Climate Change Impact Studies: Should We Bias Correct Climate Model Outputs or Post‐Process Impact Model Outputs?
Bibliographic record
Abstract
Abstract The inter‐variable dependence of climate variables is usually not considered in many bias correction methods, even though it has been deemed important for various impact studies. Another possible approach is to forgo the bias correction of climate model outputs, and instead, post‐process the outputs of the impact model. This has the advantage of circumventing the difficulties associated with correcting the inter‐variable dependence of climate variables. Using a hydrological impact study as an example, this study investigates the feasibility of bias correcting impact model outputs by comparing the performance of the pre‐processing and post‐processing of hydrological model simulations when using bias correction methods. The performance over calibration and validation periods was used to assess the transferability of both approaches. The results show that both the pre‐processing and post‐processing procedures are capable of significantly reducing the bias of simulated streamflow time series for most global climate models (GCMs), even though their performances depend on GCM simulations, hydrological models, streamflow metrics and watersheds. Both approaches were likely to perform badly over the validation period when bias correction factors have a strong seasonal variability and are therefore sensitive to bias nonstationarity of climate model outputs and/or streamflow between the calibration and validation periods. This problem is found to be more acute for the post‐processing method because streamflows often have a seasonal pattern with more abrupt changes than precipitation and temperature. For this reason, pre‐processing is recommended as it is less likely to suffer from this problem.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".