Regional characteristics of extremes and variability in Arctic temperature
Bibliographic record
Abstract
Arctic temperature is analyzed in view of its extremes based on climate indices derived from daily mean, maximum, minimum temperature. This analysis is done for the pan Arctic domain and region-specific for east and west Russian Arctic. The variability of temperature-related indices over the last four decades is discussed, in which the spatial distribution and regional differences as well as its temporal trends are discussed. The analysis is based on ERA40 data and station data in the Russian Arctic as well as the output of the regional climate model HIRHAM. Exemlarily, results for the intra-seasonal extreme temperature range (ETR) and the growing degree days (GDD) are presented. ETR is a simple but useful measure of the extreme temperature variability and GDD describe the intensity of the growing season. ETR for GSOD west in autumn is the only case where positive ETR trends occur in all analyzed time slices, and where the trend magnitude increased towards recent years (0.8 C/decade in 1958-2001; 2.6 C/decade in 19802001). The station observations, ERA40, and HIRHAM agree on this behaviour, which can be attributed to warmer September temperatures, which also prolong the growing season. Positive GDD trends are calculated over most parts of the Arctic and are significant in some areas like northern Alaska and northeastern Canada. This is confirmed by the regional, station based analysis over Russia. GDD are systematically underestimated by HIRHAM. However, the calculated spatial patterns as well as the trends and decadal-scale variability in the time series are well reproduced. A large variability of the Arctic temperature and its extremes is inherent in the analysis period. The interannual variability of both indices shows a pronounced decadal-scale variability and considerable regional and seasonal differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".