In this paper, we present a coupling scheme between the
Massachusetts Institute of Technology general circulation model (MITgcm) and
the Biogeochemical Flux Model (BFM). The MITgcm and BFM are widely used
models for geophysical fluid dynamics and for ocean biogeochemistry,
respectively, and they benefit from the support of active developers and user
communities. The MITgcm is a state-of-the-art general circulation model for
simulating the ocean and the atmosphere. This model is fully 3-D (including
the non-hydrostatic term of momentum equations) and is characterized by a
finite-volume discretization and a number of additional features enabling
simulations from global (
Coupling different models that have been specifically developed to study only limited aspects of the Earth's systems is becoming increasingly common due to the need to simulate different environmental components – and their interactions – simultaneously (Heavens et al., 2013). As regards numerical oceanography, coupled hydrodynamic–biogeochemical models are widely used to investigate and predict the physical, biogeochemical, and ecological properties of marine ecosystems across a wide range of scales and provide useful tools that support environmental management and policies.
The numerical implementation of a coupling framework between 3-D hydrodynamic models and biogeochemical models is not a trivial task (Bruggeman and Bolding, 2014) because every model focuses on processes that occur on different temporal and spatial scales and uses different numerical parameterizations and schemes. Additionally, these models might be coded in different languages or follow different coding “philosophies” with respect to memory allocation, computational schemes, and code workflow. Furthermore, hydrodynamic and biogeochemical models are often developed by different and highly specialized scientific groups, whereas coupling requires interdisciplinary expertise.
In recent decades, the increasing availability of significant computational resources has allowed substantial improvements in hydrodynamic and biogeochemical models in terms of both temporal and spatial resolution of the simulations, which required new specific programming and coding expertise (i.e. code optimization and parallel programming). In addition, biogeochemical model complexity has increased through the inclusion of new variables and processes (Robson, 2014), and model development has become a cooperative and multidisciplinary task rather than an individual effort. A large number of generic, open-source models are utilized by the scientific community, and they can be customized to match the users' specific applications. A non-exhaustive list of the main state-of-the-art, hydrodynamic community models includes the MITgcm (Adcroft et al., 2016), GOTM (Burchard et al., 2006), ROMS (Haidvogel et al., 2000), and NEMO (Madec, 2014), whereas examples of community biogeochemical models include the BFM (Vichi et al., 2015), ERSEM (Butenschön et al., 2016), PISCES (Aumont et al., 2015), and ERGOM (Neumann, 2000).
Hydrodynamic and biogeochemical models can be coupled by merging their codes into a single larger new code, in which the original parts are intertwined. In this case, biological models are inserted into the workflow of the existing hydrodynamic model code (Burchard et al., 2006; Follows et al., 2006) because, in general, hydrodynamic models have already been developed to solve the partial differential equation of tracers and provide the coding infrastructure to handle the spatial–temporal properties of the simulations (i.e. bathymetry, boundaries, computational domain discretization). Alternatively, a modular approach can be adopted: each component preserves its own peculiarities, the coupling is performed only on localized portions of the code, and there are clear application programming interfaces (APIs). The separation of the two coupled components facilitates the maintenance of each code within its development community, avoids possible large efforts in solving the language differences between models, and eliminates the need to keep models up to date with respect to the parent model. As an example, Bruggeman and Bolding (2014) proposed a set of programming interfaces (FABM) that allows communication between different hydrodynamic and biogeochemical models.
In this paper, we present a coupling scheme between the MITgcm hydrodynamic model and the BFM biogeochemical model for ocean biogeochemical simulations. The two models are widely used, as described in the next sections, and have already been coupled with several other models. For example, the MITgcm has already been coupled to low- (Parekh et al., 2005; Follows et al., 2006) or intermediate-complexity (Hauck et al., 2013; Cossarini et al., 2015a) biogeochemical models for a few specific applications and to a specific high-complexity model (Dutkiewicz et al., 2009) to explore the theoretical aspects of intraspecific competition in plankton communities. On the other side, the BFM has already been coupled to POM (Polimene et al., 2006), NEMO (Vichi and Masina, 2009; Epicoco et al., 2016), and the offline OGSTM, an upgraded version of OPA (Lazzari et al., 2012). A direct coupling between MITgcm and BFM has not been implemented yet. Thus, we developed a dedicated online modular coupler linking them. The new coupler is open source, and allows us to exploit the high potentiality of the two models, to preserve the sustainability of the programming effort, and to handle the future evolution of the two codes. Further, the online coupling of hydrodynamic and biogeochemical models allows us to drive the biogeochemistry at the same frequency of the hydrodynamic processes, avoiding the use of large files where hydrodynamic variables are saved at high frequency. It also ensures the use of consistent differential operators (advection and diffusion) for hydrodynamic and biogeochemical variables, and would eventually provide a framework to describe possible feedbacks from biogeochemistry to hydrodynamics.
We demonstrate that the new online coupled model provides reliable results when simulating different marine ecosystems by correctly reproducing the interplay between physical, chemical, and biological processes and components. The coupled model also runs with good computational performance and preserves the numerical accuracy of the solution. We consider that the MITgcm–BFM model represents a promising tool for investigating marine biogeochemistry at different spatial and temporal scales.
This paper is organized as follows. After a brief presentation of the two models (Sect. 2), we focus on the technical aspects of the coupling algorithm. In the subsequent section (Sect. 3), we describe the testing of the new coupled hydrodynamic–biogeochemical model against the idealized case of a cyclonic circulation in a closed basin and against a real case study in the central Mediterranean Sea. The paper closes with a discussion of the key issues of the coupling and future perspectives. A manual of the new code package is detailed in the Appendix.
A coupled hydrodynamic–biogeochemical model is composed of three main elements: a hydrodynamic sub-model, which solves the governing equations for oceanic flows; a tracer transport sub-model, which solves for the transport (advection and diffusion) of biogeochemical variables (commonly called tracers); and a biogeochemical sub-model, which describes the relationships (i.e. biogeochemical reactions) among the biogeochemical variables.
Following the common practice in which the biological feedback on transport
is negligible, one can assume that changes in biogeochemical properties do
not affect the water velocity, density, or other physical properties;
therefore, modifying the standard equations that underpin hydrodynamic models
is unnecessary. We adopted such an assumption for this numerical coupling
framework; however, this coupler was developed, in principle, to also handle
biological feedbacks on hydrodynamics. The coupled model solves the set of
partial differential equations specified below:
Momentum conservation equations, Eqs. (1)–(2), continuity and density
equations, Eqs. (3)–(4), and active-tracer equations (for potential
temperature
Equation (8) is an equation of state that calculates the modulation of irradiance
PAR (photosynthetic active radiation) with depth starting from
short-wave surface radiation fields (
By adopting a more explicit formulation and commonly used assumptions based
on scale analysis (see Crise et al., 1999), Eq. (7) can be rewritten as
follows:
The first three terms on the RHS of Eq. (10) represent the advection (first
term) and diffusion (second – horizontal – and third – vertical – terms)
of biogeochemical tracers, where
Within a coupled model, Eqs. (1)–(6) are solved by the hydrodynamic
sub-model, whereas Eq. (10) is solved partly by the transport sub-model,
which is usually embedded in the hydrodynamic code, and partly by the
biogeochemical sub-model. The other components, such as Eq. (8), the
biogeochemical tracers' forcing terms (
A coupler is defined as the interface that transfers the hydrodynamic information from Eqs. (1)–(6) to Eq. (10) and controls the communication between the different terms of Eq. (10). In this study, the sub-models coupled are the MITgcm (managing both hydrodynamics and transport) and BFM (for the biogeochemistry) models, which are described in Sects. 2.2 and 2.3. The algorithm used to construct the fully coupled system is detailed in Sect. 2.4.
Throughout the text, we used the following convention. In equations and text,
The MITgcm (Massachusetts Institute of Technology general circulation model;
Marshall et al., 1997) is a 3-D, finite-volume, general circulation model
used by a broad community of researchers. It can be customized to create
different simulation set-ups by modifying its packages and parameters
accordingly (Adcroft et al., 2016) and it has already been successfully
applied to a wide range of case studies for the world's ocean at various
spatial and temporal scales. The code and documentation of the MITgcm are
under continuous development. The modular Fortran77 code is open source
(copyright ©2016 MITgcm Developers and Contributors), and it can
be downloaded from the MITgcm website (
A native transport sub-model for passive tracers (the Passive TRACERS –
Because of the different length scales, horizontal and vertical turbulent processes are treated separately and are solved by adopting a selected subset of several available parameterizations: in this study, we chose a mixed Leith–Smagorinsky scheme for the horizontal processes (second term on the RHS of Eq. 10) and the K-profile parameterization (KPP, Large et al., 1994) for the vertical processes (third term on the RHS of Eq. 10).
The packages that were enabled during compilation (
BFM model: scheme of the functional interactions among the variables
in the version that was implemented in Lazzari et al. (2012), Melaku Canu et
al. (2015), and Cossarini et al. (2015b). Variable names follow the BFM
convention (Vichi et al., 2015). The subscripts indicate the chemical
components (
This code was compiled onto a Linux cluster that was equipped with Intel Xeon
Ivy Bridge processors by using both the native GNU compiler (gfortran with
openmpi libraries) and the Intel compiler (ifort: Intel Composer XE 2013 SP1)
and by adopting the optimization levels
The Biogeochemical Flux Model (BFM) is an open-source, modular Fortran90
numerical model that was designed to describe the dynamics of the major
biogeochemical processes that occur in marine ecosystems (Vichi et al.,
2015). The standard configuration of the BFM solves the cycles of carbon,
phosphorus, nitrogen, silica, and oxygen in the water-dissolved phase and in
the plankton, detritus, and benthic compartments. Plankton dynamics are
parameterized by considering a number of plankton functional groups, each
representing a class of taxa. The BFM's plankton functional groups are
subdivided into producers (phytoplankton), consumers (zooplankton), and
decomposers (bacteria). These broad functional classifications are further
partitioned into functional subgroups to create a planktonic food web (e.g.
diatoms, picophytoplankton, microzooplankton). The structure of the plankton
functional types is modular and can be adapted to specific needs. In fact,
the BFM's code is organized into several modules devoted to several plankton
function types:
For this application, we adopted version v2 (Lazzari et al., 2012, 2016;
Teruzzi et al., 2013; Melaku Canu et al., 2015; Cossarini et al., 2015b),
which can be downloaded upon request from the BFM consortium website
(
In this coupling scheme, we adopted a modular approach by considering the high complexity of the two models that were employed. The size of the codes according to the SLOCCount tool (Wheeler, 2015) is approximately 400 000 code lines for the MITgcm and approximately 20 000 for the BFM. The coupler is a package that handles the interface (APIs) between the host code (MITgcm) and the BFM to solve Eqs. (7)–(8) and to efficiently manage the matrices that contain the variables and tendencies shared by the two models and the flow of information among the different sub-model components.
The MITgcm–BFM coupling (Fig. 2) was achieved by upgrading a few routines of
the MITgcm
The
Description of the MITgcm–BFM coupling and interfaces among the different components of the coupled model.
As an interface, the
We considered several coupling strategies according to the MITgcm's code
structure (Fig. 3). Within each time step of the model integration, which is
coded in the
Workflow of the MITgcm
Different options can be used to solve the evolution of tracers (Eq. 10),
which can be controlled by the
The biogeochemical tendency, which is solved by calling the BFM through the
For the second coupling option, an operator splitting scheme is selected when
This option allows for the development of an integration scheme with different time steps for the hydrodynamic and transport parts on one side and for the biological processes on the other.
A third option is an operator splitting algorithm, which involves the MITgcm
This third method is preferred over the previous one as a possible method of
decoupling the numerical biogeochemistry solution from the hydrodynamic
solution. We tested the model to verify the trade-off between the increase in
computational performance and the loss of accuracy in the model results as a
function of the extension of the time step for the tracer equations
(LS
The core of the present coupling scheme is the new
In particular, the
A second module of
The coupled MITgcm–BFM model includes a simple parameterization of the
fluxes at the water–sediment interface, which includes the burial of
detritus (e.g. a net export flux from the ecosystem) and an incoming flux of
nutrients into the deepest cell of the water column. Burial is parameterized
as the first-order kinetics of the carbon (
In the same grid cell, the nutrient (for
The
The MITgcm and BFM must be compiled with the same compiler. We tested the
code by using both the GNU and Intel compilers on several HPC platforms.
Here, we report the results obtained by running the model (compiled with
Intel) on a Linux cluster. The BFM is compiled as an independent library by
using the following option of the BFM makefile:
We tested the new coupled hydrodynamic–biogeochemical model against two case
studies: an idealized experiment (a cyclonic gyre in a mid-latitude closed
domain) and a realistic configuration (central Mediterranean Sea). In the
first case study, which was released along with the code and the manual
(
This experiment was based on a simplified case study that consisted of an
idealized domain (
This domain was discretized by adopting a uniform grid spacing
(
Idealized case study (circulation in a
Hovmöller diagrams of the
When configuring the options for the passive tracers package
(
The model simulated a realistic cyclonic circulation with associated mesoscale variability from vertical thermohaline stratification and flow instability. Relatively well-mixed thermohaline conditions in the winter induced a more unstable cyclonic gyre with small-scale mesoscale eddies (Fig. 4a), whereas a more stable and energetic cyclonic circulation occurred from stratified thermohaline conditions in the summer (Fig. 4b).
Figure 5 shows the evolution of several physical properties and biological components within the central part of the gyre. The coupled model simulated the evolution of the thermocline and nutricline and the effect of winter vertical mixing on the temperature and nutrient profiles (Fig. 5a and b). Figure 5 also shows the formation of surface phytoplankton blooms during early winter (Fig. 5c), the formation of the deep chlorophyll maximum (DCM) during summer (as a trade-off between the light penetration and the depth of the nutricline), and the effect of the erosion of the stratification during autumn on the biogeochemical properties of the basin (deepening of mixed layer depth – MLD – Fig. 5a). Net primary production (NPP, contour plot in Fig. 5d) showed the highest values in the proximity of the DCM during spring, although high primary productivity was also simulated in the upper part of the water column, where the high level of irradiance stimulated carbon fixation, especially for small-sized phytoplankton groups (not shown), even in the presence of low phytoplankton biomass.
The region close to the DCM was the most active biological area, i.e. the concentrations of all of the living variables (small and mesozooplankton groups and bacteria; Fig. 5e and f) were the highest and the fluxes fuelled the so-called classic food chain (Legendre and Rassoulzadegan, 1995). Nevertheless, significant bacterial biomass was also simulated in the upper part of the water column, where bacteria consumed the labile organic matter, which was side-produced by phytoplankton in the well-lit upper levels. Small zooplankton (sum of micro- and hetero-trophic nanoflagellate groups) took advantage of the bacterial biomass, triggering the so-called microbial food web (Legendre and Rassoulzadegan, 1995), which dominated the upper part of the water column during summer. Oxygen (Fig. 5d) was higher in the upper part of the water column during winter because of the high level of NPP and the effect of re-aeration processes with the atmosphere. Bacterial production and the predominance of respiration over phytoplankton photosynthesis caused the autumn minimum.
Wall clock time of the main MITgcm routines clustered in selected
groups (left axes) as a function of the number of hydrodynamic time steps
between tracer time steps (LS
The computational cost of a 1-year simulation was approximately 5 h when
adopting an MPI configuration that featured 16 Ivy Bridge cores. The code
profiling (Fig. 6) indicated that most of the CPU time (i.e. up to 85 %)
was devoted to solving the differential equation for the high number of
tracers (51). Solving the transport part (Tracers
The use of a coarser time resolution for the solution of the tracer equations
implied errors with respect to the reference solution (Fig. 6). The errors
were calculated as the root mean square of the difference of the integrated
0–200 m chlorophyll between the reference run (LS
The reference run was also used to verify the mass conservation of the
coupled hydrodynamic–biogeochemical model by considering that the model
configuration (i.e. non-linear free surface) was set to properly simulate the
effects of free-surface dynamics on the concentrations of the biogeochemical
variables at the surface. Figure 7 shows the time series of the sea surface
height (SSH) averaged over the entire basin. The results indicated the
prevalence of rain over evaporation for the first part of the year and vice
versa from May to October. For example, the evolution of alkalinity, which is
a key variable for resolving the ocean carbonate system (Follows et al.,
2006), was correctly anti-correlated with the derivative of SSH in the
surface layer because the effects of concentration and dilution at the
surface are dependent on the water mass balance. This model feature was
provided along with the mass conservation capability for tracers (Fig. 7).
The errors in mass conservation over time were small (
Evolution of SSH (blue line) and alkalinity (red line) at the
surface layer together with the relative variation of total alkalinity mass
(M) with respect to the initial condition (M
The coupled model was also used to simulate a realistic domain: the central Mediterranean Sea. This area, which encompasses the Adriatic and Ionian seas (Fig. 8), was chosen because it is characterized by a wide range of interconnected ecosystems that span coastal areas, which are influenced by river discharges, and offshore regions, which are characterized by open-sea dynamics. Indeed, the northern part of the Adriatic is a continental shelf area influenced by terrestrial input (Solidoro et al., 2009; Cossarini et al., 2015a). This area is a site of dense water formation (Gačić et al., 2001; Querin et al., 2013) and represents one of the most productive areas of the Mediterranean Sea (Mangoni et al., 2008). The southern Adriatic Sea is characterized by an almost permanent geostrophic gyre modulated by deep winter mixing episodes (Gačić et al., 2002; Bensi et al., 2014), and it is connected to the Ionian Sea via the Otranto Strait. The Ionian Sea is the deepest sub-basin of the Mediterranean, and it is characterized by basin-scale circulation patterns and smaller mesoscale eddies. This sea is influenced by oligotrophic and salty waters originating from the Levantine basin and by the relatively fresh Atlantic water masses that flow from the west. The hydrodynamics of the area have been simulated by the Adriatic–Ionian implementation of the MITgcm (ADriatic IOnian System model (ADIOS), Querin et al., 2016), which we used in this study. The aim of this experiment is to show the ability of the new coupled model to properly simulate the effects of hydrodynamics on biogeochemistry within a wide range of oceanographic and ecological processes that range from a few kilometres to hundreds of kilometres and from oligotrophic to high-level trophic conditions.
Bathymetry (depth in metres) of the Adriatic–Ionian model. The plot also indicates the location of the major rivers (arrows), the Otranto Strait, and the position of the two sites (circles) that were selected to display the Hovmöller diagrams in Fig. 10.
Concentrations of tracers in the rivers.
The model domain was delimited by the Sicily channel (lon 12.2
The model set-up only considered the main rivers that flow into the Adriatic Sea, whereas the minor contributions that flow into the Ionian Sea were neglected. River contributions were introduced as local boundary conditions, imposing observed daily freshwater flow rates for the major rivers (e.g. Po) and climatological annual flow rates for the others, with spring and autumn maxima and winter and summer minima (Querin et al., 2013; Janeković et al., 2014). The tracer concentrations at the river mouths were constant in space and time (Table 1), and the mass fluxes were calculated by multiplying the concentrations by the flow rate of each river.
The boundary conditions along the Sicily Channel and along the Cretan Passage
were derived from the CMEMS MED-MFC system (Tonani et al., 2008; Lazzari et
al., 2010) for both the hydrodynamic and biogeochemical variables (OBC and
OBC
Surface meteorological forcing was derived from the Regional Climate Model
(RegCM) developed at the International Centre for Theoretical Physics (ICTP)
in Trieste. We used the 12 km horizontal resolution version with 3 h output
frequency (as in Querin et al., 2016). The heat fluxes (
Computational cost as a function of the
The specific settings for the
The simulation covered the period from January 2006 to December 2012 at a
time step of 200 s. In the following analysis, we disregarded the first
2 years of the simulation, which we considered a spin-up period for the
biogeochemical variables from the CMEMS's coarser resolution fields. The MPI
domain decomposition consisted of
We present the results for the ADIOS case study to demonstrate the ability of the new MITgcm–BFM coupled model to investigate closely interconnected hydrodynamic and biogeochemical processes for both coastal and open-sea ecosystems.
In the western coastal areas of the Adriatic Sea, the maps in Fig. 9
correctly display the patterns of low salinity, southward currents, high
nitrate and chlorophyll concentrations, and strong primary production, which
are all typical fingerprints of the Western Adriatic Current (WAC) system in
the Adriatic Sea. The effect of the input from the northern rivers and the
basin-scale cyclonic circulation generates a frontal system along the Italian
coast. As is commonly observed in satellite chlorophyll maps (Barale et al.,
2008), the width of the WAC frontal system decreases southwards, whereas
weaker recirculation patterns are also visible in the central Adriatic Sea
(Fig. 9). Other river-influenced coastal areas are simulated along the
south-eastern areas of the Adriatic Sea, where the input from the Neretva and
other south-eastern rivers triggers small-scale chlorophyll
The coastal to open-sea gradients of nutrients were accurately simulated by the coupled model. As an example, Fig. 9 shows that the nitrate patterns display a longitudinal gradient along the Adriatic and northern Ionian seas, and these results are consistent with the current climatologies (Cossarini et al., 2012; Solidoro et al., 2009; Zavatarelli et al., 1998). In the open-sea area of the Ionian Sea, the surface circulation is dominated by large mesoscale structures and a basin-scale anticyclone in the middle, and the downwelling area is characterized by minimal nitrate and chlorophyll concentrations (Fig. 9). This pattern is consistent with the climatology of Manca et al. (2004), even if the nitrate concentrations are slightly higher in the eastern Ionian Sea, which is related to overestimated eastern boundary values.
Hovmöller diagrams of chlorophyll (colour) and phosphate
(contour, mmol m
If we focus on the open-sea sub-surface dynamics, we can analyse how vertical processes affect the biogeochemistry. The vertical profiles of chlorophyll and phosphate for the two sites in Fig. 8 are depicted in Fig. 10. One site is located in the centre of the southern Adriatic gyre, which is characterized by strong winter vertical mixing, whereas the second is located in the centre of the large anticyclonic gyre in the Ionian Sea. A comparison between the two sites shows the ability of the coupled model to simulate the different regimes in the two areas. The southern Adriatic Sea presents a much higher mixed layer depth in winter, a shallower nutricline than the Ionian Sea, more intense inter-annual variability in the cyclic alternation of winter vertical mixing phases, and the onset of summer stratification.
The intense vertical mixing in the southern Adriatic area during winter drives the upwelling of nutrient-rich water, which contributes to a shallow nutricline (up to the depth of the DCM) during summer. However, winter ventilation in the Ionian Sea's open areas rarely reaches a depth of 250 m; consequently, nutrient-rich water remains confined to the deepest layers (below 200 m). The two areas are characterized by different biological regimes because of the different depths of the nutricline and the superimposed longitudinal gradient of the background light extinction factor (according to Lazzari et al., 2012).
Another interesting coupled hydrodynamic–biogeochemical feature is displayed along the southern coast of Sicily, where the entrance of modified Atlantic water (MAW, low-saline water mass in Fig. 9a) and the simulated coastal upwelling from westerly winds induce vertical transport of nutrients, consistent with the findings of Patti et al. (2010) and Rinaldi et al. (2014). Intense vertical dynamics trigger the high concentrations of nutrients and chlorophyll and the strong primary production simulated in the upper layer of the northern Sicily channel (Fig. 9b), and these results are consistent with the typical patterns observed in satellite chlorophyll maps (Volpe et al., 2012).
Fluxes of organic carbon
The computation and diagnostics of the transport components for the tracers
(e.g. zonal and meridional advection and diffusion, vertical advection and
implicit and explicit diffusion) are already implemented in the native
In this paper, we presented a coupling between two widely used models, the
MITgcm and BFM, and we showed the potential of the new coupled model. These
two models were developed by two different scientific communities that are
actively and constantly involved in improving the codes. When one model is
directly embedded in another, code developments might represent an issue
because of the constant and tedious work of keeping one code updated with
respect to the other. Therefore, the coupling in this paper was designed to
preserve the independence of the two models as much as possible. The number
of modifications that were required for the two original codes was limited,
and changes could be easily managed should each single model be upgraded. In
our solution, the MITgcm remained the host code, the BFM was compiled and
linked as an independent library, and the new
Despite the growth of computational resources, the efficiency of coupled codes can still be an issue because of the large size of the computational grids (Blom and Verwer, 2000). Domain decomposition and parallelization tools are available in several coupling environments (e.g. FABM, Bruggeman and Bolding, 2014; MESSy, Jöckel et al., 2008). Likewise, our coupling scheme has been thought to fully exploit the parallelization efficiency of the MITgcm (Marshall et al., 1997), and no additional coding effort (in terms of parallelization) is required by the users.
Other biogeochemical models of various complexity have already been embedded in the MITgcm (Dutkiewicz et al., 2009; Hauck et al., 2013; Cossarini et al., 2015a). Nevertheless, the BFM in this new coupled model has a biological complexity and a number of features (Lazzari et al., 2016) that increase the attractiveness of the model for many marine applications.
The MITgcm–BFM coupling scheme was primarily designed by considering the
direct integration scheme because this framework has the highest level of
numerical accuracy. The use of the
A direct integration scheme might be more appropriate for investigating the
feedback of the biogeochemistry on the hydrodynamics of the system. An
example is the calculation for the sinking of certain phytoplankton groups,
which is a physical 1-D process solved within
Furthermore, the new coupling scheme was designed to foster development towards a full Earth system modelling approach, in which a wide range of processes among the Earth's spheres can be simulated online and the interactions and feedback effects can be directly considered. For example, the BFM has already been coupled with other ecosystem components (e.g. online coupling with high-trophic-level model Ecopath with Ecosym, Akoglu et al., 2015). Moreover, the parameterization of Eqs. (16) and (17) can be easily substituted by a call to a benthic model function, which solves the processes that occur in a single-layer sediment model and calculates the exchanges between the pelagic environment and the sediment.
Similarly, the MITgcm has already been coupled with atmospheric models. For example, the MITgcm has been coupled online with the RegCM atmospheric model in the Mediterranean Sea region (Giorgi et al., 2006) using the OASIS coupling framework (Artale et al., 2010). Therefore, our coupling scheme can act as a link between atmosphere–hydrosphere models and biosphere models. This coupler could be successfully used to study ocean–atmosphere interactions, such as the effects of climate scenarios on high-trophic-level ecosystem components or the feedback of ocean carbon pumps on the climate.
Finally, the results of the two test cases show that the new coupled model provides a realistic representation of a wide range of marine processes from costal to open-sea ecosystems, where the interplay of hydrodynamics and biogeochemistry is crucial. The effects of river plumes, coastal upwelling, and different vertical mixing regimes on phytoplankton dynamics were reasonably reproduced by the model and found to be consistent with both theoretical knowledge (Mann and Lazier, 2006) and published experimental findings for the Mediterranean Sea.
The code can be downloaded from the link:
This package was developed as a specific interface among the MITgcm, the
Several hydrodynamic–biogeochemical coupling options were implemented
according to a previously implemented option in the
The advection–diffusion tendencies of tracers are calculated in
interface to the call to the BFM model ( calculation of the PAR, the sinking of phytoplankton and
detritus, and the atmospheric deposition of nutrients and bottom fluxes; and interface from the
The external forcing fields used by the
The
New diagnostic quantities are listed in the namelist in the
The coupled MITgcm–BFM model can use a large number of tracers; therefore,
increasing the
The
The BFM is a Fortran95 code and must be compiled separately as an external
library in advance (
When the MITgcm is compiled, the
Several specific compile time flags are set in
This package must be run with both
The authors declare that they have no conflict of interest.
This study was partially funded by Italian flagship project RITMARE. The authors thank Valentina Mosetti for the support. Edited by: S. Arndt Reviewed by: C. Lemmen and one anonymous referee