Can climate change signals be detected from the terrestrial water storage at daily timescales?
Bibliographic record
Abstract
Abstract Global terrestrial water storage (TWS) serves as a crucial indicator of freshwater availability on Earth, yet detecting climate change trends in TWS poses challenges due to uneven hydrological responses, limited observations, and internal climate variability. To overcome these limitations, we present a novel approach leveraging extensive observed and simulated meteorological data at daily scales to project global TWS based on its fingerprints embedded in weather patterns. By establishing the relationship between annual global mean TWS and daily surface air temperature and humidity fields in reanalyses and multi-model hydrological simulations till the end of 21st century, we successfully detect climate change signals emerging above internal variability noise. Our analysis reveals that, since 2016, climate change signals have been detected in approximately 50% of days for most years. Furthermore, the signals of climate change in global mean TWS have exhibited consistent growth over recent decades and are anticipated to surpass the influence of natural climate variability in the future under various emission scenarios. Our findings highlight the urgency of mitigating greenhouse gas emissions to not only mitigate warming risks but also to ensure future water security. This daily-scale detection of TWS provides valuable insights into the evolving impacts of climate change on global TWS dynamics, enhances our understanding of climate change impacts, and facilitates informed decision-making in multiple sectors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".