Basal ice but not summer temperature affects land surface greenness in parts of the landscape in high Arctic tundra
Bibliographic record
Abstract
Abstract Climate warming in the Arctic is very strong compared to other regions on Earth. Arctic winter climate and cryosphere conditions are changing towards more frequent mild spells. Precipitation is often falling as rain, followed by the formation of basal ice on frozen ground, particularly in Gulf Stream-influenced climates as in Svalbard. Such conditions encapsulate tundra plants in ice for several months, which is assumed to reduce land surface greenness due to plant damage. We investigated whether extensive basal ice (presence and thickness) and increased summer temperatures (growing degree days (GDD)) from in-situ time series impact satellite-derived land surface greenness. We measured greenness as the magnitude and timing of growing season maximum normalized difference vegetation index (NDVI). Our study covers Svalbard from 2013 to 2023, a decade with record breaking summer temperatures and many icy winters. We found lower maximum NDVI values when basal ice was present only at higher elevations (Estimated effect size: −0.0119, 95% CI: −0.0207 to −0.0031). We further found an eight-day advance in the timing of maximum NDVI (Estimated effect size: −7.56, 95% CI: −14.81 to −0.31) with basal ice presence in the region that was characterized by spatially and temporally extensive basal ice. Ice thickness, in contrast to presence, or GDD did not influence the magnitude or timing of maximum NDVI. Taken together, our findings indicate that basal ice presence could become a driver of vegetation change in the High Arctic as climatic extremes intensify, which could alter tundra greenness over larger landscapes and ultimately influence Arctic food webs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".