Using a last 2k baseline to derive a first comprehensive assessment of industrial era land heat uptake
Bibliographic record
Abstract
The anthropogenically-intensified greenhouse effect causes a radiative imbalance at the top of the atmosphere. This in turn leads to an energy surplus of the Earth system, with the ocean component absorbing the greatest part and the land the second largest. The latest observational estimates based on borehole temperature profiles quantify the land contribution to the terrestrial energy surplus to be 6 % in the last five decades, whereas studies based on state-of-the-art climate models scale it down to 2 %. This underestimation stems from land surface models (LSMs) having a too shallow representation of the subsurface, which severely constrains the land heat uptake simulated by Earth System Models (ESMs). A forced simulation of the last 2000 years with the Max Planck Institute ESM (MPI-ESM) using a deep LSM captures about 4 times more heat than the standard shallow MPI-ESM simulations in the historical period, well above the estimates provided by other ESMs. However, deepening the MPI-ESM LSM does not affect the simulated temperature at the ground surface. As a consequence, it is shown that the land heat uptake values of ESMs with shallow LSM components can be corrected considering their simulated surface temperatures and propagating them with a standalone heat conduction forward model. This result is extended to all available ground surface temperature sources, such as observational data, reanalyses, and the latest generation of ESMs. This new approach yields values of 10-16 ZJ for 1971-2018, which are in close agreement with the values derived from the MPI-ESM deep simulation (12 ZJ), and relatively close to the latest borehole-based estimates (ca. 18 ZJ).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".