Projected Changes in Extreme Event Indices for Alaska
Bibliographic record
Abstract
As climate has warmed in recent decades, Alaska has experienced a variety of high-impact extreme events that include heat waves, wildfires, coastal storms and freezing rain. Because the warming is projected to continue, it is essential to consider future changes when planning adaptation actions and building resilience. In this study, we synthesize information on future changes in extreme events in Alaska from an ensemble of regional climate model simulations performed as part of Arctic-CORDEX (Coordinated Regional Climate Downscaling Experiment). A set of 13 extreme event indices, based on those developed by the World Climate Research Programme’s Expert Team on Climate Change Detection and Indices (ETCCDI), are evaluated from the Arctic-CORDEX output for Alaska. Of the 13 indices, six pertain to temperature, five to total precipitation, one to wind and one to snow. The results for locations in seven different climate zones of Alaska include large increases (5˚C - 10˚C) in the temperature thresholds for the five hottest and coldest days of the year, and large increases in warm spell duration and decreases in cold spell duration. Changes in the cold day temperature threshold are generally larger than the changes in the hot day temperature threshold, consistent with the projections of a stronger warming in winter than in summer in Alaska yearly maximum 1-day and 5-day precipitation amounts as well as the yearly number of consecutive wet days are projected to increase at all locations. The indices for heavy snow days and high-wind days show mixed changes, although the results indicate increases in heavy snow days at the more northern locations and increases in windy days at coastal locations. The changes in the extreme event indices continue through 2100 under the higher-emission (RCP 8.5) emission scenario, while the changes generally stabilize under the lower-emission (RCP 4.5) scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".