Climate Change Impact Studies: Should We Bias Correct Climate Model Outputs or Post‐Process Impact Model Outputs?. Issue 5 (3rd May 2021)

• Record Type: Journal Article
• Authors: Chen, Jie; Arsenault, Richard; Brissette, François P.; Zhang, Shaobo
• Is Part Of: Water resources research. Volume 57:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-03
• Subjects: bias correction methods -- bias nonstationarity -- climate model outputs -- hydrological models -- pre‐processing and post processing; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR028638
• Languages: English
• ISSNs: 0043-1397

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. Key Points: The feasibility of bias correcting hydrological model simulations is tested by comparing the pre‐processing and post‐processing approaches Bias nonstationarity of global climate model outputs and sharp seasonal correction factor gradients lead to performance deterioration of correction methods. Bias correction of climate model simulations is still recommended for hydrological impact studies