Evidence of Isotopic Fractionation During Vapor Exchange Between the Atmosphere and the Snow Surface in Greenland
Bibliographic record
Abstract
Abstract Several recent studies from both Greenland and Antarctica have reported significant changes in the water isotopic composition of near‐surface snow between precipitation events. These changes have been linked to isotopic exchange with atmospheric water vapor and sublimation‐induced fractionation, but the processes are poorly constrained by observations. Understanding and quantifying these processes are crucial to both the interpretation of ice core climate proxies and the formulation of isotope‐enabled general circulation models. Here, we present continuous measurements of the water isotopic composition in surface snow and atmospheric vapor together with near‐surface atmospheric turbulence and snow‐air latent and sensible heat fluxes, obtained at the East Greenland Ice‐Core Project drilling site in summer 2016. For two 4‐day‐long time periods, significant diurnal variations in atmospheric water isotopologues are observed. A model is developed to explore the impact of this variability on the surface snow isotopic composition. Our model suggests that the snow isotopic composition in the upper subcentimeter of the snow exhibits a diurnal variation with amplitudes in δ18O and δD of ~2.5‰ and ~13‰, respectively. As comparison, such changes correspond to 10–20% of the magnitude of seasonal changes in interior Greenland snow pack isotopes and of the change across a glacial‐interglacial transition. Importantly, our observation and model results suggest, that sublimation‐induced fractionation needs to be included in simulations of exchanges between the vapor and the snow surface on diurnal timescales during summer cloud‐free conditions in northeast Greenland.
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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".