Can Correcting for Mantle Dynamics Reconcile Divergent Plio-Pleistocene Sea-Level Estimates?
Bibliographic record
Abstract
Estimates of global mean sea level (GMSL) during past warm periods provide a key constraint on ice-sheet sensitivity to future climate change and inform projections of long-term sea-level rise. Measurements from the most recent periods of enhanced warmth are especially valuable since these intervals represent the closest climatic analogues to near-future conditions. Considerable focus has therefore been placed on reconstructing sea-level during the Mid-Pliocene Warm Period (MPWP; 3.3–3.0 Ma) and the Last Interglacial (~129–116 ka), periods characterised by mean temperatures 2–3 °C and ~1 °C above preindustrial levels, respectively. Many GMSL estimates have been obtained from palaeoshoreline deposits since these geomorphic proxies provide a more direct and potentially more precise constraint on past sea-level than stable isotope records. However, estimates from different sites differ by several metres due to spatially variable vertical crustal motions caused by geodynamic processes, including glacial isostatic adjustment and dynamic topography.To tackle this issue, we integrate a suite of Australian sea-level markers and geodynamic simulations into a probabilistic inverse framework to quantify and remove the effect of vertical crustal motions at a continental scale. We find that dynamic topography accounts for most of the observed MPWP sea-level marker deflection and is also significant for the LIG. After correcting for this process and glacial isostatic adjustment, we obtain a revised MPWP GMSL estimate of +16.0/10.4–21.5 m (50th/16th–84th percentiles). We also find that post-LIG dynamic topography may account for several metres of relative displacement across the Great Barrier Reef, potentially reconciling discrepant GMSL estimates from this region. Recalibration of sea-level projections with these revised estimates suggests a more stable Antarctic Ice Sheet under future warming scenarios and appears to rule out recent high-end forecasts.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".