Modeling and understanding persistence of climate variability
Bibliographic record
Abstract
In this study, two parsimonious statistical representations of climate variability on interannual to multidecadal timescales are compared: the short‐memory first order autoregressive representation (AR1) and the long‐memory “power law” representation. Parameters for each statistical representation are fitted to observed surface air temperature at each spatial point. The parameter estimates from observations are found in general to be captured credibly in the Coupled Model Intercomparison Project 3 (CMIP3) simulations. The power law representation provides an upper bound and the AR1 representation provides a lower bound on persistence as measured by the lag‐one autocorrelation. Both representations fit the data equally well according to goodness‐of‐fit‐tests. Comparing simulations with and without external radiative forcings shows that anthropogenic forcing has little effect on the measures of persistence considered (for detrended data). Given that local interannual to multi decadal climate variability appears to be more persistent than an AR1 process and less persistent than a power law process, it is concluded that both representations are potentially useful for statistical applications. It is also concluded that current climate simulations can well represent interannual to multidecadal internal climate persistence in the absence of natural and anthropogenic radiative forcing, at least to within observational uncertainty.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".