Cloud liquid water path at the North Slope of Alaska is largely insensitive to local meteorology in Arctic winter

Hartig, Kara; Cassano, John J.; Shupe, Matthew D.; Solomon, Amy

doi:10.5194/egusphere-2026-2426

Preprints

https://doi.org/10.5194/egusphere-2026-2426

Preprints

08 May 2026

| 08 May 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Cloud liquid water path at the North Slope of Alaska is largely insensitive to local meteorology in Arctic winter

Kara Hartig, John J. Cassano, Matthew D. Shupe, and Amy Solomon

Abstract. Mixed-phase clouds in the Arctic are tightly coupled to the surface energy budget in the cold months, helping to set surface temperatures and sea ice extent. However, the meteorological conditions that give rise to these clouds and their remarkable persistence across the Arctic are not well constrained, leading to model biases. Using over a decade of observations from the North Slope of Alaska, this research investigates the relationships between cloud base temperature and moisture, bulk atmospheric moisture, wind direction, large-scale circulation, and cloud liquid and ice water path. Liquid-containing clouds are ubiquitous at this site, occurring 60–70 % of the time between November and March, although about half of those cases have a liquid water path under 10 g m^-2. We find that liquid water path is remarkably insensitive to temperature, moisture, wind direction, and large-scale circulation. Furthermore, meteorological regimes with significant differences in temperature, moisture, and cloud fraction do not produce appreciable differences in cloud liquid water path. Ice water path, on the other hand, is correlated with bulk atmospheric moisture, with particularly strong increases when precipitable water vapor exceeds the 90^th percentile, and may be responsible for the muted response of liquid water path to high atmospheric moisture. To explain the observed sensitivity of ice water path and insensitivity of liquid water path to meteorology and large-scale circulation, we propose a series of hypotheses centered around continuous radiative cooling in a stable environment and the role of ice in enabling or limiting liquid mass accumulation.

Received: 27 Apr 2026 – Discussion started: 08 May 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Kara Hartig, John J. Cassano, Matthew D. Shupe, and Amy Solomon

Status: open (until 19 Jun 2026)

Post a comment Subscribe to comment alert

RC1: 'Comment on egusphere-2026-2426', Anonymous Referee #1, 01 Jun 2026 reply

Review of „Cloud liquid water path at the North Slope of Alaska is largely insensitive to local meteorology in Arctic winter” by Kara Hartig, John J. Cassano, Matthew D. Shupe, and Amy Solomon
In this paper, the authors analyzed more than a decade of winter observations (2011–2023) from the North Slope of Alaska to investigate how Arctic cloud liquid water path (LWP) and ice water path (IWP) relate to temperature, moisture, wind direction, and large-scale atmospheric circulation. They combined radiosonde, radar, microwave radiometer, and ceilometer measurements. They found that liquid-containing clouds occur very frequently (about 60–70% of the time), but their liquid water content shows little sensitivity to local meteorological conditions. In contrast, ice water path increases strongly with atmospheric moisture, especially during very moist events, suggesting that excess moisture is preferentially converted into ice rather than liquid cloud water. Based on these findings, the authors propose several hypotheses involving continuous radiative cooling and cloud–ice interactions to explain why Arctic winter cloud liquid water remains remarkably stable across different weather regimes.
This is a well-written manuscript with a clear methodology and a carefully designed analysis. The individual processing and analysis steps are described in sufficient detail, making the study easy to follow and the results transparent and reproducible. The long observational record and the comprehensive evaluation of different meteorological regimes provide a strong basis for the conclusions.
However, I believe that some aspects would benefit from a more nuanced discussion. In particular, I don’t agree with the authors’ conclusion that liquid water path (LWP) is largely insensitive to temperature and moisture. While the presented analyses demonstrate a weaker dependence than might be expected, several results still indicate systematic and significant variations of LWP with both cloud-base temperature and atmospheric moisture. I think that a more nuanced wording is required that can be easily implemented. In this context, minor revisions are needed. However, since the conclusions change, this is rather a major change to the manuscript.
Major comments:
In Fig. 2, we clearly see an increase in LWP, i.e., a reduction in the share of thin cases and an increase in the share of opaque cases. So I would argue that we clearly see LWP sensitivity to temperature.
The authors state in line 245 ff: “Rather than shift with temperature, the liquid water path distribution changes shape, preserving both very low and very high values at almost all temperatures in the supercooled liquid range.” I’m a little puzzled by this sentence. Why did you expect a shift in the distribution? As the clouds that you sample cover also different lifetime stages, I would also expect, in high IWV environments, cases of low LWP. This implies, of course, that if higher LWP values become more likely, the pdf shape will change.
Can you also add a figure with LWP boxplots, as in Fig. 3, and examine differences in the LWP distributions (as in A5, but for different cloud base temperature regimes)? I think it is crucial to compare the LWP distributions among themselves, not just to the climatology. When looking at the counts for T > -5°C and T > -30°, your sample size is very limited, so I would be cautious in interpreting the results for these regimes. You may also explicitly mention this in the manuscript. If you compare the LWP pdfs for different cloud base temperature regimes (following the methodology for Fig. A5) are they significantly different?
Also, for different PWV regimes, the LWP distributions differ significantly (Fig. A5). I think this figure is crucial. So, I don’t see evidence to support the general claim that LWP is insensitive to moisture.
Due to the above reasons, please adjust your wording throughout the manuscript. This also implies adjusting the manuscript title.

Minor comments:
line 20: rather use “environments”
Figure captions in general: Just explain what is seen in the figure and don’t start with an interpretation of the results (e.g., as in Fig. 2).
Figure 2: I find it confusing to start with b). Simply combine a, b, c in (a) I see the pdfs being a part of (a); for clarity, please use “ Cloud base temperature” on the x-axis

(e) can you simply add the count as a written number on top of the columns of (e)? (f) can then be removed. It is hard to read the numbers from (f) anyway.
Figure 3: Can you add the counts (=sample size) as numbers on top for each PWV percentile bin? Why can you get negative LWP values even though a physical retrieval approach is applied (and not a simple regression where I would expect also negative values to occur)? How does the forward model deal with negative liquid water values?
lines 274-276: I find these sentences confusing. Can you rephrase that section? Also, the median LWP value for cases with PWV>90^th percentile is 180 gm-2. Where do you see 50 gm -2?
line 276: “…so they clearly do not require particularly moist conditions to form”.

Please be more specific here: “They do not require particularly high PWV conditions to form.” You don’t know anything about the vertical moisture structure and potential moist layers.
Fig. 5: Can you provide the sample size for each node? Just a number next to Node[x,y]
line 308: “lower left corner”

It is easy to follow when you mention “lower left corner” etc., but please add the exact figure number in these cases as well, i.e., here “(Fig. 5i)”
lines 325-327: “do not even deviate from climatology in the same direction”. This is unclear to me. Please rewrite.
Figure 7: Can you also compare the LWP/IWV distributions among each other (as in Fig A5) and integrate the results in the manuscript?

Reply

Citation: https://doi.org/10.5194/egusphere-2026-2426-RC1
RC2:
'Comment on egusphere-2026-2426', Anonymous Referee #2, 15 Jun 2026 reply
Review of TC egusphere-2026-2426
This is an interesting research paper and examines potential relationships between Arctic cloud characteristics and large-scale environmental conditions using combinations of observations and reanalysis data products. This work is a nice example of leveraging a wealth of co-located observations at a long-term Arctic site (North Slope of Alaska) to quantify cloud characteristics as well as use this information to investigate and determine the physical processes that are the major drivers for these clouds. The paper is novel and well-written, but there are some key pieces of information missing that would help to clarify the results and subsequent discussion. Additionally, additional information about the uncertainties and methods for determining the categorization of the extremely low LWP clouds is needed. I think after addressing some of the major and minor points below, this paper will be an important addition to the mixed-phase Arctic cloud discourse. I would appreciate the authors investigating and / or clarifying some of the following questions and comments.
Major Points:
The Data and Methods section needs more information / clarity and details to help better interpret the results. Also, the addition of some subheadings would help guide the reader as each paragraph tends to jump to a new instrument and / or method. Specifically, as to the following topics:
Is precipitation included / excluded in the samples used to create the cloud statistics (during determined cloudy times)? I assume that they are included as written, but it would help to clarify this point as it is only mentioned on L109 w.r.t. the Ka-Band radar observations. As precipitation (rain (there have been wintertime rain events at NSA), snow, mixed – all in different ways) can impact observations and retrievals (depending on the instrument) in different ways, it is important to address how these impacts may / could potential bias the results. Some precipitation may directly affect measurements (flagged or removed data due to attenuation / instrument limitations), and while clouds are the focus it is important to communicate some of this information to the readers (in the supplement / appendix).

For the averaging of observations for the hour after the related sounding, some added details would be helpful. The statement “For each radiosonde launch time, data from the cloud radar, microwave radiometer, and ceilometer are averaged over the following hour to provide measurements corresponding to that sounding.” (L132-134) is vague. Averaging indicates taking the mean – are all these observations expected to be normally distributed? Additionally, some of the instances may have clouds on / off in the periods following the sonde or spatially heterogeneity (i.e., broken cloud decks). Does the averaging sufficiently represent these cases (meaning – are “0s” substituted to be included in the mean, or does the instrument have NaNs – so the average is only representing the times in the hour where there were cloud measurements / data)? Related – it is unclear in the averaging over the hour if the instruments need to detect the presence of a cloud the entire time (I do not believe so). Related:
It is stated that “Missing data is ignored in the hourly average.” (L134). Is this missing meaning a bad value in the instrument, or no cloud present? This would imply that the average is more of a conditional average (condition = cloud is present), which would bias the averages higher and ignore the observations when no cloud (within the hour).

It is also stated that “for the cloud radar reflectivity at least half of the

135 times used to calculate the hourly average must have reflectivity measurements to be preserved in the average…” (L134-135). Is the reflectivity averaged (mean) before the properties are calculated? If so, is this first converted to reflectivity factor (mm^6 / m^3)? The mean of the reflectivity is not accurate as it is in log space.

As stated in the paper, there is a lot of focus on the cloud LWP values and categories. As such, there need to be additional clarity and details about the retrieval bias and errors. I also think you need to reconsider the naming of the <10 g/m2 category.
I have familiarity with MWRs and retrievals of LWP / PWV and understand the logic of the reduced uncertainty from 25 g/m^2, but there needs to be more quantification and details to back up these arguments and for communicating to readers with less background on the MWRs. For example, my understanding is that you are using the averaging time to produce “bulk statistics” to demonstrate the reduced uncertainty (L124). The explanations here are vague. I suggest adding the statistical equation / reference for what you are doing. Assuming all uncertainty is instrument noise (more on the validity of that assumption below), then you can say the uncertainty becomes the theoretical uncertainty U divided by the square root of the N (number of samples in average = 60 if 1 min resolution). This would yield a 1-sigma spread similar to what is shown in Fig. A1c. Also, be precise and quantitative. State the actual values of the 1-sigma spread instead of a “few g / m^2”.
Related, the MWR retrieval outputs a specific uncertainty for each retrieval that is run (so at each observation). I suggest also examining these values during the clear-sky conditions and assessing if these vary as a function of environmental conditions to ensure that you are not ignoring biases.
Related to this – did you examine how often the retrieval did not converge (too many iterations) during each category? You can also look at the number of iterations to converge to a valid LWP. This would be a good way to examine and assess the retrieval performance to see if there are potential biases in a specific category.

Also – is it valid to consider this averaging free of uncorrelated errors?

The clear-sky does have a high bias – and this is especially seen in the Arctic. Please clarify your statement on L124 as this is different than the uncertainty reduction with the “bulk statistics.
Cadeddu 2009: This uncertainty, in the high-pressure and low-humidity conditions often present in the Arctic, can cause a positive bias as high as 25 g/m2 in the clear-sky LWP retrievals.

The above “bulk” uncertainty analysis (and yours in the paper) assumes that all error is due to instrument / retrieval errors. However, there could be heterogeneity of the spatial distribution of the clouds over the averaged hour – meaning mismatch between where the radiosonde sees a saturated layer and the conditions directly over the MWR. I would expect this to be more likely during very low cloud LWP conditions. Additionally, cloud phase (ice) could impact the retrievals (less likely with the 2 channel retrievals, but there are scattering and emission effects for ice clouds). The MWR retrieval only has priors for CS, PWV, and cloud LWP in the model, so the iteration will force the retrieval to fit these assumed spectral shapes. The outcome of this, in my opinion, is that the lowest category should not be called “thin” liquid clouds. I do believe that this category contains thin liquid clouds, but it also is possible that there is some clear-sky (spatial heterogeneity), ice clouds (retrieval assumption errors), both more common at these low temperature conditions, and biases (possibly due to environmental conditions). So, I think the naming of the category is overstated and should be more geared to a cloud category that is “below detection limits”.

In the results, the words “anomalies” and “climatology” are used throughout, but there is little / no description as to how these are calculated (and for what / which datasets). There needs to be clarity as to how the anomalies are calculated. Also, the word “climatology” should only apply to atmospheric observational datasets that are >30 years in length (see WMO recommendations). So, I think some clarification / adjustments should be added to the methods.
On L159-160 it is stated that “Finally, anomalies are calculated at each time step 160 by removing the average over the remaining domain from the sea level pressure value at each point.” But it is not explained HOW these are calculated. Are these anomalies based on months of occurrence? 10-day running mean? Seasonal? What is the baseline used? 2000 – 2024? What is the justification for only using the recent 24 years (I recommend extending to 30 years for a true climatology or using 1979 to present [justify why only using past 30 years if that is the case]).

Later in the results, both climatology and anomalies are used when presenting observations from the NSA site (I am assuming, because it often is not explicitly stated). 2011 to 2023 ≠ climatology. You should replace the climatology mentions and labels (figures / figure captions) with the specific time range. Also, any “anomalies” calculated from the 2011 – 2023 baseline should be instead referred to as “departures” or “differences”. Anomalies imply a climatology (>30 years). Also, the method you use to determine the departures / differences for each of the observations you present (radisonde profiles, MWR retrievals) should be outlined. Did you use monthly averaged differences (e.g., Sept obs – Sept ave)? Or did you subtract the entire winter season average (I would argue that this would not be appropriate to find departures or differences.

The results need to be explicit in what you are presenting. There are several Figures / figure captions where it would be helpful to mention the observations (“Radiosonde profiles of…”). It is not always clear where you obtain the data. I am assuming all data but the SOMs are from the measurement site, but please add these details. Are the 500 m winds from the radiosonde? State that when introducing this measurement (or put this information in the data / methods). I suggest taking a critical eye and making sure that it is clear where each figures’ information originates, as it is currently ambiguous.

The Discussion is great! And a really nice way (and novel way) to organize the narrative. I appreciated the very readable and interesting hypotheses.

Lastly – a comment for throughout - I would not say that your results show no response in the LWP to water vapor. I would say that the LWP values are variable and do not show as strong as response as the IWP (for example). The high variability of the LWP dovetails nicely with your discussion of competing and multiple mechanisms.
On a related note, I would say the results in Wedum et al (2026) also more variable cloud LWP observations (in addition to a weaker response) during ARs (high PWV) conditions.

Minor:
In Figure 2, I suggest removing the labels (a) and (b) from the first panel as it is confusing. Usually these PDFs are assumed to be part of the panel.

A couple suggestions for Figure 3: Since there is some motivation and links (maybe) to extremes like atmospheric rivers (ARs), and because there are big jumps >90^th %ile, I suggest breaking out the last bins and adding 95^th and 99^th. This might give some interesting context when thinking about ARs or extremes. Additionally, I would add a panel here (below) with the %-ile categories of temperatures (calculated as monthly or wintertime bulk statistics) and again do 0 – 90^th %ile and then add 95^th and 99^th. I think this would give them results some interesting context when thinking about warm air advection and moisture extremes.

Wind / meteorological discussion and Figure 4: The use of “erly” (e.g., westerly) is very jargony and meteorology specific and can be super confusing to non-met folks. This paper is submitted to ACP, which has broad readership across the atmospheric sciences. Also, it has cross appeal in the polar / cryosphere community. Therefore, I recommend using eastward / westward and north(pole)ward / south(equator)ward (these last two are used A LOT in the polar community). These are more broadly accessible / clearer terms.

Figure 6: these data are from the radiosondes, yes? See comment above about climatology / anomaly usage. Also, how did you calculate these departures (clarify in the methods).

Figure 7: Replace “Climatology” with 2011-2023

The Discussion is great – I would say it would be worth your while to check out this recent paper by Betrand et al (2025): https://www.nature.com/articles/s41467-025-64441-8
I think it is relevant to some of your discussion of the competing methods. Also, a good paper to link to motivation / future conditions at NSA

Check caption for Figure 1A (panel b / c mixed up)

Reply
Citation: https://doi.org/10.5194/egusphere-2026-2426-RC2

Kara Hartig, John J. Cassano, Matthew D. Shupe, and Amy Solomon

Viewed

Total article views: 331 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
270	48	13	331	10	15

HTML: 270
PDF: 48
XML: 13
Total: 331
BibTeX: 10
EndNote: 15

Views and downloads (calculated since 08 May 2026)

Month	HTML	PDF	XML	Total
May 2026	249	36	7	292
Jun 2026	21	12	6	39

Cumulative views and downloads (calculated since 08 May 2026)

Month	HTML	PDF	XML	Total
May 2026	249	36	7	292
Jun 2026	21	12	6	39

Viewed (geographical distribution)

Total article views: 327 (including HTML, PDF, and XML) Thereof 327 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 16 Jun 2026

Short summary

Liquid-containing Arctic clouds have a strong influence on surface temperature and sea ice extent in winter, but how weather conditions impact cloud cover remains a mystery. This study uses observations from northern Alaska to investigate this relationship. We find that, contrary to expectations, the amount of cloud liquid is remarkably insensitive to temperature, moisture, wind direction, and the large-scale atmospheric flow.


Total:	0
HTML:	0
PDF:	0
XML:	0