Blind use of reanalysis data: apparent trends in Madden–Julian Oscillation activity driven by observational changes
Bibliographic record
Abstract
ABSTRACT Atmospheric and oceanic reanalyses are used widely by the climate science community. These products provide full three‐dimensional state fields and gapless time series, along with the confidence of being constrained by observational measurements, for atmospheric scientists and oceanographers to use in analyses of the climate system. However, as ubiquitous as reanalysis data are, it is not often considered how a scarcity of measurements in certain poorly observed regions, or over the course of a long period of time in which the observational system has changed significantly, impacts the realism of the data. This study explores this question using tropical surface pressures from the Twentieth Century Reanalysis to hindcast an index of the Madden–Julian Oscillation (MJO) over the 20th century. We show that by changing the choice of surface pressure predictor locations, and being aware of the observational measurements that have been assimilated by the reanalysis system, it is possible to control the estimated centennial‐scale trend in MJO activity from nearly zero to an increase of 30% over the 20th century. We emphasize that this is an apparent trend as it arises solely from the use of reanalyzed surface pressures from locations that have either been poorly observed or have experienced significant changes in the observing system over the 20th century. This highlights the need to be aware of the observational measurements (or lack of them), particularly their density in space and time, that have been assimilated by a reanalysis system.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".