Marine Ecosystem forecasts: skill performance of the CMEMS
Mediterranean Sea model system

Salon, Stefano; Cossarini, Gianpiero; Bolzon, Giorgio; Feudale, Laura; Lazzari, Paolo; Teruzzi, Anna; Solidoro, Cosimo; Crise, A.

doi:10.5194/os-2018-145

Cited by 10 publications

(19 citation statements)

References 39 publications

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“…from 10 −6 to 10 −4 m 2 s −1 (Table 1). This subset of simulations (with float-derived PAR) clusters at a correlation of approximately 0.8 with RMSD of DCM depth between 10 and 15 m, the same order of what was found by Salon et al (2019). Perturbing D background v over 2 orders of magnitude (from REF to MLD4) shows that the impact on DCM position is lower than 10 m (Fig.…”

Section: Vertical Mixing Modelssupporting

confidence: 74%

“…Vertical profiles of Chl concentration were qualitycontrolled according to the procedure of the international BGC-Argo programme that removes spikes and corrects for non-zero deep values and non-photochemical quenching at the surface (Schmechtig et al, 2016;Organelli et al, 2017b). Due to a factory calibration bias for WET Labs ECO series Chl fluorometers, Chl concentrations were corrected by a factor of 0.5 (Roesler et al, 2017;Organelli et al, 2017a, b;Barbieux et al, 2018).…”

Section: Bgc-argo Float Datamentioning

confidence: 99%

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Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry

et al. 2019

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Abstract. New autonomous robotic platforms for observing the ocean, i.e. Biogeochemical-Argo (BGC-Argo) floats, have drastically increased the number of vertical profiles of irradiance, photosynthetically available radiation (PAR), and algal chlorophyll concentrations around the globe independent of the season. Such data may therefore be a fruitful resource to improve performances of numerical models for marine biogeochemistry. Here we present a work that integrates 1314 vertical profiles of PAR acquired by 31 BGC-Argo floats operated in the Mediterranean Sea between 2012 and 2016 into a one-dimensional model to simulate the vertical and temporal variability of algal chlorophyll concentrations. The model was initially forced with PAR measurements to assess its skill when using quality-controlled light profiles, and subsequently with a number of alternative bio-optical models to analyse the model capability when light observations are not available. Model outputs were evaluated against co-located chlorophyll profiles measured by BGC-Argo floats. Results highlight that the data-driven model is able to reproduce the spatial and temporal variability of deep chlorophyll maxima depth observed at a number of Mediterranean sites well. Further, we illustrate the key role of PAR and vertical mixing in shaping the vertical dynamics of primary producers in the Mediterranean Sea. The comparison of alternative bio-optical models identifies the best simple one to be used, and suggests that model simulations benefit from considering the diel cycle.

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Section: Vertical Mixing Modelssupporting

confidence: 74%

Section: Bgc-argo Float Datamentioning

confidence: 99%

Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry

et al. 2019

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“…In this regard, assimilating the remotely sensed ocean color and the recently available biogeochemical Argo floats data into the ESM can bring additional gains in biogeochemical prediction skill and may eventually provide a robust time series for reforecast verification. Recent studies support this point by showing the improvement of biogeochemical reanalysis from biogeochemical assimilation and the degradation of biogeochemical predictions from the perturbations in biogeochemical initial conditions (Ciavatta et al 2014, Ford and Barciela 2017, Salon et al 2019, Ford 2021.…”

Section: Discussionmentioning

confidence: 94%

Mechanisms driving ESM-based marine ecosystem predictive skill on the east African coast

Jeon

Park

Stock

et al. 2022

Environ. Res. Lett.

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The extension of seasonal to interannual prediction of the physical climate system to include the marine ecosystem has a great potential to inform marine resource management strategies. Along the east coast of Africa, recent findings suggest that skillful Earth system model (ESM)-based chlorophyll predictions may enable anticipation of fisheries fluctuations. The mechanisms underlying skillful chlorophyll predictions, however, were not identified, eroding confidence in potential adaptive management steps. This study demonstrates that skillful chlorophyll predictions up to two years in advance arise from the successful simulation of westward-propagating off-equatorial Rossby waves in the Indian ocean. Upwelling associated with these waves supplies nutrients to the surface layer for the large coastal areas by generating north- and southward propagating waves at the east African coast. Further analysis shows that the off-equatorial Rossby wave is initially excited by wind stress forcing caused by El Niño/Southern Oscillation (ENSO)-Indian Ocean teleconnections.

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“…average moles of nitrate per unit of mass in the water column (mol/kg); 4. average moles of phosphate per unit of mass in the water column (mol/kg); 5. sea-bottom temperature (°C); 6. sea-surface temperature (°C); 7. average salinity in the water column (PSU); 8. bathymetry (m); 9. average size of grains in a sediment sample (m). These data were mainly retrieved from Copernicus (Sauzède et al, 2017;Salon et al, 2019;Clementi et al, 2021;Feudale et al, 2021) to have spatially aligned and verified data. Bathymetry was retrieved from GEBCO-2020(GEBCO, 2020.…”

Section: Environmental Componentmentioning

confidence: 99%

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

et al. 2022

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International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset.

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Marine Ecosystem forecasts: skill performance of the CMEMS Mediterranean Sea model system

Cited by 10 publications

References 39 publications

Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry

Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry

Mechanisms driving ESM-based marine ecosystem predictive skill on the east African coast

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

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