Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the <scp>CMIP5</scp> ensemble
Bibliographic record
Abstract
ABSTRACT Global climate models ( GCMs ) provide the fundamental information used to assess potential impacts of future climate change. However, the mismatch in spatial resolution between GCMs and the requirements of regional applications has impeded the use of GCM projections for impact studies at a regional scale. This study applied statistical post‐processing methods that preserve long‐term temporal trends, bias‐correction/spatial disaggregation with detrended quantile mapping ( SDDQM ) and BCSD with quantile delta mapping ( SDQDM ), to downscale 20 CMIP5 GCM climate projections for daily precipitation, minimum temperature, and maximum temperature over South Korea. Using the downscaled CMIP5 climate projections, we investigated absolute changes in extreme indices between the reference and three 30‐year future periods. In addition, the biases in change signals from GCM projections for different statistical downscaling methods were compared to evaluate how well long‐term trends in indices are preserved. The results showed that the statistical downscaling methods significantly improved the skill in reproducing extreme indices. For temperature‐related extreme indices, we found strong significant trends while trends for precipitation‐related indices varied depending on the index and climate projection horizon. Specifically, more frequent, longer duration, and more intense hot extremes may occur under the CMIP5 climate projections, while corresponding decreases may occur for extreme cold indices. Prominent upward trends are found in extreme precipitation events. Regarding analysis of the bias in change signals, SDQDM , which explicitly preserves changes in all quantiles of the underlying variables, better preserved long‐term trends in extreme indices simulated by GCMs .
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".