Pan Evaporation Trends and the Terrestrial Water Balance. II. Energy Balance and Interpretation
Bibliographic record
Abstract
Abstract Declines in pan evaporation have been reported across the USA, former Soviet Union, India, China, Australia, New Zealand and Canada, among other places. The trend is large – approximately an order of magnitude larger than model‐based estimates of top of the atmosphere radiative forcing. The pan evaporation trend also has a different sign (i.e. decline) from commonly held conceptions. These are a remarkably interesting set of observations. In the first article of this two‐part series, we discussed the measurements themselves and then presented summaries of the worldwide observations. In this, the second article, we outline the use of energy balance methods to attribute the observed changes in pan evaporation to changes in the underlying physical variables, namely, radiation, temperature, vapour pressure deficit and wind speed. We find that much of the decline in pan evaporation can be attributed to declines in radiation (i.e. dimming) and/or wind speed (i.e. stilling). We then discuss the interpretation of changes in the terrestrial water balance. This has been an area of much misunderstanding and confusion, most of which can be rectified through use of the familiar and longstanding supply/demand framework. The key in using the pan evaporation data to make inferences about changes in the terrestrial water balance is to distinguish between water‐ and energy‐limited conditions where different interpretations apply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".