Bibliographic record
Abstract
Reanalysis products provide spatially homogeneous coverage for a variety of climate variables in regions where observational data are limited. However, very little validation of reanalysis soil temperatures in the Arctic has been performed to date, because widespread in situ reference observations have historically been unavailable there. Here we validate pan-Arctic soil temperatures from eight reanalysis and Land Data Assimilation System (LDAS) products, using a newly-assembled database of in situ data from diverse measurement networks across Eurasia and North America. We find that most products have soil temperatures that are biased cold by 2–7 K across the Arctic, and that biases and Root Mean Square Error (RMSE) are generally largest in the cold season. Monthly mean values from most products correlate well with in situ data (R > 0.9) in the warm season, but show lower correlations (r = 0.6–0.8), in many cases, over the cold season. Similarly, the magnitude of monthly variability in soil temperatures is well captured in summer, but overestimated by 20 % to 50 % for several products in winter. The suggestion is that soil temperatures in reanalysis products are subject to much higher uncertainty when the soil is frozen and/or when the ground is snow-covered. We also validate the ensemble mean of all products, and find that when all seasons, and metrics are considered, the ensemble mean generally outperforms any individual product in terms of its correlation and variability, while maintaining relatively low biases. As such, we recommend the ensemble mean soil temperature product for a wide range of applications – such as the validation of soil temperatures in climate models, and to inform models that require soil temperature inputs, such as hydrological models, or for permafrost simulations.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.036 | 0.021 |
| Insufficient payload (model declined to judge) | 0.030 | 0.032 |
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".