Sensitivity of the Statistical DownScaling Model (SDSM) to reanalysis products
Bibliographic record
Abstract
Numerous general circulation models (GCMs) have been designed by climate centres to predict future climate. An outstanding issue with the use of GCM output for local applications is the coarse spatial resolution. To produce accurate daily predictions of future climate variables at the regional scale, the Statistical DownScaling Model (SDSM) is a commonly used downscaling technique. The SDSM statistically identifies relationships between large‐scale predictors (i.e., GCM) and local‐scale predictands, using a multiple linear regression model. Reanalyses, such as those produced by the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the European Centre for Medium‐range Weather Forecasts (ECMWF), are important components for the structuring of the SDSM as they supply the predictor values for the calibration and validation of the model. It is well known that the reanalysis products contain biases which may subsequently affect the development of downscaling scenarios when used with the SDSM. In this paper, separate downscaled precipitation and temperature scenarios were generated using the SDSM with the calibrations and validations derived from two different reanalyses for a climate station in southern Ontario. From these comparisons, we have identified statistically significant differences between the two time series. Therefore, it is clear that choice of the reanalysis used to calibrate the SDSM can significantly affect the downscaled scenario over a region evaluated in southern Ontario.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".