Le Groenland sera-t-il vraiment « vert » après la perte de sa masse glaciaire?

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-Par Xander Wang, Pelin Kinay, Aminur Shah et Quan Dau-

Nombreux sont ceux qui pensent qu’en raison du réchauffement climatique, un mythe de longue date concernant le Groenland pourrait devenir réalité : une terre « verte », comme son nom l’indique, au lieu de la terre blanche couverte de glace qui existe actuellement. Des preuves scientifiques récentes donnent à penser que les couches de glace du Groenland fondent rapidement en raison de la hausse de la température de l’air et du réchauffement des eaux océaniques, ce qui entraîne une élévation du niveau de la mer et menace les zones côtières. Les actions des Nations Unies en faveur du climat visent à limiter le réchauffement de la planète en réduisant les émissions de carbone, ce qui permettrait à terme de réduire la fonte des glaces dans les régions polaires, dont le Groenland. L’avenir potentiel du Groenland, en revanche, est inconnu. La question clé reste la suivante : est-il possible de stabiliser la couverture de glace du Groenland ou deviendra-t-il un continent complètement « vert » à l’avenir si les objectifs de réduction des émissions de carbone fixés par l’Accord de Paris (2015) ne sont pas atteints?

Si les preuves passées de la fonte des calottes glaciaires au Groenland ont été étudiées, la prévision de l’avenir de l’énorme couverture de glace présente également un intérêt significatif pour les scientifiques. L’établissement d’une relation directe entre le recul de la glace et le réchauffement du climat pourrait contribuer à prouver le bien-fondé du schéma « émission de carbone – hausse de la température – fonte des couvertures glaciaires – élévation du niveau de la mer ».

Une étude récente publiée dans Earth’s Future a examiné comment la largeur spatiale de la calotte glaciaire du Groenland pourrait changer dans le contexte du réchauffement climatique, en utilisant un modèle climatique régional pour quantifier les changements futurs de la calotte glaciaire du Groenland couvrant divers scénarios d’émissions.

En raison des piètres performances des modèles climatiques en matière de simulation des précipitations (y compris le modèle PRECIS utilisé dans cette étude), l’équipe n’a abordé ce sujet qu’en utilisant les estimations des températures futures. L’étude a notamment utilisé la notion de climat de calotte glaciaire pour estimer si une zone sera ou non couverte par une calotte glaciaire.

Selon les auteurs, la couverture climatique de la calotte glaciaire du Groenland diminuerait régulièrement tout au long du siècle dans le cadre des deux scénarios RCP8.5 et RCP4.5, ce qui signifie que la superficie spatiale de la calotte glaciaire diminuerait de 15 % (RCP4.5) et de 25 % (RCP8.5) d’ici la fin du siècle. En comparaison, le scénario à faibles émissions (RCP2.6) offre la possibilité de limiter la perte de la couverture de la calotte glaciaire du Groenland à moins de 10 % d’ici le milieu du siècle, aucune perte supplémentaire n’étant prévue par la suite. Bien que diverses variables de surface influent sur l’évolution du processus d’équilibre de la masse de la surface glaciaire du Groenland, les chercheurs ont décidé de recourir aux projections de température uniquement pour étudier s’il est possible de stabiliser la calotteglaciaire du Groenland, étant donné que PRECIS fonctionne raisonnablement bien pour simuler la température de l’air proche de la surface au-dessus du Groenland.

Par rapport à la période de référence 1970-2000, l’étude prévoit la couverture glaciaire future du Groenland pour trois périodes – les années 2020, 2050 et 2080 – selon trois scénarios d’émissions. « Le scénario à faibles émissions RCP2.6 a le potentiel de stabiliser le réchauffement climatique au Groenland après les années 2050 et d’empêcher toute perte supplémentaire de la couverture glaciaire », concluent les auteurs. La glace qui ne couvre que 65,5 % du pays, selon les estimations de la période de référence, pourrait passer à 56 % dans les années 2050, puis à 57 % dans les années 2080, selon un scénario à faibles émissions. Par conséquent, de faibles émissions pourraient potentiellement limiter le réchauffement du Groenland à moins de 1°C au cours des 30 prochaines années et limiter la perte de la
couverture glaciaire à moins de 10 %.

En revanche, la couverture de glace diminuera continuellement tout au long de ce siècle, car le climat local du Groenland est susceptible de se réchauffer continuellement dans le cadre des scénarios d’émissions moyennes et élevées. Le pire pourrait être attendu dans les années 2080 dans le cadre d’un scénario à fortes émissions, avec seulement environ 40 % du pays couvert par des calottes glaciaires.

Les résultats de cette étude sont essentiels pour comprendre les conséquences de divers scénarios d’émissions de carbone sur la stabilisation ou la limitation du réchauffement au Groenland et donc de la perte de couverture de la calotte glaciaire, qui est liée à l’élévation du niveau des mers. Les résultats de l’étude supposent que les scénarios à fortes et moyennes émissions entraîneraient un réchauffement continu au Groenland et donc une perte importante de la calotte glaciaire. Toutefois, le scénario à faibles émissions présente un fort potentiel de réduction du réchauffement climatique local et de la perte de la calotte glaciaire avant les années 2050. Plus particulièrement, en supposant que le scénario à faibles émissions soit respecté, aucun changement significatif n’est prévu après les années 2050.

Ces conclusions sont importantes, non seulement parce qu’elles permettent aux défenseurs du climat d’espérer que la calotte glaciaire du Groenland sera préservée et que les populations côtières seront protégées de l’élévation du niveau de la mer, mais aussi parce qu’elles incitent toutes les nations à prendre des mesures immédiates pour réduire les émissions de carbone. Il est juste de s’attendre à ce que la couche de glace couverte par le climat de la calotte glaciaire reste en place indéfiniment, mais il est difficile de prévoir quand la couche de glace au-delà de la couverture du climat de la calotte glaciaire commencera à fondre et finira par disparaître, soulignent les auteurs.

Cela va de soi que la véritable terre « verte » est à l’horizon si nous ne prenons aucune mesure pour réduire les émissions de gaz à effet de serre et le réchauffement de la planète, et les plus grandes conséquences sont évidentes. Le Groenland pourrait envier la beauté d’un paysage vert luxuriant, mais le reste du monde souffrira des pires conséquences de l’élévation du niveau de la mer et des inondations côtières.

Les changements dans les nappes glaciaires du Groenland et de l’Antarctique ont un impact sociétal important car ils ont une incidence directe sur le niveau mondial des mers. Lorsque les glaciers et les nappes glaciaires fondent, davantage d’eau pénètre dans l’océan. Heureusement, certaines politiques et actions climatiques sont en place à l’échelle mondiale et locale. Il y a donc lieu d’espérer! Pourtant, il est urgent de prendre des mesures efficaces pour réduire les émissions de carbone afin de ralentir la disparition de la calotte glaciaire du Groenland et de sauver nos communautés côtières de grandes catastrophes.

Nouvelles sur l’article publié suivant : Wang, X., Fenech, A. et Farooque, A. A. (2021). Possibility of stabilizing the Greenland ice sheet. Earth’s Future, 9(7), e2021EF002152. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021EF002152

-By Xander Wang, Pelin Kinay, Aminur Shah, and Quan Dau-

Many people believe that, due to global warming, a longstanding myth about Greenland might be the reality – a ‘green’ land, resembling its name – ‘Green’, instead of the snowwhite ice-covered land that exists now. Recent scientific evidence suggests the ice sheets in Greenland are melting quickly because of rising air temperature and warm ocean waters, which is causing sea level rise and threatening coastal areas. UN Climate Actions are aimed at limiting global warming by lowering carbon emissions, which would eventually lead to less ice melting in the polar areas, including Greenland. Greenland’s potential future, on the other hand, is unknown. The key question remains: is it possible to stabilize Greenland’s ice cover, or will it be a completely “Green” continent in the future if the Paris Agreement’s (2015) carbon reduction goals are not met?

While past evidence of melting ice sheets in Greenland has been studied, predicting the future of the huge ice cover is also of significant interest to scientists. Establishing a direct relationship between ice retreat and a warming climate could assist prove the ‘carbon emission – rising temperature – melting ice covers – sea level rise’ pathways.

A recent study published in Earth’s Future explored how the spatial breadth of the Greenland ice sheet might change in the context of global warming, using a regional climate model to quantify future changes in the Greenland ice sheet covering various emission scenarios.

Due to the poor performance of climate models in simulating precipitation (including the PRECIS model used in this study), the team only addressed this subject using future temperature estimates. The study, in particular, employed the idea of ice cap climate to estimate whether or not an area will be covered by an ice sheet.

According to the authors, ice cap climatic coverage of Greenland would diminish steadily throughout the century under both RCP8.5 and RCP4.5, meaning that the spatial area of the ice sheet would fall by 15% (RCP4.5) and 25% (RCP8.5) by the end of the century. In comparison, the low-emission scenario (RCP2.6) has the possibility of limiting the loss of Greenland ice sheet coverage to less than 10% by the middle of this century, with no more loss expected after that. Though various surface variables influence the evolution of Greenland’s ice surface mass balance process, the researchers decided to use the temperature projections only to investigate if it is possible to stabilize the Greenland ice sheet given that the PRECIS does perform reasonably well in simulating near-surface air temperature over Greenland.

Compared with the baseline period of 1970-2000, the study projects future ice coverage over Greenland for three periods – 2020s, 2050s, and 2080s under three emission scenarios. “The low-emission scenario of RCP2.6 does have the potential to stabilize the warming climate in Greenland after 2050s and prevent further loss to its ice sheet coverage”, the authors conclude. Ice covering only 65.5% of the country estimated in the baseline period could reduce to 56% in the 2050s and then increase to 57% in the 2080s under a low emission scenario. Hence, low emissions could potentially limit the warming in Greenland below 1°C within the next 30 years and constrain its loss of ice sheet coverage below 10%. 

By contrast, ice coverage will continuously decline throughout this century as the local climate in Greenland is likely to warm up continuously under both medium and high emission scenarios. The worst could be expected in the 2080s under a high-emission scenario, with only around 40% of the country covered by ice caps.

The findings of this study are critical for understanding the consequences of various carbon emission scenarios on stabilizing or limiting warming in Greenland and thus the loss of ice sheet coverage, which is connected to rising sea levels. The results of the study imply that both the high- and medium-emission scenarios would result in ongoing warming in Greenland and thus major ice sheet loss. However, the low-emission scenario has a high potential for reducing local climate warming and ice sheet loss before the 2050s. Most notably, assuming the low-emission scenario is satisfied, no significant changes are projected after the 2050s.

The findings are significant not just for giving climate activists optimism that the Greenland ice sheet will be preserved and coastal populations would be protected from rising sea levels, but also for pressing all nations to take immediate action to decrease carbon emissions. It is fair to expect that the ice sheet covered by the ice cap climate will remain in place indefinitely, but it is difficult to predict when the ice sheet beyond the ice cap climate coverage will begin to melt and eventually disappear, authors highlight. 

It goes without saying that the real ‘Green’ land is on the horizon if we do not take any action to reduce GHG emissions and global warming, and the greatest consequences are obvious. Greenland might envy the beauty of a lush green landscape; however, the rest of the world will suffer from the worst impacts of sea level rise and coastal flooding.

Changes in the Greenland and Antarctic ice sheets have a significant societal impact because they have a direct impact on world sea levels, as glaciers and ice sheets melt, more water enters the ocean. Fortunately, there are some climate policies and actions in place at the global and local scales. So, there is hope! Yet, effective actions are urgently needed to reduce carbon emissions so that we can slow down the disappearance of the ice sheet over Greenland and save our coastal communities from big disasters.

News on the following published article:

Wang, X., Fenech, A., & Farooque, A. A. (2021). Possibility of stabilizing the Greenland ice sheet. Earth’s Future, 9(7), e2021EF002152. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021EF002152

Dr. Xander Wang is an Associate Professor in the School of Climate Change and Adaptation at the University of Prince Edward Island (UPEI). He is also the Director of Climate Smart Lab in the Canadian Centre for Climate Change and Adaptation. Dr. Wang has been recently elected as a member of the Royal Society of Canada (RSC) College of New Scholars. Dr. Wang has served as the Associate Dean (Interim) in the School of Climate Change and Adaptation and is a core member leading the development of the Canadian Centre for Climate Change and Adaptation at UPEI, which is a world-leading research and teaching cluster in climate change impacts and adaptation. Before joining UPEI, Dr. Wang worked as an Assistant Professor in the School of Geosciences at the University of Louisiana at Lafayette, US.

Dr. Pelin Kinay is a postdoctoral fellow of the Climate Smart Lab in the Canadian Centre for Climate Change and Adaptation at UPEI. Her research interest focuses on climate change adaptation and its associated impacts on human health, as well as the natural and human-caused variables that influence climate change.

Dr. Aminur Shah is a postdoctoral fellow of the Climate Smart Lab in the Canadian Centre for Climate Change and Adaptation at UPEI. His research interest focuses on vulnerability and risk assessment of social-ecological systems to natural hazards, sustainable flood risk management, sustainability assessment, climate change impacts and adaptation, community risk reduction, and nature-based solutions.

Dr. Quan Dau is a postdoctoral fellow of the Climate Smart Lab in the Canadian Centre for Climate Change and Adaptation at UPEI. His research interest focuses on water science and global climate change, including but not limited to, hydrological cycle, water resources planning and management, remote sensing, artificial intelligence, climate change adaptation, irrigation water management, socio-economic projection, and reservoir operating management.

 

Correction des biais dans les estimations de l’équivalent en eau de la neige de surface au moyen de l’apprentissage automatique

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-Par Fraser King-

Pendant les hivers froids du Canada, les accumulations de neige qui ne sont pas constamment déneigées ou pelletées augmentent lentement en taille et en densité. Du point de vue du bilan hydrique, ces accumulations de neige agissent comme des châteaux d’eau éphémères, attendant que les températures printanières les réchauffent suffisamment pour les faire fondre en masse. Cette eau issue de la fonte des neiges constitue un élément essentiel des bilans d’eau locaux, car elle remplit les aquifères et alimente les rivières et les lacs voisins. Cependant, les périodes rapides de fonte des neiges peuvent rapidement saturer le sol, entraînant un ruissellement de surface et des inondations. Les inondations causées par la fonte des neiges sont devenues de plus en plus problématiques dans une grande partie du Canada au cours des dernières décennies, alors que les températures mondiales continuent d’augmenter, entraînant des millions de dollars de dommages pour les communautés locales et des perturbations pour le développement et la durabilité des écosystèmes régionaux.

La capacité de quantifier avec précision la quantité d’eau stockée dans la neige au sol représente donc un élément important de la prévision des inondations, permettant aux gouvernements locaux de mieux se préparer et d’atténuer les dommages causés par les futurs épisodes de fonte rapide des neiges. Comme le mentionne Ross D. Brown dans un bulletin de la SCMO publié récemment, le nombre de sites d’observation de la neige au Canada a chuté de plus de 50 % depuis 1995, laissant d’importantes lacunes non observées dans une grande partie du pays. Les modèles climatiques et les produits de réanalyse sont des outils puissants qui peuvent être utilisés pour combler ces lacunes spatio-temporelles dans les observations, mais aucun modèle n’est exempt de biais, d’erreurs et d’incertitudes, ce qui limite nos estimations de la teneur en eau réelle d’un manteau neigeux donné.

Dans un article soumis à Hydrology and Earth System Science en 2020, nous répondons à certaines des préoccupations susmentionnées concernant l’erreur de modèle en corrigeant le biais des estimations de l’équivalent en eau de la neige (EEN) à partir du produit EEN quadrillé du système d’assimilation de données SNOw (SNODAS). SNODAS est un ensemble de données quotidiennes de modélisation et d’assimilation de données à 1 km produit par le centre opérationnel de télédétection hydrologique du National Oceanic and Atmospheric Administration (NOAA) National Weather Service. Bien que ce produit ait été conçu principalement pour être utilisé dans la partie continentale des États-Unis, la partie nord de SNODAS chevauche le sud de l’Ontario. Grâce à la combinaison des nombreuses sources d’observations assimilées par SNODAS et de son modèle interne complexe basé sur la physique, SNODAS produit des estimations d’EEN de surface de la plus haute qualité dans la région.

L’apprentissage automatique est utilisé depuis des décennies dans le domaine des géosciences, mais les progrès rapides des ressources informatiques, combinés aux pétaoctetsde données d’observation désormais facilement accessibles, ont permis à ce domaine de recherche de prendre de l’essor ces dernières années. Alors qu’il peut être tentant de passer immédiatement à l’apprentissage automatique pour des problèmes tels que la réduction d’échelle ou la correction de biais, nous soutenons que cet état d’esprit peut être problématique. L’approche du rasoir d’Occam pour des problèmes tels que ceux-ci offrent aux chercheurs des occasions supplémentaires d’économiser sur les coûts de calcul (c’est-à-dire éviter les phases coûteuses de formation/hyperparamétrage du modèle) et de concevoir un modèle que l’on peut mieux interpréter et expliquer. Bien que nous soyons incroyablement optimistes quant à l’avenir de l’apprentissage automatique (et surtout de l’apprentissage profond) dans les géosciences, nous recommandons également aux futurs chercheurs de commencer par des méthodes simples avant de fouiller dans leurs boîtes à outils respectives.

-By Fraser King-

During Canada’s cold winters, snowpacks that aren’t consistently plowed or hovelled slowly grow in size and density. From a water-balance perspective, these snowpacks act as ephemeral water towers, waiting for spring temperatures to eventually warm them enough to melt en masse. This snowmelt-derived water is a critical contributor to local water budgets as it refills aquifers, and feeds nearby rivers and lakes. However, rapid snowmelt periods can quickly saturate the soil, leading to surface runoff and flooding. Snowmelt-derived flooding has become increasingly problematic across much of Canada in recent decades as global temperatures continue to rise, leading to millions of dollars in damage to local communities, and disruptions to regional ecosystem development and sustainability.

Figure 1: a) Relative bias in SNODAS SWE estimates when compared to in situ estimates from ECCC; and b) 7 year timeseries of SWE on ground estimates from SNODAS and ECCC.

The ability to accurately quantify the amount of water stored in snow on the ground is therefore an important component in flood forecasting, allowing local governments to better prepare for, and mitigate, damages caused by future rapid snowmelt events. As discussed in another recent CMOS bulletin from Ross D. Brown, the number of snow-observing sites across Canada has dropped by over 50% since 1995, leaving large unobserved gaps across much of the country. Climate models and reanalysis products are powerful tools which can be used to fill these spatiotemporal gaps in observations, however no model is without bias, error and uncertainty, which limits our estimates of the true water content in a given snowpack.

In a paper submitted to Hydrology and Earth System Science in 2020, we address some of the aforementioned concerns surrounding model error by bias correcting snow water equivalent (SWE) estimates from the SNOw Data Assimilation System (SNODAS) gridded SWE product. SNODAS is a daily, 1 km modelling and data assimilation dataset produced by the National Oceanic and Atmospheric Administration (NOAA) National Weather Service’s Operational Hydrologic Remote Sensing Center. While this product was primarily developed for use across the continental United States, the northern portion of SNODAS overlaps with southern Ontario. Through a combination of the many sources of observations assimilated by SNODAS, and its complex, physically-based internal model, SNODAS produces some of the highest quality estimates of surface SWE across the region.

However, when compared with independent in situ measurements recorded by the Climate Research Division of Environment and Climate Change Canada (ECCC), SNODAS displays clear spatiotemporal biases in its SWE estimates across much of southern Ontario (Figure 1). Temporally, SNODAS exhibits a strong positive bias pre-2014 (a period which marks a distinct change in the known assimilated datasets), along with strong positive spatial biases as we move further inland, away from the US border.

To address these biases, we explored a suite of increasingly sophisticated statistical bias-correction methods, culminating in the application of a nonlinear machine learning (ML) technique which displayed the best overall skill. Instead of jumping directly into ML, we followed an “Occam’s razor” methodological approach by starting with simple, well validated and interpretable methods of bias correction like mean bias subtraction (MBS) and linear regression to develop a performance baseline. The idea being that if a simple method does nearly as well as a more sophisticated ML-based method, we should use the simpler, more explainable technique.

Figure 2: RMSE and absolute mean bias for each model trained and tested using different spatiotemporal partitions of the full training dataset.

We evaluated four different models including the aforementioned MBS, a simple linear regression (SLR) model, decision tree (DT), and finally a random forest (RF). Each of these models were fit using the same training datasets over three periods

  1. December, January, February (DJF)
  2. March, April, May (MAM)
  3. DJF MAM (i.e. annual)

across two spatial domains (northern vs. southern Ontario). Each model was fit using a set of climate predictor variables (SNODAS SWE on ground, precipitation biases, surface temperature, elevation, year, and day of year) to model ECCC SWE on ground at 391 sites.

Our results indicated that the RF continually demonstrated the lowest overall RMSE and absolute mean bias over each period and across all regions (Figure 2). The overly simplistic MBS did an excellent job at removing the mean bias (by construction), however this was accomplished by overcorrecting the bias in some regions and under correcting it in others (resulting in the high RMSE for this method in Figure 2). The SLR fared better with a slightly lower overall RMSE, however these linear methods were unable to fully account for the nonlinear spatiotemporal bias from Figure 1. The best performing methods were the ML-based DT and RF, with the RF demonstrating improved performance annually (improved robustness).

Figure 3: Timeseries comparisons of monthly area-normalized discharge from SNODAS and the bias corrected SWE melt estimates at three river gauges in Ontario.

To further quantify the differences between bias-corrected and uncorrected SWE estimates, we also performed a simple water balance analysis across three watersheds in southern Ontario, anticipating that reductions in mean SWE would produce a more physically consistent fit with in situ melt measurements. Comparing monthly snow melt estimates (i.e. the negative SWE differences between consecutive monthly means from bias corrected and uncorrected SWE datasets) with area-normalized discharge across each basin (Figure 3), we found that the bias corrected RF-derived melt estimates were much closer in magnitude to in situ, and did not display the unphysical overestimation which was typical of SNODAS. These types of follow-up comparisons are incredibly useful methods for further validating the robustness of bias correction models like those explored in this work, and can be used to identify deficiencies which may be hidden upon first glance (e.g. preserving physical laws which are unknown to the ML model).

While the ML-based bias correction techniques applied here demonstrate good skill and a general robustness throughout the region, there are other options to choose from in the ML toolbox. In an upcoming study, we plan on evaluating some of these tools through a daily, ten-year Canada-wide bias correction of temperature, precipitation and radiation fields from the fifth-generation Canadian Regional Climate Model (CRCM5) (biases shown in Figure 4). With a much larger available sample in this follow-up project, we are able to experiment with more sophisticated neural network (NN) approaches for spatiotemporally bias correcting each climate variable. These bias corrected fields can then be used to drive land surface models and, in turn, bias correct surface snow estimates via a proxy correction of associated climate variables. When trained on the billions of available data points, early results suggest that NN approaches strongly outperform linear methods, and even beat out RF techniques for nonlinear biases like those present in surface temperature.

Figure 4: CRCM5 surface temperature (T2M) and precipitation (PR) biases at a) monthly and b) annual timescales; along with their corresponding climatological mean biases across Canada in c) and d).

ML has been used across the Geosciences for decades, however, rapid advancements in computing resources, combined with petabytes of now easily accessible observational data, has allowed this field of research to flourish in recent years. While it can be appealing to immediately jump to ML for problems like downscaling or bias correction, we argue that this mindset may be problematic. The Occam’s razor approach to problems such as these provide researchers with additional opportunities to save on computational costs (i.e. avoid expensive model training/hyperparameterization phases), and to develop a more interpretable and explainable model. While we are incredibly optimistic about the future of ML (and especially deep learning) in the Geosciences, we also recommend that future researchers take care by starting with simple methods before digging into their respective machine learning toolboxes.

Fraser King completed his PhD in remote sensing and machine learning of precipitation at the University of Waterloo in December, 2022. He is now a post doctoral research fellow at the University of Michigan, developing machine learning-based snowfall retrieval algorithms and using surface and spaceborne radars to improve our understanding of hydrometeor particle microphysics.

L’importance des avant-dunes côtiers en tant que solution naturelle pour la protection du littoral : ce que nous apprend l’ouragan Fiona

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Par Jeff Ollerhead, Robin Davidson-Arnott et Bernard O. Bauer
Les avant-dunes sont souvent présentés comme une solution naturelle pour empêcher l’inondation du littoral pendant les grosses tempêtes, ce qui permet d’atténuer les dommages potentiels aux précieuses infrastructures côtières et de réduire l’impact de l’érosion des vagues et des ondes de tempête. La destruction récente causée par l’ouragan Fiona (septembre 2022) sur la côte nord de l’Île-du-Prince-Édouard (Î.-P.-É.) offre une occasion idéale de valider cette affirmation.

Le changement climatique et le réchauffement des océans dans un avenir proche entraîneront trois conséquences prévues pour de nombreuses zones côtières canadiennes : i) une hausse des taux d’élévation du niveau relatif de la mer; ii) une augmentation de la fréquence et de l’ampleur des tempêtes majeures; et iii) une diminution de la couverture de glace de mer en hiver. Ces trois facteurs entraîneront une augmentation des taux d’érosion du littoral. Le sixième rapport d’évaluation du Groupe d’experts intergouvernemental sur l’évolution du climat (GIEC), l’organe des Nations Unies chargé d’évaluer les données scientifiques relatives au changement climatique, rend ces conséquences très claires, et Fiona illustre bien la tendance (figure 1).

Davidson-Arnott et Bauer (2021) ont récemment publié un article examinant les contrôles de la réponse géomorphique des systèmes plage-dune à l’élévation progressive du niveau de l’eau en lien avec d’autres contrôles à long terme de la réponse côtière (p`. ex. la climatologie du vent, la croissance de la végétation, le contexte géologique). La plupart des données donnent à penser que la plage et l’avant-dune (ainsi que le profil du littoral) peuvent migrer vers l’intérieur des terres de manière intacte, en suivant le rythme actuel de l’élévation relative du niveau de la mer (Fox-Kemper et coll. 2021). Cette situation peut toutefois être perturbée si la fréquence et/ou la gravité des tempêtes érosives augmentent. Cela réduira ou empêchera le système plage-dune de se rétablir dans une position vers l’intérieur des terres par le biais de l’apport de sable du littoral aux dunes dans des conditions de vagues de beau temps et de processus éoliens.

Un programme de recherche lancé il y a plus de 20 ans aux dunes de Greenwich (qui font partie du parc national de l’Î.-P.-É.; figure 2A) démontre que les dunes évoluent et migrent généralement vers l’intérieur des terres, comme l’ont laissé entendre Davidson-Arnott et Bauer (2021; figure 2B). Notre compréhension collective des processus en cause est éclairée par de multiples études sur le site, menées à diverses échelles spatiales et temporelles, du transport de sédiments vers les dunes pendant des événements éoliens individuels, à la variabilité saisonnière du transport de sédiments sur l’avant-dune, à l’évolution décennale du système d’avant-dunes au cours des 100 dernières années (p. ex. Walker et coll. 2017; Mathew et coll. 2010).

L’impact de Fiona sur le système plage-dune a été prononcé, avec l’érosion d’une partie importante du talus de la pente de l’avant-dune. L’intégrité des dunes est cependant restée largement intacte, et la crête des dunes n’a pas été rompue. La question qui se pose maintenant consiste à déterminer si l’avant-dune de Greenwich, et probablement d’autres endroits le long de la côte nord de l’Î.-P.-É., est susceptible de rester intacte dans un climat changeant. Il y a de l’espoir, car nous savons, grâce à des documents d’archives, que toute l’avant-dune de Greenwich a été érodée lors d’une importante tempête en 1923 et que le système d’avant-dune s’est complètement rétabli, bien que sur plusieurs décennies (Mathew et coll. 2010). Une question connexe est de savoir comment le système d’avant-dunes peut être géré pour avoir le plus de chances de « survivre » au changement climatique. Fiona donne un aperçu de ces deux questions.

-By Jeff Ollerhead, Robin Davidson-Arnott, and Bernard O. Bauer-

Figure 1: A) Foredune erosion at the Greenwich Dunes eastern beach access post-Fiona (photo taken west to east). The inset shows the boardwalk in May 2022, prior to the stairs being attached (photo taken east to west). B) Overwash and scarped foredune at Greenwich post-Fiona (photo taken east to west).

Introduction

Foredunes are oft-touted as a nature-based solution to preventing shoreline inundation during major storms, serving to mitigate potential damage to valuable coastal infrastructure and reducing the erosional impact of waves and storm surge. The recent destruction imparted by Hurricane Fiona (September 2022) on the north coast of Prince Edward Island (PEI) provides an ideal opportunity to validate this assertion.

Climate change and ocean warming in the near future will lead to three anticipated consequences for many Canadian coastal areas: i) an acceleration in rates of relative sea level rise, ii) an increase in the frequency and magnitude of major storms, and iii) a decrease in winter sea ice coverage. All three factors will drive increases in rates of shoreline erosion. The Sixth Assessment Report from the Intergovernmental Panel on Climate Change (IPCC), the United Nations body for assessing the science related to climate change, makes these consequences abundantly clear, and Fiona illustrates the trend nicely (Figure 1).

Davidson-Arnott and Bauer (2021) published a paper recently examining controls on the geomorphic response of beach-dune systems to progressive water level rise in relation to otherlong-term controls on coastal response (e.g., wind climatology, vegetation growth, geological context). Most of the evidence suggests that the beach and foredune (together with the nearshore profile) can migrate landward intact, keeping pace with current rates of relative sea- level rise (Fox-Kemper et al. 2021). This situation can be disrupted, however, if the frequency and/or severity of erosive storms increases. This will reduce or prevent the beach-dune system from re-establishing itself in a landward position via the delivery of sand from the nearshore to the dunes under fair weather wave conditions and aeolian processes.

A research program initiated more than 20 years ago at Greenwich Dunes (part of PEI National Park; Figure 2A) demonstrates that the dunes are generally evolving and migrating inland as suggested by Davidson-Arnott and Bauer (2021; Figure 2B). Our collective understanding of the processes involved is informed by multiple studies at the site, conducted at various spatial and temporal scales, from sediment transport to the dunes during individual wind events, to seasonal variability in sediment transport over the foredune, to the decadal evolution of the foredune system over the past 100 years (e.g., Walker et al. 2017; Mathew et al. 2010).

Figure 2: A) Field site at Greenwich Dunes, PEI, and locations of profile lines (Ln 1 to Ln 8). Projection is UTM (Zone 20N). B) Profile changes at line 5 over a 20-year period (2002-2022) not including the impact of Fiona. A vertical exaggeration (VE) of 2 times is used.The impact of Fiona on the beach-dune system was pronounced, with a substantial portion of the stoss slope of the foredune system being eroded. The integrity of the dunes remained largely intact, however, and the dune crest was not breached. The question now is whether the foredune at Greenwich, and likely at other locations along the north coast of PEI, is likely to remain intact with a changing climate? There is hope, because we know from archival records that all of the foredune at Greenwich was eroded away during a major storm in 1923, and that complete recovery of the foredune system occurred, albeit over several decades (Mathew et al. 2010). An associated question is how the foredune system can be managed to provide the greatest likelihood of ‘surviving’ climate change? Fiona provides insight into both questions.

Hurricane Fiona – The Storm

Although Atlantic hurricanes pass over or close to PEI every few years (Table 1), they are relatively infrequent events, with periods of several years without notable impacts (e.g., 2015-2018). By the time these warm-cored storms reach PEI, they have typically transitioned from their ‘hurricane’ or ‘tropical storm’ designation into an extra-tropical configuration with significantly reduced wind speeds, unless merging with mid-latitude cyclonic disturbances travelling through the region. Hurricane Fiona was not unusual in terms of its trajectory, extra- tropical status, and reduced wind speeds in the Maritimes, but its impact on the north coast of PEI was considerably more significant than any other tropical-origin storm in recent times.

Figure 3A shows the evolution of 4 (of 5) hurricanes to impinge on PEI since 2013, which is the period of record for the Stanhope weather station (ECCC ID#8300590-6545) which is representative of conditions on the north coast of PEI. Hurricane Ida is not included because it had slower windspeeds than the other four storms and tracked across PEI twice—once from the south and then from the north on the following day—after doing a loop in the Gulf and losing energy. It is evident that Fiona was the most significant storm during this decade, with winds peaking at 89 km h-1 and sustained winds above 60 km h-1 for 7 consecutive hours. A peak wind gust was recorded at 131 km h-1 just before midnight on September 23. An important factor for coastal impacts is that the wind was consistently out of the north for most of the storm, which lasted approximately 24 hours.

Figure 3: A) Mean hourly wind speed and direction at the Stanhope, PEI station for 4 hurricanes to impinge on PEI since 2013. B) Mean hourly wind speed and direction for Fiona relative to two of the most intense winter storms recorded at the Stanhope, PEI station since 2013. Nomenclature follows the Beaufort Scale.

In contrast, the other three hurricanes shown in Figure 3A (Teddy, Dorian, and Arthur) were much less effective in their potential to cause shoreline erosion on the north shore of PEI. Dorian, for example, never reached ‘gale’ status (Beaufort Scale) and at its peak, had speeds of only 55 km h-1. Unlike Fiona, which tracked to the east of PEI, Dorian tracked almost directly over our field site. This meant that the wind in advance of the eye was generally from the east in the alongshore direction and then transitioned rapidly to a westerly direction (also alongshore but from the opposite direction) after the eye passed. Neither direction is conducive to wave generation or significant storm surge at Greenwich. Arthur and Teddy had greatly reduced wind speeds, never quite attaining 40 km h-1, and in the case of Arthur, the wind direction was dominantly from the south, consistent with its track across the western tip of PEI.

An assessment of all Atlantic hurricanes since 1953, using data from the Charlottetown Airport (YYG) weather station (ECCC ID#8300300-6526 and ID#8300301-50621; in continuous operation since 1953) indicates that Fiona was among the most intense hurricanes to hit PEI in three decades. The barometric pressure during Fiona was the lowest recorded at 95.85 kPa and the maximum hourly windspeed of 73 km h-1 ranks it third in intensity behind Hurricanes Juan (Sept 29, 2003; 82 km h-1; 98.55 kPa) and Arthur (Jul 5, 2014; 76 km h-1; 98.1 kPa). However, the inland location of Charlottetown airport means the windspeeds experienced on the north shore of PEI are often different. As shown in Figure 4A, the records from the Stanhope station show that the windspeeds during Fiona peaked at 89 km h-1 rather than the 73 km h-1 experienced at the airport. Moreover, the Stanhope station recorded a peak of only 39 km h-1 during Hurricane Arthur, rather than the 76 km h-1 experienced at the airport, which is due to the southerly wind direction during Arthur.

Even though Fiona stands out as one of the most intense Atlantic hurricanes to impact the north shore of PEI, there are other mid-latitude frontal systems that rival its intensity. A storm on March 12/13, 2022 had barometric pressures almost as low (96.24 kPa) as during Fiona, but windspeeds were below a ‘strong breeze.’ Figure 4B shows the evolution of Fiona relative to two of the most intense storms recorded at the Stanhope station since 2013. Storm A (Feb 15/16, 2015) attained peak windspeeds of 61 km h-1, which makes it the second-most intense wind event after Fiona. Storm C (March 26/27, 2014) was of similar intensity with peak windspeeds of 60 km h-1 and near gale conditions for 9 hours continuously. In the period 2014-2022, there were a total of thirteen storms that had peak windspeeds of 61 km h-1 or greater (based on Charlottetown data), only two of which were hurricanes (Fiona and Arthur). The other eleven storms had a mid-latitude origin and all occurred in the winter or early spring (i.e., December 15 through March 31). This is of considerable importance when assessing beach-dune interaction, because beach-dune systems on PEI are often covered by snow, and the north coast is protected from wave erosion by shore-fast ice, during this period (Figure 4). Thus, many of the most significant storms to hit PEI are incapable of forcing substantial shoreline change, despite their intensity, due to the presence of snow and shore-fast ice.

 

 

 

 

Figure 4: Photo of the beach-dune system at Line 7 taken February 15, 2008, showing snow on the foredune and shore-fast ice in the nearshore (view to east).

Hurricane Fiona – Beach-Dune Impacts

As noted above, an impact of Fiona was the erosion of a substantial portion of the stoss slope of the foredune along the Greenwich shoreline (Figures 5 and 2B). Our data also show, however, that landward retreat was less and preservation of the foredune greater at the western end of the study site, the downdrift end of the local littoral system. At the eastern end of the study site, the foredune is losing volume and retreating more rapidly.

Figure 5: Profiles across the foredune at lines 2, 5, and 8 and comparative photos from May and October 2022 (before and after Fiona; taken from east to west). The dashed grey line shows mean sea level and the blue line is the estimated maximum water elevation during Fiona (storm surge + wave runup) based on measurements of wrack lines. A vertical exaggeration (VE) of 2 times is used.

Much of the eroded sand during Fiona was lost to the nearshore, but a proportion was transported over the crest and onto the lee slope. Although it cannot be seen on Figure 5 given the scale, the data show a small increase in elevation on the lee slope from May to October 2022, and freshly deposited sand was evident there when surveying post-Fiona. Where the foredune was low, some sand moved inland by overwash (Figure 1B). Much of the sand that was moved to the nearshore zone will gradually make its way back to the beach-dune system driven by fair-weather waves and aeolian processes.

One challenge we faced in assessing Fiona and its impacts, is a lack of marine data for the north shore of PEI. Fisheries and Oceans Canada does not maintain any real-time tide stations on the north shore of PEI. Water level gauges operated by the Canadian Centre for Climate Change and Adaptation (CCCCA) at the University of Prince Edward Island at North Rustico and North Lake were not working properly during Fiona. The only estimate we could obtain was from CCCCA’s gauge at Red Head, which captured a peak storm surge of at least 2 m during Fiona. ECCC has no operational buoys in the Gulf of St. Lawrence to report on wind speed, wave height and period, atmospheric pressure, etc., so we are left to hindcast likely wave characteristics using terrestrial wind records. This situation is far from ideal, as researchers attempt to quantify the impacts of climate change in general, and storms in particular, in Maritime Canada.

The Future

As Figure 2 shows, over the past 20 years the foredune crest at line 5 has translated landward and grown vertically. Sand has been transported to the lee side, maintaining total dune volume. In short, the system is evolving in a manner consistent with the Davidson-Arnott and Bauer (2021) model, with the differences in erosion along the shoreline caused by Fiona explained by differing amounts of sediment available in the littoral cell. This observation highlights the need to assess beach-dune sediment balance in three dimensions along a stretch of coastal foredune (i.e., both on-offshore and alongshore) when assessing the robustness of a foredune to provide natural protection.

Based on our studies, it is anticipated that where there is sufficient sediment supply, sand ramp emplacement will take place relatively quickly and dune healing will occur. The increasing frequency and intensity of storms brought on by a warming climate may, however, disrupt the critical equilibrium between dune scarping and healing processes that have characterized this system for the last 50 years. As ocean temperatures warm, hurricanes and tropical storms will retain their intensity longer as they move north. This will likely be exacerbated by that fact that as sea ice (and particularly shore-fast ice) declines in the coming decades, winter events like Storm A (Feb 15/16, 2015) will become more effective in eroding the dunes. So, hurricanes reaching PEI will likely become more frequent and stronger (as suggested by Fiona) but the system will also be subjected to additional erosion from strong winter storms (i.e., Nor’easters) in the near future.

As Mathew et al.’s (2010) work demonstrates, the Greenwich beach-dune system can recover from even catastrophic erosion. Recovery from the storm of 1923 happened at a decadal scale, rather than at an annual scale, but it did happen. The management imperative is therefore to facilitate dune healing along this coast after major storms via natural processes by: 1) preventing human disturbance of the natural vegetation through activities such as trampling, driving of all-terrain vehicles, and construction; and 2) by providing substantial accommodation space for the foredune to migrate inland and grow upward as relative sea level rises. Management of the Greenwich Dunes by Parks Canada has employed this strategy, and our monitoring program indicates that it played an important role in the ability of the foredune there to withstand the impact of storm surge and erosion by large waves during the passage of Fiona.

Table 1: History of Atlantic Hurricanes impinging on PEI since 1999

Jeff Ollerhead is a Professor in the Geography and Environment Department at Mount Allison University in Sackville, NB. He is a coastal geomorphologist who studies beaches and salt marshes. In recent years, he has been particularly involved in designing and monitoring salt marsh restorations in the upper Bay of Fundy.

Robin Davidson-Arnott retired from the Department of Geography, Environment and Geomatics in 2009 and is now continuing research as Professor Emeritus. In addition to work on beach and nearshore sedimentation, he has carried out research on coastal salt marshes, erosion of cohesive coasts, particularly underwater erosion, beach/dune interaction, and the dynamics of coastal sand dunes. He has published extensively in book chapters and refereed journals, and his book Introduction to Coastal Processes and Geomorphology was published by Cambridge University Press in 2010 and a second edition in 2019.

Bernard Bauer is a process geomorphologist specializing in sediment transport dynamics in aeolian, nearshore, and fluvial systems. His research is primarily directed at advancing fundamental scientific understanding of Earth systems, but increasingly he is interested in ensuring that the latest scientific knowledge is used by coastal managers and environmental decision makers to inform policy development. Bauer is Emeritus Dean of Arts & Sciences at the University of British Columbia Okanagan.

References

Davidson-Arnott, R.G.D. and B.O. Bauer, 2021. Controls on the geomorphic response of beachdune systems to water level rise. Journal of Great Lakes Research, Vol. 47(6), p. 1594-1612. https://doi.org/10.1016/j.jglr.2021.05.006

Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021. Ocean, Cryosphere and Sea Level Change. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362. https://www.ipcc.ch/report/ar6/wg1/

Mathew, S., R.G.D. Davidson-Arnott and J. Ollerhead, 2010. Evolution of a beach-dune system following a catastrophic storm overwash event: Greenwich Dunes, Prince Edward Island, 19362005. Canadian Journal of Earth Sciences, Vol. 47, p. 273-290. https://doi.org/10.1139/E09-078

Ollerhead, J. and R. Davidson-Arnott, 2022. Evolution and management of Atlantic Canadian coastal dunes over the next century. Physical Geography, Vol. 43(1), p. 98-121. https://doi.org/10.1080/02723646.2021.1936790

Walker, I.J., R.G.D. Davidson-Arnott, B.O. Bauer, P.A. Hesp, I. Delgado-Fernandez, J. Ollerhead and T.A.G. Smyth, 2017. Scale-dependent perspectives on the geomorphology and evolution of beach-dune systems. Earth-Science Reviews, Vol. 171, p. 220-253. https://doi.org/10.1016/j.earscirev.2017.04.011

Acknowledgements

Drs. I. Delgado-Fernandez, P.A. Hesp and I.J. Walker are thanked for their many contributions to this work over many years. The list of additional colleagues, students, and funding agencies who have contributed over 20 years is long and they too are thanked (see our published papers for a complete list).

Modélisation océanique à haute résolution des fjords de la Colombie britannique

Written on .

-Par Krysten Rutherford and Laura Bianucci-

L’océan côtier et proche du rivage est une région importante, qui fait office de tampon entre la terre et l’océan ouvert. Par conséquent, elle subit les effets du changement climatique en provenance de la haute mer et absorbe également les effets de la terre et des changements d’utilisation des terres. De plus, elle est souvent la plus utilisée par l’homme pour des activités récréatives, économiques et traditionnelles, ce qui peut ensuite avoir des effets, par exemple des marées noires et des opérations d’aquaculture. Les scientifiques du monde entier ont donc entrepris de mieux comprendre ces régions. L’un des outils à leur disposition est la modélisation océanique à haute résolution. 

Laura Bianucci, Ph. D., qui travaille actuellement pour Pêches et Océans Canada à l’Institut des sciences de la mer (Sidney, C.-B.), est l’une de ces chercheuses qui étudient les zones côtières de la Colombie-Britannique. Elle a toujours cherché à mieux comprendre les processus océaniques côtiers, de l’échelle du plateau continental à celle du littoral, et travaille avec des modèles numériques océaniques connexes depuis ses études supérieures. Au départ, son travail était axé sur les modèles régionaux à haute résolution, mais il a évolué au fil des ans pour se concentrer de plus en plus sur les questions relatives aux processus littoraux. Elle est d’avis que le type de modèles à haute résolution qu’elle utilise nous aide à mieux comprendre les parties de l’océan côtier qui sont si importantes pour nous en tant que société et pour l’écosystème. 

Comme le souligne Mme Bianucci, les modèles à haute résolution ne sont pas nécessairement nouveaux, mais la puissance accrue des ordinateurs a permis d’augmenter considérablement la capacité de ces modèles au cours des dernières années. Le terme « haute résolution » peut avoir plusieurs significations lorsqu’il s’agit de modèles numériques d’océan, car il existe de nombreux types d’applications pour cet outil scientifique. La résolution d’une photographie (ou d’un modèle) dépend de la taille des pixels (ou de la taille des cellules de la grille du modèle), une photographie (ou un modèle) à haute résolution ayant des pixels (cellules de la grille) plus petits pour capturer plus de détails. Trouver la résolution spatiale optimale d’un modèle dépend en grande partie de l’équilibre entre son coût de calcul et les questions scientifiques posées et les processus modélisés. Les grands domaines de modélisation et/ou les modèles à haute résolution nécessitent davantage de puissance informatique et de stockage; en d’autres termes, ils sont plus coûteux sur le plan informatique. Néanmoins, les processus ou les régions ne peuvent être résolus avec précision s’ils se situent à des échelles inférieures à la largeur de la cellule de la grille du modèle (c’est-à-dire la résolution), ce qui doit être pris en compte lors de l’élaboration de modèles pour des applications spécifiques.

Cet article a été rédigé par Krysten Rutherford, Ph. D., sur la base d’un entretien avec Laura Bianucci, Ph. D., et d’images de Glenn Cooper. 

 

 

-By Krysten Rutherford and Laura Bianucci-

The nearshore and coastal ocean is an important region, acting as a buffer between the land and the open ocean. As a result, it experiences climate change impacts from the open ocean and also absorbs impacts from the land and land-use changes. Moreover, it is often the most utilized by humans for recreational, economic, and traditional activities, which can subsequently lead to impacts from, for example, oil spills and aquaculture operations. Scientists across the globe have therefore set out to better understand these regions. One tool at their disposal is high-resolution ocean modelling.

Dr. Laura Bianucci, who currently works for Fisheries and Oceans Canada at the Institute of Ocean Science (Sidney, BC), is one such researcher studying the coastal areas of British Columbia. She has always been interested in understanding coastal ocean processes ranging from continental shelf to nearshore scales, and has been working with related numerical ocean models since graduate school. Initially, her work focused on high-resolution regional models, but it has evolved over the years to focus more and more on questions regarding nearshore processes. She believes that the type of high-resolution models she employs help us to better understand parts of the coastal ocean that are so important to us as a society and for the ecosystem.

Figure 1: Fish farms in Clayoquot Sound along the west coast of Vancouver Island (photo credit: Glenn Cooper).

As Dr. Bianucci points out, high-resolution models are not necessarily new, but increasing computer power has meant that the ability of these models has significantly increased in recent years. The term “high-resolution” can mean many things when it comes to numerical ocean models since there are many different types of applications for this scientific tool. Here, we are referring to a model’s spatial resolution, which is similar to pixels in a photograph – the resolution of a photograph (or model) depends on the size of the pixels (or size of the model grid cells), with a higher resolution photograph (model) having smaller pixels (grid cells) to capture more detail. Finding a model’s optimal spatial resolution depends largely on balancing its computational expense with the scientific questions being asked and processes being modelled.  Large model domains and/or high-resolution models require more computer power and more storage; in other words they are more computationally expensive. However, processes or regions cannot be accurately resolved if they are on scales smaller than the model grid cell width (i.e. resolution), which must be taken into consideration when developing models for specific applications.

For Dr. Bianucci, who studies the intricate nature of the many inlets and fjords of British Columbia’s coastline, high-resolution often means a model grid cell width ranging in size from 10 to 100 m. These coastal areas are made up of narrow channels that often cannot be resolved with models that have a resolution of >1km. For reference, some high-resolution regional ocean models have a resolution of 2-10km and global Earth System Models (ESMs) often have a resolution of ~100 km. With her models having such high spatial resolution, they often need to cover a smaller area to limit the computational expense. As such, Dr. Bianucci develops model applications for specific inlet and fjord systems. Currently, she is working on 4 different high-resolution models which encompass Queen Charlotte Strait, Discovery Islands, West Coast of Vancouver Island, and Quatsino Sound (see Figure 2).

Figure 2: Four model domains: Discovery Islands, West Coast Vancouver Island, Quatsino Sound and Queen Charlotte Strait. Inset shows zoomed in model grid for Queen Charlotte Strait as an example of the model resolution (credit: Krysten Rutherford).

The questions that Dr. Bianucci tries to tackle with her projects are multidisciplinary in nature. As such, she often brings together a diverse team of individuals. “Ocean science today is in deep need of interdisciplinary research and, most importantly, interdisciplinary teams. Siloed research is not the right approach when facing a multifaceted challenge like climate change,” Dr. Bianucci comments. Given that the regions Dr. Bianucci studies are highly utilized by humans, she additionally tries to involve as many stakeholders as possible in her projects. She works closely with fisheries managers as well as with First Nation partners who are interested in finding out more about the resilience of their systems that are changing very rapidly due to climate change. She keeps them up to date on the modelling side, but also integrates them into the implementation of monitoring plans of the various fjords she is studying. Many of these fjords are lacking in observations because it is difficult to visit them frequently and sample in high enough spatial resolution to constrain the system. The implementation of these monitoring plans, particularly when combined with high-resolution modelling, is therefore crucial to better understanding these systems.

Figure 3: CTD and Niskin deployment off the CME Anderson in Quatsino Sound (photo credit: Glenn Cooper).

Models in general are great tools that can be used to investigate the inner workings of the ocean without altering the real ocean, both at present-day and under future conditions, Dr. Bianucci argues. They are powerful tools that help us interpret sparse observations, and allow scientists to test mechanisms and hypotheses that can be hard to observe in the real ocean. Given the many uses of numerical ocean models, there are many different types of modelling systems available. Currently, Dr. Bianucci specifically uses one called Finite Volume Community Ocean Model (FVCOM). This type of model is unstructured, which means that the grid cell size can vary throughout the model. This feature is beneficial for the type of modelling that Dr. Bianucci is doing since it allows her to represent different types of areas, which may need different resolution, within the same model (e.g. continental shelf vs. fjords).

British Columbia’s inlets are often home to many aquaculture operators. The development of these inlet-specific models can help operators and regulators in a variety of ways, such as by helping them plan best management practices and for potential future changes. For example, Dr. Bianucci and her team have implemented particle tracking in her models, which can simulate the dispersal of pathogens or contaminants from aquaculture farms (DFO 2021). Her current work is also focused on hypoxia and deoxygenation since this is what many fjords along the British Columbia coastline are experiencing. A couple of her recent collaborative studies have found long-term deoxygenation and warming trends in four BC fjords (Jackson et al. 2021), and have explored the seasonal occurrence of near-surface hypoxia in another inlet based on recent measurements (Rosen et al. 2022). She aims to use her models to improve the understanding of the dynamics behind these observed low oxygen events as well as how these events and dynamics may change under future climate scenarios. Furthermore, by studying several inlets and fjords, Dr. Bianucci hopes to address the spatial diversity of coastal hypoxia in these geomorphologically complex regions. For example, it is not yet fully understood why a subset of the inlets along the West Coast of Vancouver Island experience hypoxia while some do not. Dr. Bianucci believes that differences in the bathymetry and sill locations, how the inlets are aligned with the main winds, and the type of freshwater forcing reaching the fjords are likely some of the key drivers in setting inlet biogeochemical properties. This work is still in progress, but Dr. Bianucci is very much looking forward to seeing the outcomes of her modelling work in this region.

Over the coming years, Dr. Bianucci hopes to expand her models to include even more coastal areas. These models can be used to see the impacts on and from any given aquaculture farm, and help to establish the best management practices. She also hopes to use these models to understand how extremes and climate change as a whole will impact the important near shore regions of British Columbia, in terms of hypoxia, acidification, and temperature. It will be incredibly important over the coming years to get a firm grasp on these stressors and how they will affect the local fish stocks and aquaculture farms; high-resolution ocean modelling is a formidable tool for the task.

 

 

This article was written by Dr. Krysten Rutherford based on an interview with and input from Dr. Laura Bianucci and images from Glenn Cooper.

Krysten Rutherford (she/her) is a postdoctoral fellow at the Institute of Ocean Science in Sidney, BC, and completed her PhD in 2021 at Dalhousie University. She implements and develops high-resolution models to better understand present-day processes and the potential future impacts of climate change on coastal systems.

Laura Bianucci (she/her) is a research scientist at the Institute of Ocean Science in Sidney, BC. Before joining Fisheries and Oceans Canada in 2017, she was a scientist at Pacific Northwest National Laboratory (Seattle, WA, USA) and a postdoc at Dalhousie University. She holds a PhD from the University of Victoria (2010).

 

References

DFO. 2021. Hydrodynamic Connectivity between Marine Finfish Aquaculture Facilities in British Columbia: in support of an Area Base Management Approach. DFO Can. Sci. Advis. Sec. Sci. Resp. 2021/042.

Jackson, J. M., Bianucci, L., Hannah, C. G., Carmack, E. C., & Barrette, J. (2021). Deep waters in British Columbia mainland fjords show rapid warming and deoxygenation from 1951 to 2020. Geophysical Research Letters, 48, e2020GL091094, https://doi.org/10.1029/2020GL091094.

Rosen S, Bianucci L, Jackson JM, Hare A, Greengrove C, Monks R, Bartlett M and Dick J (2022). Seasonal near-surface hypoxia in a temperate fjord in Clayoquot Sound, British Columbia. Front. Mar. Sci, 9:1000041, doi: 10.3389/fmars.2022.1000041.

Changements océaniques futurs : Que peuvent nous apprendre les modèles d’écosystèmes?

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– Par Andrea Bryndum-Buchholz –

Le changement climatique a une incidence sur tous les aspects de la vie dans le monde. Les océans se réchauffent et s’acidifient, entraînant une cascade de conséquences pour la vie marine — mortalité accrue, réduction de la calcification et modification de la répartition des espèces ne sont que quelques-uns des changements majeurs déjà observés.

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Communauté de pratique de la Décennie de l’océan

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– By Jia Yi Fan–

La Décennie des Nations Unies des sciences océaniques au service du développement durable (appelée Décennie de l’océan) est un effort visant à catalyser l’action et l’innovation afin de parvenir à « la science dont nous avons besoin pour l’océan que nous voulons ». Le Marine Environmental Observation, Prediction and Response Network (MEOPAR) a répondu à ce besoin au début de l’année 2021 en travaillant avec des partenaires pour mobiliser une communauté de pratique vouée à soutenir l’action canadienne dans le cadre de la Décennie de l’océan, en commençant par mettre l’accent sur l’amélioration de l’accès à l’information et la stimulation de la collaboration.

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Timothy R. Parsons – Avis de décès

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Le professeur Timothy (Tim) R. Parsons (OC, FSRC) est décédé à l’hôpital le 11 avril 2022, entouré de sa famille. Tim était l’un des plus éminents spécialistes des sciences de la mer au Canada et il a reçu de nombreux prix et honneurs nationaux et internationaux.

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