Steric Sea-Level Change and its Impact on the Gravity Field caused by Global Climate Change
Bibliographic record
Abstract
It is sometimes assumed that steric sea-level variations do not produce a gravity signal as no net mass change, thus no change of ocean bottom pressure is associated with it. Analyzing the output of two CO2 emission scenarios over a period of 2000 years in terms of steric sea-level changes, we try to quantify the gravitational effect of steric sea-level variations. The first scenario, computed with version 2.6 of the Earth System Climate Model developed at the University of Victoria, Canada (UVic ESCM), is implemented with a linear CO2 increase of 1% of the initial concentration of 365 ppm and shows a globally averaged steric effect of 5.2 m after 2000 years. In the second scenario, computed with UVic ESCM version 2.7, the CO2 concentration increases quasi-exponentially to a level of 3011 ppm and is hold fixed afterwards. The corresponding globally averaged steric effect in the first 2000 years is 2.3 m. We show, due to the (vertical) redistribution of ocean water masses (expansion or contraction), the steric effect results also in a small change in the Earth’s gravity field compared to usually larger changes associated with net mass changes. Maximum effects for computation points located on the initial ocean surface can be found in scenario 1, with the effect on gravitational attraction and potential ranging from 0.0 to −0.7·10−5 m s−2 and −3·10−3 to 6·10−3 m2 s−2, respectively. As expected, the effect is not zero but negligible for practical applications.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".