State-dependency of temperature variability in transient simulations of the last Deglaciation from models of varying complexity
Bibliographic record
Abstract
Much about the response of temperature variability to a change in the climate's mean state, as the one projected for the current century, remains uncertain. These uncertainties include spatiotemporal patterns, the magnitude, and, in some cases, even the sign. For the last Deglaciation, - the last change in global mean temperature of a similar degree to that expected in projections - variability analyses of climate model simulations and temperature proxies produce conflicting results. Here, we build a hierarchy of transient simulations covering the period since the Last Glacial Maximum about 26k years ago. We include a range of climate models, from conceptual to complex Earth System Models. The simulations cover a variety of temporal and spatial resolutions, parameterizations, and modeled processes. For annual to multi-millennial temporal as well as regional to global spatial scales, we compare variability patterns and power spectra and analyze how they relate to model properties and the background state of Earth's climate. This allows for the examination of regional temperature differences between low, middle, and high latitudes and at locations of available paleoclimate proxy records. For sets of sensitivity experiments, we investigate effects of changes to ice sheets, sea ice, and in volcanic, solar, greenhouse, and orbital forcing on modeled climate variability. Thus, our analysis provides insights into when and how models disagree with each other and with proxies, and what differences arise due to specific models, simulation setups, and boundary conditions. Based on these results, we can then gauge the degree of complexity which is required to reproduce past temperature variability and predict its changes in the future.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".