Historical model biases in monthly high temperature anomalies indicate under-estimation of future temperature extremes
Bibliographic record
Abstract
Both the mean climate state and anomalies from the mean determine the impact of extreme events, yet how models represent the latter is largely unexplored. Here, we assess skill of climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for predicting monthly high temperature anomalies relative to monthly means over time. Models project that, as the planet warms, these high temperature anomalies will increase in subtropical regions and decrease in high latitudes. Both postive and negative biases remain largely unchanged regionally and seasonally within two historical periods, 1980–2001 and 2002–2023. Globally, models have underestimated the 22-year average monthly high temperature anomalies by 2–3% and 22-year maximum anomalies by 11–12%. If historical biases of models carry forward, the on-average 2100 extreme high temperatures in some regions and months could be greater than current projections by 3 K, and greater than 5 K for the most extreme cases. Models tend to underestimate mean monthly maximum temperature anomalies by 2-3% and extreme anomalies by 11-12% over the period 1980 and 2023, which could lead to temperatures 3 °C to 5 °C higher than currently projected by 2100, according to an analysis of multi-model ensemble.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".