Multi‐model decadal potential predictability of precipitation and temperature
Bibliographic record
Abstract
A first multi‐model estimate of the long timescale potential predictability of precipitation is obtained based on over 8000 years of data from the control simulations of 21 state‐of‐the‐art coupled climate models. The analysis also updates earlier estimates of the potential predictability of temperature to provide a consistent estimate for these basic climate parameters. Long timescale potential predictability is found mainly over the oceans at middle to high latitudes and predominantly where the surface is connected to the deeper ocean. Precipitation's modest potential predictability resembles an attenuated version of that for temperature on these timescales. Over land, predictability is largely absent for precipitation and comparatively weak for temperature where it is found over the northern and western parts of northern hemisphere land masses bordering the oceans. Regions exhibiting potential predictability indicate where we may hope to find predictive skill at long timescales and also points indirectly to the processes involved.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".