A Satellite Climatology of Relative Humidity Profiles and Outgoing Thermal Radiation over Earth’s Oceans
Bibliographic record
Abstract
Abstract Satellite observations over Earth’s oceans show two distinct regimes in the relationship between sea surface temperatures (SST) and outgoing longwave radiation (OLR): a temperate regime (OLR increases with increasing SST quasi linearly) and the super greenhouse regime (OLR decrease with increasing SST). Transitions between these regimes occur via nonlinear atmospheric moistening, increasing relative humidity (RH). We perform a clustering analysis of about 450 million satellite-retrieved RH profiles and show that RH profiles over Earth’s oceans can be grouped into six and eight distinct and physically meaningful “primitive” classes for clear-sky and all-sky conditions, respectively. As the different RH-profile classes have distinct effects on OLR, can be associated with large-scale dynamical structures, and their occurrence is particular to different global ocean regions, the “primitive” clustering allows for studying large-scale radiative effects important for characterizing global climates and systematic relationships between SST, OLR, and atmospheric water vapor content. In both clear-sky and all-sky conditions three RH-profile classes correspond to the tropics. In the tropics, increasing SSTs are accompanied by systematic increases in both the occurrence probability and the observed RH magnitudes of the moistest RH-profile classes. Observations of moistest RH-profile classes are usually in a super greenhouse state (data in or likely to transition into the super greenhouse regime), and the formal characterization of them allows us to define an empirical threshold to identify which instantaneous observations are likely in a super greenhouse state. Applying this threshold we are able to identify typical regions in the super greenhouse regime.
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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 | 0.000 |
| 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 | 0.000 | 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".