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Record W4408426877 · doi:10.5194/egusphere-egu25-10679

Chemical Distribution Patterns across the west Greenland Shelf: The Roles of Ocean Currents, Sea Ice Melt, and Freshwater Runoff

2025· preprint· en· W4408426877 on OpenAlexaboutno aff
Charles E. Schmidt, Tristan Zimmermann, Katarzyna Koziorowska‐Makuch, Daniel Pröfrock, Helmuth Thomas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsOceanographySurface runoffSea iceIce shelfGeologyDistribution (mathematics)IcebergCryosphereClimatologyEcology

Abstract

fetched live from OpenAlex

The west Greenland shelf is a dynamic marine environment influenced by various physicochemical and biological processes. We captured a high-resolution, large-scale snapshot of various water column parameters across the west Greenland shelf and Davis Strait between 64°N and 71°N during July 2021. This study provides an overview of the main factors affecting the distribution of macronutrients (NOx = nitrate + nitrite, silicate, phosphate), carbonate system parameters (alkalinity (AT), dissolved inorganic carbon (CT)), and dissolved trace elements (dV, dFe, dMn, dCo, dNi, dCu, dCd, and dPb) during late summer. The key drivers include major ocean currents, melting sea ice, and terrestrial freshwater runoff, each uniquely contributing to the cycling and spatial distribution of chemical constituents.Major ocean currents, such as the southward-moving Baffin Island Current (BIC) and the northward-moving West Greenland Current (WGC), shape the chemical composition of shelf waters by introducing water masses with distinct chemical signatures. The northward-moving West Greenland Shelf Water (WGSW) was characterized as warm (2.68°C), fresh (33.57), and highly productive, with overall low nutrient concentrations. In contrast, the southward-moving Arctic water (AW) was cold (0.38°C) and fresh (33.48), with high nutrient contents due to lower biological activity. The inflow of Pacific-origin waters through the Canadian Archipelago (CAA) to Baffin Bay was responsible for elevated dFe, dMn, dCo, dNi, and dCu concentrations.The progressive melting and retreat of sea ice altered both the biological productivity and the chemical composition of surface waters in southern Baffin Bay. The east-to-west direction of sea ice retreat created a nutrient gradient, with low nutrient levels in the highly productive shelf waters to the east and high nutrient levels in areas with prolonged ice cover to the west. This process also affected the carbonate system, leading to changes in pH and aragonite saturation states, which are critical for the health of marine organisms. Furthermore, we observed sea ice meltwater as a source of dFe, dCo, dNi, dCu, and dCd to Baffin Bay surface waters. This additional source of bioactive trace elements could maintain and prolong ice-edge blooms.Terrestrial freshwater runoff from the Greenland Ice Sheet (GIS), particularly in Disko Bay and at the mouth of the Nassuttooq Fjord, replenished macronutrients in the photic zone, stimulating primary production (PP) and creating significant CO2 sinks. However, in areas along the coastline where PP was limited by low nutrient concentrations, surface waters became more susceptible to acidification via input of poorly buffered glacial freshwater.This work provides a summarized overview of the complex interplay between the chemical composition of the west Greenland shelf and major ocean currents, melting sea ice, and terrestrial freshwater runoff from the GIS. Understanding these key drivers is essential for forecasting future changes of the marine chemistry and biology of the west Greenland shelf, especially in the context of ongoing climate change within this high-latitude region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.505

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.232
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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