Remaining error sources in bias-corrected climate model outputs
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Remaining error sources in bias-corrected climate model outputs Jie Chen 1,2
3
& François P. Brissette & Daniel Caya
3,4
Received: 13 November 2019 / Accepted: 14 May 2020/ # Springer Nature B.V. 2020
Abstract
Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a “pseudo-reality” approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability. Keywords Bias correction . Climate sensitivity . Internal climate variability . Pseudo-reality . Climate change . Climate models . Impact studies
1 Introduction General Circulation Model (GCM) and Earth System Model (ESM) outputs are often considered too coarse and biased for climate change impact studies which are most often conducted at the fine spatial scale. Using raw model outputs in most environmental models would * Jie Chen [email protected] Extended author information available on the last page of the article
Climatic Change
provide a response to remove from reality to be useful to the impact and adaptation community. To overcome these problems, various downscaling approaches have been developed. These downscaling approaches can be generally classified into two categories: dynamical downscaling and statistical downscaling (Chen et al. 2012; Maraun et al. 2010; Gutiérrez et al. 2019). Dynamical downscaling achieves a higher spatial resolution by nesting Regional Climate Models (RCMs) based on dynamic formulations and using lateral boundary conditions of GCMs (Jones et al. 1995; Caya and Laprise 1999). RCMs resolve small-scale processes and small-scale variability that GCMs cannot represent and that are crucial to properly represent th
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