Atmospheric trends over the Arctic Ocean in simulations from the Coordinated Regional Downscaling Experiment (CORDEX) and their driving GCMs
Bibliographic record
Abstract
Abstract The Arctic Coordinated Regional Downscaling Experiment (Arctic-CORDEX) uses regional climate models (RCMs) to downscale selected Fifth Coupled Model Intercomparison Project simulations, allowing trend validation and projection on subregional scales. For 1986–2015, the CORDEX seasonal-average near-surface temperature (tas), wind speed (sfcWind), precipitation (pr) and snowfall (prsn) trends are generally consistent with analyses/observations for the Arctic Ocean regions considered. The projected Representative Concentration Pathway 8.5 (RCP8.5) 2016–2100 subregional annual tas trends range from 0.03 to 0.18 K/year. Projected annual pr and prsn trends have a large inter-model spread centered around approximately 5.0 × 10–8 mm/s/year and −5.0 × 10–8 mm/s/year, respectively, while projected sfcWind summer and winter trends range between 0.0 and 0.4 m/s/year. For all variables except prsn, and sometimes total precipitation, the driving general circulation model (GCM) dominates the trends, however there is a tendency for the GCMs to underestimate the sfcWind trends compared to the RCMs. Subtracting the Arctic-Ocean mean from subregional trends reveals a consistent, qualitative anomaly pattern in several variables and seasons characterized by greater-than or average trends in the central and Siberian Arctic Ocean and lesser or average trends in the Atlantic Sector and the Bering Sea, related to summer sea-ice trends. In particular, a strong proportional relationship exists between the summer sea-ice concentration and fall tas and sfcWind trend anomalies. The RCP4.5 annual, multi-model mean trends are 35–55% of the corresponding RCP8.5 trends for most variables and subregions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".