Characteristics of water-vapour inversions observed over the Arctic by Atmospheric Infrared Sounder (AIRS) and radiosondes
Bibliographic record
Abstract
Abstract. An accurate characterization of the vertical structure of the Arctic atmosphere is useful in climate change and attribution studies as well as for the climate modelling community to improve projections of future climate over this highly sensitive region. Here, we investigate one of the dominant features of the vertical structure of the Arctic atmosphere, i.e. water-vapour inversions, using eight years of Atmospheric Infrared Sounder data (2002–2010) and radiosounding profiles released from the two Arctic locations (North Slope of Alaska at Barrow and during SHEBA). We quantify the characteristics of clear-sky water vapour inversions in terms of their frequency of occurrence, strength and height covering the entire Arctic for the first time. We found that the frequency of occurrence of water-vapour inversions is highest during winter and lowest during summer. The inversion strength is, however, higher during summer. The observed peaks in the median inversion-layer heights are higher during the winter half of the year, at around 850 hPa over most of the Arctic Ocean, Siberia and the Canadian Archipelago, while being around 925 hPa during most of the summer half of the year over the Arctic Ocean. The radiosounding profiles agree with the frequency, location and strength of water-vapour inversions in the Pacific sector of the Arctic. In addition, the radiosoundings indicate that multiple inversions are the norm with relatively few cases without inversions. The amount of precipitable water within the water-vapour inversion structures is estimated and we find a distinct, two-mode contribution to the total column precipitable water. These results suggest that water-vapour inversions are a significant source to the column thermodynamics, especially during the colder winter and spring seasons. We argue that these inversions are a robust metric to test the reproducibility of thermodynamics within climate models. An accurate statistical representation of water-vapour inversions in models would mean that the large-scale coupling of moisture transport, precipitation, temperature and water-vapour vertical structure and radiation are essentially captured well in such models.
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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 | 0.001 |
| 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) | 0.000 | 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 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".