Quantifying the Spatio-Temporal Pattern Differences in Climate Change before and after the Turning Year in Southwest China over the Past 120 Years
Bibliographic record
Abstract
In conjunction with Earth’s ongoing global warming, the Southwest China (SWC) region has become a fascinating case study on the control of local climate change. Moreover, an entire period of climate change may partially mask the patterns in some stages. Therefore, in this research, we investigated the spatial patterns of the significant turning years of climatic factor change, and determined the heterogeneity of the spatial patterns of climate change before and after the significant turning years. We used the long time-series of the CRU datasets (CRU_TS4.02) from 1901 to 2017 with a piecewise linear regression model to explore the significant turning-year distribution characteristics of inter-annual and inter-seasonal climate factor changes, and further describe and quantize the differences in the spatio-temporal patterns of climate factors before and after the significant turning years on the grid scale in SWC. Overall, the trends in temperature and precipitation factors in SWC were segmented over the last 120 years, with significant turning years with different regional and stepwise characteristics. In terms of timing, temperature and precipitation factors changed significantly in 1954 and 1928, respectively, and overall temporal variability (0.04 °C/(10 a) (p < 0.05), −0.48 mm/(10 a)) masked the magnitude or direction of variability (0.13 °C/(10 a) and 0.16 °C/(10 a) both at the level of p < 0.05 before the turning year, 19.56 mm/(10 a) (p < 0.05) and 1.19 mm/(10 a) after the turning year) around the watershed years. Spatially, the significant turning years were concentrated in the periods 1940–1993 (temperature) and 1910–2008 (precipitation), and the distribution pattern of the turning years was patchy and concentrated. The turning years of temperature factors were gradually delayed from east to west, and the variability of climate factors before and after the turning years exhibited significant shifts in location (e.g., temperature decreased from southeast to northwest before the turning year and increased after the turning year). After the turning year, the warming variability of the temperature factor increased, while the increasing variability of the precipitation factor decreased. Further integrated analysis revealed that the increase in variability of the climate factor after the turning year was mainly due to the increase in winter and autumn variability (0.05 °C/(10 a), 7.30 mm/(10 a) in autumn; and 0.12 °C/(10 a), 1.97 mm/(10 a) in winter). To the extent that this study provides a necessary academic foundation for efficiently unveiling the spatio-temporal variability properties of climate factors against the background of modern global climate change, more attention should be paid to the location and phase of the study.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".