Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data
Bibliographic record
Abstract
The amount of soil moisture affects water availability, the occurrence of droughts and floods, and the frequency and intensity of heat waves in many regions across the globe. Here, we evaluate historical trends in soil moisture estimated by land-surface models (LSMs) with observed atmospheric forcing and trends simulated by global climate models participating in the Coupled Models Inter-comparison Project Phase 5 (CMIP5). We classify northern hemispheric land into wet and dry regions and analyze soil moisture changes in these regions. We find a significant decrease in soil moisture from 1951 to 2005 in the northern hemispheric land areas, in particular in dry regions, both in LSM and CMIP5 model simulations. Soil moisture trends in wet regions are less consistent among simulations. The increase in the area affected by drought (defined as the area where soil moisture is below its 10th percentile) from 1951 to 2005 is estimated to be 20 % (LSMs) and 30 % (CMIP5 models). A comparison between soil moisture simulated by LSMs and CMIP5 model output under different external forcings suggests that anthropogenic forcing contributed significantly to the observed drying and could explain the increase in the area affected by drought. As increases in atmospheric greenhouse gas concentrations will continue in the near future, dry areas are projected to become drier and larger in extent, which could negatively impact future water supply and food security.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".