Evidence of phytoplankton blooms under Antarctic sea ice
Bibliographic record
Abstract
Areas covered in compact sea ice were often assumed to prohibit upper-ocean photosynthesis. Yet, under-ice phytoplankton blooms (UIBs) have increasingly been observed in the Arctic, driven by anthropogenic changes to the optical properties of Arctic sea ice. Here, we show evidence that the Southern Ocean may also support widespread UIBs. We compile 77 time series of water column samples from biogeochemical Argo floats that profiled under compact (80%–100% concentration) sea ice in austral spring–summer since 2014. We find that that nearly all (88%) such measurements recorded increasing phytoplankton biomass before the seasonal retreat of sea ice. A significant fraction (26%) met a observationally determined threshold for an under-ice bloom, with an average maximum chlorophyll-a measurement of 1.13 mg/m 3 . We perform a supporting analysis of joint light, sea ice, and ocean conditions from ICESat-2 laser altimetry and climate model contributions to CMIP6, finding that from 3 to 5 million square kilometers of the compact-ice-covered Southern Ocean has sufficient conditions to support light-limited UIBs. Comparisons between the frequency of bloom observations and modeled bloom predictions invite future work into mechanisms sustaining or limiting under-ice phytoplankton blooms in the Southern Hemisphere.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".