Near‐surface soil thermal regime and land–air temperature coupling: A case study over Spain
Bibliographic record
Abstract
Abstract Understanding the near‐surface soil thermal regime and its connection to the atmospheric state is important for the assessment of several climate‐related processes. However, the lack of in situ soil temperatures measurements limits the analysis of such processes. In this study, we have developed a quality‐controlled soil temperature database for Spain that consists of 39 sites spanning from 1987 to 2018. We have used this database to assess the near‐surface soil thermal regime. Likewise, we evaluate at seasonal to multidecadal timescales the land–air temperature coupling over Spain by analysing the structure of the surface air temperature (SAT) and the ground surface temperature (GST) covariance and also their long‐term evolution. In addition, we have employed the ERA5‐Land reanalysis to test the consistence between observations and reanalysis. The results show that the near‐surface soil thermal structure is dominated by conduction despite some influence of hydrology‐related processes. Regarding the land–air temperature coupling, we have found a strong connection between SAT and GST. However, in the summer months there is an offset in SAT–GST at some sites due to limited evaporation and enhanced sensible heat fluxes. Furthermore, multidecadal SAT–GST decoupling may exist over some sites as a response to decreasing precipitation. The ERA5‐Land represents the observations' climatology well, but it underestimates the summer soil temperature observations and the long‐term trends at some sites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".