Connecting the Land Surface to Droughts: How Transpiration, Canopy Evaporation, and Ground Evaporation Impact Droughts Across the North American Continent
Bibliographic record
Abstract
Land surface moisture plays a crucial role in precipitation patterns across the globe. Evapotranspiration (the combination of ground evaporation (E), canopy evaporation (I), and transpiration (T)) from the land surface can influence precipitation through local recycling and the propagation of moisture to downwind regions. However, the role of the land surface and of T, E, and I individually in these two processes are not well understood and limit our understanding of the role of the land surface for both drought onset and intensification. Here we use a version of the Community Earth System Model (CESM1.2 with the Community Atmosphere Model CAM5 and the Community Land Model CLM5) with online water tracers to directly track and quantify the movement of T, E and I moisture across North America for the 1985–2015 period. Initial findings suggest that over 50% of summer precipitation for much of central and northern US and Canada comes from the land surface. The tracers also suggest that, with the exception of the US west coast and desert southwest, 40-60% of land precipitation across the continent comes from the T component. The connection between land surface moisture and drought episodes are examined for different regions of North America. The individual roles of T, E, and I in shaping droughts are also examined.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".