The need for global hydro-climatological indicators
Bibliographic record
Abstract
Global warming is expected to alter the global hydrological cycle in addition to higher temperatures, higher global sea levels and melting ice. We present an analysis of how rainfall patterns have changed over the period 1950-2020 based on the ERA5 reanalysis, involving both aggregation and 2D Haar wavelet analysis. Our results suggest that there have been pronounced changes, such as increased activity in rain events with a size of less than 400 km. Such changes call for the need for global hydro-climate indicators that provide a summary of the state of the global hydrological cycle. We show that the typical total mass of water falling on Earth’s surface each day has gradually increased over the period, and the daily fraction of the global area on which it falls has diminished. Hence, the mean rainfall intensity has also increased. These findings imply two explanations for observed trends toward more extreme rainfall: (1) a more moist atmosphere and (2) the rainfall has increasingly become more concentrated in both time and space. One hypothesis is that these changes in rainfall patterns may be connected with changed conditions for convection in the atmosphere. We found that the energies in some of the wavelet components closely track the global mean temperature, which also may suggest a possibility to downscale statistics for rainfall patterns based on expected global warming. A practical consequence of these findings is an explanation for both more flooding in some places but also more drought in others. They also call for the need to include global hydro-climatological indicators in the widely used set consisting of the global mean temperature, global sea-level, CO2 concentrations and sea-ice extent.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".