Trends and Projections in Climate‐Related Stressors Impacting Arctic Marine Ecosystems—A CMIP6 Model Analysis
Bibliographic record
Abstract
Abstract Eleven Earth System Models (ESMs) contributing to the Coupled Model Intercomparison Project (CMIP6) were evaluated with respect to climate‐related stressors impacting Arctic marine ecosystems (temperature, sea ice concentration, oxygen, ocean acidification). Stressors show regional differences and varying differences over time and space among models. Trends calculated over three consecutive 40‐year time periods are highest for 2061–2100 for temperature and O 2 . Differences between scenarios SSP2‐4.5 and SSP5‐8.5 vary among models and regions, mainly driven by sea‐ice retreat and dilution effects. Differences in biogeochemical parameterizations contribute to acidification differences. Projections indicate consistent ocean acidification until 2040 and faster progression for the high‐end emission scenario thereafter. For SSP5‐8.5 all Arctic regions show aragonite undersaturation by 2080, and calcite undersaturation for all but two regions by 2100 for all models. Most regions can avoid calcite undersaturation in a medium emission scenario (SSP2‐4.5). All variables show increases in seasonal amplitude, most prominently for temperature and oxygen. Calcium carbonate saturation state shows little change to the seasonal range but temporal shifts in extrema. Seasonal changes in may be underestimated due to lacking carbon cycle processes within sea ice in CMIP6 models. The analysis emphasizes regionally varying threats from multiple stressors on Arctic marine ecosystems and highlights the propagation of uncertainties from physical to biogeochemical variables. Large model differences in seasonal cycles emphasize the need for improved model constraints regarding the representation of sea‐ice decline, river inflow and Atlantic and Pacific water circulation to enhance the applicability of CMIP models in multi‐stressor impacts assessments.
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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".