Precipitation isotope (δ¹⁸O, δ²H, d-excess) seasonality across the Pan-Arctic during MOSAiC
Bibliographic record
Abstract
Stable isotopes of oxygen and hydrogen in precipitation (δ18OP, δ2HP, d-excess) are valuable hydrological tracers linked to ocean-atmospheric processes such as moisture source, storm trajectory, and seasonal temperature cycles. However, characteristics of δ18OP, δ2HP and d-excess and the processes governing them are yet to be quantified across the Arctic due to a lack of long-term empirical data. The Pan-Arctic Precipitation Isotopes Network (PAPIN) is a new coordinated network of 24 stations aimed at the direct sampling, analysis, and synthesis of precipitation isotope geochemistry in the north. Our ongoing event-based sampling provides a rich spatial dataset during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (“MOSAiC”) expedition and new insight into coupled climate processes operating in the Arctic today. To date, precipitation δ18O and δ2H data (2018-2019) exhibit pronounced spatial and seasonal variability that broadly conforms to theoretical and observed understanding: (1) decreasing δ18OP/ δ2HP with increasing latitude and elevation, (2) decreasing δ18OP/ δ2HP with increasing continentality, and (3) increasing δ18OP/ δ2HP with increasing SAT. However, event-based sampling reveals remarkable variability among these relationships. For example, our observed Arctic mean summer -latitude slope of -0.3‰/degree of latitude is 50% smaller than the annual latitude effect in the mid-latitudes (-0.6‰/degree). This rate decreases to -0.1‰/degree of latitude in Finland and Russia, while in Alaska and northern Canadian a -0.7‰/degree latitudinal rate is observed. Similarly, we observe marked spatial differences in mean δ18O-temperature coefficients. Using back-trajectory analysis, we attribute these nuances to divergent moisture sources and transport pathways into, within, and out of the Arctic, and demonstrate how atmospheric circulation processes drive changes in isotope geochemistry and climate that are linked to sea ice concentration. For example, Alaska moisture derived from the North Pacific Ocean, Sea of Okhotsk, and the Bering Sea remains relatively enriched in 18OP/2H due to higher sea surface temperatures, whereas moisture originating from ice-covered seas to the north is characterized by relatively depleted values. This is the first coordinated network to quantify the spatial patterns of isotopes in precipitation, simultaneously, across the entire Arctic. In combination with a Pan-Arctic network of continuous water vapor isotope analyzers, our process-level studies will resolve the patterns and processes governing the δ18O, δ2H and d-excess values of the Arctic water cycle during the MOSAiC expedition and beyond.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".