Data assimilation of oceanic proxies in the North Atlantic over the Common Era
Bibliographic record
Abstract
The mismatch between oceanic proxy data and climate model results over the past millennia has been a long-lasting challenge. Although both are valuable sources of paleoclimate information, there is a strong discrepancy in variance between models and proxies, so that they cannot be compared directly. In addition, local sea-surface temperature (SST) reconstructions are often inconsistent among proxy types. We first performed several offline data assimilation experiments with different standardized SST proxy datasets using the climate models LOVECLIM and CESM in order to investigate the effect of proxy selection on local and regional reconstructions over the Common Era (0-2000 CE). All experiments work technically at the local scale, but the spatial pattern of the reconstructions vary with the type(s), number and density of proxies, and, where there is no proxy, the choice of the model. We then developed empirical scaling factors based on independent SST observations to correct for the discrepancy between model and proxy amplitude. While it is essential to scale proxies, scaling the model leads to complications because of the biases in the sea ice extent. Data assimilation of scaled proxies results in coherent SST reconstructions at the scale of the North Atlantic, with timing and amplitude that are in agreement with those given by forced models. Finally, results are compared to online data assimilation experiments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".