Assessing land‐atmosphere coupling using soil moisture from the Global Land Data Assimilation System and observational precipitation
Bibliographic record
Abstract
Precipitation analysis and soil moisture from the Global Land Data Assimilation System (GLDAS) are used to assess the land‐atmosphere coupling in boreal summer. Correlations between antecedent soil moisture and precipitation suggest that regions of strong land‐atmosphere coupling lie mainly in arid to semiarid transition zones or in semihumid forest to grassland transition zones. They consist of central Eurasia, the region from Mongolia to northern China, southwest China, the Sahel, the northern continental United States, and southern Europe. It is found that over these regions, positive soil moisture feedback accounts for typically 10–20% of the variance of monthly precipitation anomalies with the feedback efficiency of the order of 0.3–0.9 mm month−1 (0.1 standardized soil moisture)−1. While soil moisture feedback is dominated by the positive sign, negative feedback may exist in some areas, such as India and the western part and Quebec province of Canada. Generally, the land‐atmosphere coupling strength estimated from the GLDAS data agrees well with those from the observational soil moisture in Illinois and the European Centre for Medium‐Range Weather Forecasts 40‐year reanalysis (ERA‐40) soil moisture product. Physical mechanisms responsible for the findings are further discussed. This study provides a Northern Hemisphere distribution of the land‐atmosphere coupling strength, which can be used to test the model simulations on monthly to seasonal time scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".