Sea‐surface temperature and salinity product comparison against external in situ data in the <scp>A</scp>rctic <scp>O</scp>cean
Bibliographic record
Abstract
Abstract Sea‐surface temperature and salinity (SST/S) in the Arctic Ocean (AO) are largely governed by sea‐ice and continental runoff rather than evaporation and precipitation as in lower latitude oceans, and global satellite analyses and models which incorporate remotely observed SST/S may be inaccurate in the AO due to lack of direct measurements for calibrating satellite data. For this reason, we are motivated to validate several satellite sea‐surface temperature (SST) data products and SST/S models by comparing gridded data in the AO with oceanographic records from 2006 to 2013. Statistical analysis of product‐minus‐observation differences reveals that the satellite SST products considered have a temperature bias magnitude of less than 0.5 C compared to ship‐based CTD measurements, and most of these biases are negative in sign. SST/S models also show an overall negative temperature bias, but no common sign or magnitude of salinity bias against CTD data. Ice tethered profiler (ITP) near‐surface data span the seasons of several years, and these measurements reflect a sea‐ice dominated region where the ocean surface cannot be remotely observed. Against this data, many of the considered models and products show large errors with detectable seasonal differences in SST bias. Possible sources of these errors are discussed, and two adjustments of product SST on the basis of sea‐ice concentration are suggested for reducing bias to within less than 0.01 C of ITP near‐surface temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".