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  • View in gallery

    Location of the two study areas: land area (L) (37°–42°N, 113°–118°E); ocean area (O) (37°–42°N, 151°–156°E).

  • View in gallery

    The two Ci cases: (top half) over the L area (L-) on 30 Jul 2007 and (bottom half) over the O area (O-) on 3 Aug 2007. Shown are the L- and O-(a) radar reflectivity factor from CPR (dBZ), L- and O-(b) logarithm of attenuated backscattering coefficient from CALIOP (km sr−1), L- and O-(c) IER (μm), and L- and O-(d) logarithm of IWC (mg m−3) for the respective cases.

  • View in gallery

    Histogram of total cloud (diagonal stripes + no stripes) and cirrus (diagonal stripes) frequency over the L area and O area in spring (MAM), summer (JJA), autumn (SON), and winter (DJF) from January 2007 to December 2010.

  • View in gallery

    Histograms of cirrus Hbase (gray), Htop (black), and CTH (white) in (a),(e) spring, (b),(f) summer, (c),(g) autumn, and (d),(h) winter during 2007–10 over the (a)–(d) L and (e)–(h) O area.

  • View in gallery

    Frequency of IER with (top) heights and (bottom) temperatures over the (left) L and (right) O area during 2007–10.

  • View in gallery

    Variation of mean IER (μm) and its standard deviation with (top) height and (bottom) temperature in spring, summer, autumn and winter over (left) land and (right) ocean during 2007–10. Results of linear fitting are also shown.

  • View in gallery

    Frequency (log) of IWC with height and temperature over the (left) L and (right) O area during year 2007–10: L- and O-(a) distribution with height; L- and O-(b) distribution with temperature, and L- and O-(c) variation of mean IWC and its standard deviation with temperature in spring, summer, autumn and winter.

  • View in gallery

    Frequency (log) of IWC with IER over the (left) L and (right) O area during 2007–10: (top) distribution with IER and (bottom) variation of mean IWC and its standard deviation with IER in spring, summer, autumn, and winter.

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Physical Properties of High-Level Cloud over Land and Ocean from CloudSatCALIPSO Data

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  • 1 Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Abstract

Unlike other cloud types, high-level clouds play an important role, often imposing a warming effect, in the earth–atmosphere radiative energy budget. In this paper, macro- and microphysical characteristics of cirrus clouds, such as their occurrence frequency, geometric scale, water content, and particle size, over northern China (land area, herein called the L area) and the Pacific Ocean (ocean area, herein the O area) are analyzed and compared based on CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) products from 1 January 2007 to 31 December 2010. Over both areas, statistical analysis shows that cirrus occurrence approached 33% in summer whereas it was only ~10% in winter, >50% of cirrus cloud thicknesses were in the range of ~(0.25–1.5) km, there were >98% ice particles in high-level clouds, and temperature had a closer linear relationship with ice effective radius (IER) than height. Also, the seasonal difference of this linear relationship is minor over both land and ocean. Comparisons reveal that the mean occurrence frequency, mean cloud thickness, range of cloud-base and cloud-top height, IER, and ice water content of cirrus in summer were generally greater in winter, and greater over the O area than over the L area. However, the relationship between IER and temperature over land is close to that over ocean.

Corresponding author address: Dr. Juan Huo, LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, 40 Huayanli, Chaoyang District, Beijing 100029, China. E-mail: huojuan@mail.iap.ac.cn

Abstract

Unlike other cloud types, high-level clouds play an important role, often imposing a warming effect, in the earth–atmosphere radiative energy budget. In this paper, macro- and microphysical characteristics of cirrus clouds, such as their occurrence frequency, geometric scale, water content, and particle size, over northern China (land area, herein called the L area) and the Pacific Ocean (ocean area, herein the O area) are analyzed and compared based on CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) products from 1 January 2007 to 31 December 2010. Over both areas, statistical analysis shows that cirrus occurrence approached 33% in summer whereas it was only ~10% in winter, >50% of cirrus cloud thicknesses were in the range of ~(0.25–1.5) km, there were >98% ice particles in high-level clouds, and temperature had a closer linear relationship with ice effective radius (IER) than height. Also, the seasonal difference of this linear relationship is minor over both land and ocean. Comparisons reveal that the mean occurrence frequency, mean cloud thickness, range of cloud-base and cloud-top height, IER, and ice water content of cirrus in summer were generally greater in winter, and greater over the O area than over the L area. However, the relationship between IER and temperature over land is close to that over ocean.

Corresponding author address: Dr. Juan Huo, LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, 40 Huayanli, Chaoyang District, Beijing 100029, China. E-mail: huojuan@mail.iap.ac.cn

1. Introduction

Clouds regulate the radiation energy balance of the earth’s climate system by absorbing or scattering solar radiation and longwave radiation and emitting thermal radiation. Thus, they have an important influence on global climate (Wetherald and Manabe 1988; Cess et al. 1989; Ramanathan et al. 1989; Harrison et al. 1990; Liou 1992). Along with other factors, such as aerosols, uncertainties in climate model simulations arise primarily from clouds, and one important reason could be that current descriptions and characterizations of cloud properties are inadequate due to their diversity and complexity, as well as the difficulties involved in their detection (Liou 1986; Wilson and Mitchell 1986; Hartmann et al. 1992; Stephens 2005). With this recognition, researchers working on climate/radiation models are seeking methods to simplify the parameterizations of cloud physical properties in models (i.e., the general circulation model) in the hope that model simulation accuracies can be improved (Heymsfield and Platt 1984; Heymsfield and Donner 1990; Fu and Liou 1993; Luebke et al. 2013). For example, Liou et al. (2008) found that ice water content was a relatively better parameter for describing the radiative role of cloud in radiative models than other parameters, such as cloud height or size.

High-level clouds, often referred to as cirrus clouds, are mainly composed of ice particles and stay in the upper troposphere. Unlike other cloud types, cirrus clouds generally play a warming role in the radiation budget of the earth–atmosphere system because the solar radiation reflected back to space by cirrus clouds is relatively less than the longwave radiation they absorb from below. Cirrus clouds have a wide spatial distribution. They are thin, often appear with low-level clouds, and contain various shapes of nonspherical ice particles, resulting in difficulties of physical properties retrieval.

Many previous studies on cirrus properties or their relationships with other atmospheric factors have been carried out using ground-based data or passive remotely sensed satellite data (Minnis et al. 1998; Han et al. 1999; Sassen et al. 2008; Wang and Sassen 2002). Mace et al. (2006) documented cirrus cloud properties based on 6 yr of ground-based data. Davis et al. (2007) compared and analyzed ice water content of cirrus cloud using in situ measurements. At present, radar and lidar instruments on board CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) bring us new data to perform scientific analyses on cloud vertical structure and properties. For instance, Sassen et al. (2008) studied cirrus occurrence and optical depth over the global area; Adhikari et al. (2012) analyzed the distribution of cirrus height, thickness, particle effective radius, and other parameters over the Antarctic through CloudSat and CALIPSO data.

The aim of the present study is to analyze cirrus cloud macro- and microphysical characteristics, their seasonal and vertical variations, and their differences between two types of surface, land and ocean, via CloudSat and CALIPSO data. The intention by doing so is to help further our understanding of the characteristics of high-level cloud and to improve their parameterization in radiative and climate models (i.e., general circulation models). Correlations between microphysical parameters, such as the relationship between particle size and temperature, and between particle size and water content, are also investigated. It will help to support quantified cloud-field parameters input to 3D cloud radiative transfer models [such as the Spherical Harmonic Discrete Ordinate Method (SHDOM); Evans 1998] for 3D radiation effects study over land and ocean. Section 2 describes the satellite data and methodology. Section 3 presents two cirrus cloud cases to demonstrate the typical cirrus structures and physical information. A detailed analysis of cirrus cloud physical properties over land and ocean is given in section 4, and section 5 summarizes the major conclusions of the study.

2. Data and methodology

Data used in this paper are from two satellites of the A-Train constellation, CloudSat and CALIPSO. A W-band (94 GHz) Cloud Profiling Radar (CPR) on board CloudSat explores the vertical structures of cloud (Stephens et al. 2002). CALIPSO is equipped with a Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument to detect backscattering coefficients and polarization rate profiles from aerosol and cloud (Winker et al. 2007). The sensitivities of CPR and CALIOP in responding to cloud particles are distinct due to different detection wavelengths. Compared with CPR (wavelength of 3.2 mm), CALIOP has a shorter wavelength (532 and 1064 nm) and is more sensitive to smaller cloud or aerosol particles; however, it has difficulty penetrating thick clouds because of the attenuation from cloud and aerosol. CPR has superiority in penetrating thick clouds, but it is more sensitive to larger particles and always neglects smaller particles. For example, CPR will miss supercooled small-scale cloud and some smaller ice particles in middle-level clouds (Delanoë and Hogan 2010; Chan and Comiso 2011). Therefore, retrievals of cloud microphysical parameters from a combination of CPR and CALIOP data possess better accuracy than either one on its own.

Our study objects are high-level clouds, the types of which are determined by CloudSat. The data products, 2B-CLDCLASS-lidar, released by CloudSat are cloud classification products (http://cloudsat.atmos.colostate.edu/data). CloudSat classifies clouds into eight categories: stratus (St), stratocumulus (Sc), cumulus (Cu), nimbostratus (Ns), altocumulus (Ac), altostratus (As), deep convective clouds (Dc), and high-level clouds (Ci), according to their precipitation status, cloud-base height (Hbase), cloud thickness (CTH), cloud-top height (Htop), cloud-top temperature, and radar reflectivity factor (Ze). High-level clouds include mostly cirrus so in this article we use Ci or cirrus to be concise. As far as Ci is concerned, once a cloud cluster is found, and if its average temperature at the largest Ze (single profile) of all profiles is <−22.5°C, the average largest Ze < 0.05 dBZ, average height of maximum Ze is >5 km, and the maximum average CTH < 6.1 km or average Hbase > 10 km, it is determined to be high-level cloud. It should be noted that high-level cloud as determined by CloudSatCALIPSO has somewhat different properties from the real cirrus defined in meteorology since high-level cloud is not precisely equal to cirrus. For example, cirrus clouds defined in the AMS Glossary of Meteorology are composed exclusively of ice, without the presence of liquid.

Cloud microphysical characteristics such as particle size, water content, and number concentration are estimated by the 2B-CWC-RVOD and 2C-ICE product. Macro- and microphysical properties analyzed in this paper are mainly from the two products. The 2C-ICE products are more important in this paper since ice occupy predominantly in cirrus clouds. Both 2B-CWC-RVOD and 2C-ICE products are retrieved based on measured CPR reflectivity factor and measured CALIOP attenuated backscattering coefficient data. The 2B-CWC-RVOD cloud products, the algorithm of which is a modification of the algorithm described in Austin and Stephens (2001), presents properties of water clouds. The 2C-ICE products present properties of ice clouds. The detailed algorithm of 2C-ICE is described in Deng et al. (2010). Delanoë and Hogan (2008) also presented some effective methods to improve the retrieval accuracy before that. Both the 2B-CWC-RVOD and 2C-ICE product algorithms are composed of several key parts. At the beginning, the phase of cloud is determined and it decides the next algorithm to be used to retrieve cloud physical properties. CALIOP combines the attenuated backscattering coefficient (β′) and depolarization ratio (δ) with cloud temperature to distinguish cloud phases (Hu et al. 2009; Avery et al. 2012). CloudSat, meanwhile, sets a temperature threshold to determine the cloud phase: a cloud layer of temperature >0°C is determined as water cloud, temperature <−20°C is ice, and temperature between −20° and 0°C is a mixture of ice and water. Once the cloud phase is determined, a retrieval algorithm assigns an a-priori ice water content (IWC)–liquid water content (LWC) and ice effective radius (IER)–liquid effective radius (LER) according to the measured Ze and β′ based on ground-based radar observation experience. Given the a priori assignments, the state vector would be calculated and the radar–lidar forward model would be applied to predict what measurements the instrument would observe. The retrieval algorithm adjusts the state vector by comparing these predictions with the measurements actually observed until appropriate convergence has been achieved. Uncertainties of this retrieval will be calculated finally according to convergence analysis. However, the best way to estimate the errors of the retrieval algorithm is to compare the retrieval values with those measurements from other instruments, such as those on board aircraft, or from in situ experiments. In fact, it is difficult now to make such exact comparisons because it is hard to get perfect temporally and spatially matched cases between satellite and aircraft or in situ measurements. From the results of the Small Particles in Cirrus (SPARTICUS) field campaign (Mace et al. 2009), it was shown that the mean ratios (retrieved to measured) were 1.17 and 1.22 for IWC and IER on condition that time lags were within 15 min and distances were less than 5 km.

In this paper, we compare and analyze properties of cirrus clouds over two surfaces: a land area (L area; 37°–42°N, 113°–118°E) and an ocean area (O area; 37°–42°N, 151°–156°E) (Fig. 1). The L area is located in northern China, includes Beijing, and comprises a large area of land and a very small area of water. The topography is high in the northwest (with mountains and a high plateau) and low in the southeast. The O area is located in the Pacific Ocean, far from the mainland (>3000 km from the L area). The two areas are located at the same latitude and with roughly equal incident solar radiation. The surface characteristics of the L area, such as vegetation, water bodies, urban areas, farmland, etc., are more complex than the O area, which is solely seawater. In addition, the presence and impacts of human activities in the L area are far greater than in the O area. Cloud condensation nuclei (CCN) types are different between the L area and O area: the L area has more soil, dust, or other particles from automobiles and industrial exhausts, while the majority of CCN over the O area are sea salt and biological particles from plankton, etc. The period of satellite data used in this paper spans from 1 January 2007 through 31 December 2010. In terms of the synoptic meteorology, the two areas belong to the subtropical monsoon zone, with the northwest monsoon prevailing in winter and southwest and southeast monsoon prevailing in summer. Rainfall is greater in summer, with less rain but more snow occurring in winter. The two areas chosen for comparison have distinct surface and environmental conditions, affecting the formation of cloud and producing different cloud characteristics, which should be dealt with differently by models.

Fig. 1.
Fig. 1.

Location of the two study areas: land area (L) (37°–42°N, 113°–118°E); ocean area (O) (37°–42°N, 151°–156°E).

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

3. The two Ci cases

CALIOP and CPR, detecting profiles of clouds as they move, show detailed information and vertical structural characteristics of various cloud types. Two typical Ci cases, one over land (the L-area case) and one over the ocean (the O-area case) that occurred at nearly the same time, are introduced in Fig. 2 to demonstrate the appearance of Ci clouds and their fundamental properties. The L-area Ci case took place on 30 July 2007 at an altitude of between 10 and 15 km, with an average CTH of ~2.3 km. The IER of this Ci case varied from approximately 13 to 56 μm, with a maximum value of 56 μm existing on the south side. The IER values near the cloud bottom were slightly larger. The IWC was in the range of ~(0.09–28) mg m−3. The mean IWC on the north side was greater than the south side, and the mean IWC near the cloud bottom was greater than near the cloud top. The O-area Ci case took place on 3 August 2007 at an altitude of between 7 and 14 km, with an average CTH of ~5 km that became thinner from south to north. The IER of this Ci case varied mainly in the range of ~(15–61) μm, with a maximum value of 61 μm existing on the north side near the cloud bottom. The IWC of this case was in the range of ~(0.13–80) mg m−3. The mean IWC and IER near the cloud bottom were also greater than near the cloud top. Compared with the L-area case, the O-area case had a lower average base height, a thicker average CTH, stronger radar reflectivity, greater IWC, and greater mean IER. These basic differences between the land and ocean cases are compared and analyzed in more depth later in the paper. The background noise from CALIOP over land and ocean are different because the data shown in Fig. 2b for the L case were measured at night whereas those shown in for the O case were measured during the day. The capabilities of CPR and CALIOP in detecting cirrus are also compared in Fig. 2. The cirrus case over land was thinner than that over the ocean, and CPR missed some of the particles of this thin cirrus due to their smaller sizes, while CALIOP performed better (circled areas in Fig. 2). On the other hand, radar reflectivity from CPR depicted the entire vertical structure of thicker clouds, where some CALIOP lidar signals were absorbed or attenuated (boxed areas in Fig. 2). The IWC and IER values given in Fig. 2 are both from CloudSat 2C-ICE data, which are retrieved based on both CALIOP and CPR measurements. Obviously, inversion results combined with CPR and CALIOP will overcome these shortcomings as much as possible from using individual radar or lidar data only. We also should notice that CALIPSO is attenuated severely when the cloud optical depth is >3 so that CloudSat can only retrieve the microphysical properties in such cases. CloudSat also will suffer attenuation in deep cloud. Estimation errors enhance likely for those cases and even some deep clouds will be “neglected” because of attenuation. But deep cirrus cases are relatively uncommon and their effects on statistical analysis results are minor.

Fig. 2.
Fig. 2.

The two Ci cases: (top half) over the L area (L-) on 30 Jul 2007 and (bottom half) over the O area (O-) on 3 Aug 2007. Shown are the L- and O-(a) radar reflectivity factor from CPR (dBZ), L- and O-(b) logarithm of attenuated backscattering coefficient from CALIOP (km sr−1), L- and O-(c) IER (μm), and L- and O-(d) logarithm of IWC (mg m−3) for the respective cases.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

4. Properties of Ci

Geographical locations, heights, and microphysical properties such as particle size and water content of clouds control the effects of clouds on radiative energy transfer. This section will study these detailed properties of Ci that can be used in climate model or radiative model as references.

a. Occurrence frequency

The occurrence of cloud of various types, reflecting regional climate characteristics and affecting and changing the local radiative energy budget, is important in studies of global or regional radiative energy balance for climate models. Total cloud occurrence (Pc) is defined as
eq1
where Nc is the number of total cloud profiles and Nr is the number of all profiles. Cirrus cloud occurrence (Pcc) is defined as
eq2
where Ncc is the number of Ci cloud profiles.

Figure 3 shows the Pc and Pcc distributions of the L area and O area in spring [March–May (MAM)], summer [June–August (JJA)], autumn [September–November (SON)], and winter [December–February (DJF)]. It should be noted that the value of Pc minus Pcc does not equal the occurrence of other cloud types besides cirrus because one cloudy profile may contain multiple cloud layers, resulting in the sum of each individual cloud type occurrence being greater than Pc. From Fig. 3, the Pc over the L area was greater than 50%, except in winter, with a maximum in summer (~72%). The distribution of Pcc over the L area also possessed seasonal variation: summer had maximum occurrence, winter had minimum occurrence, and the Pcc values in spring and summer were both greater than ⅓. Over the O area, Pc reached 80% during all years and was slightly higher in winter and spring than in summer and autumn. More Ci clouds occurred in summer than in winter. The Pcc values in spring, summer, and autumn were all greater than ⅓. The Pcc in winter was the minimum among all seasons, being only ~10% both over the L area and O area. The mean Pc and Pcc values over the O area were greater than over the L area, and the maximum Pcc difference between the L area and O area occurred in autumn, reaching 20%.

Fig. 3.
Fig. 3.

Histogram of total cloud (diagonal stripes + no stripes) and cirrus (diagonal stripes) frequency over the L area and O area in spring (MAM), summer (JJA), autumn (SON), and winter (DJF) from January 2007 to December 2010.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

b. Distribution of Hbase, CTH, and Htop of Ci

Statistical analysis of the Hbase, CTH, and Htop over the two areas is made in this section and the results are given in Table 1. Over the L area, the mean Hbase was ~8.54 km, the mean CTH was ~1.46 km, and the mean Htop was 10.0 km. From 2007 to 2010, the biggest Htop of Ci was 16.35 km (the regional average tropopause height, approximately), the largest CTH was 7.50 km, and the lowest Htop was 3.8 km. Both the average Hbase and Htop in summer were 1 km higher than in winter. The mean CTH in summer was ~0.5 km larger than in winter. For the O area, the highest Htop was 18.43 km and the highest CTH reached 12 km. The mean Hbase was ~9.24 km, the mean CTH was ~1.86 km, and the mean Htop was 11.1 km. The average Hbase and Htop in summer were the highest, and they were lowest in winter, with the difference being >2 km between the two seasons. It can also be seen from Table 1 that the standard deviations of all statistical quantities in summer were mostly the maximum among the four seasons (i.e., Hbase, CTH, and Htop values in summer had relatively wider distributional ranges).

Table 1.

Statistical results of Hbase, Htop, and CTH of cirrus (km) over the L and O areas in the four seasons during 2007–10 (standard deviations shown in parentheses).

Table 1.

To further elucidate the distribution of Ci height, the frequencies of Hbase, CTH, and Htop are also studied. Figure 4 shows the analysis results, revealing the interval for frequency statistics to be 0.5 km. From all the histograms of CTH in Fig. 4, we can see the frequency of CTH has a nearly lognormal distribution and the peak value was between 0.5 and 1 km. Over the L area, more than 50% of the CTHs were in the range of ~(0.25–1.5) km in all four seasons (~72.9% in winter and 54.9% in summer). The proportion of cases belonging to the range of ~(0.25–2.5) km in winter reached 89.5%, while it was ~72.9% in summer. There were more Ci occurrences with greater CTH in summer than in winter. For the O area, the CTHs in the range of ~(0.25–1.5) km accounted for ~55.5% in winter and 46.6% in summer. The proportion of CTHs in the range of ~(0.25–2.5) km reached 79.4% in winter and 69.4% in summer.

Fig. 4.
Fig. 4.

Histograms of cirrus Hbase (gray), Htop (black), and CTH (white) in (a),(e) spring, (b),(f) summer, (c),(g) autumn, and (d),(h) winter during 2007–10 over the (a)–(d) L and (e)–(h) O area.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

The histogram for Htop shows a lognormal distribution and the peak value varied among the seasons. For the L area, the range of Htop in summer was maximum and in winter it was minimum; >50% of the Htop values in summer were in the range of 9.5–12 km, whereas in winter the majority of Htop values were in the range of 8.5–11 km. Over the O area, the difference of Htop among the seasons increased, with the range in spring and winter being lower than in summer and autumn. More than 50% of the Htop values in summer were in the range of 12–14.5 km, while in winter the majority of Htop values were in the range of 9.0–11.5 km.

The histogram of Hbase also shows a lognormal distribution. The peak value of the distribution among the four seasons was different. For the L area, >50% of the Hbase values were in the range of ~7–9.5 km. The range of Hbase values occurring in summer was wider than in winter. For instance, the range of Hbase values for which the frequency accounted for >5% was ~(7–11) km in summer, whereas it was ~(6.0–9.5) km in winter. The range of Hbase values over the O area was generally higher than over the L area. For example, the range of Hbase values for which the frequency accounted for >5% was ~(7.5–13) km in summer, while it was ~(6–10) km in winter. Statistically, ~78.4% of Hbase values in winter were in the range of ~(6.5–9.5) km, while in summer the proportion was ~30.5%. More Hbase values (~52.4%) occurred in the range of ~(9.5–12.5) km in summer. Compared with the distribution of CTH, the distributions of Htop and Hbase possessed stronger seasonal variation.

c. Phases of Ci

Research has shown that ice particles occupy the majority of Ci cloud. Vertical information from CloudSat and CALIPSO helps to quantify the percentage. For Ci particles, CALIPSO divides them into three phase statuses by analyzing the depolarization ratio and backscattering coefficient from CALIOP (Hu et al. 2009; Sassen et al. 2008): liquid water only (liquid only), a mixture of liquid and solid (liquid + ice), and solid particles only (ice only). According to the phase determination results, we calculated the percentage of each phase status and the statistical results are shown in Table 2. Over both the L area and the O area, >98% of the Ci particles were ice particles, while the proportion of liquid water was <0.05%. In view of the absolute majority of ice particles in the Ci cloud, the analysis of the microphysical properties of Ci in this paper focuses on ice particles, and analysis of liquid water particles is neglected.

Table 2.

Statistics of Ci particle phases (%) during 2007–10.

Table 2.

d. IER of Ci

IER is a key parameter for describing Ci microphysical properties. IER of cloud will control the scattering and absorption radiation energy so that it is an important input parameter for radiative transfer models and climate models. The occurrence frequencies of all IER values at various heights and temperatures during 2007–10 over the L area and O area were calculated and the distributions are shown in Fig. 5. The number of particles (also referred to as the frequency) belonging to each interval box was calculated at IER intervals of 1 μm from 0 to 90 μm, at height intervals of 0.25 km from 4 to 16 km, and at temperature intervals of 1 K from 200 to 270 K. From Fig. 5 we can see that the distributions of IER in each height (temperature) box were varied; generally, IER tended to cluster around lower values when height (temperature) increased (decreased). For the L area, most IER values were in the range of ~(20–45) μm at heights of ~(7–11) km [temperatures ~(210–245) K], and there was a higher-frequency area at heights of ~(8–10) km [temperatures ~(220–240) K] for which IER values were in the range of ~(25–40) μm. Compared with the L area, the ranges of IER and heights of ice particles were increased over the O area. Most IER values were in the range of ~(15–50) μm at heights of ~(7–14) km [temperatures ~(205–250) K], and there was also a high-density area at heights of 10.3–12.3 km [temperatures ~(215–235) K] for which IER values were in the range of 20–35 μm.

Fig. 5
Fig. 5

Frequency of IER with (top) heights and (bottom) temperatures over the (left) L and (right) O area during 2007–10.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

It can be seen from Fig. 5 that there was a negative (positive) relationship between IER and height (temperature). In view of this, we perform further statistical analysis to possibly quantify the relationship between height (temperature) and IER. The distributions of mean IER and the variances with height (temperature) in the four seasons are shown in Fig. 6 In the top panels of Fig. 6, we can see that the mean IER reduced with an increase in height, and the relationship between IER and height was somewhat different among the four seasons. For the L area, the slopes of linear fitting in the four seasons (as shown by the solid lines in Fig. 6) were varied, with the minimum slope in summer and the maximum in winter. For the O area, the slopes of linear fitting in the four seasons were closer to each other, but the intercepts were very different. Compared with height, temperature had a closer relationship with IER, and the standard deviation was smaller. As shown in the bottom panels of Fig. 6, IER possessed a similar positive linear correlation with temperature in the four seasons. The slopes (intercept) of linear fitting for all data were 1.372 (183.2) over the L area and 1.362 (182.1) over the O area. The difference of slopes between land and ocean is minor. A value of IER can be estimated according to temperature with this linear relationship, which is a reference to estimate a priori of the IER for retrieval or parameterization in models.

Fig. 6.
Fig. 6.

Variation of mean IER (μm) and its standard deviation with (top) height and (bottom) temperature in spring, summer, autumn and winter over (left) land and (right) ocean during 2007–10. Results of linear fitting are also shown.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

e. IWC of Ci

IWC is related to particle size and extinction, both of which are used to determine the radiative properties of Ci cloud. The statistical results of IWC in the four seasons over the L area and O area are shown in Table 3. It can be seen that the difference between the smallest and largest IWC was quite big. The maximum IWC measured during 2007–10 over the two areas was 1268.98 μg m−3 and minimum was 0.01 μg m−3. The standard deviation values were larger than the mean values, meaning the distribution of IWC in Ci was relatively disperse. As shown in Table 3, the average IWC changed in the four seasons, with the minimum in winter and maximum in summer (over the L area) or autumn (over the O area). The mean IWC over the O area was larger than over the L area, while in winter the mean IWC over the O area was smaller than that over the L area.

Table 3.

Statistical results of IWC (μg m−3) in the four seasons over the L and O area during 2007–10, showing maximum, mean, minimum, and standard deviation.

Table 3.

The vertical distribution of IWC is shown in Fig. 7. We use the log value of IWC (LIWC) to replace the IWC itself to simplify the figure and description. The number of particles (also referred to as the frequency) belonging to each interval box was calculated according to its LIWC and height–temperature value with LIWC intervals of 0.1 from −2 to 4, height intervals of 0.25 km from 4 to 16 km, and temperature intervals of 1 K from 200 to 270 K. As shown in Fig. 7, ~94.5% (over the L area) and 92.9% (over the O area) of LIWC values were in the range from −1 to 1.5. There were two frequency peaks in the vertical distribution of LIWC: One peak (greater than the other) was near 0.6 (0.75) LIWC at 9 km (11 km) over the L area (O area), surrounded by LIWC values of >0, which accounted for around 62.5% (63.9%) over the L area (O area). Another peak was near LIWC −0.5 (−0.5) at 9.5 km (12 km) over the L area (O area), surrounded by LIWC values of <0. Figure 7b shows the distributions of frequencies of IWC with temperature. The distribution is also divided into two groups: LIWC < 0 and LIWC > 0. The average temperature for LIWC < 0 was lower than for LIWC > 0.

Fig. 7.
Fig. 7.

Frequency (log) of IWC with height and temperature over the (left) L and (right) O area during year 2007–10: L- and O-(a) distribution with height; L- and O-(b) distribution with temperature, and L- and O-(c) variation of mean IWC and its standard deviation with temperature in spring, summer, autumn and winter.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

Figures 7c show the distributions of average IWC values with temperature in the four seasons. The seasonal difference in the relationship between IWC and temperature was not significant; however, the relationships between IWC and temperature were more complex than those between IER and temperature. The standard deviations of IWC were larger, and there were three zones of change. When the temperature was <240 K, IWC increased with an increase of temperature; when temperature was in the range of ~(240–250) K, IWC changed little with temperature; and when temperature was >250 K, IWC decreased as temperature increased. The average IWC peak (or maximum) occurred at a temperature of ~(240–250) K. Also, it should be noted that LIWC values of <0 mostly occurred at lower temperatures and relatively higher heights.

f. Relationship between IWC and IER

As noted above, IER and IWC are retrieved by iteration after a priori setting, and they are treated as individual parameters in the inversion algorithm. What was the nature of the relationship between IER and IWC? Ci microphysical products released by CloudSat and CALIPSO for the period 2007–10 provide us with data to examine this question. Figure 8 shows the analysis results. From the top panels of Fig. 8, the distribution of IER with IWC formed two types: one group contained lower IWC and IER, and another group comprised relatively larger IWC and IER values. The bottom panels of Fig. 8 show a clearer relationship between IWC and IER through calculating the average IWC with IER at intervals of 1 μm. The difference of the distribution among the seasons was not significant, albeit with winter showing a slight distinction, especially over land. IER had two opposite relationships with IWC, as shown by the data presented in the bottom panels of Fig. 8. When IER was <15 μm, IER increased as IWC reduced. Meanwhile, when IER was >15 μm, IER increased with increased IWC. The bimodal relationship of IER and IWC is mainly due to the bimodal relationship between IWC and temperature (discussed in section 4e.) while IER has a nearly linear relationship with the temperature (see Fig. 6). That is, the relationship between IWC and IER is negative when IWC has a negative relation to temperature whereas it will be positive when IWC has a positive relationship with temperature. However, it was difficult to make a fitting function between IWC and IER due to the complex distribution. This section presents a superficial analysis only of the relationship between IER and IWC. As we know, the formation of Ci is affected by numerous factors, so further analysis to deepen our understanding of the relationship between IER and IWC should include other parameters such as aerosols and temperature.

Fig. 8.
Fig. 8.

Frequency (log) of IWC with IER over the (left) L and (right) O area during 2007–10: (top) distribution with IER and (bottom) variation of mean IWC and its standard deviation with IER in spring, summer, autumn, and winter.

Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00329.1

5. Summary and conclusions

Clouds with complex physical properties play an important role in radiative transfer and climate models. Radar–lidar data provide us with a powerful approach to understand the vertical characteristics of cloud. In this paper we have revealed some macro- and microphysical properties of high-level cloud over northern China and the Pacific Ocean using CALIPSO and CloudSat cloud products for the period 2007–10. Comparisons between land and ocean have been made, and the relationships among microphysical parameters investigated. The key findings of the study can be summarized as follows:

  1. More Ci clouds occurred in summer (over the O area) than in winter (over the L area). The Ci occurrence frequency in spring and summer was >1/3. In winter, the Ci occurrence frequency was only ~10% both over the L area and O area.
  2. Over the L area, the mean Hbase (Htop) was ~8.54 km (10.0 km). Over the O area, the mean Hbase (Htop) was ~9.24 km (11.1 km). The mean Hbase and Htop showed seasonal discrepancies. The mean Hbase, mean Htop, and their ranges in summer were greater than in winter. Both the average Hbase and Htop in summer were 1 km (2 km) higher over the L area (O area) than in winter.
  3. The mean CTHs were ~1.46 and 1.86 km over the L area and O area, respectively. The mean CTH in summer was higher than in winter. Over the L area, more than 50% of CTH values fell in the range of ~(0.25–1.5) km across the four seasons (~72.9% in winter and 54.9% in summer). For the O area, the CTH values in the range of ~(0.25–1.5) km accounted for ~55.5% in winter and 46.6% in summer. The Hbase, CTH, and Htop values in summer had a relatively wider distributional range, and the O area showed a wider range than the L area.
  4. Ci cloud contained >98% ice particles, while the proportion of liquid water particles was <0.05%.
  5. For the L area, most Ci IER values were in the range of ~(20–45) μm at heights from ~(7 to 11) km and temperatures from ~(210 to 245) K. Over the O area, most IER values were in the range of ~(15–50) μm at heights of ~(7–14) km and temperatures ~(205–250) K. There was a negative (positive) relationship between IER and height (temperature). Compared with height, temperature had a closer relationship with IER, and the standard deviation was smaller and the relationships among the four seasons were similar.
  6. The magnitude of Ci IWC was in the range of 0.01–1000 μg m−3 and the standard deviation values were larger than the mean values. Approximately 94.5% (over the L area) and 92.9% (over the O area) of LIWC values fell in the range of −1 to 1.5 i.e., [~(0.1–32) μg m−3]. IWC showed different changes with temperature. When the temperature was <240 K, IWC increased with increased temperature; when the temperature was ~(240–250) K, IWC changed little with temperature; and when the temperature was >250 K, IWC decreased as temperature increased. The average IWC peak (or maximum) occurred at a temperature of ~(240–250) K.

The impacts of the monsoon cycle, movement of the tropical convergence zone, local convective activity, temperature, water vapor, and other factors cause the differences of cloud physical properties over land and ocean. But we might conclude that plenty of water vapor is the main reason why average geometric scale and mean occurrence frequency of cirrus is greater over the ocean than over land. However, from microphysical point, the relationship of IER (IWC) and temperature over land is nearly same as over ocean. This feature might reduce the difficulties of the retrieval or parameterization of cloud microphysical properties in climate model and radiative transfer model since they show a close relationship over ocean and land surface.

Acknowledgments

The authors acknowledge support from the National Natural Science Foundation of China (Grant 41275040) and the Strategic Pilot Science and Technology Project of the Chinese Academy of Sciences (XDA050402).

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