An analysis of the 2023 summer and fall marine heat waves on the Newfoundland and Labrador Shelf
Bibliographic record
Abstract
Abstract. In this study, we investigated a series of moderate to severe surface marine heat waves (MHWs) impacting the Newfoundland and Labrador (NL) Shelf during the summer and fall of 2023. Using a combination of ocean model reanalysis data, in situ data collected under the Atlantic Zone Monitoring Program (AZMP), and atmospheric reanalysis data, we explored several factors that contributed to the intensity of these MHWs. We concluded that, firstly, due to an unusually cold spring and abnormally fresh conditions advected from upstream, the water column was highly stratified. Secondly, atmospheric conditions were calm and anomalously warm, and wind speeds were unusually low for prolonged periods in the summer. The combination of increased stratification and lower wind speeds caused a reduction in vertical mixing, limiting the exchange of warm surface waters with colder waters below and amplifying the retention of heat near the surface. However, by the late fall, the signature of the surface heat wave had vanished when the cooler subsurface waters were mixed vertically due to increased winds, storms, and surface cooling. During the most intense MHW in July 2023, we found that this event was confined to the surface as demonstrated by temperature anomalies along several standard transects which showed a thin layer of warm anomalies in the upper 10 m and cold anomalies below. Consequently, the vertical extent and distribution of MHWs are important considerations when exploring ecosystem impacts because not all elements of the ecosystem are equally sensitive to surface conditions. Finally, these results suggest that ocean model nowcast and reanalysis products can complement observational methods for studying MHWs in near real-time over large geographic areas and at multiple depths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".