A comparison of sea surface temperatures from microwave remote sensing of the Labrador Sea with in situ measurements and model simulations
Bibliographic record
Abstract
As one of the few places in the ocean where winter cooling and mixing creates conditions where water from the surface can penetrate into the deep ocean the Labrador Sea is an area of interest to people studying climate change in the ocean. Persistent cloud cover over this area makes it impossible to use infrared satellite imagery to relate space/time changes in sea surface temperature (SST) to changes in surface currents and air‐sea interaction. Using passive microwave SSTs from the Advanced Microwave Scanning Radiometer (AMSR‐E), we plot space/time changes in SST in the Labrador Sea and relate these changes to both simultaneous in situ measurements of temperature and numerical model SSTs. A direct comparison between the microwave SSTs, infrared SSTs, and in situ temperatures measured from profiling floats reveals that the microwave SSTs are a good representation of space/time changes in infrared SST and in ocean temperatures down to 10 m below the sea surface. Comparisons between the microwave SSTs and time series of temperatures at depths below 50 m reveal that winter/spring surface cooling makes the SST similar to temperatures at these deeper depths in the convection region of the central Labrador Sea. Detailed comparison of the annual cycle between the microwave SSTs and the model SST and 10 m currents reveals overall good agreement and some interesting differences.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".