Evaluating model‐simulated variability in temperature extremes using modified percentile indices
Bibliographic record
Abstract
ABSTRACT Percentile indices monitoring the frequency of moderate temperature extremes are widely used to assess changes in present and future temperature extremes because of their straightforward interpretation. While observed trends in such indices can be, and have been, compared with model‐simulated trends, their definition relative to each model's own climatology inhibits their use for the evaluation of model‐simulated temperature variability. This is unfortunate, as in many parts of the world, indices from observations remain the only source of publicly available information about extreme temperature variability. We approach this problem by introducing a novel adjustment to the standard method for deriving indices from climate models. This involves the removal of the bias in the mean annual cycle of the models and the use of percentile thresholds from a reference data set. We illustrate the technique by comparing daily minimum (TN) and maximum (TX) temperatures from the fifth phase of Coupled Model Intercomparison Project ( CMIP5 ) historical simulations with those from an observation‐based data set and from the National Centers for Environmental Prediction/National Center for Atmospheric Research ( NCEP/NCAR ) and the European Centre for Medium‐Range Weather Forecasts ( ERA ‐40) reanalyses. Biases in the annual cycle also translate into biases in the representation of the percentile indices in the models and reanalyses. Generally, percentile indices based on daily TX are well represented by the models and reanalyses compared to the observations. For percentile indices based on daily minimum temperature, however, large discrepancies occur particularly between the reanalyses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".