamics, and sometimes to the manganese cycle as a catalyzer for the redox reactions have been developed for such oxygen-deficient waters. Figures 7 to 10 give the schematic representation of ecosystem models of different complexity applied in the eutrophic Black Sea basin with different aims. ©Encyclopedia of Life Support Systems (EOLSS) 271 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. The comparison of these four pictures shows clearly that the structure of the model is adapted according to the objectives of the studies. Also, for instance, the ecosystem model given in Figure 7 has been coupled with a 1D vertically integrated physical model in order to simulate the complex modifications of the Black Sea ecosystem structure and functioning caused by eutrophication. The model has been applied in the Danube's discharge area and is described by 33 state variables and 26 processes linking them. The biogeochemical model described in Figure 8 has also been coupled with a 1D physical model to provide a unified representation of the dynamically coupled oxicsuboxic-anoxic system of the interior Black sea. Figure 7. Structure of a complex ecosystem model applied on the north-western Black Sea. Solid lines show an idealized description of the foodweb representative of the situation before eutrophication. Dashed lines correspond to the incorporation of autotrophic opportunistic species. The gelatinous food chain is represented by dotted lines. Primary producers are described as three different functional groups according to their different physiological properties and trophic fates: diatoms (DIA), nanoflagellates (NAN) and autotrophic opportunistic species (OPP). The secondary trophic level is composed of bacteria (BAC), micro-zooplankton (MUZ), copepods (COP) and the giant omnivorous dinoflagellates Noctiluca scintillans (NOC). Among carnivorous organisms, only the jellyfish Aurelia aurita (AUR) and the exotic ctenophore Mnemiopsis ledyi (MNE) are considered explicitely. DOM and POM respectively are dissolved and particulate detritus. Most of the parameters are determined experimentally based on processes-oriented studies. (Reprinted from Kluwer Academic Publishers, Sensitivity to change: Black Sea, Baltic Sea and North Sea, E. Ozsoy and A. Mikaelyan (Eds), NATO ASI Series, 27, 1997, Van Eeckhout D. and Lancelot C., Modeling the functioning of the north-western Black Sea ecosystem from the 1960 to present, figure 4, page 460, copyright 1997, with kind permission from Kluwer Academic Publishers). ©Encyclopedia of Life Support Systems (EOLSS) 272 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Figure 8. Prey-predator interactions between plankton groups of the Black Sea pelagic foodweb (the biological model, 54 parameters, left), and (right) pathways of biogeochemical transformations constituting elements of the nitrogen and redox cycles in the model (about 21 parameters). The biological model represents the pelagic foodweb with two groups of phytoplankton (diatoms Pd, and phytoflagellates Pf), two classes of herbivorous/omnivorous zooplankton (microzooplankton Zs, and mesozooplankton Zl), nonphotosynthetic free-living bacterioplankton B, the gelatinous carnivore group Zm of medusae Aurelia aurita, and an opportunistic species group Zn representing the omnivorous dinoflagellate Noctiluca scintillans. DON represents the dissolved inorganic nitrogen, and D are the detritus. The numbers refer to remineralisation (1), ammonification (2), first step of nitrification (3a), second step of nitrification (3b), first step of denitrification (4a), second step of denitrification (4b), oxidation of dissolved manganese by nitrate (5), oxidation of dissolved manganese by dissolved oxygen (6), oxidation of hydrogen sulfide by particulate manganese (7), oxidation of ammonium by particulate manganese (8). The dotted lines indicate oxygen dependency of nitrification, and the broken lines show couplings of the biological model with the nitrogen cycle model (Reprinted from Global Biogeochemical Cycles, 14 (4), Oguz, T., Ducklow H.W., Malanotte-Rizzoli P., pages 1336 and 1337, copyright 2000, with permission from American Geophysical Union). The model relates the annual cycle of plankton production in the form of a series of successive phytoplankton, mesozooplankton, and higher consumer blooms to organic matter generation and to the remineralisation-ammonification-nitrificationdenitrification chain of the nitrogen cycle, as well as to anaerobic sulfide oxidation in the suboxic-anoxic interface zone. ©Encyclopedia of Life Support Systems (EOLSS) 273 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Figure 9. Schematic representation of a classical 5 state variable ecosystem model showing the state variables and the interactions between them. The version presented here has been applied in the Black Sea. The mathematical expression of the interaction terms is written on the arrows. ©Encyclopedia of Life Support Systems (EOLSS) 274 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Figure 10. Schematic representation of a refined version of the 5 state variables ecosystem model presented in Figure 9. This model is still defined by a nitrogen cycle described by 13 state variables. The food web is composed of two branches: the linear food-chain leading to fish (i.e., microphytoplankton, mesozooplankton) and the microbial food chain (nanophytoplankton, microzooplankton, bacteria, dissolved detritus, ammonium). (Reprinted from Journal of Marine Systems, 16, Gregoire, M., Beckers J.M., Nihoul J. C. J. and Stanev E., page 90, copyright 1998, with permission from Elsevier Science). Models described in Figures 9 and 10 are not as complex, because they have been coupled with a 3D physical model in order to assess the influence of the hydrodynamics on the ecodynamics. Figure 9 gives a classic NPZ-type model, whereas Figure 10 shows ©Encyclopedia of Life Support Systems (EOLSS) 275 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. a refinement of this model obtained by dividing the phytoplankton and zooplankton compartments respectively in to three and two groups, by including an explicit representation of bacteria, and by taking into account the possibility of a phytoplankton growth limitation by the availability of dissolved silicate and inorganic phosphorus. The sea surface phytoplankton concentrations simulated by these two models are shown on Figure 11 and are compared with a satellite picture showing the sea surface chlorophyll concentration. This comparison shows that the refined ecosystem model simulates more intense phytoplankton blooms, which are in better agreement with the observations. Indeed, some sensitivities studies of the model solution to the ecological parameter values have shown that the grazing control on the phytoplankton growth is overestimated in the 5 state variables model, due to the simplicity of the representation of the phytoplankton and zooplankton compartment. Besides, the influence of ecological interactions on the phytoplankton horizontal distribution is more pronounced in the refined model, and is outlined by the presence of some localized bloom corresponding to the pick of the development of one particular group (in the case of the simple model, the phytoplankton distribution is essentially driven by the hydrodynamics). However, one should note that due to feasibility and reliability constraints, simple models, such as the NPZ-type model, are often used in 3D high resolution coupled ecosystem-hydrodynamical models. Figure 11. Sea surface distribution of the phytoplankton field (in mgchlam−3) simulated by the NPZ-type ecosystem model (top-left) and by the refined ecosystem model shown in Figure 10 (top-right). Below, sea surface distribution of chlorophyll concentration (in mgchlam−3) reconstructed from satellite data (CZCS data). The HNLC (high nutrient-low chlorophyll) regions covering more than 20% of the world's oceans possess a fairly constant phytoplankton biomass all year long. In these regions, spring bloom activity is rarely observed and nitrate stocks are never ©Encyclopedia of Life Support Systems (EOLSS) 276 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. significantly depleted. The observations suggest that the combination of iron deficiency for the growth of net phytoplankton, and strong microzooplankton grazing control on nanophytoplankton communities are two reasons limiting the primary production in HNLC areas. Iron deficiency prevents phytoplankton growth rates from ever being sufficiently large for a bloom to be triggered. In the equatorial Pacific waters, iron supply was shown to determine the most prominent phytoplankton size fraction for blooming, while grazing pressure was shown to keep the population below the system's carrying capacity for smaller phytoplankton. The model simulations also suggested how the 1992 El Niño event modified the ecosystem composition from larger phytoplanktondominated populations and relatively high levels of new production, to a recyclednutrients based system dominated by small phytoplankton and zooplankton. An alternative approach for the development of complex food web structure is to introduce a generic, organism size-dependent food web structure and associated rate. This approach obviates, to a large extent, the difficulty of parameter estimations by using the body size as an independent criterion to estimate most of the parameters. It has been used in some highly sophisticated ecosystem models coupled with sediments dynamics and describing the cycling of several biogenic elements in parallel with several groups of autotrophs and heterotrophs. There are also a limited number of model studies which incorporate specific details about the population dynamics of particular zooplankton species to examine the role of different copepod stages on the functioning of the ecosystems. Such individual-based population dynamics models are designed to investigate the effect of different behavioral, bioenergetic, and physiological characteristics on growth and development of the copepods. For example, this approach has been used to understand how the copepod Metridia lucens respondes to environmental conditions in the North Atlantic Ocean (see Models and Functioning of Marine Ecosystems). 4. Physical Controls On Biogeochemical Models Marine ecosystems with their endogenous time scales are confronted with the multiscale physical environment. Recent modeling efforts recognized the substantial role of the upper ocean physics on the surface layer biogeochemical processes. A wide range of space-time variations of the biochemical processes in the water column requires a thorough representation of the upper layer physics; in particular, currents, temperature, salinity and vertical mixing. The biological production is modified by physical processes such as mixing and entrainment of nutrients from below, light penetration, confinement of phytoplankton in the euphotic zone by stratification, the input of nutrients by rivers discharges, lateral diffusion of nutrients or plankton by barotropic/baroclinic instabilities giving birth to filaments, meanders, or eddies (Figure 12). The fact that biological production in the euphotic zone is closely related to the upper layer physical processes demands coupling of the ecosystem model with a hydrodynamic model capable of simulating all these features of the surface layer. (see Nested interdisciplinary three-dimensional models of the marine system). ©Encyclopedia of Life Support Systems (EOLSS) 277 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Figure 12. Typical vertical structure of the water column (a) in winter, (b) in summer. In winter, the mixing layer depth is usually deeper than the euphotic layer and thus, the development of phytoplankton is limited by the light availability. In summer, however, the mixing layer is reduced to the first upper meters of the water column by the presence of a seasonal thermocline which inhibits the vertical exchanges. As a result, the surface layers are depleted in nutrients and the phytoplankton develops below the thermocline at the base of the euphotic layer using inorganic nutrients mainly produced by the in-situ regeneration of detritus, or brought by lateral advection/diffusion. The vertical resolution in the coupled physical-ecosystem models is achieved either by introducing a few interactive, homogenous layers or a series of vertical levels with typically 5-10 m spacing. In the case of the multi-layer representation of the water column, the layers are assumed thoroughly homogeneous and mixing takes place through the entrainment and diffusion processes across the interfaces between the layers. The entrainment rate is computed in terms of the upper layer flow characteristics using a Kraus-Turner type, bulk mixed layer turbulence parameterization. The performance of the layer-averaged models were found to depend critically on the mixed layer depth variations. This is because specification from observations is a highly ambiguous matter unless continuous time series measurements exist. Moreover, the mixed layer ecosystem models can not resolve the summer biological production once ©Encyclopedia of Life Support Systems (EOLSS) 278 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. the mixed layer generally becomes shallower than the euphotic zone and the production is confined to subsurface levels below the seasonal thermocline. The layer-type models thus underestimate the biological production during the periods of shallow mixed layer structures. On the other hand, in the case of the deeper mixed layer, primary production tends to be underestimated, because of the averaging of the light limitation function over the deep mixed layer. Multi-level representation of the water column, on the other hand, removes these deficiencies of the layer averaged representation and gives rise to a more realistic representation of the upper ocean biogeochemical structure. The vertical mixing is represented by the vertical diffusion process whose depth and time dependent coefficient is either specified empirically in some functional form in terms of the water column stratification characteristics, or computed using a turbulence closure model. They simulate stronger and more active summer production below the seasonal thermocline (known as the mid-water chlorophyll maximum) in summer months. Higher vertical resolution in the multilevel models not only provides some nitrate inputs from the nitracline layer, but also allows a more efficient recycling within the lower part of the euphotic zone, below the seasonal thermocline. Under these conditions, the subsurface chlorophyll maximum layer develops at a rate depending on the local zooplankton grazing and the light intensity. During such periods of stratification, the turbulence may be strongly inhibited in the upper layer. However, internal waves may provide some upwards and downwards displacements of plankton and nutrients. The high frequency variability of the wind stress induces short-term strong vertical mixing which generates nutrient pulses into the euphotic zone and provides additional contribution to the biological production process. More detailed and accurate representation of the temperature stratification and the PAR within the water column are other advantages of the vertically-resolved models. The penetration of PAR into seawater depends on the intensity of the surface light field and on the absorbing properties of water and the substances present in it. The studies indicated that the traditional, non-spectral models of light attenuation and photosynthesis can overestimate daily primary production in the water column as much as 50% and spectral properties of PAR must be considered if higher accuracy is necessary. Comprehensive models are available that divide PAR into a large number of wavebands and explicitly include the wavelength-dependent properties of various parameters which affect light attenuation and photosynthesis. However, the majority of biological models deal with the non-spectral form of the irradiance. The seasonal development of a thermocline, in combination with increasing solar radiation in spring, is accepted as a general mechanistic requisite for the development of blooms. There are, however, some observations suggesting that the spring bloom can precede the onset of vertical water column stability, i.e., prior to the formation of the seasonal thermocline. Indeed, simulations with a vertically-resolved, coupled physicalbiogeochemical model using turbulence closure parameterizations have shown that the intense vertical mixing delays the occurrence of the bloom until the mixing layer is shallower or comparable to the euphotic layer (Figure 13). Once this level was maintained, the bloom then developed rapidly within the upper part of the deep mixed layer receiving sufficient radiation to promote the photosynthesis, until it became ©Encyclopedia of Life Support Systems (EOLSS) 279 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. limited by the reduction in the surface nutrients. Therefore, vertically resolved models have an advantage over the bulk mixed layer models. They need not to delay the bloom until the mixed layer shallows up to a certain depth to provide a sufficiently strong solar radiation, which is the case in the mixed layer averaged models. Figure 13. Seasonal evolution of the vertical distribution of the turbulent kinetic energy (top), the temperature (middle) and the phytoplankton (bottom) in the central basin of ©Encyclopedia of Life Support Systems (EOLSS) 280 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. the Black Sea, simulated by a NPZ-type ecosystem model coupled with a 3D hydrodynamical field. This picture shows that the phytoplankton bloom occurs before the establishment of the seasonal thermocline. In coastal waters, modeling studies focused on the response of phytoplankton growth to the stratification controlled by local factors (e.g., river input, wind and tidal stirring) and on the possibility of predicting the timing of the bloom as in the deep waters using the Sverdrup critical ratio model. The model results have shown that, contrary to the classical concepts of stratification and blooms in deep pelagic systems, phytoplankton population growth does not necessarily increase as the surface layer shallows. The results reveal a more complicated relation than in the deep waters depending on the surface layer depth and various competing processes, such as (i) the interaction between turbulent mixing and sinking in the surface layer, (ii) maximization of the average net growth rate in the surface layer, (iii) minimization of turbulent leakage from the surface layer, and (iv) minimization of the production occurring below the surface layer. Whereas persistent stratification greatly increases the probability of a bloom, semidiurnal periodic stratification does not increase the likelihood of a phytoplankton bloom over that of a constantly unstratified water column. Furthermore, the surface layer depth, thickness of the pycnocline, vertical density difference, and tidal current speed all contribute in equal proportions to producing conditions which promote the onset of phytoplankton blooms. The formation of subsurface phytoplankton patches as a result of wind-forced motions in many fronts is another example of the strong link between the biological and physical dynamics. Simulations with coupled physical-biological models showed that the formation and maintenance of subsurface frontal patches was strongly dependent on water column properties such as frontal characteristics, nutrient gradient, euphotic layer depth, and historical wind stress. The model results indicated that the phytoplankton respond to cross frontal nutrient fluxes and wind-driven vertical mixing and advection on a time scale of the order of about a week. To the first order, the phytoplankton dynamics at the wind-perturbed front are controlled by vertical fluxes of nutrients. However, details of the structure and biomass of the subsurface patch are strongly influenced by the wind direction. Winds aligned with the frontal jet weaken the crossfrontal density gradient, causing a flux of nutrients into the warmer waters of the front, and eroding the chlorophyll patch through deep vertical mixing. Winds aligned against the frontal jet, on the other hand, enhance the cross-frontal density gradient and isolate the subsurface patch from vertical mixing. 5. Inverse Approaches In Biogeochemical Modeling The capacity of dynamical coupled physical-ecosystem models to simulate interdisciplinary processes over specific space-time windows, and thus forecast their evolution over predictable time scales depends upon the availability of relevant observations. The models require various types of datasets to initialize and continually update the physical and biogeochemical state of the ocean, to provide relevant atmospheric and boundary forcing, to parameterize and to properly represent the subgrid scale processes, growth rates and reactions rates. A main reason for the inability of the ecosystem models to simulate the observed biogeochemical fields is the need to ©Encyclopedia of Life Support Systems (EOLSS) 281 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. specify values for a large number of external parameters (e.g., half saturation constants, zooplankton grazing, specific growth and mortality rates). Most of these biological parameters also depend on the complexity of the model structure. They are generally poorly measured and known, differ greatly for different plankton species groups, and from region to region as well. In practice, these parameters are adjusted for each specific modeled ecosystem in order to obtain the "best" consistency between computed and observed state variables. The calibration may be carried out by trial and error procedure (i.e., subjectively) or by means of mathematical methods which yield the "best" fit (i.e., objectively) for selected parameters of the model. The classical approach is to tune each of these parameters subjectively until the model reproduces the observed data reasonably well for each ecosystem. A moderately complex model usually requires a hundred individual experiments to generate a parameter set that provides a successful and realistic simulation. The task of determining the parameter set can thus quickly become tedious even for a simple model configuration. In this approach, the calibration of 3D ecosystem models is even more difficult because the calibration of the parameters are based on only several observed vertical profiles, and may not be representative of the entire basin. The accelerating rate of development in the coupled physical-ecosystem modeling studies, together with the recent advances in measurement systems and data assimilation research efforts, gave a major thrust to the data assimilation studies. The data assimilation strategy in ecosystem models helps to calibrate the models, to determine the simplest possible model structure that is consistent with the data, to identify limitations of existing model formulations and to give insight in to how to remedy this problem, and to determine the most critical data types constraining the models. The essence of the data assimilation procedure is to minimize the misfit between the data and the corresponding control variables (e.g., model parameters, initial/boundary conditions, forcing functions) under certain constraints satisfied by the model. The implementation of data assimilation procedures requires a dynamical description of the behavior of the ecosystem (forward model), observations that provide information on the ecosystem dynamics (data), and a technique used for incorporating the observations into the model dynamics (inverse model) (Figure 6). The most common inverse techniques adopted in ecosystem modeling studies are based on non-linear optimization algorithms such as adjoint variational methods, simulated annealing, or regression methods. Due to the large number of parameters often needed to be specified in the models, data assimilation procedures becomes particularly useful if some redundant parameters are removed from the model, prior to data assimilation. For instance, the nondimensionalization of the model equations helps to estimate the first order processes and their parameters, and allows a more optimal and efficient solution to the inverse problem. On the other hand, assimilation of a limited number of data sets can only constrain a few linear combinations of the parameters, and therefore is generally not sufficient to provide a realistic representation of the entire ecosystem structure. Also, it was shown that assimilating surface chlorophyll concentration alone from satellite data was sufficient to constrain primary production within the photic layer, but inadequate to constrain its regeneration in surface water and its export towards deep water. Simultaneous assimilation of surface chlorophyll, temperature and nitrate concentration ©Encyclopedia of Life Support Systems (EOLSS) 282 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. data was found to give a significant improvement over model simulation in terms of a more realistic trophic system within the euphotic layer. The application of a data assimilation algorithm to a specific ecosystem model does not always guarantee the realism of a simulation, unless the model is appropriately set up to describe all the basic biological processes. The most important constraint is the number of independent data types which are used in parameter estimations. When a specific data set from the Ocean Weather Station Papa in the Gulf of Alaska was applied to three different ecosystem models of varying complexity, the data were able to resolve up to 10 independent model parameters. This number was, however, less than the number of unknown parameters even for the simplest NPZ-type model. This implies that a more complicated ecosystem structure, such as the 7-compartment configuration (i.e., the phytoplankton and the zooplankton biomass, nitrate, ammonium, dissolved and particulate detritus and bacteria), cannot be justified, unless additional measurements are made available. A similar study designed to reproduce the Bermuda Atlantic Time Series (BATS) data using a 7 compartment model also arrived at similar conclusions. They indicated that some of the model parameters couldn't be estimated independently and that the seven-compartment model used was not appropriate for simulating the BATS ecosystem. Testing various more simplified versions of this model, its four compartment reduced form with only 11 parameters to be estimated was able to describe more successfully the annual cycle in BATS data as compared with the original model. The assimilation of both physical and biological data in 3D coupled models is relatively new. A challenging test of this approach has been applied to the Gulf Stream frontal region characterized by highly complex mesoscale physical and biogeochemical fields. This study has shown the importance of the submesoscale "event scale" processes in physical-biological interactions. It further demonstrated that the high resolution 4-D field estimation through observations is necessary to capture the essential physicalbiological interactions inferred by the data. It has been found that jet meandering and winds cause only slight enhancement in phytoplankton biomass at the Gulf stream front while ring-stream interaction events are an important process for generating phytoplankton patches at the front (see Synoptic/Mesoscale Processes). 6. Regional Applications of Coupled Physical-Biogeochemical Models The three dimensional, regional or global scale coupled physical-ecosystem models are the only possible approach to interpolate observations from different times and locations so that a dynamically-consistent picture emerges. The regional models are developed as a compromise between the structural complexity required to accurately reproduce detailed local measurements, and the conceptual simplicity required for large scale applications and interpretations. There is also a practical constraint due to the computational facilities that impose limits on the number of explicitly resolved ecosystem variables and/or vertical and horizontal resolution of the model. These limitations and restrictions put a major constraint on the widespread use of the high resolution, state-of-the-art ocean circulation-biogeochemical models. The applicability of eddy-resolving coupled models for basin wide and ocean scale applications studying ©Encyclopedia of Life Support Systems (EOLSS) 283 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. annual structure and interannual variability of the system is therefore quite limited and began only within the last few years. 6.1. Models for Open Ocean Studies and Basin Scale Applications A common feature of the current three dimensional interdisciplinary models simulating the basin-scale variations of the physical and biogeochemical environment is the relative simplicity in the treatment of both plankton and circulation dynamics. Due to computational limitations, and feasibility and reliability constraints, 3D ecosystem models generally consider the biogeochemical cycle of only one type of biogenic element (mostly nitrogen) together with a limited number of aggregate variables (Figure 9). Simplifications are introduced to the circulation models either by invoking the primitive equation dynamics with multi-layer configuration or the quasi-geostrophic dynamics using a vertically better-resolved water column. The first approach considers a more simplified and crude vertical structure in the form of several homogeneous, interactive layers. However, since it uses fully primitive equation dynamics, it is valid for mesoscale dominated circulation problems. On the other hand, the other approach is only applicable for open ocean dynamics where non-linearity is not important. Its relative computational efficiency, however, allows the introduction of a high number of vertical levels to resolve the water column. Both of these approaches therefore offer computational efficiency for the long term (decadal) integrations. The choice depends on the nature of the problem under consideration. Some problems (generally those occurring on coastal and shelf regions) are inherently non-linear and require the introduction of a high vertical resolution; they therefore dictate implementation of vertically-resolved, multi-level primitive equation circulation models coupled with a biogeochemical model of similar vertical configuration. This type of model is relatively more expensive, and time consuming. An application of the layer-type coupled model was made to investigate the seasonal variations of the surface circulation and biological production in the Arabian Sea. The simple physical and ecosystem structures were defined by introducing phytoplankton, zooplankton, nitrate and detritus compartments using a two layer system over an infinitely deep and motionless water body. The mixed layer dynamics were embedded inside the first layer to compute temporal changes of the mixed layer thickness depending on the entrainment, detrainment and vertical mixing. The particular objective of this study was to understand the relative contributions of upwelling, vertical mixing, horizontal advection and biological remineralisation in generating nutrient sources for the blooms, the role of light limitation in controlling the onset of blooms, and the relative contributions of predation and nutrient depletion for their decays. For the climatologically mean conditions of the Arabian Sea, three different types of bloom formation have been identified in response to physical processes of upwelling, detrainment and entrainment. Upwelling blooms were strong, long-lasting events that continued as long as the upwelling persisted. Detrainment blooms were found to be short-lived, intense events that developed when the mixed layer shoaled abruptly, thereby increasing the depth-averaged light intensity available for phytoplankton growth. They occurred mostly in spring during the mixed layer shallowing phase. In contrast, entrainment blooms were weaker because entrainment steadily thickens the mixed layer, which in turn decreases the depth-averaged light intensity. ©Encyclopedia of Life Support Systems (EOLSS) 284 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Alternatively, several studies adopted the vertically-resolved, multi-level models with primitive equation dynamics. In the case of basin-scale applications, like in the Northern Pacific or Atlantic, a compromise for a rather economical and practical use of the models was made by having a relatively coarser horizontal grid resolution. The emphasis was then given to the larger scale features of the circulation by ignoring the role of mesoscale dynamics on the biogeochemistry of the basin. In some of these models, a further sophistication has been introduced by implementing a higher level Mellor-Yamada type turbulence closure parameterization for vertical mixing. An application of such a sophisticated hydrodynamical model coupled with a sixcompartment ecosystem model involving the phytoplankton, zooplankton, nitrate, ammonium, particulate and dissolved detritus compartments has been given for the North Pacific, extending from 122oE to 72oW and from 23oS to 63oN. It used a 2ox2o grid interval with 45 vertical levels compressed toward the surface to properly resolve the seasonal variation of the mixed layer. The model was found to successfully simulate various observed features of the circulation and ecosystem in different domains of the North Pacific. But it also had some deficiencies such as an underestimation of surface chlorophyll concentrations in the subpolar regions and an overestimation in the subtropics and equatorial region. A limited number of three-dimensional, coupled models resolving mesoscale circulation features and implementing multi-compartmental plankton groups and multi-nutrient limitations are also available for smaller basins, such as the Black Sea (Figure 10), Baltic Sea, the North Sea (Figure 4) and for various sub-domains of the oceanic basins. Such modeling studies suggested that the primary production may be increased by at least 30 percent through the mesoscale dynamics. One of the two main mechanisms invoked to explain such enhancement of the biological productivity is the upward vertical circulation associated with eddy dynamics. This process can transfer nutrients to the enlightened surface layer in nutrient limited conditions. The second mechanism is related to the eddy-induced mixed layer shoaling. In light limited conditions, it can locally increase the mean exposure time of the photosynthetic organisms to solar radiation. In both cases, mesoscale dynamics can play a role in enhancing primary production by reducing the dominant stress. The contribution of mesoscale eddies to open ocean primary productivity has been little explored simply because the open ocean observational programs are limited in number, and usually provide biweekly to monthly measurements, and therefore are not able to capture sporadic processes on a weekly time scale. For example, the subtropical gyre of the North Atlantic has long been thought to be a mid-latitude "ocean desert" due to its downwelling vertical motion and lack of nutrient supply into its surface waters. But it becomes evident that it sustains some degree of primary productivity due to eddyinduced nutrient transport on horizontal scales of several tens to hundreds of kilometers, and over a time scale of several weeks. Indeed, the studies in the Sargasso Sea of this subtropical gyre have described the importance of eddy-induced upwelling mechanisms associated with a passage of strong cyclonic eddies to meet the nutrient demand for new production. The nitrate flux due to eddy pumping has been estimated to be an order of magnitude greater than the diapycnal diffusive flux and transport due to isopycnal transport. The mechanism of eddy upwelling has been explained by the vertical perturbation of density surfaces, as a result of formation, evolution and destruction of ©Encyclopedia of Life Support Systems (EOLSS) 285 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. mesoscale features. Shoaling density surfaces lift nutrients into the euphotic zone, which are then rapidly utilized by the biota. Deepening density surfaces serves to push nutrient-depleted water out of the illuminated surface layer. The asymmetric light field thus rectifies vertical displacements in both directions into a net upward transport of nutrients, which is balanced by a net commensurate flux of sinking particulate material. This idea has been put into a larger context to investigate the contribution of eddyinduced nutrient supply to the entire North Atlantic Ocean, by means of an eddyresolving coupled general circulation-ecosystem model including altimeter data assimilation, to achieve a more realistic representation of the real ocean eddy field. The comparison of the simulations performed with and without mesoscale eddies in the circulation field has indicated that eddy activity was able to increase the total nutrient input into the euphotic zone by about thirty percent in the subtropic and mid-latitudes. The same conclusion has been found in the North Equatorial Current (NEC) system which is a region of strong mesoscale activity (e.g., strong currents, eddies, fronts). Modeling studies have shown that meanders and eddies formed by instabilities cause upward motion of water transporting nitrogen into the euphotic zone. This upwelled nitrogen induces an enhancement of about 30% of the primary production and of the phytoplankton biomass primarily during the period of eddy-eddy interactions at the edge of eddies. In non-upwelling regions or when upwelling events are seldom present, the horizontal advection of particulate organic matter is shown to support the production. Moreover, the model studies indicated doubling of the primary production in the jet zone when the eddies are resolved. In addition to the transfer of nutrients to the upper enlightened layer, mesoscale dynamics may also induce the shoaling of the mixed layer and thus enhance the phytoplankton development by locally increasing the mean exposure time of the photosynthetic organisms to solar radiation. For instance, modeling studies applied to the north-western Mediterranean Sea have suggested that eddies, which are developed at the periphery of the dense water patch, participate very actively in the restratification of the surface waters following deep convection. Indeed, mesoscale upward motions cause shoaling of the mixed layer in the trough of the meanders, which then acquires higher rates of solar radiation and thus increase primary productivity. Phytoplankton biomass is then subducted from these sources towards the crest of the meanders. The center of the patch, on the other hand, is the least productive region where strong and deep vertical mixing is shown to inhibit photosynthesis (see Synoptic/Mesoscale Processes). 6.2. Models for Coastal Regions and Shelf Seas Recent decades have seen a degradation of the environmental quality in various basins of the world's oceans caused by eutrophication and pollution problems resulting from increased anthropogenic inputs of terrestrial origin (e.g., mineralized nutrients, organic and inorganic pollutants). Such problems particularly affected the coastal zone located at the interface between the continents and the oceans and thus exposed to increasing socio-economic pressures, led to dramatic alterations of the structure and functioning of the ecosystem. In particular, the semi-enclosed seas and enclosed inland water bodies were the regions most sensitive to natural and anthropogenic perturbations. ©Encyclopedia of Life Support Systems (EOLSS) 286 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Since the early 1980s, satellite-derived observations of the ocean surface temperature (Advanced Very High Resolution Radiometer, AVHRR), pigment distributions from CZCS (Coastal Zone Color Scanner) and SeaWiFS (Sea-viewing Wide Field-of-view Sensor) provide evidence of enhanced biological production in coastal regions. Increased production near-coastal boundaries are often related to eutrophication phenomena due to high anthropogenic load of nutrients and organic materials from rivers. Coastal waters thus tend to present very different patterns of phytoplankton biomass variability from the open ocean waters. The connections between physical variability and phytoplankton bloom dynamics are less well established for these shallow systems. Predictions of biological responses to physical variability in these environments are inherently difficult because the recurrent seasonal patterns of mixing are complicated by aperiodic fluctuations in river discharges and the high frequency components of tidal variability. In addition, shallow coastal waters have high concentrations of suspended particulate matter that originate from resuspension of bed sediments as well as riverine inputs of terrigeneous material. Light attenuation by suspended particulate materials acts as a major control on photosynthesis. On the other hand, nutrient availability is relatively high in coastal waters because of terrigeneous inputs as well as rapid recycling rates, both in the water column and the benthic boundary layer. Moreover, benthic-pelagic coupling is also a controlling factor for changes in the phytoplankton population in coastal waters. Offshore flowing jets, mesoscale eddy fields embedded within the slowly moving current systems, meandering currents as well as other transport mechanisms due to wind-induced upwellings, episodic mixing of deep water by winter convection, and diapycnal diffusion can all lead to highly complex biological structures in coastal waters. Therefore, shallow shelf waters are less predictable and have more complex bloom dynamics than the open ocean. Because the eutrophication-induced biological production has severe consequences for local tourism, fishery and economy, ecosystem modeling studies devoted to coastal regions and shelf seas have received a particularly great interest. Indeed, ecosystem models are necessary to assess the ecosystem's vulnerability to contaminants of anthropogenic origin, and to calculate the transfer and accumulation of toxic substances from one level of the food web to the next. Besides, the study of the eutrophication problems and their impacts on the basin scale ecosystem cannot be made without considering the physical processes leading to the mixing and transport of pollutants and biogeochemical constituents discharged by the rivers. The studies of the coastal threedimensional coupled physical-biogeochemical models have been approached either by exploring the dynamics of certain physical mechanisms governing the regional ecosystem, or by understanding the details of the ecosystem's functioning under given physical conditions. Several ecosystem models have been developed to study the eutrophication problems in various heavily polluted regions such as the western part of the Seto Inland Sea, the Italian coast of the northern Adriatic receiving the inputs from the River Pô, the Baltic Sea, the North Sea, and the Black Sea. For instance, the North Sea has been the subject of a series of modeling attempts during the last two decades to hindcast the long-term transformation of the North Sea ecosystem with the gradual build up of eutrophication since 1950s. They reveal that the increase in primary production associated with ©Encyclopedia of Life Support Systems (EOLSS) 287 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. eutrophication was not simply proportional to the increase in the anthropogenic nutrient load. It was shown that, once the ecosystem is perturbed, it shows a complicated response to many factors such as anthropogenic nutrient load, zooplankton grazing, temporally varying nutrient limitations, and domination of different phytoplankton groups at different seasons. A study of the sensitivity of the Black Sea's ecosystem dynamics to the macroscale and mesoscale dynamics has been made by means of a three-dimensional, mesoscale resolution hydrodynamical model coupled with different ecosystem models describing a simple nitrogen based structure defined by the phytoplankton, zooplankton, nitrate, ammonium, pelagic and benthic detritus (Figures 9 and 10). A more sophisticated version of the biological model has also been developed to test the roles of phosphate and silicon in the growth of three different phytoplankton groups. This model has further involved two zooplankton groups and the microbial loop. The results illustrated a highly complex spatial variability of the phytoplankton annual cycle imparted by the horizontal and vertical variations of the physical and chemical properties of the water column. In particular, the seasonal variability of the north-western shelf circulation induced by the seasonal variations in the Danube discharges and the wind stress intensity have been found to have a major impact on the primary production repartition of the area. In agreement with observations, the results indicated a rapid and efficient recycling of particulate organic matter in the oxygenated layer of the water column. More than 90% of the particulate organic nitrogen produced in the euphotic zone was shown to be recycled in the upper 100 m of the water column and about 79% in the euphotic layer. Comparison of the seasonally-averaged phytoplankton biomass computed by the models with the satellite-derived estimates of the seasonal surface chlorophyll field suggested the best agreement under the simulations with the mesoscale resolution of the hydrodynamical models, and with the refined ecosystem model having a better representation of the biological processes. The continental margin zones are well known as regions of enhanced biological production, partly fuelled by nutrients brought by river discharges and boosted in many regions by upwelled nutrients from the deep sea. The shelf-edge processes influence the cross-shelf exchanges of biogenic nutrients, and plankton, as well as larvae transport. The continental shelf-deep sea interactions are therefore of crucial importance, and the coupled hydrodynamics-ecosystem models are needed to study in detail the flow structure over the steep continental slope, the shelf edge processes and the transport of biogeochemical constituents along and across the continental slope. A study in the Gulf of Lion region of the Western Mediterranean indicated the complexity and temporal variability of the mechanisms governing the exchanges across the continental margin zone. In early winter, new production is very low, and sediment release represents a major source of nitrate to the margin, which is not locally consumed biologically, but carried away to the open sea by deep offshore flow along the bottom. Later in the winter, as the deep mixing intensifies in the region, the surface waters of the open sea are enriched and advected onto the margin. During the spring bloom event, new production is more active on the shelf than offshore due to an additional contribution of the Rhone River, leading to stronger depletion of nitrate in the margin as compared with the open sea. For this period, the new production is sustained in the coastal zone by both the Rhone River discharge and oceanic input. ©Encyclopedia of Life Support Systems (EOLSS) 288 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Modeling studies have examined the influence of the freshwater and nutrient discharges on the formation of inner shelf frontal structures and associated alongshore and crossshelf current systems. Alongshore currents developed in response to cross-shelf density gradients were strong, and typically about 100 ms-1 at the surface. The cross-shelf circulation is characterized by a surface-intensified offshore current near the coast and multiple cells inside the frontal zone. In addition to being recycled through biological processes, the nutrients in the frontal zone were shown to be supported physically by two sources: (i) horizontal advection from the river discharge, and (ii) advectioninduced upward nutrient flux outside the frontal zone. Multiple cells of the frontal zone acted like retention zones that recirculated the nutrients in the vertical and also restricted marine organisms from cross-frontal exchanges. The physical processes thus caused the formation of a large concentration of bottom-rich nutrients in the weak current region within the frontal zone. Cross-shelf distributions of the biological fields were further significantly modified by upwelling and downwelling favorable winds depending on their amplitude and duration. The downwelling-favorable winds tended to enhance the vertical mixing and caused a more vertically uniform pattern of nutrients, phytoplankton and zooplankton. The upwelling-favorable winds, on the other hand, tended to speed up the offshore migration of phytoplankton and nutrients in the upper layer and the onshore advection in the lower layer. This process increased the vertical gradients at the expense of reduced horizontal variations within the shelf. 6.3. Models Embedded in Global Ocean Carbon Cycle Studies A novel understanding of marine productivity is necessary to appraise the role of marine ecosystems in global changes. The present ocean carbon cycle models consider an extremely simplified ecosystem configuration for the oceanic uptake of atmospheric CO2. In the past, the majority of these models assumed no variations in the biological pump over the whole ocean, and its role was parameterized indirectly by forcing the model output towards observed surface nutrient and other biogeochemical observations. However, this technique predicted unrealistically high nutrient accumulation in the equatorial region, and suggested that a considerable amount of the new production flux to the ocean interior must be carried by dissolved organic matter. Indeed, it was thought that if the dissolved organic matter has a lifetime of years and decades, its advection would carry nutrients away from the regions of strong upwelling and thereby avoid nutrient trapping. Furthermore, specification of one globally uniform phosphorus uptake rate for the annual net effect of biological processes generally provided a better agreement between the simulated and observed seasonal amplitude of CO2 partial pressure (pCO2). However, the model yielded an almost complete depletion of phosphate in the surface waters of the subtropical gyres, due to deficiency of the circulation field in bringing sufficient phosphate to the surface layer. The enhanced incorporation of phosphate into particulate organic matter and its export from the surface layer in upwelling areas resulted in a strong PO4 depletion that was only supported by lateral PO4 advection, as in the subtropical gyres. In a general sense, the coupling of the global scale carbon cycle models with sophisticated biological models allows a better representation of the pCO2 repartition. These studies have revealed that, even under the apparent simplicity of the plankton models, they reproduced regional differences in seasonal oceanic pCO2 as expected, and generally model results were in qualitatively good agreement with the observations. Moreover, zooplankton dynamics ©Encyclopedia of Life Support Systems (EOLSS) 289 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. was shown to cause an important part of the regional variations. The interplay between the mixed layer depth (which influenced the local phytoplankton growth rate) and reduced zooplankton abundance allowed the development of strong phytoplankton blooms, while, in other regions, the North Pacific for instance, relatively shallow mixed layer depths supported a higher zooplankton standing stock. The seasonal variations of the phytoplankton populations were followed by immediate responses in the zooplankton stocks. This study confirmed that the seasonal effect of marine biology might be captured by simple plankton models. However, simple ecosystem models with globally uniform parameter settings seemed not to be able to successfully simulate the observed different seasonal cycles of primary production and nutrients between different regions. More complex ecosystem models must be developed if one wishes to parameterize the difference between ecosystem dynamics at high latitudes of the Southern and Northern Hemispheres. In this case, simple hydrodynamic models, usually global box models, have to be used. A more complex global carbon and nitrogen cycling model represented major oceanic basins in the form of interactive boxes (Figure 14). Each of these boxes involved three homogeneous layers representing surface, intermediate and deep water masses. This type of model gives a detailed description of the seasonal cycles of the surface mixed layer ecosystem and specifies different parameters for different regions. The biological model included the phytoplankton and the zooplankton biomass, the organic and inorganic parts of detritus, dissolved inorganic nitrogen, oceanic total inorganic carbon, total alkalinity and atmospheric carbon dioxide. The model also included formation of particulate organic matter and CaCO3 in the upper ocean, their export to depth, degradation of particulate organic matter and dissolution of CaCO3 in the deep ocean, and exchange of CO2 between the ocean and atmosphere. The model, primarily considered the processes of gas exchange between ocean and atmosphere, advective transport, entrainment and detrainment at the lower boundary of the mixed layer, primary production of phytoplankton, phytoplankton and zooplankton mortality, detritus remineralisation, gravitational sinking of particles, degradation of detritus, and inorganic carbon chemistry processes. Figure 14. Global box model simulating nitrogen and carbon cycling in the ocean including the biological pump. ©Encyclopedia of Life Support Systems (EOLSS) 290 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. The model includes a detailed description of the seasonal cycles of the oceanic upper mixed layer (UML) ecosystem and a refined parameterization of the role of seasonal variations of marine biota in atmospheric pCO2 regulation. The difference between ecosystem dynamics at high latitudes of the Southern and Northern Hemispheres is also parameterized. The ocean is represented as a stack of three homogeneous outcropped boxes (deep, intermediate and surface water masses) in each hemisphere. The model prognostic variables include phytoplankton P, zooplankton Z, the organic part of detritus D, dissolved inorganic nitrogen N, inorganic carbon C, the inorganic part of detritus H, total alkalinity A, and atmospheric carbon dioxide Ca. The principal processes considered by the model are: gas exchange between ocean and atmosphere, advective transport, entrainment and detrainment at the lower boundary of the UM, primary production, phytoplankton and zooplankton mortality, grazing of zooplankton, detritus remineralisation, gravitational sinking of particles, degradation of the detritus hard parts, and inorganic carbon chemistry processes. (Reprinted from Global Biogeochemical Cycles, 14 (4), Popova, E.E., V. A. Ryabchenko, M.J.R. Fasham, page 479, copyright 2000, with permission from American Geophysical Union). Also, there are some attempts to simulate the ecosystem characteristics of various contrasting regions of the global oceans such as the Southern Ocean, the North Pacific, and the North Atlantic by implementing a globally standard biogeochemical model structure. One such a model described the global ecosystem by ammonium, nitrate, multiple-size classes of phytoplankton, zooplankton, bacteria, detritus and dissolved organic nitrogen with a variable chlorophyll-to-biomass (nitrogen) ratio. It was shown that the inclusion of multiple size classes of phytoplankton and detritus, as well as the use of a variable chlorophyll-to-nitrogen ratio enabled the model to predict important biological differences not only temporally within a site, but also spatially between the different regions. Glossary Aggregation: Assimilation of data: Autotrophs: Biota: Calibration: Ciliates: Compensation depth: Unification of system components that are homogeneous in some properties into blocks, each being a new component with properties defined by the aggregation laws. Incorporation of information from observations into the model, in order to improve model predictions. Organisms able to fix carbon from inorganic to organic form using light or chemical energy. Plant and animal life of a region. Adjustment of a number of model parameters in order to obtain the best accordance between computed and observed state variables. (microns to several hundreds of microns). Present in all marine regions, they can be extremely important. They use cilia for locomotion, and eventually food capture. They feed on small phytoflagellates and zooflagellates, small diatoms and bacteria. The depth at which the photosynthetic system is at the compensation point. In field work, the compensation depth can be approximated from the depth of 1% of the surface radiation. Compensation depths may vary from a few meters in turbid coastal ©Encyclopedia of Life Support Systems (EOLSS) 291 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Compensation point Copepod: Continental shelf: Critical depth: Ctenophores: Denitrification: Detrainment: Diapycnal: Diatoms: Dinoflagellates: Dissolved Organic Matter: Downwelling: DOM: Eddies: Entrainment: Euphotic zone: waters to over 150 m in some tropical seas. The photosynthetic system is at the compensation point when the daily average net production (i.e., the gross photosynthesis production minus respiration) is zero. One of the predominant forms of zooplankton. Gradually sloping submerged extension of a continent, composed of granitic rock overlain by sediments. Has features similar to the edge of the nearby continent. The depth at which the integral net production (the gross photosynthetic production - respiration) is zero. Carnivores species (with a size ranging from centimeters to meters) which feed on fish eggs and fish larvae and compete with young fish for smaller zooplankton prey such as copepods. Process in which nitrate is reduced into nitrite and nitrogen gas. It is the opposite process of the entrainment. It represents mixing of upper layer (less dense) waters into the lower layer (more dense) across the interface between two water parcels with different densities. Detrainment results in changes in the properties of the lower layer, whereas no changes take place on the characteristics of the upper layer. Processes occurring across isopycnal surfaces. Unicellular planktonic algae with cell size ranging from about 2µm to over 1000 µm (some species form larger chains or other forms of aggregates). All species have an external skeleton made of silica and therefore diatoms need silicon for their growth. autotrophic dinoflagellates unicellular algae with a size ranging from a few tens of microns to fractions of millimeters and existing mostly singly (only a few species form chains), with flagella which give them motility not only vertically (which can also be assured by buoyancy control as in diatoms) but also horizontally.heterotrophic dinoflagellates (up to 1mm or more for Noctiluca, often observed in dense swarms in coastal areas) feed on bacteria, diatoms, other flagellates, ciliates and for Noctiluca, fish eggs. The division of the organic matter to its dissolved part and particulate part is arbitrary. Usually, this separation can be made by filtering over 0.5 µm filters. The dissolved substances are not retained by the filter. Downward movement of surface water. Dissolved Organic Matter. Patches of turbulent flow. One-directional movement of water through a boundary between two water bodies with different densities. The denser water mixes into the less dense water. This process results in changes in the properties of the less dense fluid, while the denser fluid keeps its original properties. Region where the net rate of photosynthesis is positive. The lower limit of the euphotic zone is usually assimilated to the ©Encyclopedia of Life Support Systems (EOLSS) 292 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Eutrophication: Flagellates: Heterotrophs: Initialization: Isopycnal: Kraus-Turner bulk mixed layer dynamics: Labile dissolved organic matter: Medusae: Mesoplankton: Microplankton: Mixing layer: Mixotroph: Nanoplankton: New production: Nitracline: Nitrification: Nutricline: Nutrient: NPZ-type model: compensation depth. Anthropogenic enrichment of natural waters with inorganic nutrients (ammonium, nitrate, phosphate) by which phytoplankton growth is stimulated. Eutrophication leads to increased biomass, decomposition, and in the worst cases, results in oxygen depletion and mass mortality. Planktonic species. Flagellates may be autotrophs, heterotrophs, or mixotrophs, with some other species being parasitic or symbiotic. Organisms unable to fix carbon from the inorganic to organic form, dependenton already existing biomass to develop. Introduction of initial conditions for each state variable of the model. Surfaces characterized by a constant density It is a method of calculating entrainment and detrainment velocities across the base of the surface mixed layer and the layer immediately below. Depending on the rate of entrainment (detrainment), the mixed layer undergoes deepening (shallowing). Part of the dissolved organic matter which is rapidly consumed by bacteria and so is characterized by a fast turnover. are common in open sea and coastal waters. They are carnivorous and have a size ranging from a few millimeters to a few meters, possibly with tentacles extending down to several tens of meters. Planktonic organisms of intermediate body size between 0.2 and 20 mm. Small-sized (between 20 and 200 µm) planktonic organisms. The zone being actively mixed from the surface at a given time and corresponding generally to the zone in which there is strong turbulence directly driven by surface forcing. Organism which has the capability of both autotrophic and heterotrophic functions, presumably switching from one to the other according to food availability. Planktonic bacteria and algae between 2 and 20 µm in size. Part of the primary production based on newly incorporated inorganic nutrients imported into the euphotic layer from the continent (e.g., by the rivers) and/or from the deep waters (e.g., by winter mixing/upwellings). Layer where the nitrate concentrations present an important vertical gradient. Process in which ammonium is oxidized into nitrite and nitrate. Layer where the nutrient concentrations presents an important vertical gradient. Any nourishing substance. As used in the Marine Sciences literature, it usually refers to the important and commonly measured elements needed for the growth of plants phosphorus, nitrogen and silicon. Ecosystem model defined by a nitrogen cycle described by 5 state variables which are the phytoplankton and the zooplankton biomass, nitrate, ammonium and detritus. ©Encyclopedia of Life Support Systems (EOLSS) 293 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Photosyntetically Available Radiation PAR: Particulate Organic The fraction of the total organic matter that can be filtered or centrifuged from seawater. Hence the difference between Matter: particulate and dissolved organic matter is arbitrary. Usually, a substance is considered as particulate if it is retained by a 0.5 µm filter. CO2 partial pressure. pCO2: The pelagic environment extends from the sea surface to depths Pelagic: but has no relation with the bottom layer. The pelagic environment supports two basic types of marine organisms the plankton, which is, to a large extent, transported by currents in the sea and the nekton consisting of free-swimming animals more or less independent of water movements. Phytoplankton refers to vegetal planktonic organisms and zooplankton to animal ones. The process by which autotrophs bind light energy into the Photosynthesis: chemical bonds of food with the aid of chlorophyll and other substances. The process uses carbon dioxide and water as raw materials and yields glucose and oxygen. Unicellular algae with a size ranging from a few microns to a few Phytoflagellates: tens of microns and existing mostly singly (only a few species form chains) with flagella which give them motility not only vertically (which can also be assured by buoyancy control as in diatoms) but also horizontally. Particulate Organic Matter. POM: Primary producers: Autotrophs. Fixation of carbon from inorganic to organic form, whether by Primary photo- or chemoautotrophy. production: by a population is its total elaboration of new flesh (or plant Production: material), irrespective of whether the individuals containing that production survive to the end of the time interval over which production is calculated Layer where the density presents an important vertical gradient. Pycnocline: The essential nutrients and carbon dioxide are taken up (and Redfield ratios: released, when living matter is metabolized) in approximately constant ratios to each other and the oxygen produced (or consumed). The ratios calculated by Redfield are 1P 16N 106C 138O2, where P,N,C, and O2 stand for phosphorus, nitrogen, carbon, and oxygen concentrations. They approximate the upper parts of the water column by a Reduced-gravity, number of homogeneous layers. The deeper part of the water layered models: column is assumed very deep and motionless. The advantage of this assumption is to simplify the solution of the dynamical equations governing the motion. Part of the organic matter which is not remineralized in the water Refractory column due to its very slow turnover rate, and sinks to the bottom. dissolved organic matter: Part of the primary production based on inorganic nutrients Regenerated recycled in the euphotic layer by bacteria remineralisation or by production: ©Encyclopedia of Life Support Systems (EOLSS) 294 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. - TO ACCESS ALL THE 37 PAGES OF THIS CHAPTER, Visit: http://www.eolss.net/Eolss-sampleAllChapter.aspx Bibliography Baretta J.W., Ebenhoh W. and Ruardij P. (1995). The European Regional Seas Ecosystem Model, a complex marine ecosystem model. Neth. J. Sea Res. 33, 233-246. Carlotti F. and Radach G. (1996) Seasonal dynamics of phytoplankton and Calanus finmarcicus in the North Sea as revealed by a coupled one-dimensional model. Limnology and Oceanography 41, 522-539. Gregoire M., Beckers J.M., Nihoul J. C. J. and Stanev E. (1998). Reconnaissance of the main Black Sea's ecohydrodynamics by means of a 3D interdisciplinary model. Journal of Marine Systems 16, 85-105. [This paper describes the results of a 3D coupled ecosystem-hydrodynamical model applied to the Black Sea. Focus is put on the influence of macroscale physical processes on the space-time distribution of the primary and secondary production]. Jorgensen S. (1988). Fundamentals of ecological modeling, 391pp. Elsevier. [This presents all the necessary steps in the development of a mathematical model: its conceptualization, parameterization, calibration, initialization and validation]. Lancelot C. and Billen G. (1985). Carbon-nitrogen relationships in nutrient metabolism of coastal marine ecosystems. Advances in Aquatic Microbiology 3, 263-321. Leonard, C.L., McClain C., Murtugudde R., Hofmann E., Harding L.W.Jr. (1999) An iron-based ecosystem model of the central equatorial Pacific. Journal of Geophysical Research 104, 1325-1341. ©Encyclopedia of Life Support Systems (EOLSS) 295 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. Levy M., Memery L., Madec G. (1999) The onset of the spring bloom in the MEDOC area: mesoscale spatial variability. Deep-Sea Research I 46, 1137-1160. McCreary, J.P. Jr., K. Kohler, R. R. Hood, D. B. Olson (1996) A four-compartment ecosystem model of biological activity in the Arabian Sea. Progress in Oceanography 37, 193-240. McGillicuddy D.J. Jr., Robinson A., Siegel D., Jannasch J., Johnson R., Dickey T., McNeil J., Michaels A.F., Knap A.H. (1998) Influence of mesoscale eddies on new production in the Sargasso Sea. Nature 394, 263-266. Moisan, J.R., Hofmann E., Haidvogel E. (1996) Modeling nutrient and plankton processes in the California coastal transition zone 2. A three-dimensional physical-bio-optical model. Journal of Geophysical Research 101, 22677-22691. Nihoul J. and Djenidi S. (1991). Hierarchy and scales in marine ecohydrodynamics. Earth-Science Reviews 31, 255-277. [This paper describes the ecohydrodynamic hierarchical organization in marine ecosystems and describes the effects of major marine hydrodynamic processes on the ecosystem dynamics] Oguz T., Ducklow H.W., Malanotte-Rizzoli P. (2000) Modeling distinct vertical biogeochemical structure of the Black Sea: Dynamical coupling of the oxic, suboxic and anoxic layers. Global Biogeochemical Cycles 14, NO. 4,1331-1352. [This paper describes the application of a one-dimensional, vertically resolved, physical-biogeochemical model to provide a unified representation of the dynamically coupled oxic-suboxic-anoxic system for the interior Black Sea]. Oschlies A. and Garcon V. (1999) An eddy-permitting coupled physical-biological model of the North Atlantic. 1. Sensitivity to advection numerics and mixed layer physics. Global Biogeochemical Cycles 13, 135-160. Popova E.E., V. A. Ryabchenko, M.J.R. Fasham (2000). Biological pump and vertical mixing in the Southern Ocean: Their impact on atmospheric CO2. Global Biogeochem. Cycles, 14, 477-498. [This paper describes a global box model simulating nitrogen and carbon cycling in the ocean and involving a detailed description of the seasonal cycles of the oceanic upper mixed layer ecosystem. The model is used to estimated the global ocean new production]. Prunet P., Minster J.F., Pino D.R., Dadou I. (1996) Assimilation of surface data in a one-dimensional physical-biogeochemical model of the surface ocean 1. Method and preliminary results. Global Biogeochemical Cycles 10, 111-138. Robinson A. (2000). Data Assimilation for modelling and predicting coupled physical-biological interactions in the sea. In Robinson A.R. and Lermusiaux P. (eds.), Workshop on the assimilation of biological data in coupled Physical/Ecosystem models, Globec Special Contribution N°3, pp 2-13. [This paper describes the general process of data assimilation (e.g., methodologies, purposes) and gives several examples of regions where data assimilation has been successfully applied]. Six K.D. and Maier-Reimer E. (1996) Effects of plankton dynamics on seasonal carbon fluxes in an ocean general circulation model. Global Biogeochemical Cycles 10, 559-583. Tankere S. P. C., Price N. B. and Statham P. J. (2000). Mass balance of trace metals in the Adriatic Sea. Journal of Marine Systems, 25, 269-286. [The paper presents a first order mass balance of six different trace metals (Mn, Fe, Pb, Zn, Cu, Ni) for a 1-year period for the different compartments of the Adriatic Sea]. Van Eeckhout D. and Lancelot C. (1997). Modelling the functioning of the north-western Black Sea ecosystem from the 1960 to present. In: Sensitivity to change: Black Sea, Baltic Sea and North Sea, E. Ozsoy and A. Mikaelyan (Eds), NATO ASI Series, Kluwer Academic Publishers, 27, 455-468. [This paper analyses the changes that have occurred in the northwestern Black Sea ecosystem resulting from eutrophication, and describes the results of a highly complex ecosystem model applied in the area to simulate the modifications of food-web structure and functioning]. Biographical Sketches Temel Oguz was born on 13 November 1952 in Aydın, Turkey. He has B.Sc and M.Sc degrees in Physics from the Middle East Technical University (METU), and Ph.D degree in Dynamical Meteorology ©Encyclopedia of Life Support Systems (EOLSS) 296 OCEANOGRAPHY – Vol.III - Modeling Biogeochemical Processes in Marine Ecosystems - Gregoire, M., Oguz, T. and Oceanography form the University of Reading (United Kingdom). Dr. Oguz joined Institute of Marine Sciences of the METU as an Assistant Professor in 1981. He was promoted to Associate Professor in 1986 and full Professor in 1992 at the same institution. Dr. Oguz is recognized for his work on Black Sea circulation and ecosystem dynamics. His major interests within the last 10 years were mainly concentrated on studying the transports and ecological processes that control biogeochemical cycles in the Black Sea using coupled physical-biogeochemical models with data assimilation capabilities. More specifically he has been working on circulation dynamics, their effects on the biogeochemical transports, and understanding biogeochemical processes in the Black Sea. Recent publications: Oguz, T., H. Ducklow, P. Malanotte-Rizzoli, J.W. Murray V.I. Vedernikov, U. Unluata (1999) "A physical-biochemical model of plankton productivity and nitrogen cycling in the Black Sea". Deep Sea Research I, 46, 597-636. Oguz, T. and S. Besiktepe (1999) "Observations on the Rim Current structure, CIW formation and transport in the Western Black Sea". Deep Sea Research, I, 46, 1733-1753. Oguz, T. and B. Salihoglu (2000) "Simulation of eddy-driven phytoplankton production in the Black Sea". Geophys. Res. Letters, 27(14), 2125-2128. Oguz, T., H. W. Ducklow, P. Malanotte-Rizzoli (2000) "Modeling distinct vertical biogeochemical structure of the Black Sea: Dynamical coupling of the oxic, suboxic and anoxic layers". Global Biogeochemical Cycles, 14, 1331-1352. Oguz, T. and J.W. Murray (2001) "Simulation of Suboxic-Anoxic interface zone structure in the Black Sea". Deep Sea Research I, 48, 761-787. Marilaure Grégoire was born on June 29, 1972 in Huy, Belgium. She graduated in engineering in 1995 (University of Liège). From 1995 until 1999, she worked as a research assistant, National Fund for Scientific Research (FNRS) Belgium and obtained a PhD in applied sciences (University of Liège) in 1999. She is currently working as a researcher, FNRS Belgium, in the department of geological fluid dynamics, on physical and ecological modelling of the marine environment. She develops coupled ecosystem-hydrodynamical models applied to the Black Sea in a sustainable development perspective. The study of the desertification in the Aral Sea Region is also one of her main concerns. Recent publications: Grégoire M., Beckers J.-M., Nihoul J. and Stanev E., 1998. Reconnaissance of the main Black Sea's ecohydrodynamics by means of a 3D interdisciplinary model. Journal of Marine Systems, 16:85-105. Delhez E., M. Gregoire, J.C.J. Nihoul and Beckers J.M., 1999. Dissection of the GHER Turbulence Closure Scheme, Journal of Marine Systems, 21., pp 379-397. Nezlin N., Kostianoy A. and Gregoire M, 1999. Patterns of seasonal and interannual changes of surface chlorophyll concentration in the Black Sea revealed from the remote sensed data. Remote Sensing of Environment, 69, pp 43-55. Beckers J.M., Gregoire M., Nihoul J.C.J. Stanev E., Staneva J. and Lancelot C., 2000. Hydrodynamical processes governing exchanges between the Danube, the North-Western continental shelf and the Black Sea's basin simulated by 3D and box models. Estuaries and Coastal Shelf Science, in press. Stanev E., Beckers J.M., Lancelot C., Letraon P.Y., Staneva J., Peneva E. and Gregoire M., 2000. Coastal-open ocean exchange. Black Sea examples from survey, satellite data and modelling. Estuaries and Coastal Shelf Science, in press. ©Encyclopedia of Life Support Systems (EOLSS) 297