Adaptive time step algorithms for the simulation of marine ecosystem models using the transport matrix method implementation Metos3D (v0.5.0)

Mapping Intimacies ◽

10.5194/gmd-2021-392 ◽

2022 ◽

Author(s):

Markus Pfeil ◽

Thomas Slawig

Keyword(s):

Marine Ecosystem ◽

Size Control ◽

Ecosystem Model ◽

Computational Effort ◽

Ecosystem Models ◽

Step Size ◽

Time Step ◽

Marine Ecosystem Model ◽

Marine Ecosystem Models ◽

Adaptive Step

Abstract. The reduction of the computational effort is desirable for the simulation of marine ecosystem models. Using a marine ecosystem model, the assessment and the validation of annual periodic solutions (i.e., steady annual cycles) against observational data are crucial to identify biogeochemical processes, which, for example, influence the global carbon cycle. For marine ecosystem models, the transport matrix method (TMM) already lowers the runtime of the simulation significantly and enables the application of larger time steps straightforwardly. However, the selection of an appropriate time step is a challenging compromise between accuracy and shortening the runtime. Using an automatic time step adjustment during the computation of a steady annual cycle with the TMM, we present in this paper different algorithms applying either an adaptive step size control or decreasing time steps in order to use the time step always as large as possible without any manual selection. For these methods and a variety of marine ecosystem models of different complexity, the accuracy of the computed steady annual cycle achieved the same accuracy as solutions obtained with a fixed time step. Depending on the complexity of the marine ecosystem model, the application of the methods shortened the runtime significantly. Due to the certain overhead of the adaptive method, the computational effort may be higher in special cases using the adaptive step size control. The presented methods represent computational efficient methods for the simulation of marine ecosystem models using the TMM but without any manual selection of the time step.

Download Full-text

Estimation of the Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.2419 ◽

2013 ◽

Vol 321-324 ◽

pp. 2419-2423

Author(s):

Xiao Yan Li ◽

Chun Hui Wang ◽

Xian Qing Lv

Keyword(s):

Marine Ecosystem ◽

Absolute Error ◽

Ecosystem Model ◽

Global Scale ◽

Time Step ◽

Marine Ecosystem Model ◽

Maximum Uptake Rate ◽

Influence Radius ◽

Adjoint Assimilation ◽

Spatially Varying

By utilizing spatial biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem model (NPZD-type) and its adjoint model which were built on global scale based on climatological environment and data. When the spatially varying Vm (maximum uptake rate of nutrient by phytoplankton) was estimated alone, we discussed how would the distribution schemes of spatial parameterization and influence radius affected the results. The reduced cost function (RCF), the mean absolute error (MAE) of phytoplankton in the surface layer, and the relative error (RE) of Vm between given and simulated values decreased obviously. The influence of time step was studied then and we found that the assimilation recovery would not be more successful with a smaller time step of 3 hours compared with 6 hours.

Download Full-text

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

10.5194/os-2017-39 ◽

2017 ◽

Author(s):

Yasuhiro Hoshiba ◽

Takafumi Hirata ◽

Masahito Shigemitsu ◽

Hideyuki Nakano ◽

Taketo Hashioka ◽

...

Keyword(s):

Data Assimilation ◽

North Pacific ◽

Western North Pacific ◽

Marine Ecosystem ◽

Ecosystem Model ◽

Biological Data ◽

Physiological Parameters ◽

Biological Parameters ◽

Ecosystem Models ◽

Marine Ecosystem Model

Abstract. Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3D) lower trophic level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The approach used a one-dimensional emulator that referenced satellite data. The 3D NSI-MEM with biological parameters optimised by assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to models without data assimilation. Furthermore, the model was able to simulate not only surface concentrations of phytoplankton but also subsurface maximum concentrations of phytoplankton. Our results show that surface data assimilation of biological parameters from two observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

Download Full-text

Ecological ReGional Ocean Model with vertically resolved sediments (ERGOM SED 1.0): coupling benthic and pelagic biogeochemistry of the south-western Baltic Sea

Geoscientific Model Development ◽

10.5194/gmd-12-275-2019 ◽

2019 ◽

Vol 12 (1) ◽

pp. 275-320 ◽

Cited By ~ 3

Author(s):

Hagen Radtke ◽

Marko Lipka ◽

Dennis Bunke ◽

Claudia Morys ◽

Jana Woelfel ◽

...

Keyword(s):

Baltic Sea ◽

Marine Ecosystem ◽

Nutrient Recycling ◽

Three Dimensional ◽

Ecosystem Model ◽

Ocean Model ◽

Ecosystem Models ◽

Predictive Capacity ◽

Sediment Types ◽

Marine Ecosystem Model

Abstract. Sediments play an important role in organic matter mineralisation and nutrient recycling, especially in shallow marine systems. Marine ecosystem models, however, often only include a coarse representation of processes beneath the sea floor. While these parameterisations may give a reasonable description of the present ecosystem state, they lack predictive capacity for possible future changes, which can only be obtained from mechanistic modelling. This paper describes an integrated benthic–pelagic ecosystem model developed for the German Exclusive Economic Zone (EEZ) in the western Baltic Sea. The model is a hybrid of two existing models: the pelagic part of the marine ecosystem model ERGOM and an early diagenetic model by Reed et al. (2011). The latter one was extended to include the carbon cycle, a determination of precipitation and dissolution reactions which accounts for salinity differences, an explicit description of the adsorption of clay minerals, and an alternative pyrite formation pathway. We present a one-dimensional application of the model to seven sites with different sediment types. The model was calibrated with observed pore water profiles and validated with results of sediment composition, bioturbation rates and bentho-pelagic fluxes gathered by in situ incubations of sediments (benthic chambers). The model results generally give a reasonable fit to the observations, even if some deviations are observed, e.g. an overestimation of sulfide concentrations in the sandy sediments. We therefore consider it a good first step towards a three-dimensional representation of sedimentary processes in coupled pelagic–benthic ecosystem models of the Baltic Sea.

Download Full-text

Porting marine ecosystem model spin-up using transport matrices to GPUs

Geoscientific Model Development ◽

10.5194/gmd-6-17-2013 ◽

2013 ◽

Vol 6 (1) ◽

pp. 17-28 ◽

Cited By ~ 8

Author(s):

E. Siewertsen ◽

J. Piwonski ◽

T. Slawig

Keyword(s):

Ocean Circulation ◽

Message Passing ◽

Graphics Processing Units ◽

Message Passing Interface ◽

Marine Ecosystem ◽

Computational Effort ◽

Computational Time ◽

Biogeochemical Model ◽

Ecosystem Models ◽

Marine Ecosystem Model

Abstract. We have ported an implementation of the spin-up for marine ecosystem models based on transport matrices to graphics processing units (GPUs). The original implementation was designed for distributed-memory architectures and uses the Portable, Extensible Toolkit for Scientific Computation (PETSc) library that is based on the Message Passing Interface (MPI) standard. The spin-up computes a steady seasonal cycle of ecosystem tracers with climatological ocean circulation data as forcing. Since the transport is linear with respect to the tracers, the resulting operator is represented by matrices. Each iteration of the spin-up involves two matrix-vector multiplications and the evaluation of the used biogeochemical model. The original code was written in C and Fortran. On the GPU, we use the Compute Unified Device Architecture (CUDA) standard, a customized version of PETSc and a commercial CUDA Fortran compiler. We describe the extensions to PETSc and the modifications of the original C and Fortran codes that had to be done. Here we make use of freely available libraries for the GPU. We analyze the computational effort of the main parts of the spin-up for two exemplar ecosystem models and compare the overall computational time to those necessary on different CPUs. The results show that a consumer GPU can compete with a significant number of cluster CPUs without further code optimization.

Download Full-text

The role of ecosystem function and emergent relationships in the assessment of global marine ecosystem models: a case study with ERSEM

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-6095-2015 ◽

2015 ◽

Vol 8 (8) ◽

pp. 6095-6141

Author(s):

L. de Mora ◽

M. Butenschön ◽

J. I. Allen

Keyword(s):

Ecosystem Function ◽

Large Scale ◽

Marine Ecosystem ◽

Ecosystem Model ◽

Ecosystem Functions ◽

Emergent Properties ◽

Ecosystem Models ◽

Marine Ecosystem Model ◽

Scale Properties ◽

Meta Analyses

Abstract. Ecosystem models are often assessed using quantitative metrics of absolute ecosystem state, but these model-data comparisons are disproportionately vulnerable to discrepancies in the location of important circulation features. An alternative method is to demonstrate the models capacity to represent ecosystem function; the emergence of a coherent natural relationship in a simulation is a strong indication that the model has a appropriate representation of the ecosystem functions that lead to the emergent relationship. Furthermore, as emergent properties are large scale properties of the system, model validation with emergent properties is possible even when there is very little or no appropriate data for the region under study, or when the hydrodynamic component of the model differs significantly from that observed in nature at the same location and time. A selection of published meta-analyses are used to establish the validity of a complex marine ecosystem model and to demonstrate the power of validation with emergent properties. These relationships include the phytoplankton community structure, the ratio of carbon to chlorophyll in phytoplankton and particulate organic matter, the ratio of particulate organic carbon to particulate organic nitrogen and the stoichiometric balance of the ecosystem. These metrics can also inform aspects of the marine ecosystem model not available from traditional quantitative and qualitative methods. For instance, these emergent properties can be used to validate the design decisions of the model, such as the range of phytoplankton functional types and their behaviour, the stoichiometric flexibility with regards to each nutrient, and the choice of fixed or variable carbon to nitrogen ratios.

Download Full-text

Porting marine ecosystem model spin-up using transport matrices to GPUs

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-2179-2012 ◽

2012 ◽

Vol 5 (3) ◽

pp. 2179-2214 ◽

Cited By ~ 1

Author(s):

E. Siewertsen ◽

J. Piwonski ◽

T. Slawig

Keyword(s):

Ocean Circulation ◽

Message Passing ◽

Graphics Processing Units ◽

Message Passing Interface ◽

Marine Ecosystem ◽

Computational Effort ◽

Computational Time ◽

Biogeochemical Model ◽

Ecosystem Models ◽

Marine Ecosystem Model

Abstract. We have ported an implementation of the spin-up for marine ecosystem models based on the "Transport Matrix Method" to graphics processing units (GPUs). The original implementation was designed for distributed-memory architectures and uses the PETSc library that is based on the "Message Passing Interface (MPI)" standard. The spin-up computes a steady seasonal cycle of the ecosystem tracers with climatological ocean circulation data as forcing. Since the transport is linear with respect to the tracers, the resulting operator is represented in so-called "transport matrices". Each iteration of the spin-up involves two matrix-vector multiplications and the evaluation of the used biogeochemical model. The original code was written in C and Fortran. On the GPU, we use the CUDA standard, a specialized version of the PETSc toolkit and a CUDA Fortran compiler. We describe the extensions to PETSc and the modifications of the original C and Fortran codes that had to be done. Here we make use of freely available libraries for the GPU. We analyze the computational effort of the main parts of the spin-up for two exemplary ecosystem models and compare the overall computational time to those necessary on different CPUs. The results show that a consumer GPU can beat a significant number of cluster CPUs without further code optimization.

Download Full-text

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

Ocean Science ◽

10.5194/os-14-371-2018 ◽

2018 ◽

Vol 14 (3) ◽

pp. 371-386 ◽

Cited By ~ 3

Author(s):

Yasuhiro Hoshiba ◽

Takafumi Hirata ◽

Masahito Shigemitsu ◽

Hideyuki Nakano ◽

Taketo Hashioka ◽

...

Keyword(s):

Data Assimilation ◽

North Pacific ◽

Western North Pacific ◽

Marine Ecosystem ◽

Ecosystem Model ◽

Biological Data ◽

Physiological Parameters ◽

Ecosystem Dynamics ◽

Ecosystem Models ◽

Marine Ecosystem Model

Abstract. Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a one-dimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

Download Full-text