Investigating future precipitation changes over China through a high‐resolution regional climate model ensemble
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Due to climate change, rising temperature around the world will have a great potential to influence the global hydrologic cycle, thus leading to substantial changes in the spatial and temporal patterns of precipitation. In this study, the effects of global warming on the regional hydrologic cycle, particularly on the spatiotemporal patterns of precipitation, over China are investigated through a high‐resolution regional climate ensemble. In detail, the PRECIS regional climate modeling system is employed to simulate the regional climate over China from 1950 to 2099 with a fine resolution of 25 km, driven by the boundary conditions from a four‐member HadCM3 ‐based perturbed‐physics ensemble (i.e., HadCM3Q0 , Q1 , Q7 , and Q13 ) and the ECHAM5 model. Historical simulations of the PRECIS ensemble are first compared to the observations to validate its performance in capturing both the spatial and temporal patterns of precipitation. The comparisons show that the PRECIS ensemble is likely to overestimate precipitation in the south and exhibits slight dry biases in the northwest and southeast coasts of China. The projections from the PRECIS ensemble for future periods (i.e., 2020s, 2050s, and 2080s) are then analyzed to help understand how the regional characteristics of precipitation will be affected in the context of global warming. It is shown that the annual mean precipitation over China is likely to increase throughout the 21st century (i.e., by 0.078 mm/d in 2020s, 0.218 mm/d in 2050s, and 0.360 mm/d in 2080s). This may suggest that the rising temperature due to climate change will intensify the regional hydrologic cycles in China. However, apparent spatial and temporal variations are also reported in the projected precipitations from the PRECIS ensemble. For example, bigger changes in precipitation are usually observed in summer; projected precipitation changes in the southeast are apparently higher than other regions. In addition, the results show that the fluctuation range of the ensemble simulations will increase with time periods from 2020s to 2080s, indicating that the longer the projecting periods, the more uncertain the projections will be.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it