Recommendations for the formulation of grazing in marine biogeochemical and ecosystem models

Rohr, Tyler; Richardson, Anthony J.; Lenton, Andrew; Shadwick, Elizabeth H.

doi:10.1016/j.pocean.2022.102878

Cited by 11 publications

(37 citation statements)

References 131 publications

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“…However, as more zooplankton observations and standardized products become available (Buitenhuis et al., 2010; Heneghan et al., 2020; Moriarty & O'Brien, 2013; Strömberg et al., 2009), more assessment of zooplankton becomes possible. In addition to zooplankton biomass, model developers could also consider assessment of other terms, such as physiological rates (Heneghan et al., 2020; Hirst et al., 2003; Kiørboe & Hirst, 2014), their associated model parameters (Buitenhuis et al., 2006; Stock et al., 2014b; Rohr et al., 2022), and productivity ratios (Stock & Dunne, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Zooplankton, if present, function more often as a closure term on the phytoplankton, ensuring that phytoplankton biomass does not grow uncontrollably. Zooplankton are less often considered as an ecologically or biogeochemically meaningful variable, yet, the response of phytoplankton is extremely sensitive to the formulation and parameter values of zooplankton (Gentleman & Neuheimer, 2008; Rohr et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

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Assessment and Constraint of Mesozooplankton in CMIP6 Earth System Models

Petrik

Luo

Heneghan

et al. 2022

Global Biogeochemical Cycles

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Although zooplankton play a substantial role in the biological carbon pump and serve as a crucial link between primary producers and higher trophic level consumers, the skillful representation of zooplankton is not often a focus of ocean biogeochemical models. Systematic evaluations of zooplankton in models could improve their representation, but so far, ocean biogeochemical skill assessment of Earth system model (ESM) ensembles have not included zooplankton. Here we use a recently developed global, observationally based map of mesozooplankton biomass to assess the skill of mesozooplankton in six CMIP6 ESMs. We also employ a biome‐based assessment of the ability of these models to reproduce the observed relationship between mesozooplankton biomass and surface chlorophyll. The combined analysis found that most models were able to reasonably simulate the large regional variations in mesozooplankton biomass at the global scale. Additionally, three of the ESMs simulated a mesozooplankton‐chlorophyll relationship within the observational bounds, which we used as an emergent constraint on future mesozooplankton projections. We highlight where differences in model structure and parameters may give rise to varied mesozooplankton distributions under historic and future conditions, and the resultant wide ensemble spread in projected changes in mesozooplankton biomass. Despite differences, the strength of the mesozooplankton‐chlorophyll relationships across all models was related to the projected changes in mesozooplankton biomass globally and in regional biomes. These results suggest that improved observations of mesozooplankton and their relationship to chlorophyll will better constrain projections of climate change impacts on these important animals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment and Constraint of Mesozooplankton in CMIP6 Earth System Models

Petrik

Luo

Heneghan

et al. 2022

Global Biogeochemical Cycles

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“…Even with a simple food‐web, the grazing formulation varies across three degrees of freedom: The qualitative functional response curve and two parameters. The functional response curve relates zooplankton‐specific grazing rates ( g ; d −1 ) to prey abundance ([ C phyto ]; mmolCm −3 ) and is typically described by a type II or III response (Rohr et al., 2022). A type II response increases linearly at low concentrations, while a type III increases quadratically (Supporting Figure 3 in Supporting Information ).…”

Section: Methodsmentioning

confidence: 99%

“…The K 1/2 with the maximum score was identified for each g max value, then averaged across all three sets to determine the overall optimal K 1/2 . We focus on tuning the functional response type and K 1/2 value because they exert a stronger influence on population dynamics than g max (Rohr et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

The Global Distribution of Grazing Dynamics Estimated From Inverse Modeling

Rohr,

Richardson,

Lenton

et al. 2024

Geophysical Research Letters

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Grazing dynamics are one of the most poorly constrained components of the marine carbon cycle. We use inverse modeling to infer the distribution of community‐integrated zooplankton grazing dynamics based on the ability of different grazing formulations to recreate the satellite‐observed seasonal cycle in phytoplankton biomass after controlling for physical and bottom‐up controls. We find large spatial variability in the optimal community‐integrated half saturation concentration for grazing (K1/2), with lower (higher) values required in more oligotrophic (eutrophic) biomes. This leads to a strong sigmoidal relationship between observed mean‐annual phytoplankton biomass and the optimally inferred grazing parameterization. This relationship can be used to help constrain, validate and/or parameterize next‐generation biogeochemical models.

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“…This leads to deficiencies in the representation of significant physical processes such as (sub)mesoscale currents (McWilliams, 2016) which can have large impacts on transport and mixing processes that strongly affect biological productivity (Lévy et al, 2018;Keerthi et al, 2022). Second, the descriptions of ecological interactions and of the physiology of primary and secondary producers in GOBMs are still mostly based on empirical or semi-empirical mathematical descriptions, such as e.g., the dependency of zooplankton grazing rates on prey abundance (Doney et al, 2001;Rohr et al, 2022). These contain a large number of parameters that are only partly constrained from observations, making it necessary to tune these parameters in GOBMs to some extent.…”

Section: Introductionmentioning

confidence: 99%

Ocean biogeochemistry in the coupled ocean-sea ice-biogeochemistry model FESOM2.1-REcoM3

Gürses¹,

Oziel

Karakuş

et al. 2023

Preprint

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Abstract. The cycling of carbon in the oceans is affected by feedbacks driven by changes in climate and atmospheric CO2. Understanding these feedbacks is therefore an important prerequisite for projecting future climate. Marine biogeochemical models are a useful tool there, but as any model is a simplification, need to be continually improved. In this study, we coupled the Finite-volumE Sea ice-Ocean Model (FESOM2.1) to the Regulated Ecosystem Model version 3 (REcoM3). FESOM2.1 is an update of the Finite Element Sea ice-Ocean Model (FESOM1.4) and operates on variable mesh resolution. Unlike standard structured-mesh ocean models, the mesh flexibility allows for a realistic representation of small-scale dynamics in key regions at affordable computational cost. Compared to the previous coupled model version FESOM1.4-REcoM2, the model FESOM2.1-REcoM3 utilizes a new dynamical core based on a finite volume discretization instead of finite elements, but retains central parts of the biogeochemistry model. As a new feature, carbonate chemistry including water vapor correction is computed by mocsy-2.0. Moreover, REcoM3 has an extended food web that includes macrozooplankton and fast-sinking detritus. Dissolved oxygen is added as a new tracer. In this study we assess the ocean and biogeochemical state simulated with FESOM2.1-REcoM3 in a global setup at relatively low spatial resolution forced with JRA55-do atmospheric reanalysis. The focus is on the recent period 1958–2021, to assess how well the model can be used for present-day and future climate change scenarios on decadal to centennial timescales. A bias in global ocean-atmosphere preindustrial CO2 flux present in the previous model version FESOM1.4-REcoM2 could be significantly reduced. In addition, the computational efficiency is 2–3 times higher than that of FESOM1.4-REcoM. Overall, it is found that FESOM2.1-REcoM3 is a skillful tool for ocean biogeochemical modelling applications.

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Recommendations for the formulation of grazing in marine biogeochemical and ecosystem models

Cited by 11 publications

References 131 publications

Assessment and Constraint of Mesozooplankton in CMIP6 Earth System Models

Assessment and Constraint of Mesozooplankton in CMIP6 Earth System Models

The Global Distribution of Grazing Dynamics Estimated From Inverse Modeling

Ocean biogeochemistry in the coupled ocean-sea ice-biogeochemistry model FESOM2.1-REcoM3

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