Long-term Surface Temperature (LoST) Database as a Complement for Transient and Control Preindustrial Simulations
Bibliographic record
Abstract
Estimates of climate sensitivity from Atmosphere-Ocean Coupled General Circulation Model (GCM) simulations present a large spread despite the continued improvements in climate modeling since the 1970s. This variability is partially caused by the dependence of several long-term feedback mechanisms on the reference climate state. However, it is difficult to provide a reference to assess the climatology of preindustrial control simulations as there are no long-term preindustrial observations. In the ground, recent changes in ground surface temperature are observed at shallow depths as perturbations to the quasi-steady state geothermal regime. However, if undisturbed by recent surface temperature changes, the deep ground temperatures vary linearly as a function of depth, and the extrapolation of this linear behavior to the surface can be interpreted as the past long-term surface temperature climatology. We assemble a new gridded database of past long-term ground surface temperatures (LoST database) obtained from 514 borehole temperature profiles measured across North America, and we explore its use as a potential reference for the evaluation of GCM preindustrial control simulations and past millennium simulations. All temperature profiles are truncated at 300 m depth, allowing to estimate the ground surface climatology for the period 1300-1700 of the common era. We compare the LoST database with observations from the CRU database, as well as with five past millennium simulations and five preindustrial control simulations from the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3) and the fifth phase of the Coupled Model Intercomparison Project (CMIP5) archives. Our results suggest that LoST temperatures could be employed as a reference to narrow down the spread of surface temperature climatologies on GCM preindustrial control and past millennium simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".