Sampling errors in estimation of the small scales of monthly mean climate
Bibliographic record
Abstract
Abstract We examine the sampling errors in the estimation of the small scales of monthly average mean atmospheric climate as seen in mean kinetic energy. The relationships between the small‐scale mean and transient kinetic energy in the atmosphere and atmospheric flow simulations are discussed. We elucidate how the estimation of the mean depends on the number of realizations or the length of the time period of the data. Studies based on both a barotropic model and on the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 3 general circulation model (GCM) are performed focusing on 500 hPa and vertically averaged spectra. Results for perpetual January simulations are presented for 32, 62 and 1500 member ensembles within the barotropic model and for 1, 10 and 60 month integrations with the GCM. We find that, with too few realizations in the ensemble or averaging over just one month, the mean kinetic energy has a spurious spectrum with similar power law to the transient kinetic energy but with smaller values by about two orders of magnitude. For larger ensembles or longer averaging periods, the mean kinetic energy falls off more rapidly than the transient kinetic energy. Our results lead to the conclusion that mean kinetic energy spectra based on just one month of data, such as reported in the literature, most recently by Boer (2003), are dominated by sampling errors at the small scales.
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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 | 0.000 | 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 | 0.000 |
| 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".