Multi-Model Ensemble Mean Shows Accelerating Global Below-Ground Warming
Bibliographic record
Abstract
The below-ground component of the Earth’s critical zone is crucial to human activities and underpins numerous chemical, physical and biological processes. However, previous studies primarily concentrated on below-ground temperatures (BGT) until depths up to 3 m and periods shorter than 70 years; few studies have globally analyzed the historical spatiotemporal variability of BGT beyond those ranges. The objective of this study was to investigate BGT anomalies (ΔBGT) between depths of 0–42 m during 1850–2100 using model outputs from CMIP6. The results show a three-stage accelerating warming pattern (1850–2014): weak pre-1943 warming (0.02 °C decade⁻¹, depth-average), mid-century stagnation, and post-1984 acceleration (0.33 °C decade⁻¹, depth-average) for depth mean of 0.05∼1.75 m. Future mean warming rises ∼1.7 times from SSP1‑2.6 (2.08 °C) to SSP5‑8.5 (3.45 °C), with maximum of warming mean expanding 2.6 times. Asymmetric BGT extremes drive elevated subsurface heat risk under high emissions. A robust seasonal hierarchy reversal occurs (DJF‑ to JJA‑dominated), with winter BGT most sensitive to radiative forcing. ΔBGT amplifies strongly from 60°N, and enhances in high‑altitude/coastal regions under high emissions. Heterogeneous bottom boundary condition placement (BBCP) is an important structured uncertainty source in multi-model BGT analysis, introducing non-physical sampling artifacts in ensemble-mean vertical profiles. Despite inter‑model heterogeneity, the multi‑model ensemble yields physically consistent depth‑attenuated warming, providing an ensemble-constrained reference for subsurface thermal change investigation. By 2100, low-moderate emission scenarios (e.g., SSP1‑2.6, SSP2‑4.5) will slow BGT warming. This study can provide insightful understanding of the overlooked BGT and inform future model intercomparison projects and ensemble mean analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".