Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry–climate model surface ozone fields
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acp-24-5953-2024.pdf
Description
Published version
Size
4.47 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Staehle, Christoph
Rieder, Harald E
Fiore, Arlene M
Schnell, Jordan L
Date Issued
May 24, 2024
Journal
Atmospheric Chemistry and Physics
Publisher
Copernicus GmbH
Citation
Atmos. Chem. Phys., 24, 5953–5969, 2024
Version
Final published version
Abstract
State-of-the-art chemistry–climate models (CCMs) still show biases compared to ground-level ozone observations, illustrating the difficulties and challenges remaining in the simulation of atmospheric processes governing ozone production and loss. Therefore, CCM output is frequently bias-corrected in studies seeking to explore the health or environmental impacts from changing air quality burdens. Here, we assess four statistical bias correction techniques of varying complexities and their application to surface ozone fields simulated with four CCMs and evaluate their performance against gridded observations in the EU and US. We focus on two time periods (2005–2009 and 2010–2014), where the first period is used for development and training and the second to evaluate the performance of techniques when applied to model projections. We find that all methods are capable of significantly reducing the model bias. However, biases are lowest when we apply more complex approaches such as quantile mapping and delta functions. We also highlight the sensitivity of the correction techniques to individual CCM skill at reproducing the observed distributional change in surface ozone. Ensemble simulations available for one CCM indicate that model ozone biases are likely more sensitive to the process representation embedded in chemical mechanisms than to meteorology.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.5194/acp-24-5953-2024
