Spatial partitioning of precipitation in the terrestrial water cycle and the role of dataset agreement
Bibliographic record
Abstract
The study of the water cycle at planetary scale is crucial for our understanding of large-scale climatic processes. There have been numerous studies that quantified the water cycle and its components, i.e., precipitation, evaporation, and runoff, over the land and the ocean. However, very little is known about how water fluxes are distributed across regions with different climatic or land properties. Here, we address this gap by providing robust estimates for terrestrial precipitation over a suite of land cover types, biomes, elevation zones, and precipitation intensity classes. We achieve this by estimating the mean annual precipitation of a 17-dataset ensemble between 2000 and 2019 at 0.25° spatial resolution. Our estimate of annual terrestrial precipitation is at approximately 114 000 ± 9 400 km3, with about 70% falling over one third of the grid cells, 80% over the 0 – 800 elevation zone, and two-thirds over forested regions. Our results also highlight that despite the current progress in the development of global scale data products there are still substantial uncertainties over the arid and/or high-elevation areas. Bigger discrepancies appear within the reanalysis data products, while remote sensing estimates show a better agreement with the in-situ ground truth. These results help to detect regions of high observational fidelity and pave the way to further explore and improve observational uncertainties. At the same time, we provide consistent estimates that can be used for benchmarking the precipitation partition in the climate models, and most importantly that can be used to assess future changes in global precipitation.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".