the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 May 2026
Bias in satellite-derived cloud radiative effect over Arctic sea ice relative to aircraft measurements during ARCSIX
Abstract. The surface radiation budget (SRB) strongly controls the summertime evolution of sea ice and, therefore, plays a key role for the ongoing transformations of the Arctic climate system. Clouds can have a significant impact on the SRB, which is quantified by the cloud radiative effect (CRE). Consequently, continuous, Arctic-wide monitoring of clouds and further factors governing the CRE, including surface and thermodynamic properties, is required. These persistent observations can only be provided by passive remote sensing instruments aboard polar-orbiting satellites. However, cloud detection deficiencies and the lack of accurate surface albedo data over heterogeneous sea ice limit the precision of satellite products and subsequent CRE estimates. Therefore, this study quantifies the accuracy of satellite cloud products, the surface albedo assumed therein, thermodynamic analysis data, and the resulting CRE simulations. To isolate the contributions of individual parameters to the CRE bias, satellite-derived simulation input is consecutively replaced with collocated aircraft observations that were collected over sea ice north of Greenland during the Arctic Radiation–Cloud–Aerosol–Surface Interaction Experiment (ARCSIX) between May and August 2024. It is concluded that clouds warm the surface according to simulations initialized with aircraft measurements, whereas satellite-based CRE estimates suggest a cooling effect. This discrepancy is primarily caused by a negative bias in the assumed surface albedo. Substantial biases are also identified for cloud height and low-level air temperature, but compensating effects and a relatively weak sensitivity of thermal-infrared radiation to these biases mitigate their impacts on the CRE.
- Preprint
(6512 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-2210', Anonymous Referee #1, 09 Jun 2026
-
RC2: 'Comment on egusphere-2026-2210', Anonymous Referee #2, 20 Jun 2026
Review of "Bias in satellite-derived cloud radiative effect over Arctic sea ice relative to aircraft measurements during ARCSIX," egusphere-2026-2210, by Becker et al.
This is a very thorough application of advanced aircraft cloud microphysics and atmospheric radiation measurements toward evaluating and improving satellite surface radiation budget retrievals over the Arctic. While satellite remote sensing is necessary globally, satellite-based surface radiation estimates are critical over the entire Arctic Ocean which has very few surface-based data sets and which is experiencing dramatic sea ice losses in recent decades. The sea ice decline portends a substantial increase in solar energy entering the open water column, which will ultimately have implications throughout the Arctic climate system. The primary conclusion of this study is that low biases in surface albedo parameterizations used in the satellite retrieval algorithms introduce large enough errors in surface radiation budget (SRB) and cloud radiative effect (CRE) retrievals that the satellites often estimate a cloud cooling effect during spring and summer when a cloud warming effect actually occurs over sea ice. This is an important conclusion that merits publication in Atmospheric Chemistry and Physics.
The manuscript is essentially sound and well organized, and should be acceptable for publication after the authors address a few issues:
- In the abstract, lines 11-15, it would be good to make this conclusion quantitative. How large is the satellite surface albedo bias? What are the values (W/m^2) of the actual cooling effect and the erroneous satellite warming effect?
- Line 37. The SRB equation is incomplete. It should include terms for the turbulent fluxes (sensible and latent heat) and a surface conduction term. Orders of magnitudes (or ranges for the Arctic Ocean) should be given in the text, all of which will indicate that the radiative flux terms are the largest.
- In Figure 1, where do the sea ice concentration values come from? (This will be important, below).
- Figure 2 is somewhat unclear. In panel (b) are they stacked aircraft liquid and ice water paths that add up to the total water path? Or do they represent separate traces with the ice water path actually larger than the liquid water path? The blue and white legend just to the right of panel (b) appears to belong to panel (d). Maybe it should be moved underneath panel (d). The color bar at the bottom of the panel (c) plotting area is described as indicating periods of applicable dropsondes (line 243). Is there any significance to the colors?
- Line 186. What is meant by "among manifold other in situ cloud probes"? Does it mean several other cloud probes? Or cloud probes on the aircraft's intake manifold?
- Line 189. Please clarify the sentence as "determine the size of large particles up to 1280 mm. with a resolution of 10 mm..."
- Line 270. "Cloud-induced modifications of the spectral albedo are not corrected for..." This needs some clarification. Surface albedo measured under clouds will be larger than under clear skies, because clouds substantially attenuate near-infrared irradiance compared with the equivalent cloud-free atmosphere (for recent examples, see Lubin et al., 2023, Journal of Climate, JCLI-D-22-0731.1). The correct spectral surface albedo to use as the boundary condition in a radiative transfer model (for clear or cloudy conditions) is the surface albedo measured under clear skies.
- Paragraph comprising lines 277-292. This is the most important clarification needed, as it has a direct bearing on the most important conclusion. In this description of how the satellite algorithms treat the surface albedo over sea ice, there is no mention of how the sea ice concentration is specified. If I were to naïvely design a satellite cloud and SRB retrieval algorithm for use over sea ice, I would start with passive microwave sea ice concentration retrievals from SSM/I etc., available basically since the start of the modern satellite era (and sometimes estimating sea ice type), and then parameterize the albedo as a mixture of open water and ice (and maybe ice type based on well-known field measurements by Grenfell, Perovich and others). Perhaps some refinements could involve reanalysis estimates of recent precipitation. Do these algorithms do anything like this - involving an actual observation of sea ice? Or are they just some sort of climatological parameterization? I don't see how "a rough melt parameterization that depends on the day of the year" (line 280) has any realism without a defensible value of the sea ice concentration. It is important to understand exactly how the satellite retrievals' handling of sea ice works, because the spectral albedos they use (Figures 3 and 6) are substantially too low.
Citation: https://doi.org/10.5194/egusphere-2026-2210-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 296 | 70 | 12 | 378 | 16 | 16 |
- HTML: 296
- PDF: 70
- XML: 12
- Total: 378
- BibTeX: 16
- EndNote: 16
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The manuscript presents a thorough and well-executed study of biases in satellite-derived estimates on cloud radiative effect over Arctic sea ice. The extensive research-aircraft campaign provides a unique and valuable dataset that enables a robust quantitative assessment of these biases. The analyses are comprehensive and make full use of the available satellite and aircraft observations. The care with which the analyses have been conducted is evident throughout the manuscript, giving the reader confidence in the results and their interpretation.
I have a single major comment and several minor suggestions for improvement. The issues raised should be straightforward to address. Overall, I consider this to be a high-quality contribution, and I believe the manuscript is well suited for publication in ACP following minor revisions.
Major comment
The authors should make it clear how the findings of this extensive study advance scientific understanding in the field. At present, the manuscript jumps directly from the final Results subsection (Section 4.3) to the Conclusions. The Conclusions section contains no references to earlier studies, making it difficult to assess the novelty and significance of the reported findings. The manuscript would be strengthened by either adding a dedicated Discussion section that highlights the key advances beyond existing knowledge or by incorporating a concise discussion of these advances, with appropriate references, within the Conclusions section. This would help readers better understand the study's contribution to the field and its scientific impact.
Minor comments
Line 2: Clouds have a significant impact
Lines 10-11: The phrase "between May and August" is ambiguous.
Line 18: specify the period, as the number is very sensitive to it.
Lines 33-34: Include wind forcing on ice dynamics as a factor controlling sea ice evolution. Also, in winter the origin of coldest air masses is often in northern Siberia and Canada.
Line 63: perhaps "continuous ground-based observations and temporary campaign measurements"
Lines 100-101: Do you mean "Moreover, passive satellite imagers do not provide direct information on cloud-base properties. As a result, cloud-base height (CBH) can only be inferred ..."
Lines 154 and 197: Porpoise is a small marine mammal. Perhaps you mean a purposive leg and purposive sampling.
Line 191: It is assumed that
Lines 270-276: Do multiple reflections between the cloud base and snow/ice surface play a role here?
Line 294: What do you exactly mean by aggregation for all cloud walls?
Figure 3. Explain the meaning of the grey bars?
Lines 323-332: Most of the paragraph is about weak correlations but the text ends with very positive remarks, which I found confusing.
Line 367: exhibits a behaviour similar to that in the first phase
Line 401: What do you mean by current assumptions for sea ice melt?
Line 412-414: One more challenge is representation of the the surface scattering layer (SSL) that develops through snow metamorphism and the ice-to-snow transformation process.
Line 518: This assumption seems unrealistic. Were there any alternatives for such an assumption?
Lines 545-551: This subsection is very short bu the strong impacts of surface albedo become very evident in the following sub-section. Consider merging or re-naming the subsections.
Lines 638-641: Clarify the text.
Line 649: "... and should be addressed in upcoming research."
Line 672: "... albedo values assumed ... do not well represent..."