Remote Sensing Applications and Decision Support

Optically thin midlevel ice clouds derived from Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations

[+] Author Affiliations
Vinay K. Kayetha, Richard L. Collins

University of Alaska Fairbanks, Geophysical Institute and Department of Atmospheric Sciences, 903 Koyukuk Drive, Fairbanks, Alaska 99775-7320, United States

J. Appl. Remote Sens. 10(4), 046007 (Oct 24, 2016). doi:10.1117/1.JRS.10.046007
History: Received July 1, 2016; Accepted October 3, 2016
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Open Access Open Access

Abstract.  We use measurements from the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) to identify optically thin midlevel ice clouds and determine their occurrence and properties. We identify these clouds as having cloud top heights between 2 km above the ground and the tropopause plus 1 km, cloud base temperatures less than 10°C, and cloud top temperatures greater than 38°C. Globally, we find that these clouds occur at least 5% of the time and represent 7% of all tropospheric clouds detected by CALIPSO at a horizontal scale of 10 km. The cloud type fraction decreases with increasing horizontal scale, representing 4% of clouds at 25 km, 2% at 50 km, and less than 0.1% at 100 km. These clouds occur most commonly in the Arctic, most often in winter and least often in summer. During the winter, these clouds occur up to 15% of the time in the Arctic. We found five large (500  km) distinct clouds over the Arctic and investigated their meteorological conditions and radiative effects. We find that these thin midlevel ice clouds have a warming effect on the surface from 23 to 48  W/m2. Our study highlights the importance of active satellite-based remote sensing in globally detecting and characterizing optically thin clouds.

Figures in this Article

Fundamental questions on the climate system remain unanswered because of our limited understanding of how clouds, atmospheric circulation, and climate interact. From a climate perspective, it is important to understand clouds and their radiative effects. Clouds play a critical role in regulating the Earth’s energy budget by reflecting the incoming solar radiation and trapping the outgoing thermal radiation. The radiative effects of clouds depend on their macrophysical (altitude, temperature, and geometrical thickness) and microphysical (particle phase and size distribution) properties.13 Despite their importance, the accurate representation of cloud types in global/regional climate models remains a challenge. The present work focuses on midlevel clouds. According to the World Meteorological Organization, midlevel clouds are those that typically form in the Earth’s atmosphere with their base altitudes at 2 and 7 km above the surface level.4,5 Surface observations and passive satellite measurements show that midlevel clouds cover up to 22% of the Earth’s surface.68 Midlevel clouds have been documented by several field campaigns in the tropics (e.g., Refs. 91011), midlatitudes (e.g., Refs. 121314), and polar regions (e.g., Refs. 151617). However, our understanding of midlevel cloud processes and their representation in models remains poor. Studies show that almost all operational global climate models underestimate midlevel cloud occurrences.18,19 One of the challenges in observing midlevel clouds is that when observed remotely from the surface or top of the atmosphere, they are often obscured by low- or high-level clouds. Furthermore, the range of temperatures in the middle troposphere (where ice, liquid, or both phases can exist) makes the determination of the cloud properties more difficult. There are commonly two types of midlevel clouds according to the “genera” of cloud classification:5 altostratus and altocumulus. In general, altostratus clouds are ice dominated and altocumulus clouds are water dominated.20 Altostratus clouds form as thick and dark layers, sufficient to partially or totally block the disk of sun or moon for a surface observer. Altocumulus clouds form as cellular structures and are relatively thin often with ice virga precipitating from the cloud base. Although these traditional midlevel clouds are optically thick, several studies indicate they can also exist as optically thin clouds.18,20,21 Such optically thin altostratus and altocumulus clouds are least studied because of the detection limitations of both ground-based measurements and satellite passive sensors.

Recent advances in satellite active remote sensing have supported new studies of these cloud types. Several studies have used the combination of radar and lidar aboard the Cloudsat and Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites to study midlevel clouds.22,23 The distribution and phase partition of liquid-layer topped midlevel clouds, which is a typical characteristic of traditional altocumulus type of clouds, has been reported.22 The global distribution of altostratus and altocumulus clouds has been reported.23 In this study, we focus exclusively on optically thin midlevel ice clouds as measured by the lidar onboard the CALIPSO satellite. We present the occurrence of these clouds in the context of the low-, mid-, and high-level clouds that are detected by CALIPSO. We report the seasonal and global distribution of these clouds. We determine their radiative impact in the Arctic, where these clouds are observed most commonly.

CALIPSO Data Set

The payload of CALIPSO satellite consists of three coaligned, near-nadir viewing instruments: a dual-wavelength Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP), an imaging infrared radiometer, and a high-resolution wide-field camera.24 The CALIOP uses an Nd:YAG laser that transmits pulses at both 532- and 1064-nm wavelengths, with pulse energy of 110 mJ, and a pulse length of 20 ns. The lidar pulse repetition at the rate of 20.16 Hz allows for a sampling of complete atmospheric column every 333  m on the ground. To reduce the bandwidth of the downlinked data, the resolution of the data decreases with altitude. The vertical and horizontal resolutions of the downlinked data are 30 and 333 m between the ground and 8.2 km, 60, and 1000 m between 8.2 and 20.2 km, respectively. The CALIOP measurements are calibrated and geolocated to produce attenuated backscattering profiles that are made available to users as level-1 data product. Feature detection algorithms and cloud properties retrieval techniques are applied to the level-1 attenuated backscatter profiles to yield level-2 cloud data products.

We use four years (December 1, 2006 to November 30, 2010) of CALIOP measurements in this study. We use the 5-km cloud layer data product (i.e., CAL_LID_L2_05kmCLay-Prov-V3), which provides information on all clouds detected from various horizontal signal-averaging schemes. The CALIPSO feature detection algorithm uses several resolutions (i.e., 5, 20, and 80 km) and signal averaging to increase the signal-to-noise ratio of the measurements and detect even, thin clouds and aerosol layers in the atmosphere.25 All the clouds that are detected are provided in the 5-km data product along with the horizontal averaging used in identifying those clouds.

Cloud Classification Algorithm

We summarize our cloud classification algorithm in Fig. 1. We first limit our attention to tropospheric clouds. Our algorithm employs a maximum allowable cloud top height as 1 km above the local tropopause level to avoid the inclusion of stratospheric clouds. We used a maximum allowable height of 12 km for the regions poleward of 60° N and 60° S as the tropopause altitude is often ambiguous in the polar regions. We then identify all clouds with cloud top altitudes less than 2 km above the ground as low-level clouds (L). We classify the clouds with top altitudes greater than 2 km of the ground level into six distinct types based on the cloud top temperature, cloud base temperatures, and optical depth. We classify the clouds with top temperature above 38°C as midlevel clouds (M) and below 38°C as high-level (H, i.e., cirrus) clouds. Studies show that as the cloud temperature falls below 38°C supercooled liquid droplets freeze homogeneously to initiate the formation of ice particles.2628 We use the transparency flags of the cloud layers provided by the CALIPSO data product to classify the clouds as attenuated or transparent. The CALIOP data processing algorithm characterizes the identified cloud layers as either attenuated or transparent and calculates the optical depth of transparent (i.e., optically thin) clouds with visible optical depths less than 3 to 4.29 Finally, we use the cloud base temperature of 10°C to distinguish between ice- and water-dominated clouds. Studies have shown that clouds have a minimum ice particle concentration of 1000 particles/m at temperatures less than 10°C.30 Using these rules, we categorize all the tropospheric clouds into seven types (i.e., L1, M1, M2, M3, H1, H2, and H3). We present these categories of tropospheric clouds with their descriptions in Table 1. The M1 clouds are the ice dominated optically thin midlevel clouds that are the primary focus of this study.

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Fig. 1
F1 :

Logic-based rules used to categorize tropospheric clouds detected by CALIPSO.

Table Grahic Jump Location
Table 1Description of cloud types based on logic-based rules.

We show an example of our cloud classification scheme on CALIPSO data segment drawn from over both northern and southern hemispheres in Fig. 2. Figure 2(a) shows CALIPSO measurements on December 27, 2006, over the Arctic Ocean from Bathurst Island (75° N, 99° W) to the New Siberian Islands (75° N, 143° E). We detect six of our seven cloud types (i.e., L1, M1, M2, M3, H1, and H3). Several layers of optically thin high-level clouds (H1) and optically thin midlevel clouds (M1) dominate the clouds in this observation. The M1 cloud observed over the New Siberian Islands (75° N, 143° E) spans a horizontal scale of 135 km (27 profiles). In Fig. 2(a), the temperature remains less than 10°C at the surface, thus there is no contour plotted at 10°C. Figure 2(b) shows CALIPSO measurements on May 18, 2007, over the Southern Ocean (45° S, 91° E to 57° S, 85° E). This example clearly illustrates the occurrence of optically thin midlevel ice cloud, where the lidar signals could penetrate through the cloud and detect a cloud layer lying below. The M1 cloud observed here spans a horizontal scale of 110 km (22 profiles).

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Fig. 2
F2 :

(Upper) CALIPSO backscattering (lower) and cloud type derived from our logic-based rules from an overpass (a) over the Arctic region on December 27, 2006 and (b) over the Southern Ocean on May 18, 2007. The dotted line indicates the 2-km altitude level above the surface. The temperature contours at 10°C and 38°C are also plotted.

Having first classified all tropospheric clouds into seven types, we investigate the horizontal scale of the clouds using a continuity analysis. We conclude that a cloud has a scale of n profiles if a given cloud type is present at the same altitude over n successive profiles. We choose n as 2, 5, 10, 20, and 100 corresponding to 10, 25, 50, 100, and 500 km, respectively. The use of continuity over increasing numbers of successive profiles not only provides information on the spatial scales of the cloud, but also eliminates isolated cloudy profiles from the sample (e.g., M2 and H3 in Fig. 2). After counting the total number of valid clouds in each type, we calculate the frequency of occurrence and fraction of clouds based on the total observations (profiles) divided by the value of n. We then bin the types of clouds obtained from the entire data set into a 2.5°×5° latitude–longitude grid. We derive cloud statistics for an average of four years of binned data. We define the frequency of occurrence of a cloud type as the number of cloudy profiles to the total number of profiles (clear plus cloudy). We define the cloud type fraction for a given cloud type as the ratio of the number of clouds identified as that particular type to the total number of clouds identified.

Global Distribution of Tropospheric Clouds

We first investigate the CALIPSO observations at 1-profile resolution or a 5-km spatial scale. Over the four-year period, there were 149.5 million profiles, where clouds are absent in 46.0 million profiles. This indicates that 31% of the Earth’s surface is under clear skies, while 69% is cloudy. In the cloudy profiles, 28% of the clouds are low-level clouds (L1), 36% are midlevel clouds (M1, M2, and M3), and 36% are high-level clouds (H1, H2, and H3). We find that these results are insensitive to changes of ±2°C in the cloud top and cloud base temperature thresholds of our classification scheme. In summary, we find that M1 clouds have the following characteristics, optical depths between 0.1 and 6.0 with a mean optical depth of 0.8, cloud top heights between 2.0 and 12.0 km with mean height of 6.0 km, cloud top temperatures between 38°C and 10°C with a mean temperature of 27°C, cloud base heights between 0.1 and 11.3 km with a mean height of 4.7 km, and cloud base temperatures between 37°C and 10°C with a mean temperature of 21°C.

As seen in Fig. 2, clouds of one type can be found as single profiles embedded in a larger cloud of another type (e.g., M2 in M1). We assume this may reflect a classification error. We thus present our results based on the CALIPSO observations at 2-profile resolution or a 10-km spatial scale. We tabulate the frequency of occurrence and the fraction of clouds for each category from the four years (December 1, 2006 to November 30, 2010) in Table 2. Over these four years, the total number of profiles is divided by two and amounts to 74.7 million profiles. We find a total of 45.3 million valid cloudy profiles, indicating a global cloud cover of 61% for clouds of a horizontal size of 10 km or greater.

Table Grahic Jump Location
Table 2Cloud detection statistics for CALIPSO observations at 10-km scale.

We present the global distribution of cloud frequency of occurrence and fraction at 2-profile resolution in Fig. 3. The observations show that L1 and H1 clouds (low-level and cold thin cirrus clouds) occur most commonly and represent the highest fraction of the cloud types. The L1 clouds occur more commonly over oceans than land. The lower occurrence of L1 clouds over Southern and South East Asia might be due to the presence of H3 or M3 clouds (thick high-level and midlevel clouds) that prevent the detection of underlying L1 clouds. M1 clouds (cold thin midlevel clouds) form most commonly in the extratropics. In the northern hemisphere, the M1 clouds represent a larger fraction of the clouds over land than ocean, which reflects the increased occurrence of L1 clouds over the oceans. M2 clouds (warm thin midlevel clouds) primarily occur in the tropics. M3 clouds (thick midlevel clouds) occur more commonly over ocean than land and extend into the polar regions over the North Atlantic and Southern Oceans. H1 clouds (cold thin cirrus clouds) are found primarily in the tropics and also over Antarctica. The detection of cirrus clouds over the Antarctic may reflect the formation of polar stratospheric clouds that extend into the upper troposphere. H2 clouds (warm thin cirrus clouds) are largely nonexistent. H3 clouds (optically thick high-altitude clouds) are most common in the tropics, least common in the subtropics, and also form in the midlatitudes and polar regions.

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Fig. 3
F3 :

Global (a) cloud frequency of occurrence and (b) cloud type fraction derived from CALIPSO observations at 10-km resolution.

Spatial Scales of Clouds

We have determined the number of clouds present at different scales in the CALIPSO data set by detecting clouds that are continuous over increasing larger scales. The total number of clouds detected decreases with increasing scale from 4.53×107 clouds at 2-profile (10 km) resolution to 1.15×106 clouds at 20-profile (100 km) resolution to 1.70×104 clouds at 100-profile (500 km) resolution. The global mean fraction of clouds derived from CALIPSO observations at different spatial scales is shown in Fig. 4. We see that with increasing spatial scale, the relative fraction of the different cloud types changes. The global mean fraction of clouds for L1 clouds is found to increase from 30% at 10 km to 50% at 500 km. The fraction for H1 and H3 clouds is found to stay relatively constant at 10% and 30%, respectively. The fraction of midlevel clouds decreases with increasing spatial scales. The fraction of M3 clouds decreases less rapidly, from 20% at 10 km to 10% at 500 km, than the fraction of M1 and M2 clouds. For M1 clouds, the fraction of clouds is 7.4%, 4.2%, 2.0%, and <0.1% at 2-profile (10 km), 5-profile (25 km), 10-profile (50 km), and 20-profile (100 km) resolution, respectively. At the largest spatial scale of 500 km, we find only five M1 clouds in this four-year data set. These five clouds are found in the Arctic. We will use these five large M1 clouds later to investigate the radiative effects of M1 clouds.

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Fig. 4
F4 :

Global mean fraction of clouds derived from CALIPSO observations plotted as a function of horizontal spatial scale. 1 profile corresponds to 5 km.

Seasonal and Geographic Distributions of M1 Thin Midlevel Ice Clouds

We show the seasonal variations in M1 cloud occurrence and cloud type fraction in Fig. 5. These thin midlevel ice clouds occur up to 15% over the polar regions and extend into the midlatitudes. M1 clouds occur most commonly during the northern winter [December, January, February (DJF)] and least during the northern summer [June, July, August (JJA)], in both hemispheres with an exception of the two high-altitude regions of Greenland and Tibet. These clouds appear along the midlatitude storm tracks in all seasons but are quite low in northern summer (JJA). No seasonal symmetry in M1 cloud occurrences is observed. M1 clouds occur most commonly over both the Arctic and the Southern Ocean during northern winter (DJF) indicating that M1 clouds form through a variety of processes. Over the tropics, M1 cloud occurrences are found to be minimum (5%) and are concentrated along the convergence zone, resembling the altocumulus cloud distribution.31 The fraction of clouds clearly shows that M1 clouds are more abundant in the northern hemisphere than in the southern hemisphere. This may be related to the availability of ice nuclei for the formation of M1 clouds. Studies have shown that dust particles from the arid regions act as effective ice nuclei.32,33 The occurrence of high fraction of M1 clouds over arid regions in Africa, Asia, and Australia supports the assertion that such nucleation processes contribute to the formation of M1 clouds. In the northern hemisphere, M1 clouds are most common along the western Arctic extending into the midlatitudes and less common in the synoptically active North Pacific and Atlantic Oceans, where cloud droplets grow to large sizes and produce precipitation. Over the southern hemisphere, M1 clouds are found over the oceans surrounding Antarctica.

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Fig. 5
F5 :

Global seasonal variation in frequency of occurrence and cloud type fraction of M1 clouds at 10-km resolution.

We show the zonally averaged frequency of occurrence of M1 clouds with altitude in Fig. 6. The M1 clouds occur at highest altitudes in the tropics and decrease in altitude toward the poles. The clouds are found at lowest altitudes in the wintertime at the poles. The clouds occur most often in the polar regions and least often in the subtropics. The occurrence of M1 clouds follows the atmospheric circulation, where the clouds are less common in the descending branch of the Hadley-cell in the subtropics. In the tropical regions, M1 clouds are found at altitudes of 7- to 10-km altitude, while the tropopause level varies between 16 and 18 km. This observation supports the view that tropical M1 clouds are formed through detrainment of convective towers that extend into the upper atmosphere.34,35

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Fig. 6
F6 :

Zonally averaged frequency of occurrence of M1 clouds plotted as a function of latitude and altitude.

We tabulate the macrophysical properties of M1 clouds over different latitude regions in Table 3. Here, the relative occurrence is calculated as the ratio of M1 clouds that occur in each latitudinal belt to the global M1 cloud amounts. M1 clouds are more abundant in the northern hemisphere (47%) than in the southern hemisphere (38%) extratropics (poleward of 30°). As mentioned earlier, this may be related to the availability of ice nuclei for the M1 cloud formation. Further, the average M1 cloud thickness increases from tropical to polar regions in both hemispheres. The average M1 cloud thickness is higher in the Arctic (1.4 km) than any other region. This observation is consistent with the thin midlevel ice cloud climatology derived from ground-based lidar observations that reported average thin midlevel ice cloud thickness as 1.7 km over the sub-Arctic region.36

Table Grahic Jump Location
Table 3Macrophysical properties of M1 clouds observed by CALIPSO.

We highlight the occurrence of M1 clouds in the polar regions in Fig. 7. We see distinct seasonal asymmetry between the occurrence of these clouds in the Arctic and the Antarctic. These clouds occur most commonly in the wintertime (DJF) in the Arctic and the summertime (DJF) in the Antarctic. These clouds occur least commonly in the summertime (JJA) in the Arctic and the wintertime (JJA) in the Antarctic. We also see that the clouds occur more commonly over low altitudes than high altitudes, being less common over Greenland and the Antarctic plateau than the surrounding regions. The M1 clouds are less common over the North Atlantic, where M2 clouds are more common.

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Fig. 7
F7 :

Frequency of occurrence of M1 clouds as a function of season in the Arctic and the Antarctic.

To assess the radiative impact of M1 clouds, we employ an open source radiative transfer model that has been used to study midlevel clouds.21,37 We search the CALIPSO data set to find clouds that extend over more than 500 km. We choose these large clouds to ensure that the clouds represent well-formed stable clouds, and that the scale of the clouds matches the scale of the accompanying meteorological reanalysis data.

Radiative Transfer Model

We use the library for radiative transfer (libRadtran) package to estimate the radiative properties of M1 clouds.38 LibRadtran is a software package designed to calculate the radiative quantities such as radiances, irradiances at user-defined altitudes within the atmosphere using input data of the extraterrestrial radiation, the atmosphere (pressure, temperature, humidity, O3, N2, O, and CO2 profiles), and surface properties. The complete details of the libRadtran model along with examples on how to run simulations are provided online.39 This package is publicly available and suitable for computing radiative effects of aerosols and clouds under user-defined boundary conditions.40 We use the meteorological data (i.e., pressure, temperature, relative humidity, and ozone number density) obtained from the CALIPSO data product as inputs to the model to represent the atmospheric conditions for the observed clouds. We choose the standard subarctic winter profiles for the atmospheric data above 30-km altitude.41 The aerosol microphysical properties of maritime type for the winter season represent the background aerosol conditions for the simulations. At 1  μm, the maritime aerosol has an aerosol optical thickness of 0.11 and an angstrom exponent of 0.63.42 The surface spectral albedo plays a crucial role in the simulation of radiative transfer in the solar spectral range. The libRadtran software provides an option to input any measured surface albedo or to use a standard library of spectral albedo values. These standard values are taken from International Geosphere–Biosphere Program database that provides spectral albedo of these surface types derived from satellite measurements.43 However, for our simulations, we use a modified spectral albedo of the surface-type permanent ice/snow that we have validated with published studies.21,37 The simulations in the solar spectral range (290 to 4000 nm) are performed with the discrete ordinate radiative transfer solver DISORT version 2.0.44 We use the extraterrestrial solar irradiance spectrum in the solar spectral range.45 We use the LOWTRAN band model from SBDART to account for the gaseous absorption.46 We use the two-stream radiative solver in the infrared range (4 to 100  μm).47

We estimate the cloud microphysical properties based on previous studies that reported in-situ measurements of optically thin midlevel clouds.20,21 These studies reported thin altocumulus clouds with small water droplets not exceeding 20  μm in size and thin altostratus clouds consisting solely of ice particles (columns with rounded edges) with maximum size up to 100  μm. Further, the measured liquid/ice water content for these clouds was reported to have an average value of 0.01  gm3 with a maximum value of 0.45  gm3. With this information, we have chosen effective radii of 30 and 60  μm for ice clouds and 10 and 20  μm for water clouds, respectively. For the case of mixed phase clouds, we choose clouds composed of 20-μm water drops and 60-μm ice columns and then consider four cases where the ice fraction of the total optical depth is 20%, 40%, 60%, and 80%. We have performed radiative transfer simulations using these eight scenarios of microphysical properties and the cloud altitudes and optical depths derived from CALIPSO data.

Results of Case Studies

We find five large M1 clouds that each extended over 500 km. These five clouds are all found in the Arctic and we tabulate their locations, solar zenith angle, and optical depth in Table 4. Of these five clouds, four are observed when the sun was below the horizon and one, case 5, is observed when the sun was just above the horizon. We cite the median optical depth rather than the mean value, as the median is more representative of the whole cloud. The mean values are higher than the median values and reflect the influence of a few high values. We tabulate the range and average of the optical depth, cloud top altitude, cloud base altitude, cloud top temperature, and cloud base temperature in Table 5. The median value of the optical depth is presented in parenthesis next to the average value. The synoptic meteorological conditions for these days show that these clouds are formed along the frontal zones with weakly ascending air masses.48

Table Grahic Jump Location
Table 4Distinct large-scale M1 clouds identified by CALIPSO.
Table Grahic Jump Location
Table 5Characteristics of Arctic M1 clouds.

For each of these five cases, we calculate the cloud radiative forcing (CRF) as the difference between the net downward irradiance under cloudy and clear sky conditions. A positive value of the CRF indicates that the cloud has a warming effect on the surface. For example in case 1, the clear sky has a net longwave (LW) irradiance of 55  W/m2 indicating cooling. In case 1, with the M1 cloud present, the net irradiance is 12±1  W/m2. The value of 12  W/m2 with an uncertainty of 1  W/m2 is the average and standard deviation in the values determined from the eight cloud scenarios. The small standard deviation reflects the fact that the different microphysical characteristics have little effect on the radiative properties of these thin clouds. Thus, in case 1, we estimate that the M1 has a CRF of 43±1  W/m2 and a net warming effect on the surface. We tabulate our results for all five case studies in Table 6. We estimate the LW radiative forcing for all cases and the shortwave (SW) forcing for case 5, where the cloud is illuminated by the sun. We find that the LW CRF is positive in all cases with values ranging between 28 and 48  W/m2, with a mean value of 36  W/m2. We conclude that M1 cloud has a net warming effect in the LW. In case 5, we see that the cloud has an SW CRF of 7  W/m2 indicating that the cloud has a net cooling effect in the SW. However, the combined SW and LW effect of the cloud is to yield a CRF of 23  W/m2, indicating a net warming. To further explore the balance of SW and LW radiative forcing, we conducted the CRF when changing the solar zenith angle from 85 deg to 80 deg and 70 deg. While the LW warming effect of the cloud remains constant, the SW cooling effect increases and the total CRF reduces to 10  W/m2 at 80 deg and to 6  W/m2 at 70 deg.

Table Grahic Jump Location
Table 6Radiative impact of M1 clouds.

In this study, we have used logic-based rules to categorize midlevel clouds as M1 (optically thin and ice dominated), M2 (optically thin and water dominated), and M3 (optically thick) cloud types. We find that the ice-dominated M1 clouds occur most commonly over the polar regions. The water-dominated M2 clouds occur most commonly over the tropics. The thicker M3 clouds occur most commonly in the tropics, midlatitudes over eastern Asia and Pacific Ocean, and subpolar regions. The global distributions of M1 and M2 clouds are similar to the distributions of altostratus and altocumulus clouds, respectively.23 The global distribution of M3 clouds is also similar to that of midlevel liquid-layer topped stratiform clouds.22

The global mean occurrence of M1 clouds is 4.5% and represents 7.3% of all tropospheric clouds detected by CALIPSO. This estimate of M1 cloud occurrence is a lower bound as optically thicker H3 and M3 clouds can obscure M1 clouds from CALIPSO. During Arctic midwinter, M1 clouds occur up to 15% of the time. In both the Arctic and the Antarctic, M1 clouds occur most commonly during December to January to February, indicating no seasonal hemispheric symmetry in the occurrence of these clouds. The low amount of M1 cloud cover over Antarctica in winter is consistent with the extremely low temperatures, low humidity, and the ice-sheets steep coastal slopes blocking advection from lower latitudes.49 In the Arctic, the M1 clouds occur least over Greenland in winter and spring, again consistent with very low temperatures and low humidity over Greenland.50 M1 cloud occurrences are also lower along major storm tracks, indicating they form under relatively stable conditions that would otherwise allow the cloud particles to grow large sizes and eventually precipitate. A high fraction of M1 clouds is observed over arid regions of Africa, Asia, and Australia suggesting that dust particles may form as ice nuclei that trigger the formation of M1 clouds.32 The horizontal scales for these clouds show that up to 50% of these cloud population form with sizes of less than 25 km and resemble typical thin altocumulus clouds forming under mesoscale weather processes.

While the majority of M1 clouds occur at smaller scales of 25 km, the extensive CALIPSO observations allowed us find five large clouds that extended over 500 km. We analyzed these large M1 clouds and found that they have a cloud LW radiative forcing of 36  W/m2. This indicates that M1 clouds have a significant warming effect during the Arctic winter. Four of these clouds occurred at night and one occurred in daylight. Analysis of the cloud in daylight indicated that the combined SW and LW radiative forcing decreases as the solar zenith angle reduces, decreasing from 23  W/m2 at a solar zenith angle of 85 deg to 6  W/m2 at a solar zenith angle of 70 deg, and indicates that M1 clouds have a net cooling effect during the Arctic summer.

Our current study has documented thin midlevel ice clouds. The use of satellite-based active remote sensing techniques is critical in identifying these clouds. Optically thin and thick midlevel clouds should be treated separately to better understand their impacts on the Earth’s radiation budget.

The authors thank the Arctic Facility for Atmospheric Remote Sensing, CALIPSO, and the libRadTran Science Teams for their support. The authors thank Kenneth Sassen for helpful discussions. This work was conducted with support from the National Science Foundation under Grant No. AGS 1037000. V.K.K. acknowledges support from the University of Alaska Fairbanks Graduate School for completion of this study. The authors thank two anonymous reviewers for their comments that led to improvements in this paper.

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Fleishauer  R. P., , Larson  V. E., and Vonder Haar  T. H., “Observed microphysical structure of midlevel, mixed-phase clouds,” J. Atmos. Sci.. 59, (11 ), 1779 –1804 (2002).CrossRef
Korolev  A., and Isaac  G., “Phase transformation of mixed-phase clouds,” Q. J. R. Meteorol. Soc.. 129, (587 ), 19 –38 (2003). 0035-9009 CrossRef
Smith  A. J.  et al., “Processes that generate and deplete liquid water and snow in thin midlevel mixed-phase clouds,” J. Geophys. Res. Atmos.. 114, (D12203 ), 1 –18 (2009).CrossRef
Shupe  M. D., , Matrosov  S. Y., and Uttal  T., “Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA,” J. Atmos. Sci.. 63, (2 ), 697 –711 (2006).CrossRef
Shupe  M. D.  et al., “A focus on mixed-phase clouds: the status of ground-based observational methods,” Bull. Am. Meteorol. Soc.. 89, (10 ), 1549 –1562 (2008). 0003-0007 CrossRef
de Boer  G., , Eloranta  E. W., and Shupe  M. D., “Arctic mixed-phase stratiform cloud properties from multiple years of surface-based measurements at two high-latitude locations,” J. Atmos. Sci.. 66, (9 ), 2874 –2887 (2009).CrossRef
Zhang  M. H.  et al., “Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements,” J. Geophys. Res. Atmos.. 110, (D15S02 ), 1 –18 (2005).CrossRef
Illingworth  A. J.  et al., “Cloudnet—continuous evaluation of cloud profiles in seven operational models using ground-based observations,” Bull. Am. Meteorol. Soc.. 88, (6 ), 883 –898 (2007). 0003-0007 CrossRef
Hobbs  P. V., and Rangno  A. L., “Microstructures of low and middle-level clouds over the Beaufort Sea,” Q. J. R. Meteorol. Soc.. 124, (550 ), 2035 –2071 (1998). 0035-9009 CrossRef
Lampert  A.  et al., “Microphysical and radiative characterization of a subvisible midlevel Arctic ice cloud by airborne observations—a case study,” Atmos. Chem. Phys.. 9, (8 ), 2647 –2661 (2009). 1680-7324 CrossRef
Zhang  D. M., , Wang  Z., and Liu  D., “A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements,” J. Geophys. Res. Atmos.. 115, (D00H13 ), 1 –10 (2010).CrossRef
Sassen  K., and Wang  Z., “The clouds of the middle troposphere: composition, radiative impact, and global distribution,” Surv. Geophys.. 33, (3–4 ), 677 –691 (2012). 0169-3298 CrossRef
Winker  D., , Pelon  J., and Mccormick  M. P., “The CALIPSO mission: spaceborne lidar for observation of clouds and aerosols,” Proc. SPIE. 4893, , 1 –11 (2003).CrossRef
Vaughan  M. A., , Winker  D. M., and Powell  K. A., “CALIOP algorithm theoretical basic document: part 2—feature detection and layer properties algorithms,” in PC-SCI-202.02. ,  NASA Langely Research Center ,  Hampton, Virginia  (2005).
Sassen  K., and Dodd  G. C., “Homogeneous nucleation rate for highly supercooled cirrus cloud droplets,” J. Atmos. Sci.. 45, (8 ), 1357 –1369 (1988).CrossRef
Pruppacher  H., and Klett  J., Microphysics of Clouds and Precipitation. , 2nd ed.,  Springer ,  New York  (1997).
Rosenfeld  D., and Woodley  W. L., “Deep convective clouds with sustained supercooled liquid water down to-37.5 degrees C,” Nature. 405, (6785 ), 440 –442 (2000).CrossRef
Sassen  K., “Cirrus clouds: a modern perspective,” in Cirrus. , and Lynch  D. K.  et al., Eds., pp. 11 –40,  Oxford University Press ,  New York  (2002).
Morris  T., and Braham  R. R., “The occurrence of ice particles in Minnesota cumuli,” presented at  Proc. of Weather Modification ,  American Meteorological Society , pp. 306 –315 (1968).
Sassen  K., and Wang  Z., “Classifying clouds around the globe with the CloudSat radar: 1-year of results,” Geophys. Res. Lett.. 35, (L04805 ), 1 –5 (2008). 0094-8276 CrossRef
Sassen  K., “Dusty ice clouds over Alaska,” Nature. 434, (7032 ), 456 –456 (2005).CrossRef
Sassen  K., and Khvorostyanov  V. I., “Cloud effects from boreal forest fire smoke: evidence for ice nucleation from polarization lidar data and cloud model simulations,” Environ. Res. Lett.. 3, (025006 ), 1 –12 (2008).CrossRef
Folkins  I., , Oltmans  S. J., and Thompson  A. M., “Tropical convective outflow and near surface equivalent potential temperatures,” Geophys. Res. Lett.. 27, (16 ), 2549 –2552 (2000). 0094-8276 CrossRef
Seifert  P.  et al., “Saharan dust and heterogeneous ice formation: eleven years of cloud observations at a central European EARLINET site,” J. Geophys. Res. Atmos.. 115, (D20201 ), 1 –13 (2010).CrossRef
Kayetha  V. K., “Ice clouds over Fairbanks, Alaska,” MS Thesis, University of Alaska Fairbanks (2014).
Andre  E., “The impact of ice crystals on radiative forcing and remote sensing of Arctic boundary-layer mixed-phase clouds,” PhD Dissertation, Johannes Gutenberg-Universitat Mainz, Germany (2009).
Mayer  B., and Kylling  A., “Technical note: the libRadtran software package for radiative transfer calculations—description and examples of use,” Atmos. Chem. Phys.. 5, , 1855 –1877 (2005). 1680-7324 CrossRef
Mayer  B.  et al., “Download|Libradtran,” 2008, http://www.libradtran.org/doku.php?id=download (1  June  2015).
Mayer  B.  et al., “Start|Libradtran,” 2008, http://www.libradtran.org (1  June  2015).
Anderson  G.  et al., “AFGL atmospheric constituent profiles (0–120 km),” Technical Report AFGL-TR-86-0110 (1986).
Shettle  E., “Models of aerosols, clouds and precipitation for atmospheric propagation studies,” presented at  AGARD Conf. Proc. No. 454, Atmospheric Propagation in the UV, Visible, IR and MM-Region and Related Systems Aspects , pp. 1 –359 (1989).
Belward  A., and Loveland  T., “The DIS 1 km land cover data set,” Global Change Newsletter. 27, , 7 –9 (1996).
Stamnes  K.  et al., “Numerically stable algorithm for discrete-ordinate method radiative transfer in multiple scattering and emitting layered media,” Appl. Opt.. 27, (12 ), 2502 –2509 (1988). 0003-6935 CrossRef
Gueymard  C. A., “The sun’s total and spectral irradiance for solar energy applications and solar radiation models,” Sol. Energy. 76, (4 ), 423 –453 (2004).CrossRef
Ricchiazzi  P.  et al., “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the Earth’s atmosphere,” Bull. Am. Meteorol. Soc.. 79, (10 ), 2101 –2114 (1998). 0003-0007 CrossRef
Kylling  A., , Stamnes  K., and Tsay  S. C., “A reliable and efficient 2-stream algorithm for spherical radiative transfer—documentation of accuracy in realistic layered media,” J. Atmos. Chem.. 21, (2 ), 115 –150 (1995). 0167-7764 CrossRef
Kayetha  V. K., “Investigation of thin midlevel ice clouds in the Arctic using CALIPSO data and radiative transfer modeling,” PhD Dissertation, University of Alaska Fairbanks (2015).
Bromwich  D. H.  et al., “Tropospheric clouds in Antarctica,” Rev. Geophys.. 50, , RG1004  (2012). 8755-1209 CrossRef
Shupe  M. D.  et al., “High and dry: new observations of tropospheric and cloud properties above the Greenland Ice Sheet,” Bull. Am. Meteor. Soc.. 94, , 169 –186 (2013). 0003-0007 CrossRef

Vinay K. Kayetha received his PhD from the Department of Atmospheric Sciences, University of Alaska Fairbanks, USA. Currently, he is a research scientist at Science Systems and Applications Inc., Maryland. His current research includes developing remote sensing retrievals of cloud of aerosol properties and radiative transfer modeling.

Richard L. Collins received his PhD from the Department of Electrical and Computer Engineering, University of Illinois, Urbana–Champaign, USA. Currently, he is a professor at the Geophysical Institute and Department of Atmospheric Sciences at the University of Alaska Fairbanks. His current research includes remote sensing and the weather and climate of the middle atmosphere. He maintains an active lidar research program at the Poker Flat Research Range, Alaska.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation

Vinay K. Kayetha and Richard L. Collins
"Optically thin midlevel ice clouds derived from Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations", J. Appl. Remote Sens. 10(4), 046007 (Oct 24, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046007


Figures

Graphic Jump Location
Fig. 1
F1 :

Logic-based rules used to categorize tropospheric clouds detected by CALIPSO.

Graphic Jump Location
Fig. 2
F2 :

(Upper) CALIPSO backscattering (lower) and cloud type derived from our logic-based rules from an overpass (a) over the Arctic region on December 27, 2006 and (b) over the Southern Ocean on May 18, 2007. The dotted line indicates the 2-km altitude level above the surface. The temperature contours at 10°C and 38°C are also plotted.

Graphic Jump Location
Fig. 3
F3 :

Global (a) cloud frequency of occurrence and (b) cloud type fraction derived from CALIPSO observations at 10-km resolution.

Graphic Jump Location
Fig. 4
F4 :

Global mean fraction of clouds derived from CALIPSO observations plotted as a function of horizontal spatial scale. 1 profile corresponds to 5 km.

Graphic Jump Location
Fig. 5
F5 :

Global seasonal variation in frequency of occurrence and cloud type fraction of M1 clouds at 10-km resolution.

Graphic Jump Location
Fig. 6
F6 :

Zonally averaged frequency of occurrence of M1 clouds plotted as a function of latitude and altitude.

Graphic Jump Location
Fig. 7
F7 :

Frequency of occurrence of M1 clouds as a function of season in the Arctic and the Antarctic.

Tables

Table Grahic Jump Location
Table 1Description of cloud types based on logic-based rules.
Table Grahic Jump Location
Table 2Cloud detection statistics for CALIPSO observations at 10-km scale.
Table Grahic Jump Location
Table 3Macrophysical properties of M1 clouds observed by CALIPSO.
Table Grahic Jump Location
Table 4Distinct large-scale M1 clouds identified by CALIPSO.
Table Grahic Jump Location
Table 5Characteristics of Arctic M1 clouds.
Table Grahic Jump Location
Table 6Radiative impact of M1 clouds.

References

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Fleishauer  R. P., , Larson  V. E., and Vonder Haar  T. H., “Observed microphysical structure of midlevel, mixed-phase clouds,” J. Atmos. Sci.. 59, (11 ), 1779 –1804 (2002).CrossRef
Korolev  A., and Isaac  G., “Phase transformation of mixed-phase clouds,” Q. J. R. Meteorol. Soc.. 129, (587 ), 19 –38 (2003). 0035-9009 CrossRef
Smith  A. J.  et al., “Processes that generate and deplete liquid water and snow in thin midlevel mixed-phase clouds,” J. Geophys. Res. Atmos.. 114, (D12203 ), 1 –18 (2009).CrossRef
Shupe  M. D., , Matrosov  S. Y., and Uttal  T., “Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA,” J. Atmos. Sci.. 63, (2 ), 697 –711 (2006).CrossRef
Shupe  M. D.  et al., “A focus on mixed-phase clouds: the status of ground-based observational methods,” Bull. Am. Meteorol. Soc.. 89, (10 ), 1549 –1562 (2008). 0003-0007 CrossRef
de Boer  G., , Eloranta  E. W., and Shupe  M. D., “Arctic mixed-phase stratiform cloud properties from multiple years of surface-based measurements at two high-latitude locations,” J. Atmos. Sci.. 66, (9 ), 2874 –2887 (2009).CrossRef
Zhang  M. H.  et al., “Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements,” J. Geophys. Res. Atmos.. 110, (D15S02 ), 1 –18 (2005).CrossRef
Illingworth  A. J.  et al., “Cloudnet—continuous evaluation of cloud profiles in seven operational models using ground-based observations,” Bull. Am. Meteorol. Soc.. 88, (6 ), 883 –898 (2007). 0003-0007 CrossRef
Hobbs  P. V., and Rangno  A. L., “Microstructures of low and middle-level clouds over the Beaufort Sea,” Q. J. R. Meteorol. Soc.. 124, (550 ), 2035 –2071 (1998). 0035-9009 CrossRef
Lampert  A.  et al., “Microphysical and radiative characterization of a subvisible midlevel Arctic ice cloud by airborne observations—a case study,” Atmos. Chem. Phys.. 9, (8 ), 2647 –2661 (2009). 1680-7324 CrossRef
Zhang  D. M., , Wang  Z., and Liu  D., “A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements,” J. Geophys. Res. Atmos.. 115, (D00H13 ), 1 –10 (2010).CrossRef
Sassen  K., and Wang  Z., “The clouds of the middle troposphere: composition, radiative impact, and global distribution,” Surv. Geophys.. 33, (3–4 ), 677 –691 (2012). 0169-3298 CrossRef
Winker  D., , Pelon  J., and Mccormick  M. P., “The CALIPSO mission: spaceborne lidar for observation of clouds and aerosols,” Proc. SPIE. 4893, , 1 –11 (2003).CrossRef
Vaughan  M. A., , Winker  D. M., and Powell  K. A., “CALIOP algorithm theoretical basic document: part 2—feature detection and layer properties algorithms,” in PC-SCI-202.02. ,  NASA Langely Research Center ,  Hampton, Virginia  (2005).
Sassen  K., and Dodd  G. C., “Homogeneous nucleation rate for highly supercooled cirrus cloud droplets,” J. Atmos. Sci.. 45, (8 ), 1357 –1369 (1988).CrossRef
Pruppacher  H., and Klett  J., Microphysics of Clouds and Precipitation. , 2nd ed.,  Springer ,  New York  (1997).
Rosenfeld  D., and Woodley  W. L., “Deep convective clouds with sustained supercooled liquid water down to-37.5 degrees C,” Nature. 405, (6785 ), 440 –442 (2000).CrossRef
Sassen  K., “Cirrus clouds: a modern perspective,” in Cirrus. , and Lynch  D. K.  et al., Eds., pp. 11 –40,  Oxford University Press ,  New York  (2002).
Morris  T., and Braham  R. R., “The occurrence of ice particles in Minnesota cumuli,” presented at  Proc. of Weather Modification ,  American Meteorological Society , pp. 306 –315 (1968).
Sassen  K., and Wang  Z., “Classifying clouds around the globe with the CloudSat radar: 1-year of results,” Geophys. Res. Lett.. 35, (L04805 ), 1 –5 (2008). 0094-8276 CrossRef
Sassen  K., “Dusty ice clouds over Alaska,” Nature. 434, (7032 ), 456 –456 (2005).CrossRef
Sassen  K., and Khvorostyanov  V. I., “Cloud effects from boreal forest fire smoke: evidence for ice nucleation from polarization lidar data and cloud model simulations,” Environ. Res. Lett.. 3, (025006 ), 1 –12 (2008).CrossRef
Folkins  I., , Oltmans  S. J., and Thompson  A. M., “Tropical convective outflow and near surface equivalent potential temperatures,” Geophys. Res. Lett.. 27, (16 ), 2549 –2552 (2000). 0094-8276 CrossRef
Seifert  P.  et al., “Saharan dust and heterogeneous ice formation: eleven years of cloud observations at a central European EARLINET site,” J. Geophys. Res. Atmos.. 115, (D20201 ), 1 –13 (2010).CrossRef
Kayetha  V. K., “Ice clouds over Fairbanks, Alaska,” MS Thesis, University of Alaska Fairbanks (2014).
Andre  E., “The impact of ice crystals on radiative forcing and remote sensing of Arctic boundary-layer mixed-phase clouds,” PhD Dissertation, Johannes Gutenberg-Universitat Mainz, Germany (2009).
Mayer  B., and Kylling  A., “Technical note: the libRadtran software package for radiative transfer calculations—description and examples of use,” Atmos. Chem. Phys.. 5, , 1855 –1877 (2005). 1680-7324 CrossRef
Mayer  B.  et al., “Download|Libradtran,” 2008, http://www.libradtran.org/doku.php?id=download (1  June  2015).
Mayer  B.  et al., “Start|Libradtran,” 2008, http://www.libradtran.org (1  June  2015).
Anderson  G.  et al., “AFGL atmospheric constituent profiles (0–120 km),” Technical Report AFGL-TR-86-0110 (1986).
Shettle  E., “Models of aerosols, clouds and precipitation for atmospheric propagation studies,” presented at  AGARD Conf. Proc. No. 454, Atmospheric Propagation in the UV, Visible, IR and MM-Region and Related Systems Aspects , pp. 1 –359 (1989).
Belward  A., and Loveland  T., “The DIS 1 km land cover data set,” Global Change Newsletter. 27, , 7 –9 (1996).
Stamnes  K.  et al., “Numerically stable algorithm for discrete-ordinate method radiative transfer in multiple scattering and emitting layered media,” Appl. Opt.. 27, (12 ), 2502 –2509 (1988). 0003-6935 CrossRef
Gueymard  C. A., “The sun’s total and spectral irradiance for solar energy applications and solar radiation models,” Sol. Energy. 76, (4 ), 423 –453 (2004).CrossRef
Ricchiazzi  P.  et al., “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the Earth’s atmosphere,” Bull. Am. Meteorol. Soc.. 79, (10 ), 2101 –2114 (1998). 0003-0007 CrossRef
Kylling  A., , Stamnes  K., and Tsay  S. C., “A reliable and efficient 2-stream algorithm for spherical radiative transfer—documentation of accuracy in realistic layered media,” J. Atmos. Chem.. 21, (2 ), 115 –150 (1995). 0167-7764 CrossRef
Kayetha  V. K., “Investigation of thin midlevel ice clouds in the Arctic using CALIPSO data and radiative transfer modeling,” PhD Dissertation, University of Alaska Fairbanks (2015).
Bromwich  D. H.  et al., “Tropospheric clouds in Antarctica,” Rev. Geophys.. 50, , RG1004  (2012). 8755-1209 CrossRef
Shupe  M. D.  et al., “High and dry: new observations of tropospheric and cloud properties above the Greenland Ice Sheet,” Bull. Am. Meteor. Soc.. 94, , 169 –186 (2013). 0003-0007 CrossRef

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