Changing spring air‐temperature gradients along large northern rivers: Implications for severity of river‐ice floods
Bibliographic record
Abstract
Concern exists about future changes to air‐temperature gradients along large “northward”‐flowing Arctic rivers having the potential to affect the timing and severity of spring river‐ice breakup, and associated flooding events. To evaluate the significance of this concern, an analysis was conducted of temporal and spatial changes to the spring 0°C air‐temperature isotherm (I0°C), which is also known to be a good index for the timing of spring melt/breakup conditions. Changes in I0°C were analyzed for the downstream 2000‐km main‐stem reaches of four large Arctic rivers: the Lena, Mackenzie, Ob and Yenisey. Current climatic conditions (1979–2008) were compared to those of two future climatic periods (2041–2070 and 2071–2100) projected by an ensemble of four Global Climate Models. Future projections show I0°C chronology patterns along the rivers that closely parallel current conditions, but with earlier dates varying from an ensemble mean of 7.5 (13.6) to 16.5 (25.5) days for the 2050s (2080s). Results also reveal a progressive downstream increase in warming under future climates. At the time when headwater temperatures reach 0°C, river mouth to headwater temperature differences for the four rivers decrease by an average of 0.8°C (2.4°C) to 2.1°C (3.7°C) for the 2050s (2080s). The implications of such decreases on the severity of spring ice‐jam floods are discussed.
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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 | 0.001 |
| 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 | 0.001 | 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".