Estimating the sources of global sea level rise with data assimilation techniques
Bibliographic record
Abstract
A rapidly melting ice sheet produces a distinctive geometry, or fingerprint, of sea level (SL) change. Thus, a network of SL observations may, in principle, be used to infer sources of meltwater flux. We outline a formalism, based on a modified Kalman smoother, for using tide gauge observations to estimate the individual sources of global SL change. We also report on a series of detection experiments based on synthetic SL data that explore the feasibility of extracting source information from SL records. The Kalman smoother technique iteratively calculates the maximum-likelihood estimate of Greenland ice sheet (GIS) and West Antarctic ice sheet (WAIS) melt at each time step, and it accommodates data gaps while also permitting the estimation of nonlinear trends. Our synthetic tests indicate that when all tide gauge records are used in the analysis, it should be possible to estimate GIS and WAIS melt rates greater than ∼0.3 and ∼0.4 mm of equivalent eustatic sea level rise per year, respectively. We have also implemented a multimodel Kalman filter that allows us to account rigorously for additional contributions to SL changes and their associated uncertainty. The multimodel filter uses 72 glacial isostatic adjustment models and 3 ocean dynamic models to estimate the most likely models for these processes given the synthetic observations. We conclude that our modified Kalman smoother procedure provides a powerful method for inferring melt rates in a warming world.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".