the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Nov 2023
Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry-climate model surface ozone fields
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.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(2366 KB)
-
Supplement
(4681 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2366 KB) - Metadata XML
-
Supplement
(4681 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2743', Anonymous Referee #1, 03 Jan 2024
This is an interesting study on different bias-correction methods applied to CMIP6 Earth System Models (ESMs) surface-ozone results. The analysis explores the performance of each applied statistical bias-correction method, to what extent this is sensitive to each individual ESM, and finally the nature and origin of the errors. The manuscript is well organized with qualitative and efficient presentation of the results. Yet, there are some points that need clarification and further investigation. I believe the study may be a valuable addition to the literature once the following comments are addressed.
Main Comments
1. More information on the gridded observational ozone dataset (used here for the evaluation) is needed. Do the authors use the Schnell et al. (2014) data? This assessment is for the 2000-2009 period. Is this an extension of this dataset? Is this dataset publicly available? Please describe (briefly) in the manuscript how this dataset was constructed. Is the inhomogeneous network of observations over Europe and USA affecting the results of your evaluation and how? This should be discussed. I suggest including a subsection in the Data & Methods Section about the gridded observational ozone data and relevant information.
2. To explore the error sources, the authors select the daily maximum temperature and radiation for sensitivities to meteorology. Yet, wind and especially for high-ozone events stability (stagnation) are also important drivers. How are these two represented by the individual ensemble members? Attributing model error mainly to precursors emissions needs more evidence. What are the NOx and VOC PDFs for the ensemble members? Are there any model diagnostics for ozone production (PO3) and loss (PO3) to support this?
3. It would be interesting to see results for MDA8 O3 using a different gridded observational ozone dataset (if available). Moreover, since the ultimate purpose of the study is to support reliability of ozone-health studies, the recent Global Burden of Disease (GBD) report (2019) applies the ozone season daily maximum 8 hour mixing ratio (OSDMA8) metric to estimate excess mortality from long-term ozone exposure. Gridded observational OSDMA8 data, as described in DeLang et al. (2021), are publicly available at https://zenodo.org/records/8320001. Are the statistical methods used here applicable for a long-term effect ozone metric like OSMDA8?
Comments
L18-19: This is a strong statement as this is not explicitly shown from the results. See also main comment #2.
L21-26: Tropospheric and therefore surface ozone has also a natural source, the transport from the stratosphere (Stohl et al., 2003) which over specific regions (Lin et al., 2015) or occasionally (Akritidis et al., 2010) contributes significantly.
L26: “O3 is associated with a variety of detrimental human health effects”. I suggest including here a couple of recent references on ozone effects on human health like Murray et al. (2020) and Pozzer et al. (2023).
L40: maybe “meteorology and deposition”
L65-66: Please clarify why only the first member of the ensemble is used in the main study.
L68: The period 1993 to 2014 is referred here. Are there any data used in the analysis except from the 2005-2009 and 2010-2014 periods? Please clarify.
L183: Remove t.
L285-286: As this is not explicitly shown to be related with precursors emissions but rather assumed I suggest rephrasing accordingly.
L360: EB depends
L361: EB is
L363: “the strong base period performance”, maybe “the strong performance for the base period”?
References
Akritidis D., P. Zanis, I. Pytharoulis, A. Mavrakis and Th. Karacostas,: A deep stratospheric intrusion event down to the earth’s surface of the megacity of Athens, Meteorology and Atmospheric Physics, 109 (1-2), 9-18, DOI: 10.1007/s00703-010-0096-6, 2010
DeLang MN, Becker JS, Chang KL, Serre ML, Cooper OR, Schultz MG, et al. Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017. Environ Sci Technol [Internet]. 2021 Apr 20;55(8):4389–98. Available from: https://doi.org/10.1021/acs.est.0c07742
Lin, M., Fiore, A., Horowitz, L. et al. Climate variability modulates western US ozone air quality in spring via deep stratospheric intrusions. Nat Commun 6, 7105 (2015). https://doi.org/10.1038/ncomms8105
Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet [Internet]. 2020;396(10258):1223–49. Available from: https://www.sciencedirect.com/science/article/pii/S0140673620307522
Pozzer A, Anenberg SC, Dey S, Haines A, Lelieveld J, Chowdhury S. Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates. Geohealth [Internet]. 2023;7(1):e2022GH000711. Available from: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GH000711
Stohl, A., et al. (2003), Stratosphere-troposphere exchange: A review, and what we have learned from STACCATO, J. Geophys. Res., 108, 8516, doi:10.1029/2002JD002490, D12.
Citation: https://doi.org/10.5194/egusphere-2023-2743-RC1 -
RC2: 'Comment on egusphere-2023-2743', Anonymous Referee #2, 12 Feb 2024
The study presents different bias correction methods for surface ozone and apply them to four CMIP6generation Earth System Models (ESMs). The performance of each applied method is investigated along with the sensitivities of each individual ESM to these methods, and finally recommendations. The manuscript is well-organized and easy to follow. There are few points that need further clarification before it can be accepted in ACP.
General comments
1. More information is needed on the gridded observational ozone dataset, including how this dataset was generated briefly, referring to the observation networks in Europe and USA.
2. The dataset is divided into two for evaluation and projections. It would be useful to show if projections would give similar results if other datasets would be used, or the projections would be applied in other regions such as Asia. In addition, would the conclusions change if another metric was used to evaluate the performance.
3. Daily maximum temperature and radiation are selected for sensitivity to meteorology. I would recommend looking at winds to account for transport. Is there a reason why it is not included? Another important source is stratospheric ozone, which should be discussed.
4. Why did you only use the first member of the ensemble. This should be clarified and justified.
Citation: https://doi.org/10.5194/egusphere-2023-2743-RC2 - AC1: 'Comment on egusphere-2023-2743', Christoph Stähle, 18 Mar 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2743', Anonymous Referee #1, 03 Jan 2024
This is an interesting study on different bias-correction methods applied to CMIP6 Earth System Models (ESMs) surface-ozone results. The analysis explores the performance of each applied statistical bias-correction method, to what extent this is sensitive to each individual ESM, and finally the nature and origin of the errors. The manuscript is well organized with qualitative and efficient presentation of the results. Yet, there are some points that need clarification and further investigation. I believe the study may be a valuable addition to the literature once the following comments are addressed.
Main Comments
1. More information on the gridded observational ozone dataset (used here for the evaluation) is needed. Do the authors use the Schnell et al. (2014) data? This assessment is for the 2000-2009 period. Is this an extension of this dataset? Is this dataset publicly available? Please describe (briefly) in the manuscript how this dataset was constructed. Is the inhomogeneous network of observations over Europe and USA affecting the results of your evaluation and how? This should be discussed. I suggest including a subsection in the Data & Methods Section about the gridded observational ozone data and relevant information.
2. To explore the error sources, the authors select the daily maximum temperature and radiation for sensitivities to meteorology. Yet, wind and especially for high-ozone events stability (stagnation) are also important drivers. How are these two represented by the individual ensemble members? Attributing model error mainly to precursors emissions needs more evidence. What are the NOx and VOC PDFs for the ensemble members? Are there any model diagnostics for ozone production (PO3) and loss (PO3) to support this?
3. It would be interesting to see results for MDA8 O3 using a different gridded observational ozone dataset (if available). Moreover, since the ultimate purpose of the study is to support reliability of ozone-health studies, the recent Global Burden of Disease (GBD) report (2019) applies the ozone season daily maximum 8 hour mixing ratio (OSDMA8) metric to estimate excess mortality from long-term ozone exposure. Gridded observational OSDMA8 data, as described in DeLang et al. (2021), are publicly available at https://zenodo.org/records/8320001. Are the statistical methods used here applicable for a long-term effect ozone metric like OSMDA8?
Comments
L18-19: This is a strong statement as this is not explicitly shown from the results. See also main comment #2.
L21-26: Tropospheric and therefore surface ozone has also a natural source, the transport from the stratosphere (Stohl et al., 2003) which over specific regions (Lin et al., 2015) or occasionally (Akritidis et al., 2010) contributes significantly.
L26: “O3 is associated with a variety of detrimental human health effects”. I suggest including here a couple of recent references on ozone effects on human health like Murray et al. (2020) and Pozzer et al. (2023).
L40: maybe “meteorology and deposition”
L65-66: Please clarify why only the first member of the ensemble is used in the main study.
L68: The period 1993 to 2014 is referred here. Are there any data used in the analysis except from the 2005-2009 and 2010-2014 periods? Please clarify.
L183: Remove t.
L285-286: As this is not explicitly shown to be related with precursors emissions but rather assumed I suggest rephrasing accordingly.
L360: EB depends
L361: EB is
L363: “the strong base period performance”, maybe “the strong performance for the base period”?
References
Akritidis D., P. Zanis, I. Pytharoulis, A. Mavrakis and Th. Karacostas,: A deep stratospheric intrusion event down to the earth’s surface of the megacity of Athens, Meteorology and Atmospheric Physics, 109 (1-2), 9-18, DOI: 10.1007/s00703-010-0096-6, 2010
DeLang MN, Becker JS, Chang KL, Serre ML, Cooper OR, Schultz MG, et al. Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017. Environ Sci Technol [Internet]. 2021 Apr 20;55(8):4389–98. Available from: https://doi.org/10.1021/acs.est.0c07742
Lin, M., Fiore, A., Horowitz, L. et al. Climate variability modulates western US ozone air quality in spring via deep stratospheric intrusions. Nat Commun 6, 7105 (2015). https://doi.org/10.1038/ncomms8105
Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet [Internet]. 2020;396(10258):1223–49. Available from: https://www.sciencedirect.com/science/article/pii/S0140673620307522
Pozzer A, Anenberg SC, Dey S, Haines A, Lelieveld J, Chowdhury S. Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates. Geohealth [Internet]. 2023;7(1):e2022GH000711. Available from: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GH000711
Stohl, A., et al. (2003), Stratosphere-troposphere exchange: A review, and what we have learned from STACCATO, J. Geophys. Res., 108, 8516, doi:10.1029/2002JD002490, D12.
Citation: https://doi.org/10.5194/egusphere-2023-2743-RC1 -
RC2: 'Comment on egusphere-2023-2743', Anonymous Referee #2, 12 Feb 2024
The study presents different bias correction methods for surface ozone and apply them to four CMIP6generation Earth System Models (ESMs). The performance of each applied method is investigated along with the sensitivities of each individual ESM to these methods, and finally recommendations. The manuscript is well-organized and easy to follow. There are few points that need further clarification before it can be accepted in ACP.
General comments
1. More information is needed on the gridded observational ozone dataset, including how this dataset was generated briefly, referring to the observation networks in Europe and USA.
2. The dataset is divided into two for evaluation and projections. It would be useful to show if projections would give similar results if other datasets would be used, or the projections would be applied in other regions such as Asia. In addition, would the conclusions change if another metric was used to evaluate the performance.
3. Daily maximum temperature and radiation are selected for sensitivity to meteorology. I would recommend looking at winds to account for transport. Is there a reason why it is not included? Another important source is stratospheric ozone, which should be discussed.
4. Why did you only use the first member of the ensemble. This should be clarified and justified.
Citation: https://doi.org/10.5194/egusphere-2023-2743-RC2 - AC1: 'Comment on egusphere-2023-2743', Christoph Stähle, 18 Mar 2024
Peer review completion
Journal article(s) based on this preprint
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 262 | 82 | 20 | 364 | 23 | 12 | 13 |
- HTML: 262
- PDF: 82
- XML: 20
- Total: 364
- Supplement: 23
- BibTeX: 12
- EndNote: 13
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Christoph Staehle
Harald E. Rieder
Arlene M. Fiore
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2366 KB) - Metadata XML
-
Supplement
(4681 KB) - BibTeX
- EndNote
- Final revised paper