Impacts of biological parameterization, initial conditions, and environmental forcing on parameter sensitivity and uncertainty in a marine ecosystem model for the Bering Sea

Gibson, Georgina A.; Spitz, Yvette H.

doi:10.1016/j.jmarsys.2011.04.008

Cited by 49 publications

(48 citation statements)

References 43 publications

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“…This situation is aggravating but not unusual (Schartau et al, 2016). For instance, in a sensitivity study with a regional marine ecosystem model, Gibson and Spitz (2011) stressed that collinearities exist between initial conditions and the values assigned to the biological parameters. The posterior uncertainties in the estimates of the subsistence quota, (Q 0 ), are rather small, if compared with the uncertainty ranges of the other parameter estimates.…”

Section: Uncertainty Ranges In Parameter Estimates and Variability Inmentioning

confidence: 99%

A data–model synthesis to explain variability in calcification observed during a CO<sub>2</sub> perturbation mesocosm experiment

Krishna

Schartau

2017

Biogeosciences

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Abstract. The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO 2 ) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO 2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO 2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO 2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

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Section: Uncertainty Ranges In Parameter Estimates and Variability Inmentioning

confidence: 99%

A data–model synthesis to explain variability in calcification observed during a CO<sub>2</sub> perturbation mesocosm experiment

Krishna

Schartau

2017

Biogeosciences

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“…Some large, interdisciplinary studies of the sea-ice environment have attempted to integrate observational and modelling needs from the beginning of the project design, but in the end, the results have not been integrated as well as hoped. More recently, however, several examples have generated constructive outcomes (e.g., projects known as BEST, SIMBA, INTERICE IV and Barrow 2009; Gibson and Spitz, 2011;Vancoppenolle et al, 2011;Moreau et al, 2014). An Arctic example of a project that has made progress towards coordinated collaboration between modellers and observers is the Forum for Arctic Modelling and Observational Synthesis (FAMOS, http://web.whoi.edu/famos/), which has evolved from the Arctic Ocean Model Intercomparison Project (AOMIP) and now includes marine biogeochemistry (Popova et al, 2012;Steiner et al, 2014Steiner et al, , 2015.…”

Section: Domain Editor-in-chiefmentioning

confidence: 99%

What sea-ice biogeochemical modellers need from observers

et al. 2016

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Numerical models can be a powerful tool helping to understand the role biogeochemical processes play in local and global systems and how this role may be altered in a changing climate. With respect to sea-ice biogeochemical models, our knowledge is severely limited by our poor confidence in numerical model parameterisations representing those processes. Improving model parameterisations requires communication between observers and modellers to guide model development and improve the acquisition and presentation of observations. In addition to more observations, modellers need conceptual and quantitative descriptions of the processes controlling, for example: primary production and diversity of algal functional types in sea ice, ice algal growth, release from sea ice, heterotrophic remineralisation, transfer and emission of gases (e.g., DMS, CH 4 , BrO), incorporation of seawater components in growing sea ice (including Fe, organic and inorganic carbon, and major salts) and subsequent release; CO 2 dynamics (including CaCO 3 precipitation), flushing and supply of nutrients to sea-ice ecosystems; and radiative transfer through sea ice. These issues can be addressed by focused observations, as well as controlled laboratory and field experiments that target specific processes. The guidelines provided here should help modellers and observers improve the integration of measurements and modelling efforts and advance toward the common goal of understanding biogeochemical processes in sea ice and their current and future impacts on environmental systems.

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“…The oceanography was based on the Regional Ocean Modeling System (ROMS)-Bering10 K (10 km resolution), a coupled Ocean-Sea ice model whose spatial grid is a subset of the NEP5 model described and evaluated by Danielson et al (2011), which itself was built on a model described by Curchitser et al (2005) and Hermann et al (2013). The lower trophic levels were modeled using a nutrientphytoplankton-zooplankton detritus (NPZD) model coupled to the ROMS-Bering10 K, specifically designed to incorporate the ice dynamics of the Bering Sea, and modeled nutrients, phytoplankton, copepods, euphausiids and detritus (Gibson and Spitz, 2011). Model coupling included feedback from the NPZD to the ROMSBering10K through phytoplankton density, which affects shortwave penetration (heat absorption) in the upper water column and between NPZD and the Forage Euphausiid Abundance in Space and Time (FEAST) model (Ortiz et a1.,This issue) (functionally the fish module for this effort), through predation.…”

Section: The Best Bsierp and Fetementioning

confidence: 99%

“…Hermann et al (2013) also used temperature, salinity and total chlorophyll from the Alaska Fisheries Science Center's annual Bering-Aleutian Salmon International Survey (BASIS) research cruises in a multivariate analysis. Gibson and Spitz (2011) conducted a sensitivity analysis of the NPZD portion of the end-to-end integrated model. Assessments of fish movement and distribution patterns (I. Ortiz, UW, unpublished results), biophysical processes (Ortiz et a1., This issue) and fish bioenergetics (K. Aydin, NOAA, unpublished results) were also conducted.…”

Section: What Specific Aspect Of the Prediction Is Anticipated To Be mentioning

confidence: 99%

“…The oceanographic model, the NPZD model, and the fish growth/movement model, can be seen as separate 3-year modeling projects; compared to a traditional sequential approach (completing work bottom-up from physics to fish), the overall program condensed 9 years of research into 6 years. Advances were made in physical modeling of the region (Danielson et al, 2011(Danielson et al, , 2012Hermann et al, 2013), measuring uncertainties in NPZD models (Gibson and Spitz, 2011), and quantifying seasonal versus interannual environmental effects on the growth, feeding rates, and survival of fish (K. Aydin, NOAA, unpublished results), effects of prey availability and temperature on fish distribution (I. Ortiz, UW, unpublished results), and yearround biophysical processes and their effect on fish and fisheries (Ortiz et a1.,This issue).…”

Section: Conclusion: Program Legacymentioning

confidence: 99%

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End-to-end modeling as part of an integrated research program in the Bering Sea

Punt

Ortiz

Aydin

et al. 2016

Deep Sea Research Part II: Topical Studies in Oceanography

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a b s t r a c tTraditionally, the advice provided to fishery managers has focused on the trade-offs between short-and long-term yields, and between future resource size and expected future catches. The harvest control rules that are used to provide management advice consequently relate catches to stock biomass levels expressed relative to reference biomass levels. There are, however, additional trade-offs. Ecosystembased fisheries management (EBFM) aims to consider fish and fisheries in their ecological context, taking into account physical, biological, economic, and social factors. However, making EBFM operational remains challenging. It is generally recognized that end-to-end modeling should be a key part of implementing EBFM, along with harvest control rules that use information in addition to estimates of stock biomass to provide recommendations for management actions. Here we outline the process for selecting among alternative management strategies in an ecosystem context and summarize a Fieldintegrated End-To-End modeling program, or FETE, intended to implement this process as part of the Bering Sea Project. A key aspect of this project was that, from the start, the FETE included a management strategy evaluation component to compare management strategies. Effective use of end-to-end modeling requires that the models developed for a system are indeed integrated across climate drivers, lower trophic levels, fish population dynamics, and fisheries and their management. We summarize the steps taken by the program managers to promote integration of modeling efforts by multiple investigators and highlight the lessons learned during the project that can be used to guide future use and design of end-to-end models.

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Impacts of biological parameterization, initial conditions, and environmental forcing on parameter sensitivity and uncertainty in a marine ecosystem model for the Bering Sea

Cited by 49 publications

References 43 publications

A data–model synthesis to explain variability in calcification observed during a CO<sub>2</sub> perturbation mesocosm experiment

A data–model synthesis to explain variability in calcification observed during a CO<sub>2</sub> perturbation mesocosm experiment

What sea-ice biogeochemical modellers need from observers

End-to-end modeling as part of an integrated research program in the Bering Sea

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Impacts of biological parameterization, initial conditions, and environmental forcing on parameter sensitivity and uncertainty in a marine ecosystem model for the Bering Sea

Cited by 49 publications

References 43 publications

A data–model synthesis to explain variability in calcification observed during a CO&lt;sub&gt;2&lt;/sub&gt; perturbation mesocosm experiment

A data–model synthesis to explain variability in calcification observed during a CO&lt;sub&gt;2&lt;/sub&gt; perturbation mesocosm experiment

What sea-ice biogeochemical modellers need from observers

End-to-end modeling as part of an integrated research program in the Bering Sea

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A data–model synthesis to explain variability in calcification observed during a CO<sub>2</sub> perturbation mesocosm experiment

A data–model synthesis to explain variability in calcification observed during a CO<sub>2</sub> perturbation mesocosm experiment