Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios

Lehner, Fabian; Nadeem, Imran; Formayer, Herbert

doi:10.5194/ascmo-9-29-2023

Cited by 14 publications

(4 citation statements)

References 63 publications

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“…The term "quantile mapping" summarizes a variety of similar bias correction approaches used within the climate research community (e.g., Lehner et al, 2023). Here, however, we follow the method described for CCM outputs in Rieder https://doi.org/10.5194/acp-24-5953-2024 Atmos.…”

Section: Quantile Mapping (Qm)mentioning

confidence: 99%

Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry–climate model surface ozone fields

Staehle,

Rieder,

Fiore

et al. 2024

Atmos. Chem. Phys.

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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.

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Section: Quantile Mapping (Qm)mentioning

confidence: 99%

Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry–climate model surface ozone fields

Staehle,

Rieder,

Fiore

et al. 2024

Atmos. Chem. Phys.

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“…In this study PresRATe was chosen. The PresRATe approach offers several advantages (Lehner et al 2023) First, it aligns model data with the climatological means of historical observational data. Second, it maintains the long-term climatological trends of means (the climate change signal) post-bias adjustment.…”

Section: Bias Correction Of Gcmsmentioning

confidence: 99%

Flood inundation mapping under climate change scenarios: insights from CMIP6

Sadiqzai,

Khan,

Khan

et al. 2024

Water Practice &Amp; Technology

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The present research endeavors to simulate daily stream flow by employing hydrologic and hydraulic modeling techniques to comprehensively assess the impact of climate change on flood risk. This investigation was conducted within the Shekhan basin, situated in the eighth zone of Jalalabad City, Afghanistan. The efficacy of the HEC-HMS model was meticulously evaluated for each individual flood event during both calibration (Jan/2015-October/2019) and validation (November/2019-July/2022) phases using various statistical performance indicators, notably the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). During calibration, the HEC-HMS model yielded R2, NSE, and PBIAS values of 0.8795, 0.86, and 12%, respectively, while during validation, these metrics stood at 0.85, 0.8, and 9%, respectively. Among the five GCM models (INM-CM4-8, INM-CM5-0, MIROC6, MPI-ESM1-2-LR, MRI-ESM-2-0) examined, the MPI-ESM1-2-LR demonstrated superior performance based on Taylor Skill Score and Rating Metric analysis. Additionally, the HEC-SSP was employed to scrutinize precipitation frequency and to fit ranking distributions for GCM SSP245 and SSP585 scenarios. Subsequently, the aforementioned GCM data were utilized in hydrologic modeling to generate hydrographs for various return periods, while hydraulic modeling via the HEC-RAS 2D model facilitated the creation of flood inundation maps for different return periods.

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“…The term "quantile mapping" summarizes a variety of similar bias correction approaches used within the climate research community [e.g. Lehner et al, 2023]. Here, however, we follow the method described for CCM outputs in Rieder et al…”

Section: Delta Correction (Dc)mentioning

confidence: 99%

Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry-climate model surface ozone fields

Staehle,

Rieder,

Fiore

2023

Preprint

View full text Add to dashboard Cite

Abstract. State of the art chemistry-climate models (CCMs) still show biases compared to ground level ozone observations, illustrating remaining difficulties and challenges in the simulation of atmospheric processes governing ozone production and loss. Therefore, CCM output is frequently bias-corrected in studies seeking to explore changing air quality burdens and associated impacts. Here we assess four statistical bias correction techniques of varying complexity, and their application to surface ozone fields of four CCMs, and evaluate their performance against gridded observations in the EU and US. For the evaluation of the raw CCM outputs and the performance of the individual adjustment techniques we focus on two time periods (2005–2009 & 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. Our results show, that while all methods applied are capable of significantly reducing the model bias, better results are obtained for 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 the ozone bias arises from sensitivities in chemical mechanisms or emissions rather than driving meteorology.

show abstract

Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios

Cited by 14 publications

References 63 publications

Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry–climate model surface ozone fields

Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry–climate model surface ozone fields

Flood inundation mapping under climate change scenarios: insights from CMIP6

Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry-climate model surface ozone fields

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