Variability of the Arctic atmospheric moisture budget from TOVS satellite data
Bibliographic record
Abstract
Temporal and spatial variability of the Arctic atmospheric moisture budget is investigated using a new 19‐year data set (1980 to 1998) produced from daily precipitable water retrieved from the TIROS Operational Vertical Sounder (TOVS) and upper‐level winds from the NCEP‐NCAR Reanalysis. A companion paper describes the creation and validation of these new moisture budget products [ Groves and Francis, 2002 ]. Seasonal differences in moisture transport arise from distinct winter/summer circulation regimes and meridional moisture gradients. In winter, approximately 80% of the net precipitation (precipitation minus evaporation, P‐E) is transported along well‐defined storm tracks. Summer P‐E is double that of winter and dominates the annual pattern. Decadal differences in winter P‐E reveal statistically significant increases in the Beaufort and eastern Greenland‐Iceland‐Norwegian Seas, decreases in the Canadian Archipelago (islands in far northeast Canada) and Kara Sea, and a slight increase in the Arctic as a whole. Annual differences are dominated by winter changes. When the phase of the Arctic Oscillation (AO) index is positive, the net PW flux across 70°N in winter is 6 times larger than on negative‐index days. Over the entire Arctic, P‐E is 29% larger (20% lower) than the average on days with a positive (negative) AO index. In summer the PW transport is twice as large, and P‐E is 27% higher on positive versus negative AO days. These results suggest that if the AO continues its trend toward a predominantly positive phase, we should expect to observe increasing precipitation in the Arctic overall, and particularly in regions adjacent to the marginal ice zones.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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".