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What are Marine Ecological Time Series telling us about the ocean? A status report.

2022· report· en· W7053539316 sur OpenAlexaboutno aff

Notice bibliographique

RevueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2022
Typereport
Langueen
DomaineEngineering
ThématiqueElectromagnetic Compatibility and Measurements
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMarine ecosystemEcosystemClimate changeBiodiversityOcean observationsEffects of global warming on oceansBiogeochemical cycleEcosystem-based managementEarth system scienceSeries (stratigraphy)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Sustained ocean observations, including ships, autonomous platforms, and satellites, are critical for monitoring the health of our marine ecosystems and developing effective management strategies to ensure longterm provision of the marine ecosystem services upon which human societies depend. Ocean observations are also essential in the development and validation of ocean and climate models used to predict future conditions. Ship‐based biogeochemical time series provide the high‐quality biological, physical and chemical measurements that are needed to detect climate change‐driven trends in the ocean, assess associated impacts on marine food webs, and to ultimately improve our understanding of changes in marine biodiversity and ecosystems. While the spatial ‘footprint’ of a single time series may be limited, coupling observations from multiple time series with synoptic satellite data can improve our understanding of critical processes such as ocean productivity, ecosystem variability, and carbon fluxes on a larger spatial scale. The International Group for Marine Ecological Time Series (IGMETS) analyzed over 340 open ocean and coastal datasets, ranging in duration from five years to greater than 50 years. Their locations are displayed in a world map (Discover Ocean Time Series, http://igmets.net/discover) and in the IGMETS information database (http://igmets.net/metabase). These cross‐time‐series analyses yielded important insights on climate trends occurring both on a global and regional scale. At a global level, a generalized warming trend is observed over the past thirty years, consistent with what has been published by the IPCC (2013) report as well as other research. There are regional differences in temperature trends, depending on the time window considered, which are driven by regional and temporal expressions of large‐scale climatic forcing and atmospheric teleconnections. This warming is accompanied by shifts in the biology and biogeochemical cycling (i.e. oxygen, nutrient, carbon), which impact marine food webs and ecosystem services. The surface waters of the Arctic Ocean have been steadily warming over the past 30 years, from 1983‐2012. Chlorophyll biomass, as determined by satellite observations, has increased slightly over the past fifteen years, from 1998‐2012. The complexity of the Arctic marginal seas and central basin settings, and the scarcity of in situ data, limit the analysis of biogeochemical and biological community changes across the pan‐Arctic. The first comprehensive analysis of in situ time series provided for the North Atlantic Ocean revealed that, despite being the most studied region of the global ocean, there are large areas in this region still lacking multidisciplinary in situ observations. However, over the 25‐ and 30‐year analysis periods, > 95% of the North Atlantic Ocean significantly warmed and the chlorophyll concentrations decreased (p < 0.05). At the same time, negative trends in salinity, oxygen and nutrients, as exemplified by nitrate, were noted. The analysis of existing time series showed that even in adjacent areas that appear to be relatively homogenous, there is large variability in ecosystem behaviour over time, as observed in the continental shelves at both sides of the North Atlantic Ocean. In general, over the 5‐year period prior to 2012, ~70% of the area of the South Atlantic showed cooling and 66% decreasing chlorophyll concentrations. However, over the past 30 years, > 85% of the South Atlantic increased in temperature. The paucity of in situ time series in this region, and the striking changes that have been reported in South Atlantic ecosystems over the past two decades, highlight the need to have a better observing system in place. Both long‐term trends and sub‐decadal cycles are evident in the Southern Ocean on multiple trophic levels, and they are strongly related in complex ways to climate forcings and their effects on the physical oceanographic system. Antarctic marine ecosystems have changed over the past 30 years in response to changing ocean conditions and changes in the extent and seasonality of sea ice. These changes have been spatially heterogeneous which suggests that ecological responses depend on the magnitude and direction of the changes, and their interactions with other factors. Of all the ocean basins, the Indian Ocean showed the greatest extent of warming, with 92% of its area showing a significant (p < 0.05) positive trend over 30 years, compared with the Atlantic (89%), the Pacific (66%), the Arctic (79%) and the Southern (32%) oceans. In addition to having a high degree of warming, the Indian Ocean also had the greatest proportion of its area (55%) showing a significant (p < 0.05) decline of chlorophyll between 1998 and 2012. Given the spatial scale of warming in the Indian Ocean, it does seem likely that climate impacts on marine ecosystems will be most pronounced in this basin. The Indian Ocean has very few in situ biogeochemical time series that can be used to assess impacts of climate change on biota or biodiversity. Over the past 30 years, significant (p < 0.05) surface warming has been recorded for 67% of the area of the South Pacific Ocean. A strong physical coupling with planktonic ecology and biology is evident in the South Pacific, with a dominant warming pattern and significantly declining phytoplankton populations. The North Pacific Ocean has undergone significant changes in ocean climate during the past three decades. Based on both satellite and ship‐based SST measurements, over 65% of its surface area has undergone significant warming since 1983 (p < 0.05). The patterns of change suggest that the PDO has been the dominant mode of climate variability in the North Pacific Ocean between 1983 and 2012. However, marked variability in SST has been observed, with episodes of warming in 2002, 2004 and 2010 interspersed with periods of cooling, particularly since 2008 due to the combined effects of La Niña and a negative, cooling PDO phase. Long‐term time series in the central, subarctic northeast and western North Pacific Ocean show an increase in phytoplankton biomass during the past 30 years. However, satellite observations suggest that over 65% of the surface of the North Pacific has experienced a decline in chlorophyll concentration since 1998. Available time series show an increase in zooplankton biomass in the waters off Hawaii, southern Vancouver Island and the western United States during the last 15 years but an overall decrease at most other locations, with no significant correlation between zooplankton biomass and chlorophyll. Nutrients, salinity and dissolved oxygen at the ocean surface appear to be negatively correlated with SST across the North Pacific. The IGMETS effort highlights the value of biogeochemical time series as essential tools for assessing, and predicting, global and regional climate change and its impacts on ecosystem services. The capacity to identify and differentiate anthropogenic and natural climate variations and trends depends largely on the length of the time‐series, as well as on the location. Most of the ship based ecological time series are concentrated in the coastal ocean. While coastal zones in North America and Europe are being monitored, there is a conspicuous lack of biogeochemical time‐series in other coastal regions around the world, and an almost complete absence of such observational platforms in the open ocean, which limits the capacity of analyses such as this. A more globally distributed network of time‐series observations over multiple decades will be needed to differentiate between natural and anthropogenic variability.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,562
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,009
Études des sciences et des technologies0,0020,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,252
Écart entre enseignants0,230 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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