Isotopic anomalies in water vapor during an atmospheric river event at Dome C, East Antarctic plateau, controlled by large-scale advection and boundary layer processes
Bibliographic record
Abstract
On December 19-21, 2018, an atmospheric river hit the French-Italian Concordia station, located at Dome C, East Antarctic Plateau, 3 269 m above sea level. It induced an extreme surface warming (+ 15°C in 3 days), combined with high specific humidity (multiplied by 3 in 3 days) and a strong isotopic anomaly in water vapor (+ 15 ‰ for δ18O). The isotopic composition of water vapor monitored during the event may be explained by (1) the isotopic signature of long-range water transport, and by (2) local moisture uptake during the event. In this study we quantify the influence of each of these processes. To estimate the isotopic composition of water vapor advected by long-range transport, we perform back-trajectories with the FLEXible PARTicle dispersion model FLEXPART. We retrieve meteorological conditions along different trajectories between the moisture uptake area and Concordia, and use them to compute isotopic fractionation during transport with the mixed cloud isotope model MCIM. While intermediate conditions along the trajectory do not seem to have a major impact on the final isotopic composition (less than 0.1 ‰), the latter appears sensitive to surface conditions (temperature, pressure and relative humidity) in the moisture uptake area (±5.1 ‰). As the event is characterized by the presence of liquid water clouds above Concordia, we show additional sensitivity tests exploring the impact of mixed phase clouds on the water vapor isotopic composition. Finally, we perform a water vapor mass budget in the boundary layer using observations and simulations from the regional atmospheric model MAR, ran with and without drifting snow. The presence of mixed-phase clouds during the event induced a significant increase in downward longwave radiative fluxes, which led to high turbulent mixing in the boundary layer and to heavy drifting snow (white-out conditions). Using MAR simulations, we show that a significant part of the atmospheric water vapor originates from sublimation of drifting snow particles removed from the snowpack. Consequently, the isotopic signal monitored in water vapor during this atmospheric river event reflects both long-range moisture advection and interactions between the boundary layer and the snowpack. Only specific meteorological conditions driven by the atmospheric river, and their associated intense poleward moisture transport, can explain these strong interactions.
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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 | 0.000 | 0.000 |
| 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".