the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Oct 2024
Ocean wave spectra bias correction through energy conservation for climate change impacts
Abstract. A novel bias-adjustment technique for the 2D directional wave spectra is presented, which accounts for the intraannual temporal variability of waves and the conservation of the wave energy integrated parameter and its extreme distribution, allowing for shifts in frequency and direction given by the GCM-RCM climate signal for the complete multimodal energy distribution. This work represents a first attempt to address the biases inherent in GCM-RCMs wave spectra simulations for an assessment of the magnitudes of the projected changes under a climate change scenario. The bias-correction method is applied to a multi-model ensemble of seventeen EURO-CORDEX regional simulations of wave spectra in eleven locations of the Mediterranean Sea. Climate change impacts are assessed by means of the changes between the bias-adjusted ensemble and hindcast wave spectra for mid-century conditions from 2034 until 2060 and end-of-century from 2064 until 2100. Results highlight the need for novel bias-correction techniques that address the complexity of the possible directional and frequency shifts due to climate change, in order to provide an accurate assessment of projected future changes in wave climate.
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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.
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Preprint
(8099 KB)
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Supplement
(16964 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(8099 KB) - Metadata XML
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Supplement
(16964 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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CC1: 'Comment on egusphere-2024-2947: Mangled Figures', Ole Pinner, 15 Oct 2024
Somewhere along the way from submission to preprint, the labels in the figures got mangled.
Could the authors upload the correct figures to allow for an accurate peer-review?
Even if it is just the submitted PDF again as an answer to this comment?Citation: https://doi.org/10.5194/egusphere-2024-2947-CC1 - AC1: 'Reply on CC1', Andrea Lira Loarca, 15 Oct 2024
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RC1: 'Comment on egusphere-2024-2947', Anonymous Referee #1, 20 Nov 2024
In general, the paper is well written but requires some revision. My comments in a document.
- AC3: 'Reply on RC1', Andrea Lira Loarca, 06 Mar 2025
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RC2: 'Comment on egusphere-2024-2947', Anonymous Referee #2, 03 Feb 2025
The paper presents the SEGDM-month method, a new bias-adjustment technique for 2D directional wave spectra. It effectively accounts for intra-annual variability and extreme wave distributions, which is a crucial advancement over traditional univariate corrections.
The study applies the method to 17 EURO-CORDEX regional climate models across 11 locations in the Mediterranean. This wide spatial coverage enhances the robustness of the findings. Correctly identifying and adjusting extreme values (upper-tail correction) is a key improvement. The method ensures energy conservation across frequency and direction, which is an essential requirement for accurate climate impact studies. The study follows a well-structured methodology, clearly explaining the datasets, correction process, and validation. The figures also effectively illustrate the improvements brought by the SEGDM-month method.
However, the paper mainly compares GCM-RCMs against hindcast data, but direct validation with observed wave spectra (e.g., buoy data, satellite data) is limited. I believe that including a direct comparison with observational datasets would strengthen the reliability of the bias-correction method. Further, while the statistical correction method is well-detailed, the study lacks a deep discussion of why biases occur in different sub-basins (e.g., Adriatic, Aegean, Western Mediterranean). Are biases primarily due to errors in regional wind fields? Or are they related to model resolution limitations? A more physical discussion would add value. Then, the study highlights improvements but does not discuss potential pitfalls of the method. For example, does the bias correction impact the representation of low-energy wave conditions? Could the imposed energy conservation lead to unintended distortions in wave propagation physics? Additionally, the study presents SEGDM-month as an improvement over previous methods (e.g., delta method), but a direct comparison with other advanced bias-correction techniques (e.g., quantile mapping used in atmospheric and hydrological modeling) is missing. I think it should also be discussed how the SEGDM-month methodology compares with emerging multivariate approaches to further quantify its advantages and limitations. Finally, the paper assesses bias correction but does not provide much discussion on how corrected wave projections alter climate impact assessments. How does the correction affect key climate adaptation strategies, such as coastal erosion predictions or marine energy assessments?
In brief, it seems that the SEGDM-month method offers a substantial advancement in bias correction for 2D directional wave spectra by integrating energy conservation, extreme value adjustment via the Gumbel distribution, and monthly corrections to account for seasonal variability. Compared to traditional univariate and simpler delta methods, it better preserves the complex interdependencies of the wave spectrum. However, in my opinion, challenges remain—particularly regarding validation in low-energy conditions, reliance on high-quality hindcast data, and performance in regions with strong local-scale phenomena. Consequently, I would really like the authors to further discussed their results as I believe it would strengthen the study.
Citation: https://doi.org/10.5194/egusphere-2024-2947-RC2 - AC2: 'Reply on RC2', Andrea Lira Loarca, 06 Mar 2025
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2024-2947: Mangled Figures', Ole Pinner, 15 Oct 2024
Somewhere along the way from submission to preprint, the labels in the figures got mangled.
Could the authors upload the correct figures to allow for an accurate peer-review?
Even if it is just the submitted PDF again as an answer to this comment?Citation: https://doi.org/10.5194/egusphere-2024-2947-CC1 - AC1: 'Reply on CC1', Andrea Lira Loarca, 15 Oct 2024
-
RC1: 'Comment on egusphere-2024-2947', Anonymous Referee #1, 20 Nov 2024
In general, the paper is well written but requires some revision. My comments in a document.
- AC3: 'Reply on RC1', Andrea Lira Loarca, 06 Mar 2025
-
RC2: 'Comment on egusphere-2024-2947', Anonymous Referee #2, 03 Feb 2025
The paper presents the SEGDM-month method, a new bias-adjustment technique for 2D directional wave spectra. It effectively accounts for intra-annual variability and extreme wave distributions, which is a crucial advancement over traditional univariate corrections.
The study applies the method to 17 EURO-CORDEX regional climate models across 11 locations in the Mediterranean. This wide spatial coverage enhances the robustness of the findings. Correctly identifying and adjusting extreme values (upper-tail correction) is a key improvement. The method ensures energy conservation across frequency and direction, which is an essential requirement for accurate climate impact studies. The study follows a well-structured methodology, clearly explaining the datasets, correction process, and validation. The figures also effectively illustrate the improvements brought by the SEGDM-month method.
However, the paper mainly compares GCM-RCMs against hindcast data, but direct validation with observed wave spectra (e.g., buoy data, satellite data) is limited. I believe that including a direct comparison with observational datasets would strengthen the reliability of the bias-correction method. Further, while the statistical correction method is well-detailed, the study lacks a deep discussion of why biases occur in different sub-basins (e.g., Adriatic, Aegean, Western Mediterranean). Are biases primarily due to errors in regional wind fields? Or are they related to model resolution limitations? A more physical discussion would add value. Then, the study highlights improvements but does not discuss potential pitfalls of the method. For example, does the bias correction impact the representation of low-energy wave conditions? Could the imposed energy conservation lead to unintended distortions in wave propagation physics? Additionally, the study presents SEGDM-month as an improvement over previous methods (e.g., delta method), but a direct comparison with other advanced bias-correction techniques (e.g., quantile mapping used in atmospheric and hydrological modeling) is missing. I think it should also be discussed how the SEGDM-month methodology compares with emerging multivariate approaches to further quantify its advantages and limitations. Finally, the paper assesses bias correction but does not provide much discussion on how corrected wave projections alter climate impact assessments. How does the correction affect key climate adaptation strategies, such as coastal erosion predictions or marine energy assessments?
In brief, it seems that the SEGDM-month method offers a substantial advancement in bias correction for 2D directional wave spectra by integrating energy conservation, extreme value adjustment via the Gumbel distribution, and monthly corrections to account for seasonal variability. Compared to traditional univariate and simpler delta methods, it better preserves the complex interdependencies of the wave spectrum. However, in my opinion, challenges remain—particularly regarding validation in low-energy conditions, reliance on high-quality hindcast data, and performance in regions with strong local-scale phenomena. Consequently, I would really like the authors to further discussed their results as I believe it would strengthen the study.
Citation: https://doi.org/10.5194/egusphere-2024-2947-RC2 - AC2: 'Reply on RC2', Andrea Lira Loarca, 06 Mar 2025
Peer review completion
Journal article(s) based on this preprint
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Andrea Lira Loarca
Giovanni Besio
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(8099 KB) - Metadata XML
-
Supplement
(16964 KB) - BibTeX
- EndNote
- Final revised paper