3.1 Introduction to the classification

The FM100 observations may be liquid fog, ice fog (note that we do not distinguish between ice fog and clear-sky precipitation, also known as “diamond dust”), blowing snow, or snow. Thus, it is desirable to classify the probe observations in order to identify the scenes containing liquid fogs. To do this, information about particle phase, precipitation occurrence, the presence of elevated cloud layers, and the likelihood of blowing snow is needed.

The classification procedure is as follows: (1) scenes containing near-surface particles are separated from clear boundary layer scenes using a number concentration (N_c, cm⁻³) threshold in the 10 m FM100, (2) elevated tropospheric cloud layers are identified, followed by (3) occurrences of blowing snow and snow, and then (4) the phase of the particles is determined for all cases where particles were observed near the surface (step 1) but the sky was otherwise clear (step 2) and there was no blowing snow (step 3). Next, we will describe the individual classifications.

3.2 Classification steps

3.2.1 Identification of near-surface particles

A large proportion of the observations at Summit are of particle concentrations with low density (<1 cm⁻³), such as snow and light blowing snow. It is desirable to set a threshold low enough to capture these conditions. Using an FM100, Spiegel et al. (2012) set a threshold N_c>10 cm⁻³ for similar purposes, which is low enough to observe snow (Braham, 1990). Figure 3 shows frequency of occurrence of FM100 observations classified as containing any type of surface-based cloud (red and black lines) as a function of N_c. The other lines in the figure represent classifications of cloud types that are discussed later. Overall, particles are identified ∼80 % of the time at 10 m when the threshold for detection is 10⁻³ cm⁻³. Due to the consistent volume size of the FM100, this is also roughly the lowest N_c it can measure. Therefore, $N_{\mathrm{c}}={\mathrm{10}}^{-\mathrm{3}}$ cm⁻³ is a natural threshold for this study.

Figure 3Percent of time clouds are identified in FM100 data at 2 m (black dashed) and 10 m (red dashed) as a function of threshold in number concentration. Colors show the same for the classifications that are reported in Fig. 5.

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3.2.4 Phase classification

Events composed of ice are distinguished from those composed of liquid using the AERI data, which are collected at intervals of about 20–75 s. These spectra are linearly interpolated to the regular 1 min sampling used for the FM100 data. The imaginary component of the complex index of refraction, which is proportional to absorption, has different spectral dependencies for ice and liquid in the infrared. Previous studies have exploited these dependencies to infer particle phase using spectral differencing techniques (Strabala et al., 1994; Turner et al., 2003). Particle size and habit also exhibit spectral dependencies, which are a large source of uncertainty in these methods, as is uncertainty in water vapor amount and cloud temperature. In general, the uncertainty also increases for optically thin clouds because of reduced signal and for optically thick clouds because of loss of spectral structure (Turner, 2005). However, the spectral differencing approach is justified for this work because the cases of interest are less sensitive to the associated uncertainties. First, atmospheric transmission between the surface and the cloud is close to unity in the dry Arctic atmosphere (Turner et al., 2003) and can be assumed to be unity for the present purposes because the focus is on clouds with bases at the surface. Second, cloud temperature is well-characterized because it is measured in situ. Finally, because the scenes that are tested feature fogs that were observed when the sky was otherwise clear, it is reasonable to assume that these scenes were single-layer, negating ambiguity from multiple cloud layers.

Calculations of cloud emissivity are used for the phase identification using spectral microwindows that are sensitive to cloud detection (Turner et al., 2003). The necessary radiative transfer calculations were performed using the Line-by-line Radiative Transfer Model (LBLRTM), version 12.2 (Clough et al., 2005). Inputs to LBLRTM include twice daily radiosonde profiles from Summit and estimates of trace-gas profiles, as described by Cox et al. (2014). A small positive bias common in AERIs (less 0.5 mW m⁻² sr⁻¹ (cm⁻¹)⁻¹) was estimated empirically for each microwindow by comparison to the radiative transfer calculations during clear days and was subtracted from the microwindow radiance before analysis.

We use two spectral cloud emissivity differencing tests. The first, adapted from Strabala et al. (1994), is a threshold set to the 11 minus 12 µm emissivity versus the 11 minus 8.1 µm emissivity. The threshold to separate the clusters was qualitatively set to $y=-\mathrm{2.5}\left({\mathit{\epsilon }}_{\mathrm{11}-\mathrm{12}}\right)$. As indicated by the example in Fig. 4a, the clusters separate distinctly and therefore the precise slope of the threshold is not important. The second test is applied in the far-infrared spectral region where ice and liquid absorption characteristics are different and determines whether the 11 minus 17.8 µm emissivity is greater or less than zero (see example in Fig. 4b). If both tests agree, the identification is deemed valid. If the tests disagree, the observation is considered ambiguous. Ambiguous identifications are expected when the microwindow differences are near zero, which typically occurs when the emissivity of the fog is low. However, Fig. 4b also indicates that a small number of cases are not clustered as expected in the far-infrared region, even when the microwindow signal differences are large (red points near the center of the figure). The reason for these ambiguous identifications is not known and their source may be from instrumental errors or environmental conditions. These cases represent <1 % of the data, and the methodology has successfully screened them out.

Figure 4Example of phase classification using microwindow cloud emissivity measured by the AERI for the month of June. Panel (a) shows the first test described in the text and panel (b) shows the second. The colors indicate the classification. Only scenes containing identified events where the AERI 11 µm emissivity was >0.02 and no upper-level clouds were detected are shown (n=4527).

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3.3 Summary of classification

Figure 5 summarizes the fractional occurrence of the classified cloud types. Particles were observed in the lowest 10 m for a majority of the time in all months, from 65 % of the time in July to >85 % of the time in winter. Liquid fogs under otherwise clear skies were classified between 1 % of the time (April) and 12 % of the time (September); if liquid fogs could be reliably detected in the presence of tropospheric clouds, these percentages would likely be higher. While most common in late summer, liquid fogs were also identifiable 3 %–7 % of the time during January–March. The fact that there were fewer ice identifications than liquid identifications does not necessarily indicate that in situ formation of ice near the surface at Summit is less common than liquid formation for three reasons: (1) the number density of liquid fog is expected to be higher, so liquid is more likely to be identifiable using the AERI, (2) some ice fog events may be incorporated within the blowing snow or snow categories, and (3) the phase partitioning for events from which phase could not be retrieved using the AERI is unknown.

Figure 5Composite monthly frequencies of occurrence of each classification. BLSN and SN refer to blowing snow and snow, respectively. Precipitation occurrence is the sum of SN and BLSN+SN.

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The classification scheme reveals several key features of hydrometeor occurrence over the Greenland Ice Sheet:

1. Precipitation occurred predominantly from July through to October (similar to the study by Pettersen et al., 2018). This seasonal cycle is shifted later in the year compared to total precipitation amount, which peaks in July (Castellani et al., 2015).

2. In months when blowing snow occurred most frequently (winter and spring), precipitating snow was less common; and during summer when most of the precipitating snow occurred, blowing snow was least common.

3. A high frequency of events (composite annual average =27.3 %) was detected by the 10 m FM100 under tropospheric clouds, which are common at Summit (Miller et al., 2015).

4. Identification of ice fog was less frequent than liquid fog and ice fog had a distinctly different seasonal cycle than that of liquid, with a peak in April (9.2 %) and a low in July (0.3 %).

4.1 Case of 16 June 2013

Clear skies persisted from 15 to 24 June 2013, and liquid fogs were observed in the early morning on most days during this period. While this case represents an ideal illustration of fog formation at Summit, several similar cases can be found in the dataset. Figure 6a and b show the time–particle-size cross sections of N_c from the FM100s beginning at 20:00 UTC on 15 June extending through 14:00 UTC on the 16 June. During this period, the 10 m air temperature was −15 to −22 ^∘C with light southerly winds (Fig. 6e). At 10 m, condensate first occurred ∼00:10 UTC as the solar elevation angle (SEA) dipped to 10^∘ (<20 W m⁻² SW_net, Fig. 6d) and 7 h into development of the surface-based inversion (Fig. 6d). Initiation was followed by a period of uniform growth lasting 4–5 h that closely aligns with a theoretical growth curve (Houghton, 1985), calculated assuming a constant supersaturation of 0.1 % (Fig. 6a). During the first hour of growth, the droplets reached approximately 25 µm in diameter after which the growth slowed and deviated from the theoretical curve, with particles eventually reaching ∼40 µm. The curve neglects the gradual decrease in supersaturation that accompanies condensation without replacement of moisture, implying a decrease in supersaturation during development and indicating that the moisture source did not introduce new vapor as quickly as it was condensed. While most of the droplets closely followed the main growth curve, nucleation of new droplets continued until about 03:00 UTC. The fog disappeared completely by ∼10:30 UTC when the sun reached an elevation angle of ∼30^∘.

Figure 616 June 2013 case study. Number concentration and particle diameter from FM100 at (a) 10 m and (b) 2 m. (c) Total number concentration (N_c) in all bins for the 10 m FM100 (red) and the 2 m FM100 (black). (d) Temperatures at surface (skin), 2 and 10 m (reds) and downwelling shortwave radiation (SWD) and net shortwave radiation (SW_net) (blues). (e) Wind direction (blue) and velocity (red). (f) Longwave cloud radiative effect (LWCRE) and total cloud radiative forcing (CRF) (reds) and liquid water path (LWP) (blue). (g) Latent (LH) and sensible (SH) (reds) turbulent fluxes and net flux (blue). The white dashed line in panel (a) is a theoretical growth curve calculated from Houghton (1985).

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The sodar detects thermal turbulence where mixing occurs within a vertical temperature gradient. Note that structure in the sodar record may only reflect structure in temperature and not necessarily the boundaries of the fog; although in some cases radiative cooling at the top of the fog may account for the thermal contrast that is observable with the sodar. The sodar record shows a typical pattern of a “nighttime” stable surface layer 10–25 m deep before 07:00 and after 20:00 UTC (Fig. 7a). This surface layer represents a shallow, but strong, temperature inversion embedded within a deeper inversion that extended about 100 m above the surface at initiation, further deepening to several hundred meters 12 h later. The surface layer increased in depth from about 15 to about 65 m during the course of the duration of the fog layer, transitioning from statically stable to a shallow convective layer in association with the diurnal cycle (Fig. 7a). The convective plumes are visible in Fig. 7a as vertically oriented echoes below the red dashed line with intervals on the order of minutes. Dissipation occurred shortly after the onset of convection ∼08:45 UTC (SEA ∼23^∘, SW_net=61 W m⁻², SWD =426 W m⁻²), and the fog disappeared when the convection reached a developed stage around 10:00 UTC (SEA ∼29^∘, SW_net=87 W m⁻², SWD =562 W m⁻²).

Figure 7Sodar facsimile records between the surface and 160 m height for the summer case (16 June 2013, a). Panel (b) shows an expansion of a brief period from 03:23 to 03:37 UTC and 0–40 m height from panel (a) to highlight the Kelvin–Helmholtz instabilities observed at this time. Panel (c) is similar to (a) but for the winter case on 16 January 2014. Dark features that extend to the top of the plot in panel (c) (e.g., near 20:00 UTC) are likely noise from station activities and the horizontal feature in panel (c) near 40 m height is due to reflections of a side lobe from an object nearby on the ice surface.

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Particles were observed at 2 m 10–20 min after initiation at 10 m. Both the N_c and the width of the size distribution were larger at 2 m than 10 m, consistent with particle formation near 10 m followed by settling and evaporation (∼ 0.01 m s⁻¹). At Summit, condensation in radiation-induced fog frequently occurs near 10 m because nonlinearity between temperature and saturation within the inversion leads to supersaturation first between 3 and 18 m, with lower vapor pressures both above and below this layer (Berkelhammer et al., 2016). A lag in N_c of 3 to 4 min between 10 and 2 m near the peak particle size is evident in the time series (Fig. 6c), implying a maximum settling rate of 0.03 to 0.04 m s⁻¹. Theoretical calculations of settling rates assuming laminar conditions following Pruppacher and Klett (2010) agree with these estimates. Specifically, the calculations indicate settling rates up to 0.01 m s⁻¹ during the first hour of the case and 0.03–0.035 m s⁻¹ during the mature phase of the fog between 06:00 and 08:00 UTC.

Between 02:00 and 05:00 UTC, the 2 m air temperature deviated by $\sim +\mathrm{5}$ ^∘C from the smooth diurnal cycle that is evident both before and after the fog, and the LWP increased to ∼15 g m⁻² with no clouds observed above the fog layer by the radar or lidar. This corresponded to an increase in CRF from <10 to ∼65 W m⁻², caused primarily by increased downwelling longwave radiation, as evidenced by the LWCRE (Fig. 6f). These values are within the range of the thickest clouds at Summit (Cox et al., 2014; Miller et al., 2015) and large enough to drive latent and sensible heat fluxes to near-neutral conditions (Fig. 6g), indicating a well-mixed surface layer. As we will see later, fogs generally produce closer to ∼10–20 W m⁻², but larger values such as the 16 June case occur occasionally. Because of these factors, the temperature inversion within the surface layer completely eroded by around 03:30 UTC. Surprisingly, this did not dissipate the fog, and, in fact, an increase in N_c at 2 m occurred in conjunction with a brief interruption in the otherwise smooth growth rate. Following the brief interruption, the particle size and N_c were larger than before with N_c at 2 m exceeding 200 cm⁻³; as we will see later, such high N_c as observed from 04:00 to 05:00 UTC are atypical.

Thus, while the fog was likely induced by radiation initially, it was maintained, and ultimately continued to grow, without additional infrared loss at the surface driving saturation in the air column. Indeed, with warming of the surface layer, introduction of large quantities of vapor would have been necessary to maintain the relative humidity supporting the fog. The large LWP (Fig. 6f) implies significant cloud-top radiative cooling may have occurred; therefore, buoyancy-driven mixing is a plausible mechanism to have supplied the moisture that maintained the fog. However, the moisture was more likely introduced to the surface layer from above by mixing generated by wind shear. This is supported by the sodar data, which shows signatures of turbulence in the form of Kelvin–Helmholtz instabilities (Fig. 7b) driven by shear at the top if the surface layer (30–50 m) between 03:00 and 05:00 UTC, corresponding to the period of time with enhanced LWP, CRF, and N_c, and generally more variable particle size at both measurement heights. Indeed, in June, the firn is generally colder than the surface (Miller et al., 2017) so vapor transfer tends to be downward, toward the surface, from the atmosphere because the mixing ratio is higher in the (saturated) air immediately above the surface than in the firn (Berkelhammer et al., 2016). The latent heat flux (LH) was positive (defined positive into the surface) for the duration of the case study, consistent with the hypothesis that the fog was driven by moisture from aloft (Fig. 6g). Therefore, the moisture source for this case was likely the atmosphere and not the local surface. Interestingly, the N_c at 10 m was consistently lower than at 2 m. The reasons for this are unclear but since there were also more small particles at 2 m, it is possible that the concentration of particles at 2 m was associated with partial evaporation and subsequent slowing of the particles descent. The difference may also be associated with the heights of the instruments relative to the height of maximum supersaturation (see Fig. 2 by Berkelhammer et al., 2016).

Figure 816 January 2014 case study. Number concentration from FM100 at 10 m (a) and 2 m (b). (c) Total number concentration (N_c) in all bins for the 10 m FM100 (red) and the 2 m FM100 (black). (d) Cloud mask from the MPL: yellow is ice, green is liquid, blue is clear, and black is below the minimum height for acceptable data. The height scale is log. (e) Temperatures at surface (skin), 2 and 10 m (reds), and downwelling shortwave radiation (SWD), net shortwave radiation (SW_net), and solar zenith angle (minus 93 then divided by 10 to fit on the figure) (blues); (f) wind direction (blue) and velocity (red); (g) longwave cloud radiative effect (LWCRE) and total cloud radiative forcing (CRF) (reds) and liquid water path (LWP) (blue). (h) Latent (LH) and sensible (SH) (reds) turbulent fluxes and net flux (blue).

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4.2 Case of 16 January 2014

Figure 8 is similar to Fig. 6, but for a wintertime case. The fog was detected at ∼05:30 UTC at 10 m, and the particles grew quickly to ∼35 µm within approximately 30 min (Fig. 8a, b). The N_c at both heights was about an order of magnitude smaller than the June case (Fig. 8c). The troposphere was clear with intermittent ice clouds above 2 km (Fig. 8d). Similar to the June case, the wind was southerly and light with lower wind speeds during the period of the fog (Fig. 8f). The net atmospheric heat flux was generally negative (Fig. 8h) due to radiative cooling at the surface that maintained a temperature inversion with a gradient of ∼10 ^∘C between 2 and 10 m (Fig. 8e). There was not enough LWP for reliable detection by the MWR (uncertainty ∼5.5 g m⁻²), but the CRF was between 10 and 40 W m⁻²; note that the upper-level clouds contributed some to this forcing, in particular after 14:30 UTC. The CRF quickly spiked to 40 W m⁻² during the initial fog development around 06:00 UTC, which was also the time period when the N_c was the highest. Later increases in CRF also corresponded in time to increased N_c. Consistent with variable wind direction, the apparent intermittence of N_c may be explained by a spatially heterogeneous fog periodically passing the tower instruments.

This case was likely a mixed-phase fog with a liquid formation layer precipitating into an underlying settling layer composed of homogeneously frozen ice. The air temperature at 10 m (near the formation height) was between −35 and −38 ^∘C. While the homogeneous freezing point for liquid is imprecise and dependent on conditions (generally about −40 ^∘C), the 2 m air temperature and skin temperatures were $\sim -\mathrm{47}$ and −50 ^∘C, respectively, both too cold to support liquid particles. Thus, it is likely that the particles observed at 2 m were frozen droplets. While we can only infer the phase of the underlying layer from the measured temperature, we do have confidence that the droplets that formed aloft were liquid: the AERI classifications were not ambiguous as 176 of 177 samples for this event were identified as liquid; the AERI classification should not have been confounded by the presence of the ice because the viewport for the AERI is a few meters above the surface, and therefore near the top or above the ice layer; the AERI results are also supported by the MPL data, which indicate mostly liquid at levels where the MPL has sensitivity, between 100 and 150 m (Fig. 8d).

The ceilometer (not shown) and MPL (Fig. 8d) data indicate a deeper fog layer than the previous case, which may have been enabled by generally deeper and more persistent inversions in winter (Miller et al., 2013); the height of the maximum temperature within the troposphere for this case was 300–400 m compared to ∼100 m at initiation for the June case. However, the sodar record (Fig. 7b) shows that the depth of the surface layer embedded within the deeper inversion for the winter case was actually much shallower than in the summer case, <10 m during the duration of the fog. The particles within the surface layer were sampled by the measurements made at the tower (e.g., Fig. 8a, b) and may be distinct from the deeper fog layer visible in the MPL data: the sodar record also shows a significant amount of structure throughout the upper fog layer indicating additional stable layers (Fig. 7c). This structure is physically decoupled from the surface layer, separated by a thin layer of low reflectivity that may be indicative of a low-level jet, which could suppress mixing between the layers. However, the layers may be dynamically coupled in other ways. Buoyancy waves with periods of a few minutes to 15 min in the upper layer have an observable remote influence on the surface layer through fluctuations in the horizontal pressure field that produce a moving pattern of convergence and divergence (not shown). It is unknown if the observed microphysics below 10 m is representative of the fog above the surface layer. However, radiatively, the combined physically thick layer of fog compensates somewhat for the low N_c observed near the surface, enhancing the CRF relative to the summer case with a larger optical depth. The strength of the inversion also contributed to enhancing the CRF because the particles were warmer than the surface.

Interestingly, the fog development coincides closely with the weak early-season diurnal cycle. In mid-January, the sun does not rise above the horizon at Summit but is about 3^∘ below the horizon at solar noon (Fig. 8e). Atmospheric scattering in these twilight conditions produced 1–3 W m⁻² of diffuse incident solar radiation (Fig. 8e) that corresponded in time with the fog initiation. Though the mechanism is unknown, there is a possibility that the fog was diurnally forced, similar to the June case; yet, unlike summer, the timing was coincident with a peak in solar radiation rather than the minimum.

The stably stratified surface layer was largely isolated from the free troposphere in this case. The temperature gradient within the firn (which is warmer at depth than at the surface) produces a constant supply of vapor towards the surface from below, providing moisture for the fog, which is then returned via settling (Berkelhammer et al., 2016). As before, we observed higher concentrations and more small particles at 2 than 10 m.

5.1 Physical properties

Figure 9 shows distributions of the particle sizes for liquid fogs (blue) compared to those for the ice categories; the figure displays sensor height as rows (10 m – top row, 2 m – bottom row) and season as columns (left column – low-light winter season, NDJF, and right column – sunlit summer season, JJAS). Ice particles are nonspherical and, thus, the distributions represent an effective size with reference to the scattering properties of spherical liquid; asphericity and orientation are important factors in the sizing of ice using a scattering spectrometer that imposes significant uncertainties (Borrmann et al., 2000). Thus, the distributions of ice classes should be treated cautiously and are shown here for context. The liquid classification stands out distinctly from the ice both in the shape of the distribution and the overall N_c within each size bin. For smaller particles, liquid particles are present in higher concentrations than ice particles in summer and are smaller in winter, while the opposite is generally true for larger particles. Despite the uncertainties in sizing ice particles, the relative N_c within each size bin separates logically between the different types. For example, there are more particles in blowing snow at 2 m than at 10 m, while the two heights show similar concentrations of ice fog. Also, the occurrences of snow have consistently low N_c.

Figure 9Average number concentration (N_c) within the FM100 size bins for different classifications measured at 10 m (top row; a, c) and 2 m (bottom row; b, d). The left column (a, b) is for June–September (JJAS) and the right column (c, d) is for November–February (NDJF). The bin centers for the sizes are 1, 6, 11, 13, 15, 17, 19, 21.5, 24.5, 27.5, 30.5, 33.5, 36.5, 39.5, 42.5, 45.5, and 48.5 µm (refer also to the Supplement for additional information on FM100 sizing). Because the FM100 bin sizes are variable, the bin counts have been normalized such that the integral of the curves equals average concentration for all bins. The values over the grey background are the number of 1 min samples in each distribution.

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As implied by the case studies, fogs in winter have lower N_c overall compared to summer. This relationship between temperature and fog N_c is evident when analyzed directly: N_c is smaller in colder fogs, with temperature being correlated with the log of N_c (r=0.54). Gultepe et al. (2002) and Gultepe and Isaac (2004) reported similar findings in tropospheric Arctic clouds containing supercooled liquid. Note that while aerosol concentration is likely a factor, dynamical and thermodynamical processes can also play a role (Gultepe et al., 2002; Gultepe and Isaac, 2004).

The distribution of liquid particles in summer peaks between 20 and 25 µm in diameter at 2 m (Fig. 9b) while the distribution at 10 m (Fig. 9a) is broader with more small particle sizes. This is consistent with particles preferentially forming near 10 m before settling to 2 m. Specifically, multiple growth stages are likely represented in the distribution at 10 m, while the distribution at 2 m is more idealized because it is composed primarily of mature particles. Though the winter liquid distributions are more difficult to interpret owing to a limited number of data, this finding is supported by a calculation of the effective diameter (the ratio of the third and second moments of the size distribution) for all liquid fog scenes in all months, which shows a correlative relationship between effective diameter and N_c at 10 m (Fig. 10a) ($r=-\mathrm{0.55}$), but not at 2 m (Fig. 10b). This indicates that at 10 m, when the N_c is high, the particles are small (forming), while at the same time low N_c for large particles is consistent with loss via settling out of the layer. While a similar relationship might be expected from a range of aerosol concentrations, if aerosols were the explanation for the observations at 10 m, a similar result should be found at 2 m, but it is not.

Figure 10Number concentration (N_c) as a function of particle size for liquid fogs measured by FM100s at 10 m (a) and 2 m (b). Note that the y axis is log.

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The overall distributions of N_c (Fig. 11) at both heights show concentrations in winter that are smaller than in summer. In winter, there are typically more particles at 2 m than 10 m, whereas during summer the differences between the heights are less discernible. The median value of N_c was typically <7 cm⁻³ in winter and between 5 and 20 cm⁻³ in summer (Fig. 11).

Figure 11Box and whisker plots ($\ast =$ mean, boxes are 25th, 50th, and 75th percentiles, whiskers are 1st and 99th percentiles) for all observations at times when both probes were operational in winter (NDJF, blue) and summer (JJAS, red) at 2 and 10 m.

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Figure 12 shows the temperature distributions for times classified as liquid fog and ice fog (the two types associated with in situ formation). In Fig. 12, N_c was only used for identification of events. Thus, precise estimates of N_c are not important, and the threshold for wind direction is less restrictive than the microphysical results shown in Figs. 9–11. Consequently, larger sample sizes are incorporated into this analysis. Three different temperatures are plotted beginning with brightness temperatures derived from near-saturated CO₂ emission between 675–680 cm⁻¹ measured by the AERI in Fig. 12a. These frequencies are sensitive to the lowest few tens of meters above the instrument and may be most similar to the fog thermodynamic temperature. Since liquid freezes homogeneously near −40 ^∘C, observations at lower temperatures are not possible; less than 1 % of observations classified as liquid occur at temperatures $<-\mathrm{40}$ ^∘C in Fig. 12a. The air temperatures at 10 m (Fig. 12b) are similar to the AERI-derived temperatures. Air temperatures closer to the surface at 2 m (Fig. 12c) are generally colder and include many more instances with temperatures below −40 ^∘C. Note that this is not an indication of water existing in a liquid phase below −40 ^∘C but rather indicates that the liquid layer lies above this cold air where the temperature is warm enough to permit the existence of liquid droplets. Between November and March, two-thirds of the liquid fog identifications occurred when the 2 m air temperature was below −40 ^∘C. This result indicates that the 16 January case is typical. A stretch of such occurrences was observed when the 2 m air temperature was $<-\mathrm{45}$ ^∘C during an extended clear-sky period during the last 2 weeks of March 2013. Unlike the 16 January case, for cases during the spring and autumn, there was sufficient daylight for images acquired by cameras mounted to the tower to show evidence of optical phenomena caused by liquid droplets. Images of fogbows appeared at times that coincided with the identification of liquid fogs in March 2013. Fogbows are formed by scattering processes dominated by diffraction when the size of spherical particles is within the Mie regime (e.g., Lynch and Schwartz, 1991). Note that the presence of a fogbow is suggestive of the presence of liquid, but it does not rule out the presence of ice. Additionally, spherical or quasi-spherical ice formed by freezing of supercooled liquid has been reported by other studies (e.g., Thuman and Robinson, 1954), though the preferred habits of ice fog particles remain controversial (see Gultepe et al., 2015) and we are unaware of any studies linking ice fogs to optical phenomena normally associated with liquid droplets.

Figure 12Box and whisker plots ($\ast =$ mean, boxes are 25th, 50th, and 75th percentiles, whiskers are 5th and 95th percentiles) for each month for the ice fog category (cyan) and liquid fogs (blue); (a) AERI 675–680 cm⁻¹ brightness temperatures (BT), (b) 10 m air and (c) 2 m air temperature; Panel (d) shows wind velocity at 10 m.

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For both ice and liquid identifications in Fig. 12a–c, the temperatures at which they occur are lower in winter and higher in summer. Liquid generally occurs during cooler temperatures than ice in summer and thus the seasonal cycle for liquid is muted compared to that of ice. This is likely a result of diurnally forced radiation fogs occurring during the colder part of the day in summer. There is also a notable difference in the timing of the seasonal cycle with the liquid tending to be warmer than ice in the spring transition and cooler than ice in fall. It is unclear whether this is more closely tied to the seasonal cycle in aerosols (e.g., Schmeisser et al., 2018) or to meteorology. The wind regimes are similar between ice and liquid in winter but liquid fogs in summer occur during particularly calm conditions in association with the diurnal development of stable stratification (Fig. 12d).

5.2 Radiative properties

The impact on the surface radiation budget from the various classifications is evaluated in Fig. 13. The sample sizes in Fig. 13 are analogous to those in Fig. 12. First, distributions of LWCRE for the classes appear alongside those for all observations in Fig. 13a. The peaks for both liquid fog and ice fog are close to 10 W m⁻². Both ice and liquid fogs show long tails to larger values and thus the mean is 19.8 (median =14.8) W m⁻² for ice and 26.1 (median =18.1) W m⁻² for liquid fogs. It is notable that unlike distributions of temperature and microphysics shown earlier, the distributions of LWCRE for ice and liquid are similar. It is therefore possible that a similar amount of downward longwave radiation is needed, when both liquid and ice fogs form, to achieve radiation balance between the surface and the lower atmosphere. However, note that the sample selection was radiative in origin in the first place, and the proportions of missed classifications, in particular at the low end of LWCRE, may be different for the two types. The other ice classes, snow, blowing snow, and a mixture of both, are also plotted in the panel for context. Blowing snow is distributed across the range of LWCRE with the larger values more likely to be associated with higher wind speeds (r²=0.31). Snow also is distributed over a wide range of values, but generally higher than blowing snow alone, which is expected because LWCRE during snowy conditions is associated with a precipitating cloud, whereas blowing snow may occur under otherwise clear skies. When snow and blowing snow occur together, the distribution is clustered near the largest values, a condition mostly associated with storms.

Figure 13Distributions for all seasons for classified data types of (a) longwave cloud radiative effect (LWCRE), defined as the perturbation to the downwelling longwave radiation (LWD) caused by clouds, LWD – LWD_clear-sky; (b) Cloud radiative forcing (CRF); (c) For just liquid fog classifications, box and whisker plots ($\ast =$ mean, boxes are 25th, 50th, and 75th percentiles, whiskers are 5th and 95th percentiles) for each month for CRF of identified cases (blue) and CRF normalized by frequency of occurrence of liquid fog (red). Panel (d) as in panel (c) but for the ice fog category.

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When the total CRF is considered (Fig. 13b), the results are similar. The forcing is smaller because of the addition of shortwave cloud cooling but only slightly smaller because the high year-round albedo at Summit limits the ability of clouds to cool the surface there (Miller et al., 2015). Notably, there is increased separation in CRF compared to LWCRE between the ice fog class (μ=12.1 W m⁻², median =7.9 W m⁻²) and liquid fog (μ=22.1 W m⁻², median =17.1 W m⁻²). This is because a larger proportion of ice fog occurs at higher sun angles when the shortwave cloud-cooling effect is larger. Therefore, the time of day when liquid fogs typically occur maximizes their net radiative forcing.

Figure 13c and d give the statistics for each month and annually for liquid (Fig. 13c) and ice (Fig. 13d). The annual mean CRF for liquid fogs under otherwise clear skies was 1.5 W m⁻² when normalized by the frequency of occurrence. The maximum occurred in July (2.6 W m⁻²) and the minimum was in April (∼0 W m⁻²). Due to subsampling, this estimate is probably slightly lower than the total annual CRF from the fogs under all sky types because fogs that were present under optically thin cloud cover may have contributed additional CRF. However, events that were missed for being too thin contributed little radiatively (and consequently, no identification was possible); only a small number of “too thick” (0.8 %) fogs were identified, limiting the influence of this class on the mean CRF; and when ambiguous identifications are included in the analysis (not shown), the annual mean CRF decreases slightly to 1.2 W m⁻². For ice, the normalized annual mean is 0.7 W m⁻². While the magnitude of the values is similar between ice and liquid, the seasonal cycles are different with ice peaking in July and September (2.2 W m⁻²). For reference, the mean annual CRF for all sky conditions at Summit is 33 W m⁻² and is positive in all months (Miller et al., 2015).