Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Schartau, Markus; Wallhead, Philip; Hemmings, John; Löptien, Ulrike; Kriest, Iris; Krishna, Shubham; Ward, Ben A.; Slawig, Thomas; Oschlies, Andreas

doi:10.5194/bg-14-1647-2017

Cited by 68 publications

(73 citation statements)

References 343 publications

(540 reference statements)

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“…Parameter estimation and the quantification of biogeochemical model uncertainty is a major field of research (see the review by Schartau et al, 2017). Our study demonstrates the importance of considering transient model behaviour in both parameter estimation and estimates of uncertainties for biogeochemistry in OGCMs and ESMs.…”

Section: Rcp 85 Transient Simulationsmentioning

confidence: 75%

“…the initial value of the photosynthesisirradiance curve). Exploring the uncertainty associated with multiple parameter manipulations is costly and better left to offline approaches that can objectively and systematically assess the solution space (see the review by Schartau et al, 2017), though as far as we know, offline methods for threedimensional models are currently restricted to steady-state analysis. It is also possible that including a fully resolved radiative transfer model and explicit IOPs for multiple phytoplankton types could damp the Southern Ocean response we find in K1 and K2 and the low-latitude response we find in the higher light attenuation simulations (Gregg and Rousseaux, 2016).…”

Section: Rcp 85 Transient Simulationsmentioning

confidence: 99%

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Primary production sensitivity to phytoplankton light attenuation parameter increases with transient forcing

Kvale

Meißner

2017

Biogeosciences

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Abstract. Treatment of the underwater light field in ocean biogeochemical models has been attracting increasing interest, with some models moving towards more complex parameterisations. We conduct a simple sensitivity study of a typical, highly simplified parameterisation. In our study, we vary the phytoplankton light attenuation parameter over a range constrained by data during both pre-industrial equilibrated and future climate scenario RCP8.5. In equilibrium, lower light attenuation parameters (weaker self-shading) shift net primary production (NPP) towards the high latitudes, while higher values of light attenuation (stronger shelf-shading) shift NPP towards the low latitudes. Climate forcing magnifies this relationship through changes in the distribution of nutrients both within and between ocean regions. Where and how NPP responds to climate forcing can determine the magnitude and sign of global NPP trends in this high CO 2 future scenario. Ocean oxygen is particularly sensitive to parameter choice. Under higher CO 2 concentrations, two simulations establish a strong biogeochemical feedback between the Southern Ocean and low-latitude Pacific that highlights the potential for regional teleconnection. Our simulations serve as a reminder that shifts in fundamental properties (e.g. light attenuation by phytoplankton) over deep time have the potential to alter global biogeochemistry.

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Section: Rcp 85 Transient Simulationsmentioning

confidence: 75%

Section: Rcp 85 Transient Simulationsmentioning

confidence: 99%

Primary production sensitivity to phytoplankton light attenuation parameter increases with transient forcing

Kvale

Meißner

2017

Biogeosciences

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“…In other words, data assimilation through the physiological parameter change with environmental conditions might play the part in a calibration of simplified formulations of LTL marine ecosystem models. However, four-dimensional changes of physiological parameters complicate scientific interpretation (Schartau et al, 2017), even though marine ecosystem models have been developed in order to simplify real-world marine ecosystems and facilitate scientific interpretation. The spatial parameter estimation was conducted in this study be- cause we would like to also discuss the physiological effects of parameters changing in detail.…”

Section: Physiological Parameter Changes With Ambient Conditionsmentioning

confidence: 99%

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

et al. 2018

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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 onedimensional 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.

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“…Data assimilation, which refers to methodologies that systematically combine a mathematical model with observations, is often used in biogeochemical applications Friedrichs, 2001, 2002) to improve estimates of model parameters that are frequently poorly known (Lawson et al, 1995(Lawson et al, , 1996Matear, 1995;Fennel et al, 2001;Friedrichs, 2002;Schartau and Oschlies, 2003;Hemmings et al, 2004;Bagniewski et al, 2011;Doron et al, 2013;Xiao and Friedrichs, 2014a, b;Melbourne-Thomas et al, 2015;Song et al, 2016;Gharamti et al, 2017;Schartau et al, 2017). This entails a smoothing or optimization procedure, in which elements of the model are adjusted to minimize differences between the model output and the observations.…”

Section: Introductionmentioning

confidence: 99%

Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea

Kaufman¹,

Friedrichs²,

Hemmings³

et al. 2018

Biogeosciences

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Abstract. The Ross Sea is a region characterized by high primary productivity in comparison to other Antarctic coastal regions, and its productivity is marked by considerable variability both spatially (1-50 km) and temporally (days to weeks). This variability presents a challenge for inferring phytoplankton dynamics from observations that are limited in time or space, which is often the case due to logistical limitations of sampling. To better understand the spatiotemporal variability in Ross Sea phytoplankton dynamics and to determine how restricted sampling may skew dynamical interpretations, high-resolution bio-optical glider measurements were assimilated into a one-dimensional biogeochemical model adapted for the Ross Sea. The assimilation of data from the entire glider track using the micro-genetic and local search algorithms in the Marine Model Optimization Testbed improves the model-data fit by ∼ 50 %, generating rates of integrated primary production of 104 g C m −2 yr −1 and export at 200 m of 27 g C m −2 yr −1 . Assimilating glider data from three different latitudinal bands and three different longitudinal bands results in minimal changes to the simulations, improves the model-data fit with respect to unassimilated data by ∼ 35 %, and confirms that analyzing these glider observations as a time series via a one-dimensional model is reasonable on these scales. Whereas assimilating the full glider data set produces well-constrained simulations, assimilating subsampled glider data at a frequency consistent with cruise-based sampling results in a wide range of primary production and export estimates. These estimates depend strongly on the timing of the assimilated observations, due to the presence of high mesoscale variability in this region. Assimilating surface glider data subsampled at a frequency consistent with available satellite-derived data results in 40 % lower carbon export, primarily resulting from optimized rates generating more slowly sinking diatoms. This analysis highlights the need for the strategic consideration of the impacts of data frequency, duration, and coverage when combining observations with biogeochemical modeling in regions with strong mesoscale variability.

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Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Cited by 68 publications

References 343 publications

Primary production sensitivity to phytoplankton light attenuation parameter increases with transient forcing

Primary production sensitivity to phytoplankton light attenuation parameter increases with transient forcing

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

Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea

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