Changes in Relative Humidity Profiles over Earth’s Oceans in a Warming Climate: A Satellite-Data-Based Inference
Bibliographic record
Abstract
Abstract Recently, we presented a classification of “primitive” relative humidity (RH) profiles into eight distinct clusters over Earth’s oceans, based on about 18 years (2003–20) of observations from the AIRS on NASA’s Aqua satellite. Here we investigate the seasonal variability and decadal trends, both in the vertical structure of these RH profiles, and in their associated area of occurrence. Since vertical structures (except in the marine boundary layer) of each RH class are generally robust across all seasons and change only weakly in a warming climate, seasonal or decadal changes to their occurrence areas shift patterns of global moisture distribution. Globally, the marine boundary layer exhibits nonlinear moistening effects after about 2010, the end of the warming hiatus. Annual time series of ocean areas dominated by RH classes have linear trends, which are positive only for the most moist and driest RH classes (in terms of the free troposphere) associated with deep convection and large-scale subsidence favoring conditions for low-level stratocumulus clouds, respectively. Based on estimated linear trends of RH-class occurrences and sea surface temperatures, we infer projected linear responses of RH in a warming climate. Ocean areas dominated by most moist and driest RH classes (in terms of the free atmosphere) are estimated to increase by about 1% and 2%, respectively (corresponding to about 2.5% K−1 and 4.5% K−1, respectively). The averaged global and tropical RH structure remain almost constant in a warming climate. While this is consistent with other studies, our results show how increases in most moist and dry areas compensate each other, indicating possible increases in the frequency or persistence of future extreme events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".