Moisture origin as a driver of temporal variabilities of the water vapour isotopic composition in the Lena River Delta, Siberia
Bibliographic record
Abstract
In the context of the Arctic amplification of climate change affecting the regional atmospheric hydrological cycle, it is crucial to characterize the present-day moisture sources of the Arctic. The isotopic composition is an important tool to enhance our understanding of the drivers of the hydrological cycle due to the different molecular characteristics of water stable isotopes during phase change. This study introduces 2 years of continuous in situ water vapour and precipitation isotopic observations conducted since July 2015 in the eastern Siberian Lena delta at the research station on Samoylov Island. The vapour isotopic signals are dominated by variations at seasonal and synoptic timescales. Diurnal variations of the vapour isotopic signals are masked by synoptic variations, indicating low variations of the amplitude of local sources at the diurnal scale in winter, summer and autumn. Low-amplitude diurnal variations in spring may indicate exchange of moisture between the atmosphere and the snow-covered surface. Moisture source diagnostics based on semi-Lagrangian backward trajectories reveal that different air mass origins have contrasting contributions to the moisture budget of the Lena delta region. At the seasonal scale, the distance from the net moisture sources to the arrival site strongly varies. During the coldest months, no contribution from local secondary evaporation is observed. Variations of the vapour isotopic composition during the cold season on the synoptic timescale are strongly related to moisture source regions and variations in atmospheric transport: warm and isotopically enriched moist air is linked to fast transport from the Atlantic sector, while dry and cold air with isotopically depleted moisture is generally associated with air masses moving slowly over northern Eurasia.
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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 | 0.001 | 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".