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Enregistrement W4393515029 · doi:10.5281/zenodo.5593381

High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019

2021· dataset· en· W4393515029 sur OpenAlexaffabout
Robert Izett, Philippe D. Tortell

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Langueen
DomaineEarth and Planetary Sciences
ThématiqueGeological Studies and Exploration
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMixed layerArcticThe arcticLayer (electronics)Environmental scienceResolution (logic)ClimatologyGeographyRemote sensingMeteorologyGeologyOceanographyComputer scienceMaterials scienceArtificial intelligenceNanotechnology

Résumé

récupéré en direct d'OpenAlex

<strong>Dataset overview</strong> This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework. Please cite as: Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381. This dataset is supplement to: Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations. <strong>Abstract</strong> We present ΔO<sub>2</sub>/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O<sub>2</sub> budget. Large positive mixing fluxes, exceeding 100 mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup>, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (&lt;10 mmol O<sub>2</sub> m<sup>-2 </sup>d<sup>-1</sup>, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O<sub>2</sub> gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO<sub>2</sub>/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters. <strong>Lay summary</strong> Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O<sub>2</sub>) concentrations. However, while vertical mixing may be a significant component of the surface water O<sub>2</sub> budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: <em>Lâbradorip Imappinga</em>), North Atlantic, Hudson Strait (<em>Ikirasarjuaq</em>) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (<em>Saknirutiak Imanga</em>) and southern CAA. Riverine freshwater input to Hudson Bay (<em>Tasiujarjuar</em>) and the Queen Maud Gulf (<em>Ugjulik</em>) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters. <strong>Acknowledgements</strong> This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada. Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval. The model simulation was run by P. Myers (University of Alberta). The underway gas data were collected by R. Izett &amp; P. Tortell (University of British Columbia) All data were archived by R. Izett. <strong>Files and variables:</strong> data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities. <em>Variable</em> <em>Description </em> <em>Unit</em> time UTC YYYY Julian Day (year-day since YYYY-01-01) UTC Days lat Latitude N Decimal degrees N long Longitude E Decimal degrees E dist Along-track distance km reg_index Regional index region_mask_lat Latitude for region indices mask Decimal degrees N region_mask_long Longitude for region indices mask Decimal degrees E region_mask Regional masks sst Sea surface temperature measured in the instrument laboratory deg. C sal Sea surface salinity measured in the instrument laboratory chl_fluor Calibrated mixed layer Chl a fluorescence in the instrument laboratory (mg Chl a)/m3 do2ar Biological O2 saturation anomaly, deltaO2/Ar % kwo2 Weighted O2 gas transfer velocity m/d bioflux_ncp Bioflux-NCP mmol O2/m2/d cor_ncp corrected-NCP mmol O2/m2/d uw_kz Underway model-based eddy diffusivity at the base of the mixed layer m2/s bling_ncp BLING model-based mixed layer NCP, matched to underway cruise time/position mmol O2/m2/d prof_time UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations UTC Days prof_lat Latitude N at CTD profile stations Decimal degrees N prof_long Longitude E at CTD profile stations Decimal degrees E prof_dist Along-track distance at CTD profile stations km prof_reg_index Regional index at CTD profile stations prof_do2bdz Subsurface O2b gradient, dO2B/dZ at CTD profile stations mmol O2/m4 prof_mld Mixed layer depth, calculated at CTD profile stations m prof_pycnocline_dep Pycnocline depth, calculated at CTD profile stations m nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output. Varaible Description Unit time Model time, UTC YYYY Julian Day (year-day since YYYY-01-01) UTC Days lat Latitude N Model Decimal degrees N long Longitude E Model Decimal degrees W depth_grid_kz kz depth m depth_grid depth m kz Eddy diffusivity coefficient m2/s T Temperature deg-C sal Salinity oxy Oxygen concentration mol O2/m3 ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling. Varaible Description Unit time UTC; Days since 2010-01-01 UTC Days lat Latitude N Decimal degrees N long Longitude E Decimal degrees E reg_index Regional index prof_do2bdz Subsurface O2b gradient, dO2B/dZ at CTD profile stations mmol O2/m4 prof_mld Mixed layer depth, calculated at CTD profile stations m prof_pycnocline_dep Pycnocline depth, calculated at CTD profile stations m

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,235
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0050,002

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 Opus0,053
Tête enseignante GPT0,230
Écart entre enseignants0,177 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

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

En bref

Citations7
Publié2021
Routes d'admission2
Résumé présentoui

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