Projections of Future Trends in Biogeochemical Conditions in the Northwest Atlantic Using CMIP5 Earth System Models

Lavoie, Diane; Lambert, Nicolas; Gilbert, Denis

doi:10.1080/07055900.2017.1401973

Cited by 30 publications

(40 citation statements)

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“…To investigate potential future distributions of DO, we currently rely on biogeochemical ocean models that couple physical, chemical, and biological processes. High-resolution regional models are best suited to address changes in the coastal ocean, since global models still lack sufficient resolution to represent continental shelf processes properly, particularly in the Scotian Shelf area (Lavoie et al, 2013). Regional ocean models can simulate past and present conditions, for which they can be validated against observations from ships, moorings, buoys, satellites, etc.…”

Section: Introductionmentioning

confidence: 99%

Ocean biogeochemical models as management tools: a case study for Atlantic wolffish and declining oxygen

et al. 2015

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Society is moving towards a no-analogue climate that will fundamentally affect ocean ecosystems and the socioeconomic activities that depend on them. Warming has led to displacements of various populations, calling for an adaptation of fisheries management plans and Species at Risk recovery strategies. Dissolved oxygen (DO) has declined, but its impacts on habitat are much less studied. Severe hypoxia is lethal, but even sublethal hypoxia can trigger species displacements. We use Atlantic wolffish (Anarhichas lupus) as a case study to investigate the impact of DO on optimal habitat on the Scotian Shelf, Canada, considering that their habitat becomes suboptimal at DO lower than 65% saturation. First, we demonstrate that DO has decreased using two observational climatologies before and after 1980, and that the spatial pattern of the associated expansion of low oxygen waters (DO , 65% saturation) over the shelf is consistent with the observed contraction of Wolffish population. Then, we use a spatially explicit regional ocean model that couples physical and biological processes to simulate a scenario in which a continued decline of DO in the open ocean leads to a further expansion of low oxygen waters over the shelf. The future low DO extends to regions that currently have high Wolffish biomass, and likely other species as well. While fishing pressure likely drives the observed decline in Wolffish, both observations and model scenario suggest that DO can further constrain habitat. We argue that management/recovery plans should consider DO as one of the potential stressors of not just Atlantic wolffish but any oxygen-sensitive species. Finally, we emphasize that biogeochemical ocean models can inform management by elucidating the direction and ranges of future changes in ocean environmental conditions.

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Section: Introductionmentioning

confidence: 99%

Ocean biogeochemical models as management tools: a case study for Atlantic wolffish and declining oxygen

et al. 2015

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“…Rickard et al (2016) summarized that oceanographic variables in CMIP5 models reveal better agreement across all models compared to biochemical ones. Lavoie et al (2013) detected that GFDL and MPI models better represent nitrate concentrations, and the GFDL model best represents salinity among other considered models in the Labrador Sea. In our study, these models were also selected as the best for the Labrador Sea.…”

Section: Resultsmentioning

confidence: 93%

Simulation of factors affecting <i>Emiliania huxleyi</i> blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection

Gnatiuk

Radchenko

Davy³

et al. 2020

Biogeosciences

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Abstract. The observed warming in the Arctic is more than double the global average, and this enhanced Arctic warming is projected to continue throughout the 21st century. This rapid warming has a wide range of impacts on polar and sub-polar marine ecosystems. One of the examples of such an impact on ecosystems is that of coccolithophores, particularly Emiliania huxleyi, which have expanded their range poleward during recent decades. The coccolithophore E. huxleyi plays an essential role in the global carbon cycle. Therefore, the assessment of future changes in coccolithophore blooms is very important. Currently, there are a large number of climate models that give projections for various oceanographic, meteorological, and biochemical variables in the Arctic. However, individual climate models can have large biases when compared to historical observations. The main goal of this research was to select an ensemble of climate models that most accurately reproduces the state of environmental variables that influence the coccolithophore E. huxleyi bloom over the historical period when compared to reanalysis data. We developed a novel approach for model selection to include a diverse set of measures of model skill including the spatial pattern of some variables, which had not previously been included in a model selection procedure. We applied this method to each of the Arctic and sub-Arctic seas in which E. huxleyi blooms have been observed. Once we have selected an optimal combination of climate models that most skilfully reproduce the factors which affect E. huxleyi, the projections of the future conditions in the Arctic from these models can be used to predict how E. huxleyi blooms will change in the future. Here, we present the validation of 34 CMIP5 (fifth phase of the Coupled Model Intercomparison Project) atmosphere–ocean general circulation models (GCMs) over the historical period 1979–2005. Furthermore, we propose a procedure of ranking and selecting these models based on the model's skill in reproducing 10 important oceanographic, meteorological, and biochemical variables in the Arctic and sub-Arctic seas. These factors include the concentration of nutrients (NO3, PO4, and SI), dissolved CO2 partial pressure (pCO2), pH, sea surface temperature (SST), salinity averaged over the top 30 m (SS30 m), 10 m wind speed (WS), ocean surface current speed (OCS), and surface downwelling shortwave radiation (SDSR). The validation of the GCMs' outputs against reanalysis data includes analysis of the interannual variability, seasonal cycle, spatial biases, and temporal trends of the simulated variables. In total, 60 combinations of models were selected for 10 variables over six study regions using the selection procedure we present here. The results show that there is neither a combination of models nor one model that has high skill in reproducing the regional climatic-relevant features of all combinations of the considered variables in target seas. Thereby, an individual subset of models was selected according to our model selection procedure for each combination of variable and Arctic or sub-Arctic sea. Following our selection procedure, the number of selected models in the individual subsets varied from 3 to 11. The paper presents a comparison of the selected model subsets and the full-model ensemble of all available CMIP5 models to reanalysis data. The selected subsets of models generally show a better performance than the full-model ensemble. Therefore, we conclude that within the task addressed in this study it is preferable to employ the model subsets determined through application of our procedure than the full-model ensemble.

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“…This might be improved by implementing zooplankton projections into the hydrodynamic model through a physical–biogeochemical coupling approach (e.g. Lavoie, Lambert, & Gilbert, ). Zooplankton are considered when hydrodynamic model is coupled to biogeochemical model (physical–biogeochemical coupling) in which nutrient, phytoplankton, zooplankton and detritus are state variables of the biogeochemical model.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, this study revealed zooplankton abundances as a key variable in determining mackerel spawning habitat but could not be incorporated in the projections. This might be improved by implementing zooplankton projections into the hydrodynamic model through a physical-biogeochemical coupling approach (e.g Lavoie, Lambert, & Gilbert, 2017)…”

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Modelling Atlantic mackerel spawning habitat suitability and its future distribution in the north‐west Atlantic

Mbaye

Doniol‐Valcroze

Brosset

et al. 2019

Fisheries Oceanography

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We investigated the effect of environmental conditions on Atlantic mackerel spawning habitat in the southern Gulf of St. Lawrence (sGSL). Based on generalized additive models, we (i) modelled the optimal spawning habitat of mackerel in the sGSL using daily egg production (DEP) in June, (ii) predicted known and new potential present spawning habitats in the GSL and the north‐west Atlantic, and (iii) assessed how they respond to future climate change. Our findings showed that both mackerel presence–absence and given‐presence DEP were associated with sea surface temperature (10–16.5°C), salinity above 31 and depth < 120 m. Adding zooplankton showed a marked effect on the DEP given‐presence compared to the presence–absence. Predictions of spawning habitats under present (1999–2012) and future scenario (2066–2085) conditions were estimated from the presence–absence model without zooplankton, using physical conditions of the BNAM. Under present conditions, our model predicted well the main spawning habitat in the sGSL and other known secondary spawning habitats in the northern GSL (nGSL), the western and southern Newfoundland, and the north‐west Atlantic coast. Under future conditions, our study suggests that spawning habitats in the sGSL and the nGSL would expand. Our results, therefore, suggest that mackerel could benefit from a warmer GSL, minimizing the potential for a northward migration of the stock due to decreasing suitability of the sGSL as its main spawning ground, and a new but spatially limited potential habitat in Newfoundland coasts. These results can be used to inform stock management and develop adaptive management plans in the context of climate change.

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Projections of Future Trends in Biogeochemical Conditions in the Northwest Atlantic Using CMIP5 Earth System Models

Cited by 30 publications

References 100 publications

Ocean biogeochemical models as management tools: a case study for Atlantic wolffish and declining oxygen

Ocean biogeochemical models as management tools: a case study for Atlantic wolffish and declining oxygen

Simulation of factors affecting <i>Emiliania huxleyi</i> blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection

Modelling Atlantic mackerel spawning habitat suitability and its future distribution in the north‐west Atlantic

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Projections of Future Trends in Biogeochemical Conditions in the Northwest Atlantic Using CMIP5 Earth System Models

Cited by 30 publications

References 100 publications

Ocean biogeochemical models as management tools: a case study for Atlantic wolffish and declining oxygen

Ocean biogeochemical models as management tools: a case study for Atlantic wolffish and declining oxygen

Simulation of factors affecting &lt;i&gt;Emiliania huxleyi&lt;/i&gt; blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection

Modelling Atlantic mackerel spawning habitat suitability and its future distribution in the north‐west Atlantic

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Simulation of factors affecting <i>Emiliania huxleyi</i> blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection