Projected timing of perceivable change in climate extremes for terrestrial and marine ecosystems
Bibliographic record
Abstract
Human and natural systems have adapted to and evolved within historical climatic conditions. Anthropogenic climate change has the potential to alter these conditions such that onset of unprecedented climatic extremes will outpace evolutionary and adaptive capabilities. To assess whether and when future climate extremes exceed their historical windows of variability within impact-relevant socio-economic, geopolitical, and ecological domains, we investigate the timing of perceivable changes (time of emergence; TOE) for 18 magnitude-, frequency- and severity-based extreme temperature (10) and precipitation (8) indices using both multi-model and single-model multi-realization ensembles. Under a high-emission scenario, we find that the signal of frequency- and severity-based temperature extremes is projected to rise above historical noise earliest in mid-latitudes, whereas magnitude-based temperature extremes emerge first in low and high latitudes. Precipitation extremes demonstrate different emergence patterns, with severity-based indices first emerging over mid-latitudes, and magnitude- and frequency-based indices emerging earliest in low and high latitudes. Applied to impact-relevant domains, TOE patterns suggest: (i) unprecedented consecutive dry day occurrence in >50% of 14 terrestrial biomes and 12 marine realms pr¬¬ior to 2100, (ii) earlier perceivable changes in climate extremes in countries with lower per capita GDP, and (iii) emergence of severe and frequent heat extremes well-before 2030 for the 590 most populous urban centers. Elucidating extreme-metric and domain-type TOE heterogeneities highlights the challenges adaptation planners face in confronting the consequences of elevated 21st century radiative forcing.
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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 | 0.001 |
| 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".