Quantification of Warming Climate-Induced Changes in Terrestrial Arctic River Ice Thickness and Phenology
Bibliographic record
Abstract
Abstract A land process model [the coupled hydrological and biogeochemical model (CHANGE)] is used to quantitatively assess changes in the ice phenology, thickness, and volume of terrestrial Arctic rivers from 1979 to 2009. The CHANGE model was coupled with a river routing and discharge model enabling explicit representation of river ice and water temperature dynamics. Model-simulated river ice phenological dates and thickness were generally consistent with in situ river ice data and landscape freeze–thaw (FT) satellite observations. Climate data indicated an increasing trend in winter surface air temperature (SAT) over the pan-Arctic during the study period. Nevertheless, the river ice thickness simulations exhibited a thickening regional trend independent of SAT warming, and associated with less insulation and cooling of underlying river ice by thinning snow cover. Deeper snow depth (SND) combined with SAT warming decreased simulated ice thickness, especially for Siberian rivers, where ice thickness is more strongly correlated with SND than SAT. Overall, the Arctic river ice simulations indicated regional trends toward later fall freezeup, earlier spring breakup, and consequently a longer annual ice-free period. The simulated ice phenological dates were significantly correlated with seasonal SAT warming. It is found that SND is an important factor for winter river ice growth, while ice phenological timing is dominated by seasonal SAT. The mean total Arctic river ice volume simulated from CHANGE was 54.1 km3 based on the annual maximum ice thickness in individual grid cells, while river ice volume for the pan-Arctic rivers decreased by 2.82 km3 (0.5%) over the 1979–2009 record. Arctic river ice is shrinking as a consequence of regional climate warming and coincident with other cryospheric components, including permafrost, glaciers, and sea ice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".