scholarly journals One size fits all? – Calibrating an ocean biogeochemistry model for different circulations

2020 ◽  
Author(s):  
Iris Kriest ◽  
Paul Kähler ◽  
Wolfgang Koeve ◽  
Karin Kvale ◽  
Volkmar Sauerland ◽  
...  
Keyword(s):  
Particle Flux ◽  
Circulation Model ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Fixation Rate ◽  
Biogeochemical Fluxes ◽  
Flux Parameter ◽  
Oxygen Utilisation ◽  
Characteristic Features ◽  
Model Tuning

Abstract. Global biogeochemical ocean models are often tuned to match the observed distributions and fluxes of inorganic and organic quantities. This tuning is typically carried out by hand. However, this rather subjective approach might not yield the best fit to observations, is closely linked to the circulation employed, and thus influenced by its specific features and even its faults. We here investigate the effect of model tuning, via objective optimisation, of one biogeochemical model of intermediate complexity when simulated in five different offline circulations. For each circulation, three of six model parameters have adjusted to characteristic features of the respective circulation. The values of these three parameters – namely, the oxygen utilisation of remineralisation, the particle flux parameter and potential nitrogen fixation rate – correlate significantly with deep mixing and ideal age of NADW and the outcrop area of AAIW and SAMW in the Southern Ocean. The clear relationship between these parameters and circulation characteristics, which can be easily diagnosed from global models, can provide guidance when tuning global biogeochemistry within any new circulation model. The results from 20 global cross-validation experiments show that parameter sets optimised for a specific circulation can be transferred between similar circulations without losing too much of the model's fit to observed quantities. When compared to model intercomparisons of subjectively tuned, global coupled biogeochemistry-circulation models, each with different circulation and/or biogeochemistry, our results show a much lower range of oxygen inventory, OMZ volume and global biogeochemical fluxes. Export production depends to a large extent on the circulation applied, while deep particle flux is mostly determined by the particle flux parameter. Oxygen inventory, OMZ volume, primary production and fixed nitrogen turnover depend more or less equally on both factors, with OMZ volume showing the highest sensitivity, and residual variability. These results show a beneficial effect of optimisation, even when a biogeochemical model is first optimised in a relatively coarse circulation, and then transferred to a different, finer resolution circulation model.

scholarly journals One size fits all? Calibrating an ocean biogeochemistry model for different circulations

2020 ◽  
Vol 17 (12) ◽  
pp. 3057-3082 ◽  
Author(s):  
Iris Kriest ◽  
Paul Kähler ◽  
Wolfgang Koeve ◽  
Karin Kvale ◽  
Volkmar Sauerland ◽  
...  
Keyword(s):  
Particle Flux ◽  
Circulation Model ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Mode Water ◽  
Fixation Rate ◽  
Minimum Zone ◽  
Flux Parameter ◽  
Characteristic Features ◽  
Model Tuning

Abstract. Global biogeochemical ocean models are often tuned to match the observed distributions and fluxes of inorganic and organic quantities. This tuning is typically carried out “by hand”. However, this rather subjective approach might not yield the best fit to observations, is closely linked to the circulation employed and is thus influenced by its specific features and even its faults. We here investigate the effect of model tuning, via objective optimisation, of one biogeochemical model of intermediate complexity when simulated in five different offline circulations. For each circulation, three of six model parameters have been adjusted to characteristic features of the respective circulation. The values of these three parameters – namely, the oxygen utilisation of remineralisation, the particle flux parameter and potential nitrogen fixation rate – correlate significantly with deep mixing and ideal age of North Atlantic Deep Water (NADW) and the outcrop area of Antarctic Intermediate Waters (AAIW) and Subantarctic Mode Water (SAMW) in the Southern Ocean. The clear relationship between these parameters and circulation characteristics, which can be easily diagnosed from global models, can provide guidance when tuning global biogeochemistry within any new circulation model. The results from 20 global cross-validation experiments show that parameter sets optimised for a specific circulation can be transferred between similar circulations without losing too much of the model's fit to observed quantities. When compared to model intercomparisons of subjectively tuned, global coupled biogeochemistry–circulation models, each with different circulation and/or biogeochemistry, our results show a much lower range of oxygen inventory, oxygen minimum zone (OMZ) volume and global biogeochemical fluxes. Export production depends to a large extent on the circulation applied, while deep particle flux is mostly determined by the particle flux parameter. Oxygen inventory, OMZ volume, primary production and fixed-nitrogen turnover depend more or less equally on both factors, with OMZ volume showing the highest sensitivity, and residual variability. These results show a beneficial effect of optimisation, even when a biogeochemical model is first optimised in a relatively coarse circulation and then transferred to a different finer-resolution circulation model.

scholarly journals Using satellite-derived backscattering coefficients in addition to chlorophyll data to constrain a simple marine biogeochemical model

2009 ◽  
Vol 6 (2) ◽  
pp. 4201-4231
Author(s):  
H. Kettle
Keyword(s):  
Carbon Cycle ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Ocean Carbon Cycle ◽  
Empirical Relationships ◽  
Biogeochemical Models ◽  
Ocean Carbon ◽  
Model Tuning ◽  
Different Characteristics ◽  
Satellite Chlorophyll

Abstract. Biogeochemical models of the ocean carbon cycle are frequently validated by, or tuned to, satellite chlorophyll data. However, ocean carbon cycle models are required to accurately model the movement of carbon, not chlorophyll and due to the high variability of the carbon to chlorophyll ratio in phytoplankton, chlorophyll is not a robust proxy for carbon. Using inherent optical property (IOP) inversion algorithms it is now possible to also derive the amount of light backscattered by the upper ocean (bb) which is related to the amount of particulate organic carbon (POC) present. Using empirical relationships between POC and bb, a 1-d biogeochemical model is used to simulate bb at 490 nm thus allowing the model to be compared with either remotely-sensed chlorophyll or bb data. Here I test the hypothesis that using bb in conjunction with chlorophyll data can help to constrain more model parameters than using chlorophyll alone. This is done by tuning the parameters of the biogeochemical model with a genetic algorithm, so that the model is fitted to either chlorophyll or to both chlorophyll and bb data at three sites in the Atlantic with very different characteristics. There are several IOP algorithms available for estimating bb. Four of these are investigated and three of them used for model tuning. The effect of the different bb datasets on the behaviour of the tuned model is examined to ascertain whether the uncertainty in bb is significant. The results show that the addition of bb data can have a large effect on the modelled detritus and that differences in the IOP algorithms are not particularly significant.

scholarly journals Marine productivity response to Heinrich events: a model-data comparison

2012 ◽  
Vol 8 (5) ◽  
pp. 1581-1598 ◽  
Author(s):  
V. Mariotti ◽  
L. Bopp ◽  
A. Tagliabue ◽  
M. Kageyama ◽  
D. Swingedouw
Keyword(s):  
North Atlantic ◽  
General Circulation ◽  
Circulation Model ◽  
Global Ocean ◽  
Biogeochemical Model ◽  
Heinrich Events ◽  
The North ◽  
Heinrich Event ◽  
The North Atlantic ◽  
Marine Productivity

Abstract. Marine sediments records suggest large changes in marine productivity during glacial periods, with abrupt variations especially during the Heinrich events. Here, we study the response of marine biogeochemistry to such an event by using a biogeochemical model of the global ocean (PISCES) coupled to an ocean-atmosphere general circulation model (IPSL-CM4). We conduct a 400-yr-long transient simulation under glacial climate conditions with a freshwater forcing of 0.1 Sv applied to the North Atlantic to mimic a Heinrich event, alongside a glacial control simulation. To evaluate our numerical results, we have compiled the available marine productivity records covering Heinrich events. We find that simulated primary productivity and organic carbon export decrease globally (by 16% for both) during a Heinrich event, albeit with large regional variations. In our experiments, the North Atlantic displays a significant decrease, whereas the Southern Ocean shows an increase, in agreement with paleo-productivity reconstructions. In the Equatorial Pacific, the model simulates an increase in organic matter export production but decreased biogenic silica export. This antagonistic behaviour results from changes in relative uptake of carbon and silicic acid by diatoms. Reasonable agreement between model and data for the large-scale response to Heinrich events gives confidence in models used to predict future centennial changes in marine production. In addition, our model allows us to investigate the mechanisms behind the observed changes in the response to Heinrich events.

scholarly journals SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

2015 ◽  
Vol 8 (10) ◽  
pp. 3441-3470 ◽  
Author(s):  
J. A. Bradley ◽  
A. M. Anesio ◽  
J. S. Singarayer ◽  
M. R. Heath ◽  
S. Arndt
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Microbial Growth ◽  
Model Development ◽  
Biogeochemical Cycling ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Glacier Forefield ◽  
Unconstrained Model

Abstract. SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.

scholarly journals Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation

2020 ◽  
Vol 17 (173) ◽  
pp. 20200886
Author(s):  
L. Mihaela Paun ◽  
Mitchel J. Colebank ◽  
Mette S. Olufsen ◽  
Nicholas A. Hill ◽  
Dirk Husmeier
Keyword(s):  
Model Selection ◽  
Mean Squared Error ◽  
Formal Model ◽  
Circulation Model ◽  
Noise Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Imaging Data ◽  
Micro Computed Tomography ◽  
Model Mismatch

This study uses Bayesian inference to quantify the uncertainty of model parameters and haemodynamic predictions in a one-dimensional pulmonary circulation model based on an integration of mouse haemodynamic and micro-computed tomography imaging data. We emphasize an often neglected, though important source of uncertainty: in the mathematical model form due to the discrepancy between the model and the reality, and in the measurements due to the wrong noise model (jointly called ‘model mismatch’). We demonstrate that minimizing the mean squared error between the measured and the predicted data (the conventional method) in the presence of model mismatch leads to biased and overly confident parameter estimates and haemodynamic predictions. We show that our proposed method allowing for model mismatch, which we represent with Gaussian processes, corrects the bias. Additionally, we compare a linear and a nonlinear wall model, as well as models with different vessel stiffness relations. We use formal model selection analysis based on the Watanabe Akaike information criterion to select the model that best predicts the pulmonary haemodynamics. Results show that the nonlinear pressure–area relationship with stiffness dependent on the unstressed radius predicts best the data measured in a control mouse.

scholarly journals Regional impacts of iron-light colimitation in a global biogeochemical model

2010 ◽  
Vol 7 (3) ◽  
pp. 1043-1064 ◽  
Author(s):  
E. D. Galbraith ◽  
A. Gnanadesikan ◽  
J. P. Dunne ◽  
M. R. Hiscock
Keyword(s):  
General Circulation ◽  
Photosynthetic Efficiency ◽  
Light Harvesting ◽  
Circulation Model ◽  
Field Studies ◽  
Ocean General Circulation Model ◽  
Iron Limitation ◽  
Light Limitation ◽  
Biogeochemical Model ◽  
Ocean Environment

Abstract. Laboratory and field studies have revealed that iron has multiple roles in phytoplankton physiology, with particular importance for light-harvesting cellular machinery. However, although iron-limitation is explicitly included in numerous biogeochemical/ecosystem models, its implementation varies, and its effect on the efficiency of light harvesting is often ignored. Given the complexity of the ocean environment, it is difficult to predict the consequences of applying different iron limitation schemes. Here we explore the interaction of iron and nutrient cycles in an ocean general circulation model using a new, streamlined model of ocean biogeochemistry. Building on previously published parameterizations of photoadaptation and export production, the Biogeochemistry with Light Iron Nutrients and Gasses (BLING) model is constructed with only four explicit tracers but including macronutrient and micronutrient limitation, light limitation, and an implicit treatment of community structure. The structural simplicity of this computationally-inexpensive model allows us to clearly isolate the global effect that iron availability has on maximum light-saturated photosynthesis rates vs. the effect iron has on photosynthetic efficiency. We find that the effect on light-saturated photosynthesis rates is dominant, negating the importance of photosynthetic efficiency in most regions, especially the cold waters of the Southern Ocean. The primary exceptions to this occur in iron-rich regions of the Northern Hemisphere, where high light-saturated photosynthesis rates allow photosynthetic efficiency to play a more important role. In other words, the ability to efficiently harvest photons has little effect in regions where light-saturated growth rates are low. Additionally, we speculate that the phytoplankton cells dominating iron-limited regions tend to have relatively high photosynthetic efficiency, due to reduced packaging effects. If this speculation is correct, it would imply that natural communities of iron-stressed phytoplankton may tend to harvest photons more efficiently than would be inferred from iron-limitation experiments with other phytoplankton. We suggest that iron limitation of photosynthetic efficiency has a relatively small impact on global biogeochemistry, though it is expected to impact the seasonal cycle of plankton as well as the vertical structure of primary production.

Biogeochemical controls on the Black Sea oxygen dynamics : relevant diagnostics, key processes and adequacy of the monitoring infrastructure.

2020 ◽  
Author(s):  
Arthur Capet ◽  
vandenbulcke Luc ◽  
Grégoire Marilaure
Keyword(s):  
Black Sea ◽  
3D Models ◽  
The Black Sea ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Biogeochemical Processes ◽  
Low Oxygen ◽  
Future Evolution ◽  
Basin Scale ◽  
Set Up

<p>An important deoxygenation trend has been described in the Black Sea over the five past decades from in-situ observations [1]. While the implications for basin-scale biogeochemistry and possible future trends of this dynamics are unclear, it is important to consolidate our means to resolve the dynamics of the Black Sea oxygen content in order to assess the likelihood of future evolution scenario, and the possible morphology of low-oxygen events. </p><p>Also, it is known that current global models simulate only about half the observed oceanic O2 loss and fail in reproducing its vertical distribution[2]. In parts, unexplained O2 losses could be attributed to illy parameterized biogeochemical processes within 3D models used to integrate those multi-elemental dynamics.</p><p>Biogeochemical processes involved in O2 dynamics are structured vertically and well separated in the stratified Black Sea. O2 sources proceed from air-sea fluxes and photosynthesis in the<br>photic zone. Organic matter (OM) is respired over a depth determined by its composition and<br>sinking, via succeeding redox reactions. Those intricate dynamics leave unknowns as regards the biogeochemical impacts of future deoxygenation on associated cycles, for instance on the oceanic carbon pump. Here we use the Black Sea scene to derive model-observation strategies to best address the global deoxygenation concern.</p><p>First, we decipher components of the O2 dynamics in the open basin, and discuss the way in which O2-based indicators informs on the relative importance of processes involved. Using 1D biogeochemical model set-up, we then conduct a sensitivity analysis to pin-point model parameters, ie. biogeochemical processes, that bears the largest part in the uncertainty of simulated results for those diagnostics. Finally, we identify among the most impacting parameters the ones that can most efficiently be constrained on the basis of modern observational infrastructure, and Bio-Argo in particular. </p><p>The whole procedure aims at orienting the development of observations networks and data assimilation approaches in order to consolidate our means to anticipate the marine deoxygenation challenge. </p><p>[1] Capet A et al., 2016, Biogeoscience, 13:1287-1297<br>[2] Oschlies A et al., 2018, Nature Geosci, 11(7):467–473</p>

scholarly journals Constraining parameters in state-of-the-art marine pelagic ecosystem models – is it actually feasible with typical observations of standing stocks?

2015 ◽  
Vol 12 (1) ◽  
pp. 227-274
Author(s):  
U. Löptien ◽  
H. Dietze
Keyword(s):  
Ocean Circulation ◽  
Carbon Assimilation ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Additional Parameter ◽  
Ecosystem Models ◽  
Standing Stocks ◽  
Estimation Of Model Parameters ◽  
Model Components ◽  
Parameter Retrieval

Abstract. In a changing climate, marine pelagic biogeochemistry may modulate the atmospheric concentrations of climate-relevant species such as CO2 and N2O. To-date, projections rely on earth system models featuring simple pelagic biogeochemical model components, embedded into 3-D-ocean circulation models. Typically, the nucleus of these biogeochemical components are ecosystem models (i.e., a set of partial differential equations) which describe the interaction between nutrients, phytoplankton, zooplankton, and sinking detritus. Most of these models rely on the hyperbolic Michaelis–Menten (MM) formulation which specifies the limiting effect of light and nutrients on carbon assimilation by autotrophic phytoplankton. The respective MM constants, along with other model parameters, are usually tuned by trial-and-error exercises where the parameters are changed until a "reasonable" similarity with observed standing stocks is achieved. Here, we explore with twin experiments (or synthetic "observations") the demands on observations that allow for a more objective estimation of model parameters. We start with parameter retrieval experiments based on "perfect" (synthetic) observations which we, step by step, distort to approach realistic conditions and finally confirm our findings with real-world observations. In summary, we find that MM constants are especially hard to constrain because even modest noise (10%) inherent to observations may hinder the parameter retrieval already. This is of concern since the MM parameters are key to the model's sensitivity to anticipated changes of the external conditions. Further, we illustrate problems associated with parameter estimation based on sparse observations which reveals (additional) parameter dependencies. Somewhat counter to intuition we find, that more observational data can degrade the ability to constrain certain parameters.

scholarly journals Simulating the growth and distribution of planktic foraminifer using an ecophysiological multi-species model

2011 ◽  
Vol 8 (1) ◽  
pp. 1-49
Author(s):  
F. Lombard ◽  
L. Labeyrie ◽  
E. Michel ◽  
L. Bopp ◽  
E. Cortijo ◽  
...  
Keyword(s):  
Growth Rates ◽  
Food Concentration ◽  
Growth Potential ◽  
Global Ocean ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Field Observations ◽  
Lower Efficiency ◽  
Data Set ◽  
Chl A

Abstract. We present an eco-physiological model reproducing the growth of eight foraminifer species (Neogloboquadrina pachyderma, Neogloboquadrina incompta, Neogloboquadrina dutertrei, Globigerina bulloides, Globigerinoides ruber, Globigerinoides sacculifer, Globigerinella siphonifera and Orbulina universa). By using the main physiological rates of foraminifers (nutrition, respiration, symbiotic photosynthesis), this model estimates their growth as a function of temperature, light availability, and food concentration. Model parameters are directly derived or calibrated from experimental observations and only the influence of food concentration (estimated via chl-a concentration) was calibrated against field observations. Growth rates estimated from the model show positive correlation with observed abundance from plankton net data suggesting close coupling between individual and assemblage growth rates. This observation was used to directly estimate potential abundance from the model-derived growth. Using satellite data, the model simulate the dominant foraminifer with a 70.5% efficiency when compared to a data set of 576 field observations worldwide. Using outputs of a biogeochemical model of the global ocean (PISCES) instead of satellite images as forcing variables gives also good results, but with lower efficiency (58.9%). The model also correctly reproduces the relative worldwide abundance and the diversity of the eight species when compared to core tops observations both using satellite and PISCES data. This model allows prediction of the season and water depth at which each species has its highest growth potential. This offers promising perspectives for both an improved quantification of paleoceanographic reconstructions and for a better understanding of the foraminiferal role in the marine carbon cycle.

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