the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Variability and properties of liquid-dominated clouds over the ice-free and sea-ice-covered Arctic Ocean
Marcus Klingebiel
André Ehrlich
Elena Ruiz-Donoso
Nils Risse
Imke Schirmacher
Evelyn Jäkel
Michael Schäfer
Kevin Wolf
Mario Mech
Manuel Moser
Christiane Voigt
Manfred Wendisch
Abstract. Due to their potential to either warm or cool the surface, liquid-phase clouds and their interaction with the ice-free and sea-ice-covered ocean largely determine the energy budget and surface temperature in the Arctic. Here, we use airborne measurements of solar spectral cloud reflectivity obtained during the ACLOUD campaign in summer 2017 and the AFLUX campaign in spring 2019 in the vicinity of Svalbard to retrieve microphysical properties of liquid-phase clouds. The retrieval was tailored to provide consistent results over sea-ice and open ocean surfaces. Clouds including ice crystals that significantly bias the retrieval results were filtered from the analysis. A comparison with in-situ measurements shows a good agreement with the retrieved effective radii and an overestimation of the liquid water path and a reduced agreement for boundary-layer clouds with varying fractions of ice water content. Considering these limitations, retrieved microphysical properties of clouds observed over ice-free ocean and sea-ice in spring and early summer in the Arctic are compared. In early summer, the liquid-phase clouds have a larger median effective radius (9.5 µm), optical thickness (11.8) and liquid water path (72.3 g m-2) compared to spring conditions (8.7 µm, 8.3, 51.8 g m-2, respectively). The results show larger cloud droplets over the ice-free Arctic Ocean compared to sea-ice in spring and early summer caused mainly by the temperature differences of the surfaces and related convection processes. Due to their larger droplet sizes the liquid clouds over the ice-free ocean have slightly reduced optical thicknesses and lower liquid water contents compared to the sea-ice surface conditions. The comprehensive data set on microphysical properties of Arctic liquid-phase clouds is publicly available and could, e.g., help to constrain models or be used to investigate effects of liquid-phase clouds on the radiation budget.
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Marcus Klingebiel et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2022-848', Anonymous Referee #1, 21 Mar 2023
This paper presents a fairly straightforward application of a bispectral retrieval for liquid cloud optical properties from airborne data in the arctic. There is a limited validation of the retreival against in-situ probe data. It is found that the effective radius agrees well with the in-situ data whereas the liquid water path can be overestimated, presumably due ot the presence of ice. The retrieval is applied to two deployments in different seasons and the optical properties from two seasons are contrasted. I have a few minor comments listed below and two more significant comments. First, I would like to see the optical depth retreival validated to explain the liquid water path biases. Second, I believe there is lidar data for these flights and I would like to see the lidar data compared to bispectral retreivals in addition to the radar since the two instruments together provide a more complete picture of hydrometeor phase throughout the vertical profile.
Line 86: please describe the simulations of the spectral flux.
Line 90: Can you provide some estimate of uncertainty in the phase identification either from the references or collocated measurements (e.g. lidar? or in-situ probes). What is the False Alarm Rate, Probability of Detection etc.?
Figure 3: you should spell out MIZ in the figure caption.
Line 150: neglection -> neglect
Line 188: does -> do
Line 215: obtains -> results
Equation 4: how are zbase and ztop chosen? Is this the entire profile or only the liquid cloud layer at the top of the profile?
Line 233- 236: This paragraph is not written clearly. Please rewrite it for clarity. What do you mean by both cloud layers? Do you mean that the integral is over the entire liquid water content profile? In lIne 235, what does the ‘first section’ refer to?
Section 4.2: You have the measurements to be more quantitative with regard to the bias caused by ice crystals on you retrieved LWP. You should convert the liquid and ice drop size distributions to optical extinctions. Then you can integrate the liquid + ice extinction and compare that with your retrieved optical depths. This will allow you to demonstrate to what extent the scattering by ice crystals is biasing your LWP. You should include profile plots of the calculated liquid and ice extinction.
Section 4.3 From what I can tell from Wendisch, 2019 it seems like there was an airborne lidar flying as well. You should be able to get a much more precise idea of cloud top phase using the lidar LDR and backscatter.
Figure 9: change a-e -> a-d.
Citation: https://doi.org/10.5194/acp-2022-848-RC1 - AC1: 'Reply on RC1', Marcus Klingebiel, 19 Jul 2023
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RC2: 'Comment on acp-2022-848', Anonymous Referee #2, 10 Apr 2023
Summary:
This manuscript presents results from arctic airborne campaigns (ACLOUD and AFLUX), where they measured low arctic clouds over sea ice and open sea. The manuscript is very well written and is quite pertinent to ACP, particularly with respect to advances in the Arctic low cloud, which remains highly difficult to measure.
This is great manuscript to read, however there are a few minor comments to address, mostly on some clarification of some points (see list below). After these minor comments are addressed, it is recommended for publication in ACP.
General Comments:
- There is combined measurements of in situ cloud drop/ice crystal sizes and remote sensing measurements. While this may be outside the scope of the paper, at least a mention on the actual shape of the size distribution should be included. It would be interesting to see how that matches the commonly expected gamma distribution, with alpha =7 that are typically used in Nakajima & King bi-spectral retrievals for quantifying the effective radius.
- The date format for ACP is dd month yyyy, e.g., 25 July 2007. There are a few instances of a varying date format.
- The spectral slope in the measured snow albedo leaves to believe that there may be other factors, like haze or aerosol layer, present. While that may be not so important given much of the remote sensing is focused on the near infrared regions, at least a mention of the haze/aerosol conditions should be made, and if available more details on how that would impact these retrievals. A note that haze was present is found later in manuscript (line 313). Potential impact of this layer should be explored
Specific Comments:
- Line 24: What is (TR 172)? If it is a reference, then it is not in the reference list.
- Line 38: Please add the caveat that cloud top properties is from passive remote sensing from reflectances, not all passive remote sensing techniques, see Platnick 2000. Additionally, some active (lidar) techniques are also limited to the topmost portion of the cloud. There are transmitted-light based passive remote sensing that have a more even distribution of sampling through the cloud. e.g., McBride et al., 2011, LeBlanc et al., 2015, and Smith et al., 2017
- Figure 2: For that many drop sondes, one wonders how representative are these averages? What is the deviation to the median, and the standard deviation?
- Line 73: I’m not certain that the reference to the AISA Hawk instrument requires the book Pu 2017.
- Line 77-79: Why is there missing measurements? Instrument issues, lack of cloud, or measurement quality is not sufficient?
- Line 84: How accurate are the simulated downwelling irradiance? Did you remove the conditions with high clouds? What were the sun angles modeled?
- Section 2.4: What is the expected uncertainty in the combined in situ cloud probes for effective radius, LWC and IWC?
- Line 121: How low were the sun angles? Arctic often suffers from sun being near the horizon which are hard to model and measure.
- Line 137: The spectral shape of the measurements vs the modeled snow albedo, particularly in the visible, (shorter wavelength range), seems to indicate that there is something else ins the measurement scene that is not accounted for by the model. Is there any indication of aerosol near surface? Additionally, there may be issues with the Langley scattering in the modeled radiances. At the very least, please explain why you have solely attributed the differences to snow grain size and the stratification.
- Line 150-152: This is good to identify potential 3D radiative transfer issues, however the abstract and other sections of the text do not make such a distinction, and presents the effective radius and LWP as equally valid. Maybe some bounding of the expected error for Ref, tau, and LWP should be mentioned. A citation might be all that is needed, like Schäfer et al., 2015.
- Line 215: grammar error: “obtaines in”
- Figure 8 gives a great statement to how well the filtering process is successful.
- Line 277: How many days/cases does the 2% of the data represent?
- Line 355: many question marks: bad format or is the author unsured that the document is in preparation?
- Data availability: There is no link to the access of the data, but rather a list of papers that describe it.
References:
LeBlanc, S. E., Pilewskie, P., Schmidt, K. S. and Coddington, O.: A spectral method for discriminating thermodynamic phase and retrieving cloud optical thickness and effective radius using transmitted solar radiance spectra, Atmos. Meas. Tech., 8(3), 1361–1383, doi:10.5194/amt-8-1361-2015, 2015.
McBride, P. and Schmidt, K.: A spectral method for retrieving cloud optical thickness and effective radius from surface-based transmittance measurements, Atmos Chem …, 11(14), 7235–7252, doi:10.5194/acp-11-7235-2011, 2011.
Platnick, S.: Vertical photon transport in cloud remote sensing problems, J. Geophys. Res., 105(D18), 22919–22935, 2000.
Schäfer, M., Bierwirth, E., Ehrlich, A., Jäkel, E. and Wendisch, M.: Airborne observations and simulations of three-dimensional radiative interactions between Arctic boundary layer clouds and ice floes, Atmos. Chem. Phys., 15(14), 8147–8163, doi:10.5194/acp-15-8147-2015, 2015.
Smith, W. L., Hansen, C., Bucholtz, A., Anderson, B. E., Beckley, M., Corbett, J. G., Cullather, R. I., Hines, K. M., Hofton, M., Kato, S., Lubin, D., Moore, R. H., Rosenhaimer, M. S., Redemann, J., Schmidt, S., Scott, R., Song, S., Barrick, J. D., Blair, J. B., Bromwich, D. H., Brooks, C., Chen, G., Cornejo, H., Corr, C. A., Ham, S. H., Kittelman, A. S., Knappmiller, S., LeBlanc, S., Loeb, N. G., Miller, C., Nguyen, L., Palikonda, R., Rabine, D., Reid, E. A., Richter-Menge, J. A., Pilewswskie, P., Shinozuka, Y., Spangenberg, D., Stackhouse, P., Taylor, P., Thornhill, K. L., Van Gilst, D. and Winstead, E.: Arctic radiation-icebridge sea and ice experiment: The Arctic radiant energy system during the critical seasonal ice transition, Bull. Am. Meteorol. Soc., 98(7), 1399–1426, doi:10.1175/BAMS-D-14-00277.1, 2017.
Citation: https://doi.org/10.5194/acp-2022-848-RC2 - AC2: 'Reply on RC2', Marcus Klingebiel, 19 Jul 2023
Marcus Klingebiel et al.
Marcus Klingebiel et al.
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