Comparing historical and modern methods of sea surface temperature measurement – Part 2: Field comparison in the central tropical Pacific
Bibliographic record
Abstract
Abstract. Discrepancies between historical Sea Surface Temperature (SST) datasets have been partly ascribed to use of different adjustments for variable measurement methods. Until recently adjustments had only been applied to bucket temperatures from the late 19th and early 20th century, with the aim of correcting their supposed coolness relative to engine cooling water intake temperatures (EIT). In the UK Met Office Hadley Centre SST 3 dataset (HadSST3) adjustments are applied to observations over its full duration, including those obtained by other methods. Here we evaluate such adjustments by direct field comparison of historical and modern methods of SST measurement. We compare wood, canvas and rubber bucket temperatures to 3 m seawater intake temperature along a Central Tropical Pacific transect conducted in May and June 2008. In contrast to the prevailing view we find no average differences between bucket temperatures obtained with different bucket types. Moreover, we observe strong near-surface temperature gradients day and night, indicating intake and bucket temperatures cannot be considered equivalent in this region. We suggest engine intake temperatures are unreliable as a source of SST given that they are often obtained by untrained non-scientist observers with low precision, inaccurate instruments at unknown intake depth. Using a physical model we demonstrate that warming of intake seawater by engine room air is unlikely a cause of negative average bucket-intake temperature differences, as sometimes suggested. We propose removal of intake temperatures and bucket adjustments from historical SST records and posit this will lead to their better capture of real long-term trends.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".