the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Sep 2024
Insights into Greenland Ice Sheet Surface Roughness from Ku-/Ka-band Radar Altimetry Surface Echo Strengths
Abstract. Surface roughness is an important factor to consider when modelling mass fluxes at the Greenland Ice Sheet (GrIS) surface (i.e., surface mass balance, SMB). This is because it can have important implications for both sensible and latent heat fluxes between the atmosphere and the ice sheet and near-surface ventilation. While surface roughness can be quantified from ground-based, airborne and spaceborne observations, satellite radar datasets provide the unique combination of long-term, repeat observations across the entire GrIS and insensitivity to illumination conditions and cloud cover. In this study, we investigate the reliability and interpretation of a new type of surface roughness estimate derived from the analysis of Ku- and Ka-band airborne and spaceborne radar altimetry surface echo powers by comparing them to contemporaneous laser altimetry measurements. Airborne data are those acquired during the 2017 and 2019 CryoVEx campaigns while the satellite data (ESA CryoSat-2, CNES/ISRO SARAL, and NASA ICESat-2) are those acquired in November 2018. Our results show that because surface roughness across the GrIS is primarily scale-dependent, a revised empirical mapping of quantified radar backscattering to surface roughness gives a better match to the coincident laser altimetry observations than an analytical model that assumes scale-independent roughness. We also show that the radar altimetry-derived surface roughness is best interpreted as the wavelength-baseline linear projection of the scale-dependent surface roughness observed at hundreds of meter scales and is therefore not representative of individual small-scale features. These results provide critical context for interpreting the datasets and evaluating their applicability in modelling GrIS SMB.
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Status: open (until 15 Nov 2024)
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RC1: 'Comment on egusphere-2024-2832', Anonymous Referee #1, 16 Oct 2024
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Review of “Insights into Greenland Ice Sheet Surface roughness from Ku-/Ka-band Radar Altimetry Surface Echo Strengths” submitted to The Cryosphere in 2024 by Kirk M. Scanlan et al.
This study focuses on the quantification of surface topographic roughness of the Greenland ice sheet using CryoSat-2 and SARAL satellite radar measurements. The Radar Statistical Reconnaissance (RSR) method, developed in a recent publication by the same authors, is used for this purpose. The derived roughness properties are compared against airborne radar and laser scanner data from two campaigns, and with ICESat-2 ATL06 (20m intervals) data. The main question is whether the surface roughness can be accurately estimated using spaceborne radar, the main motivation being that the surface roughness is an important yet poorly constrained parameter for SMB models. This makes this study relevant for The Cryosphere. This study is also original, as novel insights are presented regarding the estimation of surface roughness using satellite radar datasets, such as the empirical correction for the RSR method (Eq. 4) that gives a better match with ICESat-2 data, and the unique spatio-temporal analysis of surface roughness (Fig. 10), which form a basis of future work using such data. The potential significance of this work is also high, since improved estimation of ice sheet roughness properties allow not only for potential improved SMB modelling but also improved radar postprocessing. The employed datasets and methods are sound, as far as I can assess as a non-radar expert.
Overall, the paper is well written. It makes use of adequate referencing from the remote sensing and SMB modelling perspectives, although it could benefit from more referencing in the field of the snow bedform quantification. Some examples are given below. The introduction allows for the non-remote sensing expert to understand the overall topic. However, the methods and results sections are long and, in some places in the methods, technical terms are introduced which do not appear to be very relevant for the results. On the other hand, there is no mention on how the laser scanner and ICESat-2 data were processed and filtered, even though these data are used as reference. The results are very interesting, yet I have some concerns which are listed below. The discussion is a relevant and a welcome part as it presents the main limitation of the novel results for improved SMB modelling. However, some more work is required to make this radar roughness estimation useful for SMB modeling, which I recommend should be done or at least discussed more quantitively before final publication. Is there absolutely no way to use spaceborne radar for improved roughness estimation relevant for SMB models ? At present a constant aerodynamic roughness value is still chosen over the entire ice sheet in climate models.
To summarise, I would recommend publication after the main comments are clarified, and once the derived roughness can be (indirectly) used for SMB modelling.
Major comments
This study uses an airborne laser scanner (ALS) and ICESat-2 ATL06 data at 20m resolution as independent reference data. First of all, it is not clear what the accuracy of the ALS data is, and how the 1m effective ALS resolution (L114) relates to the resolution required to detect the relevant surface features in the areas of interest (sastrugi, snow dunes, etc…). Even with ~1m resolution and ~10cm vertical accuracy, the ALS is limited in capturing all the scales relevant for SMB modelling, which is the main aim of this work (according to the introduction). Therefore, I would invite the authors to also critically assess the ALS data in relation to the physical processes of interest.
Similarly, at 20m resolution and >10cm vertical accuracy, the ICESat-2 ATL06 will evidently be even more limited limited in quantifying roughness features such as sastrugi and all the way down to the radar wavelength scale. Hence, I am skeptical about the interpretation of the empirical correction to the RSR method in Eq. 4. I would argue that this is an empirical or “data-based” correction that generates a better match of CryoSat-2 and SARAL data with ICESat-2 data, but it is not formally proved in this study that this correction allows for a more accurate roughness quantification. For this, roughness data at all the relevant scales (1cm – 100m) should be used, as obtained though terrestrial LiDAR, UAV photogrammetry , eddy covariance data or even manual probing.
Furthermore, in Section 4 (L252-253), a main result of this work is given based on the match that is found between the RSR derived roughness from the airborne radars, and the RMS roughness from ALS “at larger baselines (e.g. between 200 and 700 m)” (L249). Perhaps this is a lack of understanding of the RSR algorithm on my part, but I do not understand how the radar RSR roughness and the laser RMS roughness are related to each other. These two methods seem fundamentally very different and sensitive to different scales of surface roughness: ~radar wavelength scale (cm) for radar RSR, and >100 m for ALS and ICESat-2 RMS, depending on the (arbitrary?) choice of the extrapolation interval. Hence the found match between RSR and RMS “between 200 and 700 m” in Fig 2 could as well be a coincidence, or the result of the postprocessing, such as how the data was detrended (L154). At least it does not seem to be physically based. Perhaps the authors could explain this better and perform a sensitivity test by varying the extrapolation intervals (e.g. 50-500m, 200m-600m, 300-1000m, etc…) and detrending methods (linear, polynomial, high-pass filtering,...)
Finally, as a non-radar expert yet interested in improved SMB modelling, I am hoping that the estimated RSR roughness (with values between 0.01 mm and 1 mm on the ice sheet) could be translated to a more useful metric (such as standard deviation of heights, average obstacle height, etc... ), which is then directly compared and shown as a map together with the same metric as estimated from ICESat-2 in Figure 10. It becomes clear at the end in section 6.1 that this new map can’t unfortunately be used directly for aerodynamic roughness calculations, and I agree, yet I would invite the authors to make this clearer from the start and in the abstract/conclusion, and propose possible alternatives to overcome this limitation. Could this not be expected before the analysis, given the large footprint of the satellite radars?
Despite the limitations, perhaps an assumption could be made that the RMS between a certain scale range (e.g. 1 cm – 100m) is relevant for surface drag calculations, which can be indirectly estimated from the RSR or RMS algorithms. A typical roughness height from CryoSat-2, SARAL and ICESat-2 for these scales could be plotted as the final result, thereby paving the way for improved SMB modelling.
Other comments
- Title: Consider removing “Insights into” .
- L16: (mentioned in Major comments) As far as I understand, it is an assumption by the authors and not a proved result that the revised empirical correction gives a better match to ICESat-2 because the “surface roughness across the GrIS is primarily scale-dependent”. The better match is obtained by construction.
- L20: Please rephrase “wavelength-baseline linear projection of the scale-dependent surface roughness”.
- L40: Rephrase into “Numerical estimates of GrIS SMB are based on coupled climate/subsurface models”.
- L85: What are conventional surface roughness metrics ?
- Section 2.1: please include some information about the ALS data processing and accuracy (also mentioned in major comments)
- Figure 1: Is this actual data from the ALS ? If so, please include the location and time of this measurements. If not, consider using real data with a quantitative x-axis which would help the reader to better understand the “raw” data.
- L154: Wouldn’t it be more consistent to filter out the same larger wavelengths using the same high-pass filter for all ALS and ICESat-2 data ? The background plane depends on the length of the dataset, which makes it hard to compare short ALS data (1 km) with longer ICESat-2 data (10km), as done later in Figure 4.
- L182: How was this 1.5 degree threshold chosen? What are the potential effects of off-nadir instrument point on the results?
- L189: I would strongly recommend that the authors better explain how the coherent and incoherent powers are estimated, as these are the backbone of the methods. Perhaps some formulas or a schematic (possibly in the appendix) would be beneficial.
- L194-208. These sentences could be shortened while still conveying the same information and being clearer at the same time.
- L200: what data selection / what metrics have been applied to ensure quality of the ICESat-2 data ?
- L209-211: Is it possible that some words are missing from this sentence ?
- L214: What is effectively the highest sigma_h that can be estimated using CryoSat-2 using this method ? How does this compare to the RMS of the actual expected topography on the Greenland ice sheet ? If this method only works for sigma_h < ~6 mm (for CryoSat-2), that leaves out most of the ice sheet surface with ice hummocks, sastrugi, dunes, etc…
- L215: please define the “surface correlation length”.
- L230: How are the densities recovered using RSR ? Consider adding a bit more background information in Section 3.2.
- Figure 2: In panels c and d, the triangles can’t be discerned. In panel d, the resolution of the T21 ALS seems much higher than for the other locations.
- L239 On what is the assumption based that the RSR radar roughness corresponds to the RMS at the wavelength scale? please also specify “radar wavelength” to avoid confusion about which wavelength was used. How were the shaded areas 200-700 m chosen if not arbitrarily?
- L244: It took me quite some time to understand Figure 2c/d, so perhaps “immediately clear” is not the most accurate phrasing
- L252-253: mentioned in major comments
- Figure 3: Please adjust the color scale of Fig3d.
- Figure 4: The ALS and ICESat-2 ATL06 data are very different and show different behavior in the 100m-1km range, which is unexpected. Is this due to the different detrending of the data ?
- L380: This is very interesting, but perhaps adding the elevation lines in Figure 9 would help to better place this specific area with anomalous RSR retrievals in a broader context. What are the ICESat-2 RMS values in this region ? Can this also be related to noise in the ICESat-2 data ?
- Figure 10: why is the average roughness only shown for 2 transects ? If the data is available, consider plotting the data for the entire ice sheet. Also, why is only SARAL roughness shown and not also CryoSat-2 and ICESat-2 ?
- L420: Consider renaming section 6.1
- L429: This seems to be the first moment where the actual topography of the ice sheet is discussed. Perhaps the typical surface features could be introduced in an earlier stage for better overall understanding of what the satellite radars are supposed to detect (e.g. Between sections 2 and 3, or in the introduction). Some inspiration for this purpose :
- Filhol, S., & Sturm, M. (2015). Snow bedforms: A review, new data, and a formation mode. Journal of Geophysical Research: Earth Surface, 1645–1669. https://doi.org/10.1002/2015JF003529
- Picard, G., Arnaud, L., Caneill, R., Lefebvre, E., & Lamare, M. (2019). Observation of the process of snow accumulation on the Antarctic Plateau by time lapse laser scanning. Cryosphere, 13(7), 1983–1999. https://doi.org/10.5194/tc-13-1983-2019
- Zuhr, A. M., Münch, T., Steen-Larsen, H. C., Hörhold, M., & Laepple, T. (2021). Local-scale deposition of surface snow on the Greenland ice sheet. The Cryosphere, 15(10), 4873–4900. https://doi.org/10.5194/tc-15-4873-2021
- L465: Please include in 3.2 how the density is computed, which helps the reader to understand this adjustment term.
Citation: https://doi.org/10.5194/egusphere-2024-2832-RC1
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