Mitigating Spatial Leakage in Monthly GRACE/GRACE-FO Gravity Fields for the Separation of Barystatic Sea-Level Variations and Residual Ocean Circulation Effects
Bibliographic record
Abstract
Temporal variations in the total ocean mass representing the barystatic part of present-day global mean sea-level rise can be unambiguously inferred from time-series of global gravity fields as provided by the GRACE and GRACE-FO missions. A spatial integration over all ocean regions, however, largely underestimates present-day rates as long as the effects of spatial leakage along the coasts of in particular Antarctica, Greenland, and the various islands of the Canadian Archipelago are not properly considered. Based on the recent release 06 of monthly gravity fields processed at GFZ, we quantify (and subsequently correct) the contribution of spatial leakage to the post-processed mass anomalies of continental water storage and ocean bottom pressure. Utilising the sea level equation allows to predict spatially variable ocean mass trends out of the (leakage-corrected) terrestrial mass distributions from GRACE and GRACE-FO. Consistent results for the global mean barystatic sea-level rise are obtained also from spatial integrations over ocean masks with different coastal buffer zones ranging from 400 to 1000 km, thereby confirming the robustness of our method. Residual month-to-month variations in ocean bottom pressure are indicative for errors in the monthly-mean estimates of the applied de-aliasing model AOD1B RL06 and will be thus contrasted against very recent MPIOM experiments considered for AOD1B RL07. The in this way improved leakage correction will be implemented in future GravIS versions (http://gravis.gfz-potsdam.de).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".