Bibliographic record
Abstract
Climatic simulations suggest that only slight changes in climatic variability are likely to be induced owing to greenhouse warming, and that these changes will project onto existing modes of climatic variability. A lesser studied aspect of the interaction of climatic variability with climatic change is how such a mutual interaction manifests itself as interannual climatic fluctuations. A number of examples are examined in this paper using simulations made with the Commonwealth Scientific and Industrial Research Organization (CSIRO) Mark 2 coupled climatic model. The simulations included an ensemble based on four Special Report on Emission Scenarios (SRES) cases, as well as individual ensembles for two selected SRES cases. In general, the intra‐ensemble variability for a given SRES case was quite similar to the inter‐ensemble variability of the four individual SRES cases. Time series of selected climatic variability and probability density function displays are used to illustrate the character of climatic variability for simulations out to 2100. Other examples include variations in droughts and pluvial events, and associated runoff, as the greenhouse effect progresses. The differing responses in the frequency of dry events among four SRES cases are also illustrated. The outbreak of cold events around 2050 is used to highlight the impact of climatic variability. Finally, case studies involving two large‐scale climatic phenomena are used to show the ongoing dominance of climatic variability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".