the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Aug 2023
Broadband and filter radiometers at Ross Island, Antarctica: Detection of cloud ice phase versus liquid water influences on shortwave and longwave radiation
Abstract. Surface radiometer data from Ross Island, Antarctica, collected during the austral summer 2015–16 by the US Department of Energy Atmospheric Radiation Measurement (ARM) program West Antarctic Radiation Experiment (AWARE), are used to evaluate how shortwave and longwave irradiance respond to changing cloud properties as governed by contrasting meteorological regimes. Shortwave atmospheric transmittance is derived from pyranometer measurements, and cloud conservative-scattering optical depth is derived from filter radiometer measurements at 870 nm. With onshore flow associated with marine air masses, clouds contain mostly liquid water. With southerly flow over the Transantarctic Mountains, orographic forcing induces substantial cloud ice water content. These ice and mixed-phase clouds attenuate more surface shortwave irradiance than the maritime-influenced clouds, and also emit less longwave irradiance due to colder cloud base temperature. These detected irradiance changes are in a range that can mean onset or inhibition of surface melt over ice shelves. This study demonstrates how basic and relatively low-cost broadband and filter radiometers can be used to detect subtle climatological influences of contrasting cloud microphysical properties at very remote locations.
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RC1: 'Comment on egusphere-2023-1665', Anonymous Referee #1, 03 Oct 2023
This paper uses basic radiometric measurements to calculate cloud optical depths and cloud transmissions and infer the presence of ice or mixed phase clouds. The cloud optical depth calculated using wavelength 870 is not affected by ice clouds whereas broadband shortwave transmission is because of ice bands in the near-infrared. In other words cloud radars, lidars, and other sophisticated equipment are not necessary to obtain fundamentally useful data that can distinguish ice and mixed phase clouds from water clouds.
I think the authors demonstrate their point that fairly fundamental radiation measurements can be used at sites in the Antarctic. However, I am not sure that the measurements that they suggest can detect the 10 W/m^2 changes in the net surface radiation (lines 56-58) that could induce ice melt or retention.
Line 70: indicates about six weeks of measurements; is that correct?
Line 150: "... one describes with a unique large-scale circulation pattern." Is this correctly stated?
A map with labels of the geographic sites discussed in these paragraphs would help for those not familiar with Antarctic geography.
Citation: https://doi.org/10.5194/egusphere-2023-1665-RC1 -
RC2: 'Comment on egusphere-2023-1665', Anonymous Referee #2, 27 Nov 2023
Review of the research article entitled “Broadband and filter radiometers at Ross Island, Antarctica: Detection of cloud ice phase versus liquid water influences on shortwave and longwave radiation” by Kristopher Scarci et al.. (MS No.: egusphere-2023-1665)
The authors present a study examining the impact of cloud phase and properties on the surface energy budget based on ground-based radiometric instruments, supported by reanalysis and satellite observations. Their primary objective is to establish a connection between flux measurements and cloud properties using basic instruments deployable in remote locations. This holds particular relevance in Antarctica, where even small changes in the net surface budget could initiate melting processes.
The study is well-written, carefully argued and gives important insights into the role of the meteorological patterns and cloud properties on the radiative budget. However, their method is applied to a limited dataset without really enlightening our comprehension of the Antarctica atmospheric system and surface energy balance. As it stands, the research appears more as a proof of concept, aligning more closely with a technical journal such as AMT. To align with the scope and standards of ACP, the study requires a more in-depth analysis to better integrate with the current state-of-the-art research and contribute to a broader understanding of how cloud microphysical properties influence the surface energy budget. Before publication in ACP, several key points need addressing to elevate the study to meet the journal's standards.
Major comments:
1. Scientific significance for the ACP community
The authors leverage data from the AWARE campaign (2015-2016) to scrutinize the impact of atmospheric regimes and cloud properties on the surface radiative budget. However, the study encounters limitations arising from the relatively sparse number of samples, both spatially and temporally (e.g., regime 3 was observed on only 8 days), prompting questions about its representativeness or applicability to Antarctica's meteorological patterns. Notably, the AWARE campaign coincided with a period of high global-average temperatures during an El Niño event, further raising considerations about the broader context of the study.
To enhance the paper's scope, it is imperative to contextualize the analyses and conclusions within the current state-of-the-art research. This approach not only amplifies the scientific significance but also extends the benefits to a broader community. In this regard, I make a couple of suggestions, non exhaustive, that could help enhancing the generalizability of the conclusions:
- The identification of the meteorological regimes could be put in the context of the paper from Scott et al., 2019. How the four clusters identified in this paper relate to the nine they found, and to the trends they identified.
- The cloud properties retrieved in this study could be compared with the literature you cited. For example, the histogram of cloud optical depth (Figure 5) could be compared with the exponential fit of Fitzpatrick and Warren (2005).
2. Up-to-date reanalysis data set
ERA-5 replaced ERA-Interim in 2019, with improved vertical and spatial resolutions, and a newer Integrated Forecasting System. The ECMWF reanalyses are still experiencing warm biases for 2 m air temperature in polar regions, when compared with ground-based observations, with significant differences between ERA-Interim and ERA5 (see, for example, Jonassen et al., 2019; King et al., 2022; Zhu et al., 2021; Wang et al., 2019).
In the manuscript, you wrote “For the purposes of this work ERA5 reanalyses are essentially identical to ERA-Interim.”, but I would like to understand what the expected implications are on the identification of the meteorological regimes. I understand that updating the reanalysis data set would require a lot of work, but could you justify why it is not necessary, and if it is supported by the AWARE observations.
3. Geographical components
The paper is missing a map locating the ARM sites and the regions mentioned in the manuscript. It is important for the readers that are not very familiar with those regions, to better understand the links with the meteorological regimes. See for example the figure 1 in Scott et al., 2019, or the figure 1 in Silber et al., 2019.
Also, the Figure 1 shall contain latitudes and longitudes.
4. Relevance of the MODIS cloud products for this study
In this paper, MODIS is used to assess the cloud phase and cloud top height over McMurdo station during the summer 2015-2016. As mentioned in the manuscript, some improvements have been made in polar regions (Frey, 2008), but inconsistencies still exist for cloud occurrence (e.g., Cossich et al., 2021; Marchant et al., 2016) and cloud properties (e.g., Wilson et al., 2018).
The impact of uncertainties in MODIS products on the analysis is an important consideration, especially in the presence of multi-layer clouds or potential omissions of certain clouds. The authors should explicitly address how these uncertainties might affect their findings, particularly in scenarios where the MODIS data might misrepresent the actual cloud conditions.
My other comment is why the authors didn’t use ground-based observations (radar, lidar, ARM VAP, …) acquired during AWARE to do their analysis or at least assess the cloud properties retrieved by MODIS (see Minor comment below on Figures 2 and 3).
Minor comments:
Page 4, Line 123: In the past, maintaining the measurement quality of some radiometers has been a challenge when used in polar environment, for example when icing appears on the optics (Cox et al., 2021). Could you comment on how feasible it would be to have the suite of instruments you suggest in unattended remote location?
Page 8, Table 1: The date January 26, 2016, appears 2 times.
Pages 8 and 9, Figure 2a and 3: Could you comment on the differences between MODIS clear sky and TSI cloud cover. For example, MODIS identified clear sky during 15%, while TSI observed clear sky for more than 25 %.
Page 12, Figure 6: The figure may be easier to read using a log-scale.
Page 13, Line 306: Particle size could be another important parameter influencing the emissivity of optically thin clouds.
Pages 14 and 16: Figures 7 (b, d, f) and 8 (b, d, f): It would be easier to read with the dashed line crossing 0 on both y-axes, and a legend for the red bars and black line.
References:
Cossich, W., Maestri, T., Magurno, D., Martinazzo, M., Di Natale, G., Palchetti, L., Bianchini, G., and Del Guasta, M.: Ice and mixed-phase cloud statistics on the Antarctic Plateau, Atmos. Chem. Phys., 21, 13811–13833, https://doi.org/10.5194/acp-21-13811-2021, 2021.
Cox, C. J., Morris, S. M., Uttal, T., Burgener, R., Hall, E., Kutchenreiter, M., McComiskey, A., Long, C. N., Thomas, B. D., and Wendell, J.: The De-Icing Comparison Experiment (D-ICE): a study of broadband radiometric measurements under icing conditions in the Arctic, Atmos. Meas. Tech., 14, 1205–1224, https://doi.org/10.5194/amt-14-1205-2021, 2021.
Fitzpatrick, M. F., & Warren, S. G.: Transmission of Solar Radiation by Clouds over Snow and Ice Surfaces. Part II: Cloud Optical Depth and Shortwave Radiative Forcing from Pyranometer Measurements in the Southern Ocean. Journal of Climate, 18(22), 4637-4648. https://doi.org/10.1175/JCLI3562.1, 2005.
Jonassen, M. O., Välisuo, I., Vihma, T., Uotila, P., Makshtas, A. P., & Launiainen, J.: Assessment of atmospheric reanalyses with independent observations in the weddell sea, the antarctic. Journal of Geophysical Research: Atmospheres, 124, 12468–12484. https://doi.org/10.1029/2019JD030897, 2019.
King, J. C., Marshall, G. J., Colwell, S., Arndt, S., Allen-Sader, C., & Phillips, T.: The performance of the ERA-Interim and ERA5 atmospheric reanalyses over Weddell Sea pack ice. Journal of Geophysical Research: Oceans, 127, e2022JC018805. https://doi.org/10.1029/2022JC018805, 2022.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Scott, R. C., Nicolas, J. P., Bromwich, D. H., Norris, J. R., and Lubin, D.: Meteorological drivers and large-scale climate forcing of West Antarctic surface melt, J. Clim, 32, 665-684, doi:10.1175/JCLI-D-18-0233.1, 2019.
Silber, I., Verlinde, J., Cadeddu, M., Flynn, C. J., Vogelmann, A. M., and Eloranta, E. W.: Antarctic cloud macrophysical, thermodynamic phase, and atmospheric inversion coupling properties at McMurdo Station - Part II: Radiative impact during different synoptic regimes, J. Geophys. Res., 124, 1697-1719, doi:10.1029/2019JD029471, 2019
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019.
Wilson, A., Scott, R. C., Cadeddu, M. P., Ghate, V., & Lubin, D.: Cloud optical properties over West Antarctica from shortwave spectroradiometer measurements during AWARE. Journal of Geophysical Research: Atmospheres, 123, 9559–9570. https://doi.org/10.1029/2018JD028347, 2018.
Zhu J, Xie A, Qin X, Wang Y, Xu B, Wang Y. An Assessment of ERA5 Reanalysis for Antarctic Near-Surface Air Temperature. Atmosphere, 12(2):217. https://doi.org/10.3390/atmos12020217, 2021.
Citation: https://doi.org/10.5194/egusphere-2023-1665-RC2 -
AC1: 'Comment on egusphere-2023-1665', Dan Lubin, 08 Jan 2024
The reviewers have made some valuable suggestions and corrections to significantly improve the quality of the manuscript. All of these suggestions are straightforward to implement, as discussed in detail within the supplemental PDF, and we look forward to preparing a stronger paper in response to these reviews.
Status: closed
-
RC1: 'Comment on egusphere-2023-1665', Anonymous Referee #1, 03 Oct 2023
This paper uses basic radiometric measurements to calculate cloud optical depths and cloud transmissions and infer the presence of ice or mixed phase clouds. The cloud optical depth calculated using wavelength 870 is not affected by ice clouds whereas broadband shortwave transmission is because of ice bands in the near-infrared. In other words cloud radars, lidars, and other sophisticated equipment are not necessary to obtain fundamentally useful data that can distinguish ice and mixed phase clouds from water clouds.
I think the authors demonstrate their point that fairly fundamental radiation measurements can be used at sites in the Antarctic. However, I am not sure that the measurements that they suggest can detect the 10 W/m^2 changes in the net surface radiation (lines 56-58) that could induce ice melt or retention.
Line 70: indicates about six weeks of measurements; is that correct?
Line 150: "... one describes with a unique large-scale circulation pattern." Is this correctly stated?
A map with labels of the geographic sites discussed in these paragraphs would help for those not familiar with Antarctic geography.
Citation: https://doi.org/10.5194/egusphere-2023-1665-RC1 -
RC2: 'Comment on egusphere-2023-1665', Anonymous Referee #2, 27 Nov 2023
Review of the research article entitled “Broadband and filter radiometers at Ross Island, Antarctica: Detection of cloud ice phase versus liquid water influences on shortwave and longwave radiation” by Kristopher Scarci et al.. (MS No.: egusphere-2023-1665)
The authors present a study examining the impact of cloud phase and properties on the surface energy budget based on ground-based radiometric instruments, supported by reanalysis and satellite observations. Their primary objective is to establish a connection between flux measurements and cloud properties using basic instruments deployable in remote locations. This holds particular relevance in Antarctica, where even small changes in the net surface budget could initiate melting processes.
The study is well-written, carefully argued and gives important insights into the role of the meteorological patterns and cloud properties on the radiative budget. However, their method is applied to a limited dataset without really enlightening our comprehension of the Antarctica atmospheric system and surface energy balance. As it stands, the research appears more as a proof of concept, aligning more closely with a technical journal such as AMT. To align with the scope and standards of ACP, the study requires a more in-depth analysis to better integrate with the current state-of-the-art research and contribute to a broader understanding of how cloud microphysical properties influence the surface energy budget. Before publication in ACP, several key points need addressing to elevate the study to meet the journal's standards.
Major comments:
1. Scientific significance for the ACP community
The authors leverage data from the AWARE campaign (2015-2016) to scrutinize the impact of atmospheric regimes and cloud properties on the surface radiative budget. However, the study encounters limitations arising from the relatively sparse number of samples, both spatially and temporally (e.g., regime 3 was observed on only 8 days), prompting questions about its representativeness or applicability to Antarctica's meteorological patterns. Notably, the AWARE campaign coincided with a period of high global-average temperatures during an El Niño event, further raising considerations about the broader context of the study.
To enhance the paper's scope, it is imperative to contextualize the analyses and conclusions within the current state-of-the-art research. This approach not only amplifies the scientific significance but also extends the benefits to a broader community. In this regard, I make a couple of suggestions, non exhaustive, that could help enhancing the generalizability of the conclusions:
- The identification of the meteorological regimes could be put in the context of the paper from Scott et al., 2019. How the four clusters identified in this paper relate to the nine they found, and to the trends they identified.
- The cloud properties retrieved in this study could be compared with the literature you cited. For example, the histogram of cloud optical depth (Figure 5) could be compared with the exponential fit of Fitzpatrick and Warren (2005).
2. Up-to-date reanalysis data set
ERA-5 replaced ERA-Interim in 2019, with improved vertical and spatial resolutions, and a newer Integrated Forecasting System. The ECMWF reanalyses are still experiencing warm biases for 2 m air temperature in polar regions, when compared with ground-based observations, with significant differences between ERA-Interim and ERA5 (see, for example, Jonassen et al., 2019; King et al., 2022; Zhu et al., 2021; Wang et al., 2019).
In the manuscript, you wrote “For the purposes of this work ERA5 reanalyses are essentially identical to ERA-Interim.”, but I would like to understand what the expected implications are on the identification of the meteorological regimes. I understand that updating the reanalysis data set would require a lot of work, but could you justify why it is not necessary, and if it is supported by the AWARE observations.
3. Geographical components
The paper is missing a map locating the ARM sites and the regions mentioned in the manuscript. It is important for the readers that are not very familiar with those regions, to better understand the links with the meteorological regimes. See for example the figure 1 in Scott et al., 2019, or the figure 1 in Silber et al., 2019.
Also, the Figure 1 shall contain latitudes and longitudes.
4. Relevance of the MODIS cloud products for this study
In this paper, MODIS is used to assess the cloud phase and cloud top height over McMurdo station during the summer 2015-2016. As mentioned in the manuscript, some improvements have been made in polar regions (Frey, 2008), but inconsistencies still exist for cloud occurrence (e.g., Cossich et al., 2021; Marchant et al., 2016) and cloud properties (e.g., Wilson et al., 2018).
The impact of uncertainties in MODIS products on the analysis is an important consideration, especially in the presence of multi-layer clouds or potential omissions of certain clouds. The authors should explicitly address how these uncertainties might affect their findings, particularly in scenarios where the MODIS data might misrepresent the actual cloud conditions.
My other comment is why the authors didn’t use ground-based observations (radar, lidar, ARM VAP, …) acquired during AWARE to do their analysis or at least assess the cloud properties retrieved by MODIS (see Minor comment below on Figures 2 and 3).
Minor comments:
Page 4, Line 123: In the past, maintaining the measurement quality of some radiometers has been a challenge when used in polar environment, for example when icing appears on the optics (Cox et al., 2021). Could you comment on how feasible it would be to have the suite of instruments you suggest in unattended remote location?
Page 8, Table 1: The date January 26, 2016, appears 2 times.
Pages 8 and 9, Figure 2a and 3: Could you comment on the differences between MODIS clear sky and TSI cloud cover. For example, MODIS identified clear sky during 15%, while TSI observed clear sky for more than 25 %.
Page 12, Figure 6: The figure may be easier to read using a log-scale.
Page 13, Line 306: Particle size could be another important parameter influencing the emissivity of optically thin clouds.
Pages 14 and 16: Figures 7 (b, d, f) and 8 (b, d, f): It would be easier to read with the dashed line crossing 0 on both y-axes, and a legend for the red bars and black line.
References:
Cossich, W., Maestri, T., Magurno, D., Martinazzo, M., Di Natale, G., Palchetti, L., Bianchini, G., and Del Guasta, M.: Ice and mixed-phase cloud statistics on the Antarctic Plateau, Atmos. Chem. Phys., 21, 13811–13833, https://doi.org/10.5194/acp-21-13811-2021, 2021.
Cox, C. J., Morris, S. M., Uttal, T., Burgener, R., Hall, E., Kutchenreiter, M., McComiskey, A., Long, C. N., Thomas, B. D., and Wendell, J.: The De-Icing Comparison Experiment (D-ICE): a study of broadband radiometric measurements under icing conditions in the Arctic, Atmos. Meas. Tech., 14, 1205–1224, https://doi.org/10.5194/amt-14-1205-2021, 2021.
Fitzpatrick, M. F., & Warren, S. G.: Transmission of Solar Radiation by Clouds over Snow and Ice Surfaces. Part II: Cloud Optical Depth and Shortwave Radiative Forcing from Pyranometer Measurements in the Southern Ocean. Journal of Climate, 18(22), 4637-4648. https://doi.org/10.1175/JCLI3562.1, 2005.
Jonassen, M. O., Välisuo, I., Vihma, T., Uotila, P., Makshtas, A. P., & Launiainen, J.: Assessment of atmospheric reanalyses with independent observations in the weddell sea, the antarctic. Journal of Geophysical Research: Atmospheres, 124, 12468–12484. https://doi.org/10.1029/2019JD030897, 2019.
King, J. C., Marshall, G. J., Colwell, S., Arndt, S., Allen-Sader, C., & Phillips, T.: The performance of the ERA-Interim and ERA5 atmospheric reanalyses over Weddell Sea pack ice. Journal of Geophysical Research: Oceans, 127, e2022JC018805. https://doi.org/10.1029/2022JC018805, 2022.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Scott, R. C., Nicolas, J. P., Bromwich, D. H., Norris, J. R., and Lubin, D.: Meteorological drivers and large-scale climate forcing of West Antarctic surface melt, J. Clim, 32, 665-684, doi:10.1175/JCLI-D-18-0233.1, 2019.
Silber, I., Verlinde, J., Cadeddu, M., Flynn, C. J., Vogelmann, A. M., and Eloranta, E. W.: Antarctic cloud macrophysical, thermodynamic phase, and atmospheric inversion coupling properties at McMurdo Station - Part II: Radiative impact during different synoptic regimes, J. Geophys. Res., 124, 1697-1719, doi:10.1029/2019JD029471, 2019
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019.
Wilson, A., Scott, R. C., Cadeddu, M. P., Ghate, V., & Lubin, D.: Cloud optical properties over West Antarctica from shortwave spectroradiometer measurements during AWARE. Journal of Geophysical Research: Atmospheres, 123, 9559–9570. https://doi.org/10.1029/2018JD028347, 2018.
Zhu J, Xie A, Qin X, Wang Y, Xu B, Wang Y. An Assessment of ERA5 Reanalysis for Antarctic Near-Surface Air Temperature. Atmosphere, 12(2):217. https://doi.org/10.3390/atmos12020217, 2021.
Citation: https://doi.org/10.5194/egusphere-2023-1665-RC2 -
AC1: 'Comment on egusphere-2023-1665', Dan Lubin, 08 Jan 2024
The reviewers have made some valuable suggestions and corrections to significantly improve the quality of the manuscript. All of these suggestions are straightforward to implement, as discussed in detail within the supplemental PDF, and we look forward to preparing a stronger paper in response to these reviews.
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