Shelf and coastal sea processes extend from the atmosphere
through the water column and into the seabed. These processes reflect
intimate interactions between physical, chemical, and biological states on
multiple scales. As a consequence, coastal system modelling requires a high
and flexible degree of process and domain integration; this has so far hardly
been achieved by current model systems. The lack of modularity and
flexibility in integrated models hinders the exchange of data and model
components and has historically imposed the supremacy of specific physical driver
models. We present the Modular System for Shelves and Coasts (MOSSCO;
http://www.mossco.de), a novel domain and process coupling system
tailored but not limited to the coupling challenges of and applications
in the coastal ocean. MOSSCO builds on the Earth System Modeling Framework
(ESMF) and on the Framework for Aquatic Biogeochemical Models (FABM). It goes
beyond existing technologies by creating a unique level of modularity in both
domain and process coupling, including a clear separation of component and
basic model interfaces, flexible scheduling of several tens of models, and
facilitation of iterative development at the lab and the station and on the
coastal ocean scale. MOSSCO is rich in metadata and its concepts are
also applicable outside the coastal domain. For coastal modelling, it
contains dozens of example coupling configurations and tested set-ups for
coupled applications. Thus, MOSSCO addresses the technology needs of a
growing marine coastal Earth system community that encompasses very different
disciplines, numerical tools, and research questions.
Introduction
Environmental science and management consider ecosystems as their primary
subject, i.e. those systems in which the organismic world is fundamentally
linked to the physical system surrounding it; there are neither
unequivocally defined spatial nor processual boundaries between the
components of an ecosystem . Consequently, holistic
approaches to ecological research , biogeochemistry
originally 1926, and environmental science in
general have been called for.
The need for systems approaches is perhaps most apparent in coastal research.
Shelf and coastal seas are described by components from different spatial
domains, such as the atmosphere, ocean, soil, and they are driven by manifold
interlinked processes: biological, ecological, physical, and geomorphological,
amongst others. Crossing these domain and process boundaries, the dynamics of
suspended sediment particles (SPM; see Appendix for
abbreviations), living particles, and the interaction between water
attenuation and phytoplankton growth, for example, are both scientifically
challenging and relevant for the ecological state of the coastal system
e.g. .
For historical and practical reasons, the representation of the coastal
ecosystem in numerical models has been far from holistic. Most often,
ecological and biogeochemical processes are described in modules that are
tightly coupled to one or a few hydrodynamic models. For example, the Pelagic
Interactions Scheme for Carbon and Ecosystem Studies
PISCES; has been integrated into the Nucleus for
European Modelling of the Ocean NEMO; and the Regional
Ocean Modeling System ROMS;. The Biogeochemical
Flux Model (BFM) has been integrated into the Massachusetts Institute of
Technology Global Circulation Model (MITgcm) and ROMS.
These tight couplings not only exclude important processes at the edges of or
beyond the pelagic domain, but they also lack flexibility to exchange or to test
different process descriptions.
To stimulate the development, application, and interaction of ecological and
biogeochemical models independently of a single-host hydrodynamic model,
presented the Framework for Aquatic Biogeochemical
Models (FABM), which serves as an intermediate layer between the
biogeochemical zero-dimensional process models and the three-dimensional
geophysical environment models. FABM has been implemented in the Modular
Ocean Model MOM;, NEMO, the Finite-Volume Coastal
Ocean Model FVCOM;, and the General Estuarine
Transport Model GETM;. With more than
20 biogeochemical and ecological models included, FABM has enabled marine
ecosystem researchers to describe the system's many aquatic processes.
The process-oriented modularity realised within FABM, however, lacks the
means to describe cross-domain linkages. Historically rooted in
atmosphere–ocean circulation models , the coupling of
Earth domains is the standard concept in Earth system models (ESMs). Domain
coupling is also a major challenge in coastal modelling and has been used,
for example, in the Coupled Ocean–Atmosphere–Wave–Sediment Transport
COAWST; system. COAWST is comprised of the Regional Ocean
Modeling System (ROMS) with a tightly coupled sediment transport model, the
Advanced Research Weather Research and Forecasting (WRF) atmospheric model,
and the Simulating Waves Nearshore (SWAN) wave model. Each of the components
in domain coupling is usually a self-sufficient model that is run in a
special “coupled mode”. Interfacing to other components is done via
coupling infrastructure, such as the Flexible Modeling System
FMS;, the Model Coupling Toolkit
MCT; and/or the Ocean Atmosphere Sea Ice Soil
(OASIS) coupler , or the Earth System Modeling Framework
(ESMF; ; see for an example
intercomparison of coupling technologies). Recently,
introduced the Oceanographic Multipurpose Software Environment (OMUSE) and
demonstrated nested ocean and ocean–wave domain couplings. Their intention is
to provide a high-level user interface and infrastructure for coupling
existing and new oceanographic models whose spatial representations differ
greatly, in particular between Lagrangian- and Eulerian-type representations.
The Community Surface Dynamics Modeling System CSDMS;
even allows for the coupling of models implemented in many different languages, as long
as all of these describe their capabilities in basic model interface
BMI; descriptions. Typically, however, only three to
five domain components are coupled through one of the above technologies
.
The differentiation between domain and process coupling is not a technical
necessity: a typical domain coupling software like ESMF can also be used to
couple processes. With the Modeling Analysis and Prediction Layer
MAPL;, the Goddard Earth Observing System version 5
(GEOS-5) encompasses 39 process models coupled hierarchically through ESMF.
The
development of these modules, however, is strictly regulated within the
developing laboratory. Vice versa, a typical process coupling infrastructure
like the Modular Earth Submodel System MESSy;, which
initially linked mostly atmospheric processes, has been generalised to
support linking at a user-chosen granularity regardless of the process
versus domain dichotomy e.g..
Up to now, there has been no coastal modelling environment that enables a
modular and flexible process (model) integration and cross-domain coupling at
the same time that is open to a larger community of independent
biogeochemical and ecological scientists. The underlying long-term goal for
increasingly holistic model systems conflicts with the evolving and diverse
research needs of individual scientists or research groups to address very
specific problems; it remains difficult to link up-to-date research that is
delivered on the (local) process scale to the Earth system scale. Thus we
present the Modular System for Shelves and Coasts (MOSSCO,
http://www.mossco.de), a novel system for domain and process coupling
that is tailored but not limited to the coupling challenges of and
applications in the coastal ocean. This new system builds on the flexibility
of FABM and on the infrastructure provided by ESMF with its cross-domain and
many-component hierarchical capability. We present the major design ideas of
MOSSCO and briefly demonstrate its usability in a series of coastal
applications.
MOSSCO concepts
The modularity and coupling concepts proposed in this paper describe a novel
software system that addresses the needs of researchers who want to make
maximum use of their existing knowledge in a specific field (e.g.
geomorphology or marine ecology) but wish to conduct integrative research in
a wider and flexible context. In strengthening the
independence from specific physical drivers, the
new concept should, in addition to addressing the problems listed above,
support (1) liaisons between traditionally separated modelling communities
(e.g. coastal engineers, physical oceanographers, and biologists),
(2) intercomparison studies of physical, geological, and biological modules,
and (3) up-scaling studies in which models developed on the laboratory scale
in a non-dimensional context are applied to regional, global, and Earth
system scales.
The design of MOSSCO is application oriented and driven by the demands for
enabling and improving integrated regional coastal modelling. It is targeted
towards building coupled systems that support decision making for local
policies implementing the European Union Water Framework Directive (WFD) and
Marine Strategic Planning Directive (MSPD). From a design point of view we
envisioned a system that is foremost flexible and equitable.
Flexibility
means that the system itself is able to deal on the one hand
with a diverse small or large constellation of coupled model components and
on the other hand with different orders of magnitude of spatial and temporal
resolutions; it is able to deal equally well with zero-, one-, two-, and
three-dimensional representations of the coastal system. Flexibility implies
the capability to also encapsulate existing legacy models to create one or
more different “ecosystems” of models. This feature should allow for the seamless
replacement of individual model components, which is an important procedure
in the continual development of integrated systems. Flexibly in replacing
components finally creates a test bed for model intercomparison studies.
Equitability
means that all models in the coupled framework are treated as
equally important and that none is more important than any other. This
principle dissolves the primacy of the hydrodynamic or atmospheric models as
the central hub in a coupled system. Also, data components are as important
as process components or model output; any de facto difference in model
importance should be grounded in the research question and not on
technological legacy. As complexity grows by coupling more and more models,
this equitability also demands that experts in one particular model can rely
on the functionality of other components in the system without having to be
an expert in those models as well.
The equitability design extends to participation: contributions to the
development of components or the coupling framework itself is allowed and
encouraged. Anyone can use and modify the coupled framework or parts of both
in a legal sense through open source licencing and in an accessibility sense
through template codes and extensive documentation.
Wrapping legacy models – first steps in PARSE
MOSSCO's adoption of legacy code follows the two-layer paradigm of
BMI–CMI (basic model interface–component model interface) suggested by
. An existing legacy code (illustrated by “some model”)
is enhanced by model-specific code that exhibits basic coupling functionality
(BMI) and is framework agnostic. In a second step, a component (CMI)
is added that uses the BMI in the specific application programming
interface of the coupling framework. In addition to model interfaces that can
be used in MOSSCO-independent contexts, MOSSCO provides coupling concepts and
working implementations for coupled applications.
As MOSSCO is built around the ESMF hierarchy of components, any existing code
that can be wrapped in an ESMF component can be a component in MOSSCO, too.
The ESMF user guide suggests a best
practice method, PARSE, to achieve this componentisation of a legacy code.
P
repare the user code by splitting it into three phases that initialise, run, and finalise a
model.
A
dapt the model data structures by wrapping them in ESMF infrastructure like states and
fields.
R
egister the user's initialise, run, and finalise routines through
ESMF.
S
chedule data exchange between components.
E
xecute a user application by calling it from an ESMF driver.
This PARSE concept allows for a smooth transition from a legacy model to
an ESMF component. In this concept, the first three steps have to be
performed on the model side and the latter two on the framework side and
have been taken care of by the MOSSCO coupling layer. The preparation
of the code is independent of the use of ESMF and provides the basic coupling
ability – or “coupleability” – of the model; many existing models already
implement this separation into initialise, run, and finalise phases, either
structurally or more formally by implementing a BMI. For the run phase, it
is mandatory to refer to a single model timestep and not to the entire run
loop.
The adaption of a model's internal structures to ESMF consists of
technically wrapping data into ESMF communication objects and providing
sufficient metadata for communication. Among these are the grid definition and
decomposition, units, and semantics of data, optimally following a metadata
scheme like the widespread Climate and Forecast CF; or
the more bottom-up Community Surface Dynamics Modeling System (CSDMS;
following a scheme like object + operation + quantity;
). Both are currently being included in the
emerging Geoscience Standard Names Ontology (GSN;
http://geoscienceontology.org).
ESMF provides the interfaces for models written in either the Fortran or
C programming languages; data arrays are bundled together with related
metadata in ESMF field objects. All field objects from components are then
bundled into exported and imported ESMF state objects to be passed between
components. As a third step, the ESMF registration facility, needs to
be added to a user model; this step is achieved by using template code from
any one of the examples or tutorials provided with ESMF. The second and third
step (adapt and register) are typical tasks of what
refer to as a component model interface (CMI); it is
very similar between models (and thus easily accessible from template code)
and targets the interface of a specific coupling framework.
MOSSCO contains CMIs for ESMF in all of its provided components
(Fig. ). The current naming scheme follows the CF convention
for standard names except for quantities that are not defined by CF; these
names (often from biological processes) are modelled onto existing CF
standard names as much as possible. MOSSCO also allows for specification
within other naming schemes and includes a name-matching algorithm to mediate
between different schemes. For future development, adoption of the GSN
ontology is foreseen.
Scheduling in a coupled system – the “S” in PARSE
Scheduling of three coupled component instances A, B, C and their
data exchanges according to a pairwise coupling specification (see
Fig. b); shown along a simulation time axis, which is
independent of the type of (sequential or concurrent) deployment. Note how
each individual component instance has varying run lengths resulting from the
interference of all coupling intervals with this component. The time steps of
the (anonymous) scheduler component tk+n (grey bars) vary according to
the interference pattern of all coupling intervals. Coupling specification
(Fig. ): A couples bidirectionally to B at interval ΔtAB (green), A couples unidirectionally to C via a coupler D at
interval ΔtAC (blue), C couples unidirectionally to B at
interval ΔtCB (black).
MOSSCO adds onto ESMF a scheduling system (corresponding to the fourth step
in PARSE) that calls the different phases of participating coupled
models. The coupling time step duration of this new scheduler relies on the
ESMF concept of alarms and a user specification of pairwise coupling
intervals between models. The scheduler minimises calls to participating
models by flexibly adjusting time step duration to the greatest common
denominator of coupling intervals pertinent to each coupled model. Upon
reading the user's coupling specification, (i) models are initialised in
random order but with consideration of special initialisation dependencies
set by the user; (ii) a list of alarm clocks is generated that considers all
pairwise couplings a model is involved in; (iii) special couplers associated
with a pairwise coupling are executed; (iv) the scheduler then tells each
model to run until that model reaches its next alarm time; and (v) the advancing of
the scheduler to the minimum next alarm time repeats until the end of the
simulation.
The MOSSCO scheduler allows for both sequential and concurrent coupling
of model components or a hybrid coupling mode. In the concurrent mode,
components run at the same time on different computing resources; in the
sequential mode, components are executed one after another on the same set of
computing resources. Recently, demonstrated how a hybrid
coupling mode and fine granularity could be used to increase the performance
of a system that consists of both highly scalable and less scalable
components. In their system, an ocean and an atmosphere component run
concurrently; within the atmospheric component, the radiation code is
executed concurrently to a composite component that encompasses
a sequential coupling of all non-radiative atmospheric processes.
For both concurrent and sequential modes, coupling between components is
explicit: the MOSSCO scheduler runs the connectors and mediator components
that exchange the data before the components are run. For sequential mode,
the coupling configuration also allows for a memory efficient scheme in which
consecutive components operate on shared data that always reflect the most
recently calculated data from the previous component
(Fig. ; see also Sect. ); such
sequential coupling on shared data potentially introduces mass imbalances.
Examples of coupling configurations (a, b) and the steps
from installation to deployment (c). The configurations exhibit a
minimal default coupling specification (a) and a more complex one
(b, see Fig. ) that makes use of dependencies,
instantiation, and different coupling intervals. The line-numbered
installation steps (c) include environment variable specification
(export), download of the system with git, loading
additional external models with make, installation of the
mossco executable script, and finally deployment of the coupling
specification jfs in a predefined set-up called “helgoland”, a 1-D
station near the island of Helgoland in the southern North Sea.
Users specify the coupling in a text format using YAML (short for YAML Ain't
Markup Language; http://yaml.org) notation, a
human-reader-friendly data serialisation
standard. The item coupling contains a list of components
that itself contains a list of coupled components; in the simple example
(Fig. a) two components named “A” and “B” are coupled. By
default, these components are coupled in sequential mode with the default
connector sharing their data; the execution order of A and B is not
specified. In a more elaborate example (Fig. b), the order of
components in the scheduler is specified in the dependency section,
indicating a run sequence of first A, then B, and last C; all components run
on the same set of computing resources in (default) sequential coupling mode.
The instances section declares that the component named C is an
instance (or copy) of A; this makes it possible to reuse components
multiple times in (possibly different) configurations. Typically, data reader
or writer components are instantiated from a generic input–output component
to access different files for model input and output. Multiple couplings
between the three components A, B, and C are present with
coupling intervals that lead to the scheduling of coupling events
according to Fig. . Between A and C, a special
coupler “D” handles the data exchange instead of the default connector.
Deployment of the coupled system – the “E” in PARSE
MOSSCO provides a Python-based generator that dynamically creates an ESMF
driver component in a star topology that then acts as the scheduler for the
coupled system. This generator reads the specification of pairwise couplings
(Fig. ) and generates a Fortran source file that represents the
scheduler component. The generator takes care of compilation dependencies of
the coupled models and of coupling dependencies, such as grid inheritance; in
addition to the basic init–run–finalise BMI scheme, it also honours
multi-phase initialisation (as in the National Unified Operational Prediction
Capability, NUOPC, ESMF extension) and a restart phase. The generated code
structurally and functionally resembles a NUOPC driver, but it does not require implementation of the NUOPC
extension, which is currently restricted to handling only structured
grid-based sub-models.
A MOSSCO command line utility provides a user-friendly interface to
generating the scheduler, (re-)compiling all source codes into an executable
and submitting the executable to a multi-processor system, including
different high-performance computing (HPC) queueing implementations; this is
the fifth step in PARSE. By designing this command line utility and
automatic scheduler component creation based on the simple YAML textual
coupling specification, MOSSCO provides a fast way to reconfigure, rearrange,
extend, or reduce coupled systems very quickly in contrast to more elaborate
graphical coupling tools such as the CUPID Eclipse interface only
for NUOPC.
MOSSCO has been successfully deployed at several national HPC centres, such
as the Norddeutsche Verbund für Hoch- und Höchstleistungsrechnen (HLRN),
the German Climate Computing Center (DKRZ), and the Jülich Supercomputing
Centre (JSC). Equally, MOSSCO is currently functioning on a multitude of
Linux and macOS laptops, desktops, and multiprocessor workstations using the
same MOSSCO (bash-based) command line utility on all platforms.
The MOSSCO coupling layer is coded in Fortran, while most of the supporting
structure is coded in Python and partially in bash syntax. The system
requirements are a Fortran 2003 compliant compiler, the CMake build system,
the Git distributed version control system, Python with YAML support
(version 2.6 or greater), a Network Common Data Form
NetCDF; library, and ESMF (version 7 or greater). For
parallel applications, a message-passing library (e.g. OpenMPI) is required.
Many HPC centres have toolchains available that already meet all of these
requirements. For an individual user installation, all requirements can be
taken care of with one of the package managers distributed with the operating
system, except for the installation of ESMF, which needs to be manually
installed; MOSSCO provides a semi-automated tool for helping in this
installation of ESMF. The steps to get MOSSCO running quickly on any suitable
computer system are outlined in Fig. c. These instructions
should get a reader started on carrying out initial simulations with a coupled
system by typing a dozen lines of code, provided that all requirements are
met.
MOSSCO components and utilities
Components currently integrated into MOSSCO and described shortly in
this paper. Several other components are under development and not listed
here.
Driven by user needs, MOSSCO currently entails utilities for I/O, an
extensive model library, and coupling functionalities
(Fig. and Table ). As a utility
layer on top of ESMF, MOSSCO also extends the application programming
interface (API) of ESMF by providing convenience methods to facilitate the handling of time, metadata
(attributes), configuration, and to unify the provisioning and transfer of
scientific data across the coupling framework. The use of this utility layer
is not mandatory; any ESMF-based component can be coupled to the
MOSSCO-provided components without using this utility layer.
One of the major design principles of MOSSCO is seamless deployment from
zero-dimensional to three-dimensional spatial representations, while
maintaining the coupling configuration to the maximum extent possible. This
design principle builds on the dimensional independency concept of FABM
achieved by the local description of processes (often referred to as a box
model), in which the dimensionality is defined by the hydrodynamic model to
which FABM is coupled. MOSSCO generalises this concept to enable
the developers of new biological and chemical models to scale up from a
box model (zero-dimensional) to a water column (one-dimensional), sediment
plate, or a vertically resolved transect (two-dimensional) and a full
atmosphere or ocean (three-dimensional) set-up. As a concrete example, the
novel Model for Adaptive Ecosystems in Coastal Seas
MAECS; has been developed, iterating
between an application for the lab (zero-dimensional) scale and the
three-dimensional regional coastal ocean scale.
Modular components of MOSSCO. The blue branch collects newly created
sub-models and components that wrap around legacy codes; the violet branch
collects coupling functionalities and the orange branch the input–output
utilities.
All utility functions and components, especially the model-independent I/O
facilities from MOSSCO, are able to handle data of any spatial dimension.
Components that do not define their own spatial representation as a grid or
mesh are able to inherit the complete spatial information from a coupled
component that provides such a grid: usually (but not necessarily) biological
and chemical models inherit the spatial configuration from a hydrodynamic
model. Equally, this information can be obtained from data in
standardised grid description formats like Gridspec or the
Spherical Coordinate Remapping and Interpolation Package
SCRIP;. Grid inheritance is specified as a dependency
in the coupling specification.
Model library: model interfaces for scientific model components
The model library (right branch in Fig. ) includes
new models (e.g. for filter feeders and surface waves) and wrappers to
legacy models and frameworks such as FABM or GETM. Some of these wrappers are
under development, among them the Hamburg Shelf Ocean Model
HamSOM; and a Lagrangian particle tracer model. Here, we
briefly document the model collection, particularly with respect to their
preparation and functioning within the new coupling context.
Pelagic ecosystem component
The pelagic ecosystem component (fabm_pelagic_component) collects
(mostly biological) process models for aquatic systems. This component makes
use of the Framework for Aquatic Biogeochemical Models .
FABM is a coupling layer to a multitude of biogeochemical models which
provide the source-minus-sink terms for variables, their vertical local
movement (e.g. due to sinking or active mobility), and diagnostic data. Each
model variable is equipped with metadata, which is transferred by the
ecosystem component into ESMF field names and attributes. Similarly, the
forcing required by the biogeochemical models is communicated within the
framework and linked to FABM. The pelagic ecosystem component includes a
numerical integrator for the boundary fluxes and local state variable
dynamics. Advective and diffusive transport are not part of this component
but are left to the hydrodynamical model through the
transport_connector (Sect. ). The close
connection between transport and the pelagic ecosystem requires that the
spatial representation of the FABM state variables be inherited from the
hydrodynamic model component that performs the transport calculations.
Many well-known biogeochemical process models have been coded in the FABM
standard by various institutes, such as the European Regional Seas Ecosystem
Model ERSEM;, ERGOM , PCLake
, and the Bottom RedOx Model BROM;. All
pelagic biogeochemical models complying with the FABM standard can equally be
used in MOSSCO, while retaining their functionality.
Sediment and soil component
The sediment component fabm_sediment_component hosts (mostly
biogeochemical) process descriptions for aquatic soils. To allow for efficient
coupling to a pelagic ecosystem, the sediment component inherits a horizontal
grid or mesh from the coupled system and adds its own vertical coordinate, a
number of layers of horizontally equal height for the upper soil (typical
domain heights range from 10–50 cm). State variables within the
sediment are defined through the FABM framework within the 3-D grid or mesh
in the sediment. As in the pelagic ecosystem component, the state variables,
metadata, diagnostics, and forcings are communicated via the ESMF framework to
the coupled system. The sediment component is the numerical integrator for
the tracer dynamics within each sediment column in the horizontal grid or
mesh, including diffusive transport, driven by molecular diffusion of the
nutrients or bioturbative mixing. Additionally, a 3-D variable porosity
defines the fraction of pore water as part of the bulk sediment, while all
state variables are measured per volume of pore water in each cell. The FABM
infrastructure of state variable properties is used to label the new boolean
property particulate in FABM models to define whether a state
variable belongs to the solid phase within the domain. A typical model used
in applications of the sediment component is the biogeochemical model of the
Ocean Margin Exchange Experiment OMEXDIA ; a version of
this model with added phosphorous cycle is contained in the FABM model
library as OMEXDIA_P .
1-D Hydrodynamics: General Ocean Turbulence Model (GOTM))
The General Ocean Turbulence Model
GOTM; is a one-dimensional water
column model for hydrodynamic and thermodynamic processes related to vertical
mixing. MOSSCO provides a component for GOTM and a component hierarchy that
considers a coupled GOTM with internally coupled FABM within one component
(gotm_fabm_component), as many existing available model set-ups
rely on the direct coupling of FABM to GOTM. This way, the modularisation –
taking a coupled GOTM–FABM apart and recoupling it through the MOSSCO
infrastructure – can be verified; the encapsulation of GOTM is implemented in
the gotm_component.
3-D Hydrodynamics: General Estuarine Transport Model (GETM)
MOSSCO provides an interface to the 3-D coastal ocean model GETM
. GETM solves the Navier–Stokes equations under
Boussinesq approximation, optionally including the non-hydrostatic pressure
contribution . A direct interface to GOTM (see
Sect. ) provides state-of-the-art turbulence closure in the
vertical. GETM supports horizontally curvilinear and vertically adaptive
meshes . The interface to GETM is provided
by the getm_component; any model coupled to GETM via the transport
component can have its state variables conservatively transported by GETM
(see Sect. ). In the component, the GETM-created spatial
topology is made available as an ESMF grid object; typically this grid and
subdomain decomposition is communicated to the coupled system in which the
spatial and parallelisation information is inherited by other components.
Model components for erosion, sedimentation, and their biological alteration
The erosion and sedimentation routines of the Deltares Delft3D model
EROSED; were encapsulated in a MOSSCO component.
EROSED uses a Partheniades–Krone equation for
calculating the net sediment flux of cohesive sediment at the water–sediment
interface for multiple SPM size classes. The MOSSCO BMI
uses the current version of
EROSED maintained by Deltares; it isolates with the help of subsidiary
infrastructure the original code from the deeply intertwined dependencies in
the Delft3D system. The erosed_component can provide its own
spatial representation as a structured grid or unstructured mesh; it can also
inherit the spatial information from a coupled component. The functionality
of the erosion and sedimentation component is described in more detail by
.
Flow and sediment transport can be affected by the presence of benthic
organisms in many ways. Protrusion of benthic animals and macrophytes in the
boundary layer changes the bed roughness and thus the bed shear stress and
consequently the sediment transport. The erodibility of sediment can be
modified by the mucus produced by benthic organisms; the erodibility of the
upper bed sediment can be altered by bioturbation generated by macrofauna
. In the benthos_component, these biological
effects of microphytobenthos and of benthic macrofauna on sediment
erodibility and critical bed shear stress are parameterised and provided to
other coupled components (e.g. the erosion and sedimentation component) as
additional erodibility and critical shear stress factors. The benthos effect
model is described in detail by .
Filter feeding model
The filtration_component describes the instantaneous filtration by
suspension feeders within the water column. This biological filtration model
follows and describes the filtration rate as a function of
food supply; it can be adapted to different species of filter feeders and was
recently applied to describing the ecosystem effect of blue mussels on
offshore wind farms as the filtration_component of MOSSCO
. The filtration model uses an arbitrary chemical species
or compound, for example phytoplankton carbon, as the “currency” for processing. The
ambient phytoplankton carbon concentration is sensed by the model
organisms and filtered along with the other nutrients (in
stoichiometric proportion) out of the environment, creating a sink term for
subsequent numerical integration in the pelagic ecological model.
Wind waves
A simple wind wave model is part of the MOSSCO suite. Based on the
parameterisation by , significant wave height and peak
wave period are estimated in terms of local water depth, wind speed, and fetch
length. This wave data enable the inclusion of wave effects, especially for
idealised 1-D water column studies, e.g. the consideration of erosion
processes due to wave-induced bottom stresses. Coupling to 3-D ocean models
and the calculation of additional wave-induced momentum forces,
following either the radiation stress or vortex force formulation
, is possible as well. For the inclusion of wave–wave or
wave–current interactions in realistic 3-D applications, coupling to a
more advanced third-generation wind wave model like SWAN, WaveWatch III, or
a wave atmospheric model (WAM) would be necessary.
Input–output utilities
The input and output (I/O) utilities include general purpose coupling
functionalities that deal with boundary conditions, provide a restart
facility, and add surface, lateral, and point source fluxes (lower left branch in
Fig. ).
NetCDF output
This component of MOSSCO provides an output facility
netcdf_component for any data that are communicated in the coupling
framework. The component writes one- to three-dimensional time-sliced data
into a NetCDF file and adds metadata on the simulation to
this output. Multiple instances of this component can be used within a
simulation such that output of different variables, differently processed
data, and output at various time steps can be recorded. The output
component is fully parallelised with a grid decomposition inherited from one
of the coupled science or data components. In order to reduce interprocess
communication during runtime, each write process considers only the part of
the data (its data tile) that resides within its computing domain. This comes
at a cost to the user, who has to post-process the output tiles to combine
for later analysis; a Python script is provided with MOSSCO that takes care
of joining tiled files.
The output component also adds metadata that is collected from the system and
the user environment at the creation time of the output files. Diagnostics
on the processing element and run time between output steps are recorded.
The structure of the NetCDF output follows the Climate and Forecast
CF; convention for physical variables, geolocation,
units, dimensions, and methods modifying variables. When (mostly biological)
terms are not available in the controlled vocabulary of CF, names are built
to resemble those contained in the standard.
NetCDF input
The netcdf_input_component of MOSSCO reads from NetCDF files and
provides the file content wrapped in ESMF data structures (fields) to the
coupling framework. It inherits its decomposition from other components in
the coupled system. Data can be read from a single file for the entire domain
or from distributed files for all decomposed computing elements separately.
Upon reading data, fields can be renamed and filtered before they are
passed on to the coupled system.
The input component is typically used to initialise other components for
restarting, to provide boundary conditions, and for assimilating data into
the coupled system. The input facility supports the interpolation of data in time
upon reading the data with nearest, most recent, and linear interpolation.
It also supports reading climatological data and translates the
climatological time stamp to a simulation present time stamp in the coupling
framework.
MOSSCO connectors and mediators
Information in the form of ESMF states that contain the output fields of
every component are communicated to the ESMF driver; requests for data by
every component are also communicated to the ESMF driver component. MOSSCO
connectors are separate components that link the output and requested fields
between pairwise coupled components. MOSSCO informally distinguishes between
connector components that do not manipulate the field data on transfer at all
(or only slightly) and mediator components that extract and compute new data
out of the input data.
Link, copy, and nudge connectors
The simplest and default connecting action between components is to share a
reference (i.e. a link) to a single field that resides in memory and can be
manipulated by each component; in contrast, the copy_connector
duplicates a field at coupling time. The consideration of a link or copy
connector is important for managing the data flow sequence in a coupled
system: the copy mechanism ensures that two coupled components work on the
same lagged state of data, whereas the link mechanism ensures that each
component works on the most recent data available.
The nudge_connector is used to consolidate output from two
components through the weighted averaging of the connected fields. It is typically
used as a simple assimilation tool to drive model states towards observed
states or to impose boundary conditions.
These connectors can only be applied between components that run on the same
grid (but maybe with a different subdomain decomposition). The
link_connector can only be applied between components with an
identical subdomain decomposition so that the components have access to the
same memory. Components on different grids require regridding, which is
currently under development in MOSSCO.
Transport connector
A model component qualifies as a transport component when it offers to
transport an arbitrary number of tracers in its numerical grid; this facility
is present, for example, in the current gotm_component and
getm_component. The transport_connector collects state
variables to be transported from any coupled component and communicates this
collection to the hydrodynamic component based on the availability of both
the tracer concentrations and their rate of vertical movement
independent of the water currents. This connector is usually called only once
per coupled pair of components during the initialisation phase.
Mediators for soil–pelagic coupling
One aspect of the generalised coupling infrastructure in MOSSCO is the use of
connecting components that mediate between technically or scientifically
incompatible data field collections. The soil–pelagic coupling of
biogeochemical model components with a variety of different state variables
raises the need for these mediators. The use of mediators leaves the level of
data aggregation, data disaggregation, and unit conversion to the coupling
routine instead of requiring specific output from a model component
depending on its coupling partner component.
For soil–pelagic (or benthic–pelagic) coupling, the
soil_pelagic_connector mediates the soil biogeochemistry output
towards the pelagic ecosystem input and the pelagic_soil_connector
mediates the pelagic ecosystem output towards the soil biogeochemistry input.
Examples include the following: (i) disaggregation of dissolved inorganic nitrogen to dissolved
ammonium and dissolved nitrate; (ii) filling missing pelagic state fields for
phosphate using the Redfield equivalent for dissolved inorganic nitrogen; and
(iii) calculation of the vertical flux of particulate organic matter (POM)
from the water column into the sediment depending on POM concentrations in
the near-bottom water, its sinking velocity, and a sedimentation efficiency
depending on the near-bottom turbulence. The effective vertical flux is
communicated into the pelagic ecosystem component to budget the respective
loss and is communicated to the soil biogeochemistry component to account
for the respective new mass of POM. The mediator also handles
(iv) disaggregation of a single oxygen concentration (allowing for positive and
negative values) into dissolved oxygen concentration, if positive, and
dissolved reduced substances, if negative, and (v) aggregation of pelagic POM
composition (variable nitrogen to carbon ratio) into fixed stoichiometry POM
pools in the soil biogeochemistry.
Selected applications as feasibility tests and use cases
MOSSCO was designed for enhancing flexibility and equitability in
environmental data and model coupling. These design goals have been helpful
in generating new integrated models for coastal research with applications at
different marine stations (1-D), transects (2-D), and sea domains (3-D).
Below, we describe from a user perspective the added value and success of the
design goals obtained from using MOSSCO in selected applications; here, the
focus is not on the scientific outcome of the application (these are
described elsewhere by ,
, and ). All set-ups described in
the use cases are available as open source (with limited forcing data due to
space and bandwidth constraints).
Helgoland station
Coupling set-up and exemplary results from a 1-D system simulating
the nutrient and SPM dynamics near the island of Helgoland, Germany with
soil–pelagic coupling from 2002 to 2005. (a) Coupling set-up with
seven ESMF components (highlighted in red, leaves) and three FABM sub-models
(side text); (b) soil denitrification rate; (c) surface SPM
dynamics resulting from EROSED and pelagic FABM–SPM; (d) middle
water column nitrogen and phosphorous dynamics from pelagic FABM–NPZD.
The seasonal dynamics of nutrients and turbidity emerge from the interaction
of physical, ecological, and biogeochemical processes in the water column and
the underlying sea floor. We resolve these dynamics in a coupled application
for a 1-D vertical water column for a station near the German offshore island
of Helgoland. Average water depth around the island is 25 m; tidal currents are
affected by the M2 and S2 tides with a characteristic spring–neap cycle,
with current velocity not exceeding 1 m s-1.
The Helgoland 1-D application is realised by a coupled system consisting of
GOTM hydrodynamics, the pelagic FABM component with a
nutrient–phytoplankton–zooplankton–detritus (NPZD) ecosystem model
, and two SPM size classes interacting with the erosion
and sedimentation module, the sediment component with the OMEXDIA_P early
diagenesis sub-model, and coupler components for soil–pelagic,
pelagic–soil,
and tracer transport. This system and set-up are described in more detail by
.
Simulations with this application show a prevailing seasonal cycle in the
model states (Fig. ). Dissolved nutrients (referred to as
dissolved inorganic nitrogen) are taken up by phytoplankton, which fills the
pool of particulate organic nitrogen during the spring bloom
(Fig. d). The particulate organic matter sinks into the
sediments, where it is remineralised along axis, sub-oxic, and anoxic
pathways; denitrification, for example, shows a peak in late summer
(Fig. b). At the end of a year, nutrient concentrations
are high in the sediment and diffuse back into the water column up to winter
values of 20–25 mmol m-3. The seasonal variation of turbidity is a
result of higher erosion in winter and reduced vertical transport in summer
(Fig. c).
Idealised coastal 2-D transect
The coastal nitrogen cycle is resolved in an idealised coupled system for a
tidal shallow sea. This two-dimensional set-up represents a vertically
resolved cross-shore transect 60 km in length and at 5–20 m of water depth and
has been used by to simulate sustained horizontal
nutrient gradients by particulate matter transport towards the coast. Within
the MOSSCO coupling framework, the 2-D transect scenario additionally
provides insights into the horizontal variability of erosion–sedimentation and
benthic biogeochemistry. Its coupling configuration builds on the one used
for the 1-D station Helgoland (Sect. ); the water column
hydrodynamic model GOTM, however, is replaced by the 3-D model GETM; a local
wave component and data components for open boundaries and restart have been
added.
A 2-D idealised cross-shore transect off the German coastline is
used to investigate the feedback loop among estuarine circulation, sediment
transport, and nutrient cycling across the benthic–pelagic interface.
(a, c) Hovmöller diagrams showing the soil–pelagic fluxes of
particulate organic carbon (POC) and the soil BGC denitrification and oxygen
consumption rates for the 60 km long transect. (b) Coupling diagram
including components for hydrodynamics, erosion–sedimentation, waves, pelagic
ecology, suspended particles, and soil ecology. This example uses both
ESMF modularity (the components) and FABM modularity (the different
ecological–biogeochemical models within the pelagic and sediment
environmental components). (d) Spatial set-up of the idealised 2-D
cross-shore transect.
Figure shows exchange fluxes between the water column and
the sediment for 1 year of simulation. The simulation of turbidity, as a
result of pelagic SPM transport and resuspension by currents and wave stress,
provides the light climate for the pelagic ecosystem. The flux of particulate
organic carbon (POC) into the sediment reflects bloom events in summer during
calm weather conditions. Macrobenthic activity in the sea floor brings fresh
organic matter into the deeper sub-oxic layers of the sediment, where
denitrification removes nitrogen from the pool of dissolved nutrients. The
coupled simulation reveals decoupled signals of benthic respiration,
denitrification and nutrient reflux into the water column, which is not
resolved in monolithically coded regional ecosystem models of the North Sea
.
Southern North Sea bivalve ecosystem applications
Building flexible applications with MOSSCO. Two bivalve-related
scientific applications are showcased: investigated
the effect of bottom-dwelling Fabulina fabula (a, showing
parts of the southern North Sea) on suspended sediment
concentration (c) with a coupled application integrating
hydrodynamics, three pelagic SPM classes in the ecosystem model, the mediation of
erodibility by benthic bivalves, and an explicit description of bed erosion
and sedimentation (b); see Sect. 4.4 and Fig. 5.
investigated the effect of epistructural Mytilus edulis(d)
on phytoplankton concentration (f) with a coupled application
integrating hydrodynamics, the FABM–MAECS ecosystem model, and filtration by
mussels (e).
A southern North Sea (SNS) domain was used in two studies concerning the
effects of bivalves on the pelagic ecosystem. investigated
how the accumulation of epifauna on wind turbine structures
(Fig. d) impacts pelagic primary production and ecosystem
functioning in the SNS on larger spatial scales. This study is the first of
its kind that extrapolates the ecosystem impacts of anthropogenic offshore wind
farm structures from a local to a regional sea scale. The authors use a
MOSSCO coupled system consisting of the hydrodynamic model GETM, the
ecosystem model MAECS as described by , the transport
connector, the filter feeder component, and several input and output
components (Fig. e). They assess the impact of
anthropogenically enhanced filtration from blue mussel (Mytilus
edulis) settlement on offshore wind farms that are planned to meet the
40-fold increase in offshore wind electricity in the European Union
by 2030. They find a small but non-negligible large-scale effect in both
phytoplankton stock and primary production, which possibly contributes to
better water clarity (Fig. f).
Biological activities of macrofauna on the sea floor mediate suspended
sediment dynamics, at least locally. In the study by
, the large-scale biological contribution of benthic
macrofauna, represented by the key species Fabulina fabula
(Fig. a), to the suspension of sediment was investigated.
Simulation results for a typical winter month revealed that SPM is increased
not only locally but beyond the mussel-inhabited zones. This effect is not
limited to the near-bed water layers but can be observed throughout the
entire water column, especially during storm events
(Fig. c). In this coupled application, the hydrodynamic
model GETM, the pelagic ecosystem component with three SPM size classes, the
erosion–sedimentation and benthic mediation components, several input and
one output components, and the transport connector were used
(Fig. d).
Exemplary workflow
For the SPM bivalve example above ( and
Fig. c), the coupled system contains 13 modular components:
the hydrodynamic getm_component and simplewave_component,
a pelagic fabm_component, the benthic erosion–sedimentation
erosed_component and benthos_component, one output and four input
components, the default link_connector, the
nudge_connector, and the transport_connector. Each
of these 13 components is involved in at least one pairwise coupling
described in a couplings section of the YAML coupling configuration
(Fig. b). This coupled application is to be deployed in
sequential mode on the same set of computing resources for all 13 components.
The horizontal spatial representation and domain decomposition are provided
by the grid that is created in the hydrodynamic model and that is
communicated to the wave, pelagic ecosystem, benthic, and input components;
this is achieved by specifying the hydrodynamic model as a dependency of
these components (dependencies in Fig. b) Four
instances of the netcdf_input_component (see Sect.
and instances in Fig. b) are created to provide
macrofauna forcing, lateral open ocean boundaries, rivers fluxes, and restart
information from netCDF files. In the first of two initialisation phases, the
output components and the hydrodynamic component are initialised first, as
they have no dependencies. Dependent components then receive the spatial
grid information from the hydrodynamic component. All components advertise
what information they can provide (e.g. a certain quantity) and what
information they need (e.g. grid information) in the coupled system.
In the second initialisation phase, the transport_connector ensures
that all fields from the ecosystem component are made available in the
hydrodynamic component for advection and diffusion. For all other pairwise
couplings, the link_connector communicates advertised data from a
sending component as a pointer to the receiving component; passing pointers
to data instead of copies of the data is only possible in sequential mode and
on identical grids. In the restart phase, additional initialisation data are
communicated to all components implementing this (optional) phase; here, the
bed mass and SPM concentrations are updated in the ecosystem and erosion
components via a coupling to an instance of the input component that reads
data from a file created
in prior model runs (“restart”).
In the run phase, all pairwise couplings are called in the same order as
during the initialisation phase. First, the connector (or coupler) is called
to synchronise the two components' data, then each of the coupled components
in this pairwise coupling is executed for the minimum time interval to the
next coupling time step of the involved components (see
Fig. ). With the boundary conditions read with the input
component from files at each coupling interval, the SPM fields that reside in
the ecosystem component are updated by way of connecting these components
with the nudge_connector. Finally, at the end of a simulation, all
output components are run once more to ensure that the final state of the
system is recorded; then, all components go through their finalisation phase and
clean up reserved memory.
Discussion and outlook
In merging existing frameworks that address distinct types of modularity and
by developing a superstructure for making the multi-level coupling approach
applicable in coastal research, the MOSSCO system largely meets the design
goals of flexibility and equitability. In doing so, the structural deficiencies of
legacy models and the need for practical compromises became very
apparent.
For legacy reasons, equitability is the harder to achieve design goal.
Both the distribution of computing resources and the spatial grid
definition can in principle be determined by any one of the participating
components; de facto, in marine or aquatic research, they are prescribed by
the hydrodynamic models that have so far not been enabled to inherit a grid
specification or a resource distribution from a coupler or coupled system.
With the ongoing development and diversification of hydrodynamic models and
no immediate benefit for the different physical models to outsource
grid and resource allocation, this situation is not likely to change. MOSSCO
compromises here with its flexible grid inheritance scheme and with the grid-provisioning component that delivers this information to the coupled system
whenever a hydrodynamic component is not used.
Beyond grid and resource allocation, however, the equitability concept is
successfully driving independent developments of sub-modules. We found that
experts in one particular model, e.g. the erosion module, could rely on the
functionality of the other parts of the system without having to be experts
themselves in all of the constituent models in the coupled application. The
limitations to this black-box approach became evident in the scientific
application and evaluation of the coupled model system, which was only
possible when collaboration with experts in these other model systems was
sought. By taking away the inaccessibility barrier and by enforcing a clear
separation of tasks, the modular system stimulated a successful collaboration.
Sustained granularity also helped in terms of alignment with ongoing development in
external packages. These can be integrated fast into the coupled system,
which does not rely on specific versions of the externally provided software
unless structural changes occur. Long-term supported interfaces on the
external model side facilitate MOSSCO being up to date with, for example, the fast-evolving GETM and FABM code bases.
When legacy codes were equipped with a framework–agnostic interface,
we encountered four major difficulties.
For organising the data flow between the components, MOSSCO
uses standard names and units compatible with the infrastructure and library
of standard names and units provided in the pelagic component for the FABM
framework (mostly modelled on CF). Other components, such as the BMIs of
wrapped legacy models, do not provide such a standard name in their own
implementation and, in particular, often do not adhere to a naming standard.
We found ambiguity arising, e.g. with temperature to be represented as
temperature vs. sea_water_temperature vs.
temperature_in_water. While this can be resolved based on CF for
temperature, most ecological and biogeochemical quantities currently lack a
consistent naming scheme. The forthcoming GSN ontology building on
CSDMS names; could adequately address this coupling
challenge.
Deep subroutine hierarchies of existing models made it difficult
to isolate desired functionality from the structural external overhead. In
one example, in which a single functional module was taken out of the context of
an existing third-party coupled system, the module depended on many routines
dispersed throughout that third-party system repository.
Components based on stand-alone models are developed and tested
with their own I/O infrastructure and typically supply a BMI implementation
only for part of their state and input data fields. A new coupled
application or data provisioning and/or requesting within a coupled system can
therefore easily require a change in a model BMI. The implementation
potential input and output for all quantities, including replacement of the
entire model-specific I/O in the BMI, is therefore desirable for new
developments and re-factoring.
Mass and energy need to be conserved across the coupled components.
Mediators communicate conservatively regridded
mass and energy fluxes into pairs of coupled components. These fluxes then
need to be appropriately integrated by the coupled components, even when their internal
time discretisation differs and for asynchronous scheduling that can incur different
coupling time steps. The conservative integration of exchanged mass and energy fluxes
cannot automatically be ensured by the coupling system, and the user
has to carefully consider time steps in the preparation of the coupling set-up.
Efforts to make legacy models coupleable, either for MOSSCO or similar
frameworks, however, can have additional benefits besides the immediate
applicability in an integrated context. Coupleability strictly demands the
communication of sufficient metadata, which stimulates the quality and
quantity of documentation and the scientific and technical reproducibility of
legacy models. Indeed, transparency has been greatly increased by wrapping
legacy models in the MOSSO context. All participating components performed
the introspection and leveraging of a collection of metadata at the assembly time of
the coupled application and during output. Transparency is expected to be
continuously increased by new coupling demands and more generous metadata
provisioning from wrapped science models. MOSSCO is moving towards adopting
the Common Information Model (CIM) that is also required by Climate Model
Intercomparison Project (CMIP) participating coupled models
.
With a current small development base of 12 contributors, the openness
concept of MOSSCO in terms of including contributions from outside the core
developer team has not yet been tested; in the categorisation by
internal governance with simple structure is sufficient at
this size. Formally, external contributions can be included in MOSSCO by way
of contributor licence agreements. The openness concept has been useful in
instigating discussions about the need for explicit (and preferably open)
licencing of related scientific software and data as demanded in current open
science strategies e.g..
Scalability in MOSSCO applications depends on the scalability of the coupled
model components and on the potential overhead of the coupling
infrastructure. Strong scaling experiments were performed with a coupled
application using GETM, FABM with MAECS (≈ 20 additional transported
3-D tracers), and FABM with OMEXDIA_P, including bidirectional
benthic–pelagic coupling, on Jureca . They show linear
(perfect) scaling from 100 to 1000 cores and a small levelling-off (to 85%
of perfect scaling) at 3000 cores. We have not observed a loss of computing time
due to the infrastructure and superstructure overhead of ESMF, which remained
below 0.1 % in the run phase of the scaling experiment. A typical
operational computation speed achieved, e.g. in the bivalve wind farm
application (Sect. ; 175 000 grid cells), on 192 processors
is 2000 computed hours per elapsed wall clock hour: such a performance allows
for decadal to multi-decadal simulations. One of the identified bottlenecks
(that varies strongly with the HPC system used) is data transfer from memory
to disc: this will be addressed in the future by the use of parallel NetCDF
and/or leveraging the XML I/O server XIOS;.
Multi-component systems may also suffer from low acceptance by the research
community. They are much harder to implement and maintain by
individual groups, in the context of which researchers solve coastal ocean problems of a large
range of complexity, from purely hydrodynamic applications via coupled
hydrodynamic–sediment dynamic applications to fully coupled systems. Many
academic problems focus on specific mechanisms and thus do not require the
complete and fully coupled modular system such that the application of the
full system might mean a large structural overhead and additional workload.
There is, however, the necessity of following a holistic approach when
tackling grand research questions in environmental science, such as those related to
system responses to anthropogenic intervention. Yet, it is not clear whether
the bottom-up approach of many interacting modular components leads to an
emergent system behaviour that is desirable and exhibits new insights or
whether the system gets tangled up in coupling complexity.
As evident from the test cases (Sect. ), MOSSCO also encourages
coupled applications that are far from a complete system-level description.
With few coupled components, the technical threshold to getting an
application running on an arbitrary system is relatively low. The user can
quickly reach initial success. MOSSCO provides a full documentation, step by
step recipes, and a public bug tracker; it adopts abundant error reporting
from ESMF and a fail fast design that stops a coupled application as soon as
a technical error is detected . Usability is especially high
due to an available master script that compiles, deploys, and schedules a
coupled application. To address a wide range of users, the system is designed
to run on a single processor or on a user's laptop equally well as on a
high-performance computer using several thousand computing nodes.
An obvious advantage of modular coupling is the opportunity to bridge the gap
between different scientific disciplines. It allows in principle for the
combination of, for example,
hydrodynamic models from oceanography with sediment transport models
from coastal engineering. Thus different experts can work on their individual
models but benefit from all others' progress. This seeming advantage,
however, also poses a drawback for modular coupling approaches. An initial
effort which is necessary for individual models to meet the requirements of a
modular modelling framework has to be invested. This will only happen if
there is either urgent pressure to include specific model capabilities,
which will be difficult to include otherwise, or if convincing examples of
possible benefits can be presented. It cannot be expected that the coastal
ocean modelling community will agree about one coupler or one way of
interfacing modules, so it will still require considerable
implementation work to transfer a module from one modular system to another.
To solve this problem, coupling standards need to become more general, but in
turn this might even increase the structural overhead involved in using these systems.
For certain applications it might be preferable for different reasons to
hardwire sub-models and exercise strong control over such a monolithic coupled
system. But at the least, such sub-models should be made coupleable by
following the minimal requirements set forth by the BMI specification. This
ensures that the monolithic model system or parts of it can be reused or
expanded in a more modular way. And by strictly separating the BMI from any
framework-specific CMI specification, the effort spent on wrapping an
existing model or on equipping a new model with a basic model interface is
not tied to a particular coupling framework or even a particular coupling
framework technology. A model that follows BMI principles will be more easily
interfaced to other models no matter what coupler is used. Wrapped legacy
models from MOSSCO can thus be useful in non-ESMF contexts as well, and
models with an existing BMI can be integrated into MOSSCO more easily in turn.
One demand for integrative modelling, which is likely best practised in open
and flexible system approaches, arises from current European Union
legislation. The Water Framework Directive and the Marine Strategic Planning
Directive require the description of marine environmental conditions and the
development of action plans to achieve a good environmental status. These
objectives can initially be met by a monitoring programme to determine
present-day conditions but ultimately rely on numerical model studies to
evaluate anthropogenic measures. This ecosystem-based approach to management
e.g. demands modelling systems which are capable
of taking into account hydrodynamics, biogeochemistry, sedimentology, and
their interactions to properly describe the environmental status. As further
legal requirements can be expected for many coastal seas worldwide, numerical
modelling systems applied for this task need to be flexible in terms of
integrating additional (e.g. site-specific) processes. In this ongoing
process, the initial effort of creating a modular system may be the only way
forward that can take into account all relevant processes in the long run.
Outlook
The suite of components provided or encapsulated so far meets the demands
that were initially formulated by our users; they already allow for a wide
range of novel coupled applications to investigate the coastal sea. To
stimulate more collaboration, however, and to bring forward a general
“ecosystem” of modular science components, several legacy models could
interface to MOSSCO components in the near future by building on
complementary work at other institutions. For example, the Regional Earth
System Model RegESM; provides ESMF
interfaces for MITgcm, ROMS, and WAM, amongst others. The convergence of the
development of MOSSCO and RegESM is feasible in the near term. Also, the
recently developed Icosahedral Non-Hydrostatic Atmospheric Model
ICON; is currently being equipped with an ESMF
component model interface.
Once ESMF interfaces have been developed for a legacy model, it is desirable that these
developments move out of the coupler system and become integrated into the development of
the legacy model itself. This has been successfully achieved with the ESMF interface
for the hydrodynamic model GETM, which is now distributed with the GETM code.
Much of the utility layer developed in MOSSCO, or likewise in MAPL or
in the ESMF extension of the WRF model, is expected to be propagated upstream into
the framework ESMF itself.
The interoperability of current coupling standards will increase. While
currently there are three flavours of ESMF (basic ESMF as in MOSSCO,
ESMF–MAPL as in the GEOS-5 system, and ESMF–NUOPC as in the RegESM), only a
minor effort would be required to provide the basic ESMF and ESMF–MAPL
implementations with a NUOPC cap and make them interoperable with the entire
ESMF ecosystem. Even a coupling of ESMF-based systems to OASIS-MCT-based
systems has been proposed, and investigation is ongoing on a coupling of
MOSSCO to the formal BMI for CSDMS.
Conclusions
We problematised both the primacy of hydrodynamic models and the limited modularity in
coupled coastal modelling that can stand in the way of developing and applying novel and
diverse biogeochemical process descriptions. Such developmental potential is likely
needed to progress towards holistic regional coastal systems models. We presented the
novel Modular System for Shelves and Coasts (MOSSCO) that is built on coupling
concepts centred around equitability and flexibility to resolve the issue of obstructed modularity.
These concepts also bring about openness, usability, transparency, and scalability. MOSSCO
as a current Fortran implementation of this concept includes the wrapped Framework
for Aquatic Biogeochemical Models (FABM) and a usability layer for the Earth System Modeling
Framework (ESMF).
The MOSSCO design principles emphasise basic coupleability and rich meta-information. Basic
coupleability requires that models communicate about flow control, computing resources, and
exchanged data and metadata. We demonstrated that the design principles of flexibility
and equitability enable the building of complex coupled models that adequately
represent the complexity found in environmental modelling. In this first version, the MOSSCO
software wrapped several existing legacy models with basic model interfaces (BMIs);
we added ESMF-specific component model interfaces (CMIs) to these wrappers and other
models and frameworks to build a suite of ESMF components that when coupled represent
a small part of a holistic coastal system. These components deal with hydrodynamics,
waves, pelagic and sediment ecology and biogeochemistry, river loads, erosion,
resuspension, biotic sediment modification, and filter feeding.
In selected applications, each with a different research question, the
applicability of the coupled system was successfully tested. MOSSCO
facilitates the development of new coupled applications for studying coastal
processes that extend from the atmosphere through the water column into the
seabed and that range from laboratory studies to 3-D simulation studies of
a regional sea. This system meets an infrastructural need that is defined by
experimenters and process modellers who develop biogeochemical, physical,
sedimentological, or ecological models on the lab scale first and who would
like to seamlessly embed these models into the complex coupled
three-dimensional coastal system. This upscaling procedure may ultimately
also support the global Earth system community.
Code and data availability
The MOSSCO software is licenced under the GNU General
Public License 3.0, a copyleft open source licence that allows the use,
distribution, and modification of the software under the same terms. All
documentation for MOSSCO is licenced under the Creative Commons Attribution
Share-Alike 4.0 (CC-BY-SA), a copyleft open document licence that allows the use,
distribution, and modification of the documentation under the same terms.
Development code and documentation are currently primarily hosted on
Sourceforge (https://sf.net/p/mossco/code) and mirrored on Github
(https://github.com/platipodium/mossco-code). The release version 1.0.1
is permanently archived on Zenodo and accessible under the digital object
identifier 10.5281/zenodo.438922.
All wrapped legacy models are open source and freely available from the
developing institutions; free registration is required for accessing the
Delft3D system at Deltares. Selected test cases are available from a separate
Sourceforge repository, https://sf.net/p/mossco/setups, where all of the
data on which the presented use cases are based are freely available, with
the exception of the meteorological forcing fields. These are, for example,
available by request online at http://www.coastdat.de from the
coastDat model-based database developed for the assessment of long-term
changes by Helmholtz-Zentrum Geesthacht .
Acronyms and model abbreviations used in the text
bashGNU Bourne Again SHellBFMBiogeochemical Flux Model(ecosystem model)BGCBiogeochemistryBMIBasic model interface (coupling concept)CC-BY-SACreative Commons AttributionShare-Alike licenceCFNetCDF Climate and Forecast conventionCIMCommon Information Model(metadata standard)CMIComponent model interface(coupling concept)CMIPClimate Model Intercomparison ProjectCOAWSTCoupled Ocean–Atmosphere–Wave–Sediment TransportCSDMSCommunity Surface DynamicsModeling SystemDKRZDeutsches Klimarechenzentrum (HPC centre)ESMEarth system modelESMFEarth System Modeling FrameworkFABMFramework for AquaticBiogeochemical ModelsFMSFlexible Modeling System(coupling technology)FONAForschung für Nachhaltigkeit (funding scheme)FVCOMFinite-Volume Coastal Ocean ModelGCCGNU Compiler CollectionGETMGeneral Estuarine Transport Model(3-D coastal ocean model)GEOS-5Goddard Earth Observing System version 5GNUGNU is Not UnixGOTMGeneral Ocean Turbulence Model(1-D water column model)GPLGeneral Public LicenseGSNGeoscience Standard Names OntologyHLRNNorddeutscher Verbund für Hoch-und HöchstleistungsrechnenHPCHigh-performance computingICONIcosahedral Non-Hydrostatic ModelI/OInput and outputJSCJülich Supercomputing CentreMAPLModeling Analysis and Prediction LayerMAECSModel for Adaptive Ecosystemsin Coastal SeasMCTModel Coupling ToolkitMESSyModular Earth Submodel SystemMITgcmMassachusetts Institute of TechnologyGlobal Circulation ModelMOMModular Ocean ModelMOSSCOModular System for Shelves and CoastsMPIMessage-passing interface
MSPDEuropean Union Marine StrategicPlanning DirectiveNEMONucleus for European Modellingof the OceanNetCDFNetwork Common Data FormNPZDNutrient, phytoplankton, zooplankton,detritus (ecosystem model)NUOPCNational Unified OperationalPrediction CapabilityOASISOcean Atmosphere Sea Ice Soil couplerOMEXDIAOcean Margin Exchange Experimentearly diagenetic modelOMEXDIA_POMEXDIA with added phosphorousOMUSEOceanographic Multipurpose SoftwareEnvironmentPARSEPrepare, adapt, register, schedule,execute methodologyPISCESPelagic Interactions Scheme forCarbon and Ecosystem StudiesPOMParticulate organic matterPOCParticulate organic carbonRegESMRegional Earth System ModelROMSRegional Ocean Modeling SystemSCRIPSpherical Coordinate Remapping andInterpolation PackageSNSSouthern North SeaSPMSuspended particulate matterSWANSimulating Waves NearshoreWAMWave atmospheric modelWFDEuropean Union Water FrameworkDirectiveWRFAdvanced Research WeatherResearch and ForecastingXIOSXML input–output serverXMLExtensible Markup LanguageYAMLYAML Ain't Markup Language
Author contributions
CL, RH, KK, and HN developed the MOSSCO components (CMI)
and wrappers (BMI). KW, CL, and KK
designed the coupling philosophy, and CL developed the user interface and the
utility library. KW, HN, RH, OK, and CL carried out and analysed simulations
based on contributions from all authors. CL, KW, and RH wrote the paper
with contributions from all other authors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
MOSSCO is a project funded under the Küstenforschung Nordsee–Ostsee
programme of the Forschung für Nachhaltigkeit (FONA) agenda of the German
Ministry of Education and Science (BMBF) under grant agreements 03F0667A,
03F0667B, and 03FO668A. This research contributes to the PACES II programme
of the Hermann von Helmholtz-Gemeinschaft Deutscher Forschungszentren.
Further financial support for Knut Klingbeil and Hans Burchard was provided
by the Collaborative Research Centre TRR181 on Energy Transfers in Atmosphere
and Ocean funded by the German Research Foundation (DFG). The authors
gratefully acknowledge the computing time granted by the John von Neumann
Institute for Computing (NIC) and provided on the supercomputer JURECA at
Forschungszentrum Jülich. We thank those MOSSCO developers that are not
co-authors of this paper, amongst them Markus Kreus, Ulrich Körner, and
Niels Weiher, and acknowledge the support of Wenyan Zhang and Kaela Slavik in
preparing the model set-ups. This research is based on tremendous efforts by
the open source community, including but not limited to the developers of
Delft3D, GETM, GOTM, FABM, ESMF, OpenMPI, Python, GCC, and NetCDF, who share
their codes openly.The article processing
charges for this open-access publication were covered by a
Research Centre of the Helmholtz Association. Edited by: Sophie Valcke Reviewed by: two
anonymous referees
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