Modeling Northern Hemisphere Heat Extremes in Current and Warmer Climates: Intensity, Duration, and Physical Drivers
Bibliographic record
Abstract
Abstract We examine the intensity, duration, and physical drivers of Northern Hemisphere summer heat extremes in observations, reanalyses, CMIP6 models, and prescribed sea surface temperature (SST) simulations representing historical- and future-projected climates. Extremes are defined using a 90th percentile anomaly defined relative to a 29-day × 11-yr running mean to examine how the tail of temperature anomaly distributions changes in projections. Models generally capture the observed regional variations of intensity and duration. The magnitude of 90th percentile temperature anomalies shows a generally positive meridional gradient with some zonal variation but with warm biases over eastern India and southern North America. Both the reanalysis and climate model simulations exhibit positive biases in the duration of heat extremes in regions where the lowest model level temperature budget shows that diabatic effects dominate: Mexico extending through Central America, northeastern South America, and India. By investigating both prescribed SST forced by observations and coupled ocean simulations, heat extreme biases do not seem to be SST driven. In the warming simulations, projections under medium [shared socioeconomic pathway (SSP) 2–4.5] and high emission (SSP5–8.5) scenarios for late twenty-first century demonstrate sign change agreement for more intense anomalies over the Southeast United States, the Sahel and central Africa, and polar regions, along with weaker anomalies over Greenland brought on by melting ice. In terms of duration, the models have lesser agreement, likely due to internal variability, but nonetheless project increased duration over Northwest and Southeast United States and a reduction in northwest Canada and northern Africa.
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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 | 0.000 |
| 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".