1. Introduction
Recent satellite observations have revealed that strong sea surface temperature (SST) fronts associated with the western boundary currents exert strong influences on the overlying atmosphere (Xie 2004; Minobe et al. 2008; Small et al. 2008). Through the adjustment of the marine atmospheric boundary layer (MABL; Small et al. 2008), SST fronts cause stronger scalar surface winds (Xie 2002; Liu et al. 2013), surface wind convergence (Chelton et al. 2004), higher clouds (Liu et al. 2014; Minobe and Takebayashi 2015), and intensified precipitation (Minobe et al. 2008) over warmer water. Several mechanisms have been proposed (Hayes et al. 1989; Wallace et al. 1989; Lindzen and Nigam 1987; Samelson et al. 2006), but the underlying processes remain intensively debated (Xie 2002; Liu et al. 2013; Xu and Xu 2015).
Low clouds markedly affect Earth’s radiative balance (Hartmann et al. 1992) by strongly reducing incoming solar radiation (Chen et al. 2000). Since the morphological features of low clouds reflect the MABL structure (Albrecht et al. 1995; Norris 1998a), the changes in low clouds help uncover the underlying processes in the MABL adjustment to SST fronts. Indeed, the effects of SST fronts on low clouds are clear at the synoptic time scale. Stratocumulus cloud bands often originate from the Gulf Stream north wall during cold-air outbreaks (Young and Sikora 2003). Li et al. (2004) observed that a cloud line only aligns with the Gulf Stream axis in satellite images. Tomita et al. (2013) captured a cloud hole over a cold meander of the Kuroshio Extension using shipborne observations. The above case studies, generally based on a small number of observations, cannot depict a climatological picture.
Based upon in situ observations from several cruises, Tokinaga et al. (2009) observed sharp transitions in cloud-base and cloud-top heights across the Kuroshio Extension front during both winter and summer and found that the effects of the front on clouds extend beyond the MABL. Recent spaceborne lidar measurements suggest that low-level cloud top increases by about 500 m from the cold to the warm flank of the Gulf Stream front in winter (Liu et al. 2014). The previous climatological studies reveal steep cross-frontal changes in cloud base and top, suggestive of morphological variations in low clouds.
As one of the strongest western boundary currents in the ocean, the Kuroshio Current produces a sharp SST front in the East China Sea especially in winter and spring (Xie 2002). Previous studies show that the East China Sea Kuroshio (ECSK) front significantly modulates the regional climate (Xie 2002; Xu et al. 2011; Zhang et al. 2011; Sasaki et al. 2012; Minobe and Takebayashi 2015). The ECSK lies in the East Asian monsoon region where the atmospheric response to the ECSK front exhibits evident seasonal variations. For example, a deep atmospheric response occurs only from spring to early summer (Xu et al. 2011; Sasaki et al. 2012). How SST fronts regulate clouds in the monsoon region remains poorly understood because of the lack of cloud observations over the ocean.
Here we close this data gap by synthesizing high-resolution cloud observations from a spaceborne lidar and long-term visual cloud types from ship reports. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measure cloud-top heights with very high spatial resolution (30 m in vertical and 333 m along track; Vaughan et al. 2009). The cloud-layer product from CALIPSO is capable of detailing small-scale vertical distribution of clouds (Liu et al. 2015). The International Comprehensive Ocean–Atmosphere Data Set (ICOADS; Worley et al. 2005), on the other hand, provides a large number of cloud-type reports (~4 × 105) near the ECSK by visual observations. Based upon ICOADS, Norris (1998b) shows a large-scale cloud-regime transition in the eastern subtropical North Pacific. How low-cloud types vary across such narrow SST fronts (~200–400-km wide) is still unclear. The combination of CALIPSO and ICOADS offers a unique view of clouds near SST fronts.
Based upon CALIPSO and ICOADS, the present study investigates how low clouds respond to the ECSK front under different monsoonal backgrounds from a climate perspective. The large-scale circulations in the East China Sea vary with season, resulting in distinct thermal and dynamical structures in MABL that favor different types of low clouds in winter and spring. For the first time, we show that there is a sharp cross-frontal transition in low clouds in morphological detail, and the cloud responses exhibit quite different behavior between winter and spring. Moreover, the cross-frontal transitions in low clouds are closely tied to the changes in MABL structure. By depicting the long-term cross-frontal transitions in low clouds, our results lay a climatological and observational foundation for future MABL studies related to SST fronts.
We organize this paper as follows. Section 2 describes the datasets. Section 3 investigates MABL conditions in winter and spring. Section 4 examines the cross-frontal transitions in clouds. Section 5 analyzes the dominant processes determining low-cloud-top height. Section 6 investigates the cross-frontal changes in MABL structure. Section 7 concludes this paper with a summary and discussion.
2. Data
We use high-resolution satellite observations in synergy with the latest reanalysis and in situ atmospheric soundings that cover eight boreal winters and springs (December 2006–13 and January–May 2007–14) when the ECSK front is sharp. The climatology of low-cloud types is constructed using synoptic visual cloud-type observations during 1954–2014.
a. Satellite observations
We use the level-2 cloud-layer product (http://www-calipso.larc.nasa.gov/; Vaughan et al. 2009) with 333-m along-track resolution from the CALIPSO lidar to depict the vertical distribution of low-cloud top across the ESCK front. CALIPSO identifies up to five cloud layers below 8.5 km through a selective iterative algorithm from 532-nm attenuated backscatter (Winker et al. 2009). The minimal vertical distance between two cloud layers is 30 m. Following Zhang et al. (2012) and Liu et al. (2014), we regard the highest cloud tops below 4 km as low-cloud top (LCT). CALIPSO passes the ECSK region around 0430 and 1730 UTC (1230 and 0130 local time) and repeats its sun-synchronous orbit every 16 days. Our analysis involves more than 3 × 106 individual CALIPSO profiles for reliable results. Because lidar cannot penetrate optically thick clouds, low clouds can be observed only without higher clouds blocking. This is not a problem for winter when low clouds are dominant (not shown). In spring, however, higher clouds become more frequent than in winter, introducing some uncertainties.
The radar–lidar (DARDAR)-MASK product compensates for the above limitations of the CALIPSO lidar by merging the measurements from the CALIPSO lidar and CloudSat radar, and the latter is capable of penetrating thick clouds. Since the radar and lidar backscatter are proportional to different powers of particle size, the combination of CALIPSO and CloudSat can infer the accurate particle size with height and hence accurate cloud properties. The sensitivities of the lidar and radar to ice clouds are different, so it is feasible to retrieve cloud properties seamlessly (http://www.icare.univ-lille1.fr/projects/dardar/; Delanoë and Hogan 2010). DARDAR-MASK includes a range of cloud phase categories, including rain, supercooled liquid water, liquid water, ice, and mixed ice and supercooled liquid water. The spatial resolution is 1.5 km in horizontal and 240 m in vertical. We use DARDAR to characterize the cross-frontal changes in cloud phases and examine the depth of cloud response to the ECSK front. The combination of radar and lidar provides better retrievals than do standalone methods using either one (Ceccaldi et al. 2013; Delanoë et al. 2013), although there are still some uncertainties in DARDAR-MASK for the clouds below 1 km (Huang et al. 2012). Using DARDAR, Liu et al. (2014) found that the wintertime Gulf Stream front causes cross-frontal variations in cloud phases by modulating the melting level.
In addition, the ECSK front climatology is constructed using daily Optimum Interpolation SST (OISST) on a 0.25° × 0.25° grid (Reynolds et al. 2007).
b. ICOADS low-cloud types
We employ visual cloud-type observations from ships derived from ICOADS (Worley et al. 2005). WMO (1975) gives nontechnical descriptions of each low-cloud type classified according to the synoptic code and its priority in designating the low-cloud-type code (CL). Based upon ICOADS, Norris (1998b) constructed global climatologies of the frequency of occurrence of each low-cloud type over the ocean. To construct cross-frontal climatologies, we include about 4 × 105 low-cloud-type reports from 1954 to 2014 for consistent cloud-type identification (Warren 1988). The nighttime observations are excluded because of difficulty in identifying low-cloud types at night (Rozendaal et al. 1995; Norris 1998b).
c. Reanalysis
We use ERA-Interim, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF; Dee et al. 2011), to examine the atmospheric background circulations and the thermal and dynamical structure of the MABL near the ECSK front. ERA-Interim fields are on a 0.75° × 0.75° grid with seven levels below 850 hPa. The spatial resolution is high enough to depict the cross-frontal changes in climatological MABL structure. We composite the 6-hourly ERA-Interim fields at 0600 and 1800 UTC according to the occurrence of LCT to examine the MABL structure responsible for the cross-frontal changes in low clouds, following Liu et al. (2014).
d. Soundings at Cheju and Naze
The atmospheric soundings at the Cheju (33.28°N, 126.16°E), South Korea, and Naze (28.38°N, 129.55°E), Japan, weather stations from the University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html) are used to characterize MABL structure in winter and spring. There are typically 8–12 levels below 850 hPa in the Cheju soundings, suitable for the present study. The Cheju station is at 73-m elevation with less than 0.5-km distance from the west coast of the island, so its soundings are relatively less affected by the island under the prevailing northwesterlies from winter to spring (Fig. 1). The Naze station is at 295 m in elevation with about 3-km distance from the coast, and its soundings are in a coarser resolution (6–9 levels below 850 hPa) than Cheju. To eliminate the effect of daytime land surface heating on Naze soundings, we include only the nighttime (2100 LST) soundings. Cheju and Naze are situated on the cold and warm flanks of the ECSK front (Fig. 1b), respectively. The differences between their soundings help reveal the signature of the SST front on MABL.
DJF and MAM climatologies: (a),(b) SST [°C; contour interval (CI) = 1°C]. The thick contours indicate 10° and 20°C, respectively. (c),(d) SST-SAT (°C; color shading) and surface winds (m s−1; vectors) with SST contours superimposed. The white contour in (d) indicates SST-SAT equals 0°C. (e),(f) EIS (K; color shading) and upward motion (Pa s−1; CI = 0.05 Pa s−1) at 800 hPa. The red and blue lines in (a) are the daytime and nighttime CALIPSO tracks, respectively, and the solid legs of the tracks represent the range of CALIPSO observations included in this study. The red contour of 19°C SST in (b) is used as the ECSK front center for DJF and MAM. The blue filled square and red filled circle denote the locations of the Cheju, South Korea, and Naze, Japan, stations, respectively. The SST climatology is based on OISST and others on ERA-Interim.
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
3. Background MABL conditions
a. SST and SST-SAT
The ECSK flows in the East Asian monsoon region (Wang and LinHo 2002) where northerlies and southerlies prevail in winter [December–February (DJF); Fig. 1c] and summer, respectively. During spring [March–May (MAM)], the large-scale atmospheric circulation is at a transitional stage from winter to summer monsoon. The northerlies weaken significantly with weak southeasterly winds in the west of the Yellow and East China Seas, forming a surface anticyclone (Fig. 1d). This shallow anticyclone is primarily caused by the thermal contrast among the warm continent, cold shelf waters, and warm ECSK waters (Zhang et al. 2011).
In winter and spring, SST features rich structure in the Yellow and East China Seas (Figs. 1a and 1b, respectively). The spatial pattern of SST generally follows the topography (not shown) because the bottom depth of the shelf determines the thermal inertia of a water column when the ocean is vertically well mixed (Xie 2002). The SST fronts in the Yellow and East China Seas are broader in winter (Fig. 1a), while the SST front associated with the ECSK (27°–31°N, 124°–128°E) becomes narrower and steeper in spring (Fig. 1b).
We define SST-SAT as an index of the instability of the ocean–atmosphere interface, which exhibits strong seasonal variations. In winter, the northerlies bring cold air from the continent to the relatively warm ocean, resulting in the strong instability of the ocean–atmosphere interface with SST-SAT greater than 3°C over most of the Yellow and East China Seas. South of the Korea Peninsula, the strong offshore northwesterlies cause an SST-SAT maximum (>8°C). The springtime instability of the ocean–atmosphere interface, however, weakens significantly (Fig. 1d) since the thermal contrast between land and ocean reverses (Zhang et al. 2011). The ocean–atmosphere interface becomes stable in the Yellow Sea and weakly unstable on the cold flank of the ESCK front (between the white and red contours in Figs. 1d and 1b) and remains strongly unstable over the ESCK. The SST-SAT pattern is close to SST distribution for DJF and MAM, especially near the SST fronts (Figs. 1c,d), suggestive of the strong modulation of the SST fronts on the instability of the ocean–atmosphere interface.
b. MABL structure
We use nighttime atmospheric soundings at the Cheju and Naze stations to examine changes in MABL structure from winter to spring (Fig. 2). Although the Cheju and Naze stations are located on the cold and warm flanks of the ECSK front (Figs. 1a,b), respectively, their soundings consistently reveal a clear seasonal variations of MABL for this region.
The long-term-averaged nighttime (a) potential temperature (K) and (b) relative humidity (%) profiles at Cheju (solid) and Naze (dotted) in DJF (black lines) and MAM (gray lines). The nighttime PDFs (%) of synoptic (c) inversion base heights (km) and (d) inversion strengths (K) at Cheju (bars) and Naze (dotted lines) during DJF (black) and MAM (gray).
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
In the mean state, the wintertime potential temperature is relatively uniform in the lower MABL, while in spring a clear inversion with a steep increase in potential temperature with altitude covers the island surface (Fig. 2a). Such seasonal change in atmospheric boundary layer structure is consistent with relative humidity, which reaches a maximum around 0.8 km and below 0.2 km in DJF and MAM, respectively (Fig. 2b), suggestive of the altitudes with frequent clouds.
The differences in MABL structure between winter and spring are evident not only in the mean state but also at the synoptic time scale. Following Iacobellis et al. (2009), we identify synoptic inversion top and base heights and inversion strengths from Cheju and Naze soundings below 4 km. The inversion strength is defined as the potential temperature difference between the inversion top and base. In the case when more than one inversion occurs, the inversion with the largest temperature increment is selected.
Figures 2c and 2d show the probability density functions (PDFs) of inversion base heights and strengths. Take Cheju PDFs for example to illustrate the seasonal variations in MABL. During winter, the PDF of inversion base heights reaches its maximum around 1.4 km, and another maximum occurs below 0.2 km (Fig. 2c), the latter probably due to the radiative cooling of island surface at night. In spring, inversions are most prevalent below 0.5 km, consistent with the mean state. The inversion strength decreases from winter to spring (Fig. 2d). The PDF of inversion strength peaks around 5 K in winter and 3 K in spring, and the averaged strength declines from wintertime 7.0 K to springtime 5.3 K (not shown). This winter-to-spring weakening in inversion strength supports the reanalysis results. The inversion strength in observational soundings is a little stronger than the reanalysis EIS, which is approximately 6.0 and 4.0 K near Cheju for DJF and MAM, respectively. Naze soundings show similar seasonal variations in MABL to Cheju but with higher inversion base during both winter and spring (dashed lines in Fig. 2c).
The winter-to-spring changes in MABL structure result from the seasonal variations of background atmospheric and oceanic environments. In winter, the strong vertical mixing driven by the warm ocean surface is balanced with downward motion that promotes deeper MABL with stronger inversion (Medeiros et al. 2005). In spring, by contrast, when the air–sea thermal contrast and downward motion weaken, shallow inversions are usually caused by warm air advections near SST fronts (e.g., Rogers 1989; Gao et al. 2007; Zhang et al. 2009). Different MABL conditions favor different types of low clouds (Norris 1998a). As shown later, cumulus is more prevalent in winter, while stratus and fog favor a springtime MABL condition.
The differences between the Cheju and Naze soundings reveal the influence of the ECSK front on MABL. The lower layer with relatively uniform potential temperature from the Naze soundings seems to be deeper than those from Cheju for both winter and spring (Fig. 2a). The wintertime relative humidity for Naze reaches up to 75% at 1.5 km, much larger than that for Cheju at the same altitude (~52%), suggesting that the low clouds are higher near Naze. This is supported by the vertical distribution of inversion base heights. In both winter and spring, the altitude with maximal frequency of inversion base is significantly higher for Naze than that for Cheju (Fig. 2c), indicative of the signature of the ECSK on MABL.
Note that nighttime surface cooling might cause radiation inversions in land-based soundings. We also compared the MABL conditions between early morning (0900 LST; not shown) and early night (2100 LST). The results show that overall MABL structures are similar, but the lower inversions (below 0.5 km) are more frequent at 2100 LST, indicative of the influence from island surface.
4. Cross-frontal transitions in clouds
a. ERA-Interim and OISST climatologies
We composite the cross-frontal transections according to the minimal distance to the center of the ESCK front (positive denotes to the southeast). In this paper, we define the 19°C SST contour in spring between 122° and 130°E (the red contour in Fig. 1b) as the ECSK front center. Although the wintertime ESCK front somewhat deviates from the springtime, for easy comparison, we still use the above composite baseline (x = 0) for the DJF composite. The results do not change much when we use other SST contours.
Figure 3 shows the cross-frontal transections of the ERA-Interim and OISST climatologies, which are linearly interpolated along CALIPSO tracks (indicated by solid blue and red lines in Fig. 1a) prior to the composite. In winter, the SST gradient is strong from x = −5 to x = 1.5 (Fig. 3a, bottom). The 300-km-wide band of SST gradient centered on x = 0 is primarily associated with the ESCK. The wintertime background atmospheric circulation features strong descending northerlies (Fig. 3a, top). A shallow secondary circulation within MABL induced by the ESCK front is discernible with weak ascending and enhanced descending motions on the warm and cold flanks of the SST front, respectively. The formation of the secondary circulation supports the sea level pressure (SLP) adjustment mechanism proposed by Lindzen and Nigam (1987). Warmer SST increases the MABL temperature and lowers the SLP there. In addition, a well-mixed layer forms in the lower MABL and deepens as the thermal stratification (e.g., EIS) declines across the ECSK, consistent with the results of Samelson et al. (2006). Both the secondary circulation within MABL and the cross-frontal changes in thermal stratification might contribute the cross-frontal variations in low clouds.
(a) The cross-front transections of DJF climatology from ERA-Interim: (top) potential temperature (K; contours), upward motion (10−2 Pa s−1; color shading), and meridional wind (m s−1; vectors) and (bottom) SST (°C; blue line) and EIS (K; red line). (b) As in (a), but for MAM. The vectors heading upward and leftward denote ascending motion and southerlies, respectively. All variables are composited referenced to the ECSK front center (x = 0; 19°C SST contour in Fig. 1b) according to the minimal distance (×100 km) to the front center with positive x indicating the southeast (similarly defined hereafter).
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
During spring, the ESCK front becomes sharper. The effects of the ESCK front are more evident as the background circulation is weak (Fig. 3b, top). The ascending branch of the secondary circulation on the warm flank of the SST front (0.5 < x < 1.8) penetrates above MABL and reaches up to 10 km (not shown). The well-mixed layer disappears in spring, indicative of a shallow MABL and consistent with the Cheju and Naze soundings. The clear negative correlation between SST and EIS in space for both DJF and MAM suggests the strong modulation of the ECSK front on MABL structure.
b. CALIPSO LCT
Figure 4 shows the cross-front transection of relative frequency of CALIPSO LCTs on 25-km horizontal by 0.12-km vertical grids. We adopt the composite method of Liu et al. (2013) and make a slight change. The reanalysis is linearly interpolated along CALIPSO tracks and then composited according to its minimal distance to the ECSK front center (the red contour in Fig. 1b). Our composite includes only the interpolated values where and when an LCT is reported.
(a) The DJF relative frequency of LCT (%) from CALIPSO as a function of the distance to the ECSK front and altitude. (top) Composites of potential temperature (K; contours), upward motion (10−2 Pa s−1), and meridional wind (m s−1; vectors). (bottom) EIS (K; red line) is constructed according to CALIPSO LCT occurrence and the blue line indicates SST (°C) as in Fig. 3a. (b) As in (a), but for MAM.
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
The LCT elevation from the cold to the warm flank of the ESCK front is evident in both winter and spring. LCT is more prevalent in winter, and its relative frequency reaches 4% near the front (Fig. 4a, top). The LCT height increases gradually from the cold to the warm flank. A closer examination suggests that LCT approximately increases by 600 m within 400-km cross-frontal distance, resembling the wintertime condition of the Gulf Stream front where LCT increases by 500 m over a distance of 5° in latitude (Liu et al. 2014). The descending northerlies concurrent with low clouds are stronger than the mean state (vectors in Figs. 3a and 4a), indicating that the wintertime low clouds are more prevalent under the northerlies during cold-air outbreaks. Cold-air outbreaks bring cold air to the relatively warm ocean and cause larger SST-SAT (not shown), resulting in deeper MABL than the mean state (Fig. 4a, top) through a “deepening–warming mechanism” proposed by Bretherton and Wyant (1997). The vertical mixing driven by longwave radiative cooling at LCT might also play a role, but its contribution is relatively small in this area (Medeiros et al. 2005).
During spring, the cross-front changes in LCT are more evident than in winter (Fig. 4b). Most LCTs are higher than 0.8 km with much broader vertical range to the southeast of the ECSK front, while the relative LCT frequency peaks beneath 0.6 km to the northwest of the ECSK front, indicative of the prevalence of sea fog and stratus. Compared with the mean state (Fig. 3b, top), the layer with relatively uniform potential temperature becomes deeper on the warm flank of the ECSK front in the LCT composite (Fig. 4b, top), suggesting that the lower MABL there is better mixed. The composite atmospheric circulation is noisy especially around x = −1.5 and x = 2.5 (Fig. 4b).
c. DARDAR cloud categories
We use DARDAR-MASK, which is less affected by higher clouds, to depict the cross-frontal changes in cloud phase categories. Figure 5 shows cross-frontal transections of cloud phases for DJF and MAM. The frequencies of supercooled liquid water as well as mixed ice and supercooled liquid water are lower than 4% (not shown in Fig. 5). During winter, the background descending motion suppresses the influence of the ECSK front on cloud phases within 4 km (Fig. 5a, top). On the cold flank of the SST front (x < −2), the frequency of all phases is about 30% around 1.3 km in altitude and dominated by ice clouds. Over warmer water (−1 < x < 4), the frequency of all phases reaches its maximum (>40%), and the cloud layer with cloud frequency larger than 30% becomes much thicker across the front. In addition, the cloud phases on the warm flank of the ECSK generally consist of liquid and rain below the melting level, suggesting an enhancement of precipitation. All this illustrates a gradual cross-frontal transition of cloud phases in winter, which resembles the results of Skyllingstad and Edson (2009) based on a large-eddy simulation near the Gulf Stream front. In their sensitive run without the SST front, the cross-frontal cloud deepening almost disappears (Skyllingstad and Edson 2009, their Fig. 7b).
(top) Cross-frontal transections in frequencies (%) of DARDAR cloud phases in (a) DJF: all phases (including liquid water, supercooled liquid water, rain, mixed ice and supercooled liquid water, and ice; black contours), liquid water (yellow shading), rain (blue contours), and ice (blue shading). The dashed line represents melting level where the air temperature is 0°C. (bottom) As in Fig. 4a, bottom. (b) As in (a), but for MAM.
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
In spring, the maximal frequency of all cloud phases (>30%) lies only on the warm flank of the ECSK front (x = 0.8; Fig. 5b, top). The secondary peak on the cold flank of the SST front (−4 < x < −3) originates from the CALIPSO profiles near the continent (not shown). The altitude of the primary frequency maximum of all cloud phases reaches up to 7 km with a flat cloud deck above, implying that the effects of the ECSK front extend beyond the MABL top and intensify convective precipitation. This is consistent with the results of Xu et al. (2011, their Fig. 4), based on the Tropical Rainfall Measuring Mission, that both convective and stratiform precipitation peaks over the ECSK, where convective precipitation is much stronger than stratiform. Based on numerical simulation, Xu et al. (2011, their Fig. 7) show that the rainband over the ECSK disappears when the ECSK front is replaced by a smoothed SST field. The numerical experiments in Skyllingstad and Edson (2009) and Xu et al. (2011) demonstrate that the cross-frontal changes in low clouds are associated with the ECSK front.
d. ICOADS low-cloud types
The LCT elevation and cloud-layer deepening are usually related to cloud-type transition from stratiform to convective clouds (e.g., Norris 1998a; Zhou et al. 2015). Here we depict cross-frontal variations in low-cloud-type frequencies using ICOADS (Fig. 6). Similar to the calculation in Fig. 4, we use individual observations to construct the cross-frontal composites according to their minimal distance to the central ECSK front. Following Norris (1998b), we merge CL 1 and 2 into cumulus, CL 8 and 4 into cumulus with stratocumulus, and CL 3 and 9 into cumulonimbus. The sky-obscuring fog includes the observations with present-weather code (ww) between 10 and 12 or 40 and 49.
(a) Cross-frontal changes in frequencies (%) of low-cloud types from ICOADS for DJF: stratocumulus (CL 5; blue line), stratus (CL 6; black line), cumulus with stratocumulus (CL 8 and 4; green line), cumulonimbus (CL 3 and 9; red line), cumulus (CL 1 and 2; dashed red line), and fog (ww = 10–12 or 40–49; dashed black line). (b) As in (a), but for MAM. Nighttime observations are omitted.
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
Figure 6 illustrates an apparent seasonal change in low-cloud types, especially northwest of the ECSK front (x < −1). The frequencies of cumulus and cumulus with stratocumulus decline from DJF to MAM, while fog and stratus over the cold water (−5 < x < −2) become more prevalent in spring. Cumulus dominates over the entire area from winter to spring (Fig. 6), probably because of the frequent passage of synoptic disturbances.
Figure 6 also suggests evident cross-frontal transitions in low-cloud types. From the cold to the warm flank of the ECSK front (−1.5 < x < 1.5), the frequency of cumulus with stratocumulus increases in both winter and spring, while the increase in cumulus is significant just in spring. Cross-frontal declines are apparent in fog and springtime stratus. Stratocumulus frequency peaks between x = −1 and x = 0 and decreases southeastward. Cumulonimbus frequency for both DJF and MAM and stratus frequency for DJF keep steady across the ECSK front. In general, low-cloud types feature a cross-frontal transition from stratiform to convective clouds, which is steeper in spring, supporting the CALIPSO and DARDAR results (Figs. 4b and 5b).
The nighttime low clouds are more frequent than the daytime low clouds (not shown) because the daytime solar radiation burns off the liquid cloud water (Caldwell et al. 2005). Moreover, CALIPSO might miss some clouds because of the small signal-to-noise ratio in the daytime solar background (Kacenelenbogen et al. 2011). The daytime and nighttime LCTs share a similar cross-frontal pattern with the diurnal mean (not shown). The ICOADS results, including only daytime observations, differ from CALIPSO, which might introduce some minor inconsistencies between the two.
5. What determines LCT height?
The thermal and dynamical processes within the MABL are mutually related. For example, stronger inversions are typically associated with enhanced subsidence (Myers and Norris 2013). In the cross-frontal transect, the SST front induces secondary circulation and changes in thermal stratification, as mentioned in section 4a, both of which probably contribute cross-frontal elevation in LCT. To interpret the cross-frontal changes in LCT, it is necessary to clarify whether the dynamical or thermal process dominates. We express LCT as a function of EIS and ascending motion at 800 hPa (−ω800; Figs. 7a and 7b) and as a function of LCL and −ω800 (Figs. 7c and 7d). The 800-hPa level is close to the typical MABL height (1.6–2.2 km) over the ECSK area. The results are similar when we use vertical velocity at 700 hPa.
LCT (km; color) is expressed as a function of EIS (K) and upward motion (Pa s−1) at 800 hPa for (a) DJF and (b) MAM and as a function of LCL (km) and upward motion at 800 hPa for (c) DJF and (d) MAM. LCTs are the mean values within (EIS, −ω800) or (LCL, −ω800) grids after excluding the top and bottom 25% of the samples, following Kawai et al. (2015).
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
Figures 7a,b show that LCT is positively correlated to −ω800 and negatively to EIS, suggesting that both EIS and −ω800 are independent factors to determine the LCT, consistent with Myers and Norris (2013). The vertical velocity at the MABL top seems to be more important with −ω800 > 0.2 Pa s−1 when cumulus is dominant, while the MABL stratification plays a more important role in the stratocumulus regime with −ω800 < 0. In spring, LCL is positively correlated with LCT with −ω800 < 0.2 Pa s−1, implying that the near-surface moist condition is important for stratocumulus and stratus cloud top, since LCL is approximately equal to 123 times the depression of the dewpoint temperature at 2 m. The obscure relationship between LCL and LCT in winter suggests that the near-surface condition is not important when the low-cloud layer decouples from the surface moisture supply.
6. MABL decoupling
The averaged LCT also features a prominent elevation from the cold to the warm flank of the ECSK front for both winter and spring (Fig. 8a). Figure 8b shows the spatial standard deviation (STD) of LCT within 10-km bins along the CALIPSO tracks. The calculation includes only the bins with more than 50% of the profiles reporting LCT. Figure 8b shows that the LCT smoothness declines concurrent with the LCT elevation. The cross-frontal LCT elevation, decrease in cloud-top smoothness, and cloud-type transitions indicate that the MABL increasingly tends to decouple from the cold to the warm flank of the ECSK front (Norris 1998b; Bretherton and Wyant 1997).
DJF (black lines) and MAM (gray lines) cross-frontal transections of (a) averaged LCT (km) and (b) averaged along-track LCT STD (m).
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
MABL decouples when the turbulence driven by longwave cooling at LCT cannot sustain mixing of the positively buoyant entrained air over the entire MABL (Bretherton and Wyant 1997; Wood 2012). The well-mixed cloud layer and subcloud layer separate (Nicholls 1984), and a weak stable layer forms between them and cuts off the moisture supply for cloud layer from the surface (Bretherton and Wyant 1997; Wyant et al. 1997). A full evaluation of MABL decoupling requires in situ atmospheric soundings with high vertical resolution (e.g., Jones et al. 2011). Here we use the combination of CALIPSO LCT and routine atmospheric soundings to qualitatively infer the MABL decoupling.
In a well-mixed MABL, water vapor is fairly constant (e.g., Jones et al. 2011) and the difference of water vapor mixing ratio between the lower and upper MABL is small. When the MABL decouples, the stable layer between the cloud layer and lower MABL inhibits moist mixing, creating moisture stratification. Thus, the moisture stratification between the MABL top and bottom is a good index for MABL decoupling (Norris 1998a; Jones et al. 2011).
We compare the moisture stratification in the Cheju and Naze soundings. For easy comparison, we use water vapor mixing ratio difference δq between 300 m and the inversion base since the elevation of the Naze station (295 m) is higher than Cheju (73 m). Following Norris (1998a), δq is normalized by SST saturation water vapor mixing ratio qs(SST) to eliminate the influence from the steep SST front. The PDFs of δq normalized by qs(SST) for Cheju and Naze are very different (Fig. 9a). The PDF peaks around 3 × 10−2 at Cheju but reaches a maximum at 9 × 10−2 at Naze. The averaged δq/qs(SST) is 14.0 × 10−2 and 18.6 × 10−2 for Cheju and Naze, respectively. The difference between Naze and Cheju in δq/qs(SST) is significant at the 95% confidence level based on a Student’s t test, suggesting that MABL decoupling is more prevalent over Naze than Cheju.
(a) PDFs (%) of synoptic δq/qs(SST) for Cheju (gray bars) and Naze (black line). The value of δq is the q difference between the inversion base and 300 m. (b) PDFs of the synoptic differences (km) between CALIPSO LCT and LCL based on soundings for Cheju (gray bars) and Naze (black line). The LCL is calculated based upon moisture and thermal conditions at 300 m, which are linearly interpolated from soundings. LCTs are averaged from CALIPSO profiles within 100-km distance from the Cheju and Naze stations, respectively. The synoptic observations are during DJF and MAM.
Citation: Journal of Climate 29, 12; 10.1175/JCLI-D-15-0589.1
Jones et al. (2011) found that “the mixed layer cloud thickness,” defined as the difference between the LCT and LCL, is strongly correlated with decoupling. Figure 9b shows the PDFs of differences between CALIPSO LCT and LCL based upon 300-m moisture and thermal conditions from soundings. The occurrence of a mixed layer cloud that is shallower (deeper) than 0.75 km is more (less) frequent for Cheju than that for Naze. On average, the mixed layer cloud thickness over Naze (1.1 km) is much deeper than that over Cheju (0.72 km). The difference of mixed layer cloud thickness between the two weather stations is significant at the 95% confidence level. All this suggests that the tendency of MABL decoupling increases from the cold to the warm flank of the ECSK front, which is responsible for the cross-frontal cloud-type transition.
7. Summary and discussion
A sharp SST front associated with the ECSK is well developed in winter and spring. The present study investigates the low-cloud responses to the ECSK front under different monsoonal backgrounds by synthesizing observations from spaceborne lidar, in situ atmospheric soundings, cloud-type reports, and reanalysis data. The independent shipboard visual observations consistently suggest prominent cross-frontal transitions in low clouds during both winter and spring. Concurrent with MABL decoupling, the cross-frontal transitions in low clouds exhibit distinct features under different seasonal MABL conditions.
The East Asian monsoon significantly varies from winter to spring, setting quite different MABL conditions for low clouds. In winter, strong northerlies bring the cold continental air to the warm ocean, causing strong instability of the ocean–atmosphere interface over the Yellow and East China Seas. The strong instability drives a deep well-mixed MABL capped by a strong descending inversion (Medeiros et al. 2005). In spring, by contrast, the instability of the ocean–atmosphere interface becomes weakly unstable on the cold flank of the ECSK front and stable in the Yellow Sea. Frequent shallow and weak inversions are produced by warm air advection near SST fronts. The ocean–atmosphere interface over the ECSK remains strongly unstable from winter to spring. The effects of the ECSK on atmospheric circulation are also apparent with a secondary circulation near the SST front, which is confined within the MABL in winter and extends beyond the MABL on the warm flank of the ECSK front in spring.
The ECSK front exerts a strong influence on clouds and results in different cloud-response behavior between winter and spring. In winter, low clouds feature a gradual LCT elevation from the cold to the warm flank of the front. The effects of the ECSK front on clouds are confined within 4 km according to DARDAR. This supports the cross-frontal increase of cumulus-with-stratocumulus frequency from ICOADS. Moreover, the ice clouds on the cold flank of the ECSK front transform into liquid water clouds and rain on the warm flank. The springtime low clouds, by contrast, are characterized by a sharp cross-frontal transition. Most LCTs are lower than 0.6 km on the cold flank of the ECSK, while they are typically higher than 0.8 km with much broader vertical range on the warm flank. DARDAR shows that the effects of the ECSK front on clouds penetrate above the MABL and reach up to 7 km. These changes derived from satellite observations are consistent with the stratiform-to-convective transition in cloud types from the cold to the warm flank of the ECSK: increases in cumulus and cumulus-with-stratocumulus frequencies and a sharp decline in sky-obscuring fog frequency.
Previous studies from numerical simulations support that the above cross-frontal transitions in low clouds are closely tied to the ECSK front (Skyllingstad and Edson 2009; Xu et al. 2011). Our joint PDF analysis shows that both vertical motion capping the MABL and MABL stratification contribute the cross-frontal changes in LCT during winter and spring, while the near-surface moist condition becomes important in spring when MABL is shallower. Thus, both the cross-frontal secondary circulation and the changes in MABL temperature stratification induced by the ECSK are important for the cross-frontal transitions in low clouds.
In both winter and spring, LCT homogeneity declines as LCT height increases from the cold to the warm flank of the ECSK. This indicates that MABL increasingly tends to decouple across the ECSK front, consistent with the cross-frontal intensification in moisture stratification within MABL.
The cross-frontal transitions in low clouds resemble the large-scale cloud-regime transition over the eastern subtropical Pacific (Bretherton and Wyant 1997; Wyant et al. 1997). Regimes of stratocumulus, cumulus with stratocumulus, and cumulus progressively occur along trajectories of the northeasterly trade winds (Norris 1998b). Both the decrease in surface divergence and increase in SST contribute to the MABL deepening that promotes the large-scale MABL decoupling and cloud-regime transition (Bretherton and Wyant 1997; Wyant et al. 1997). By contrast, cumulus clouds dominate the entire area near the ECSK. The morphology in low clouds across the narrow SST front features a prominent transition from stratiform to convective clouds. Schubert et al. (1979) compared the large-scale and cross-SST-frontal low clouds and MABL evolution based on numerical experiments. They found that the boundary layer deepens much more rapidly with the wintertime flow of cold air across the Kuroshio because of the different adjustment time scale from the large-scale cloud-regime transition in the subtropical eastern Pacific.
There remains intense debate on how SST fronts modulate the overlying MABL, primarily because of the lack of observations in the ocean. The present study lays a climatological and observational foundation by depicting the prominent cross-frontal low-cloud transition in morphology associated with MABL decoupling. The results help shed light on how MABL responds to SST fronts. For example, we show that MABL decoupling is more prevalent on the warm flank of the ECSK front. Further numerical simulations with budget analysis of the cross-frontal MABL and its relevant clouds are desirable.
Acknowledgments
The data in this study are obtained from the Atmospheric Scientific Data Center of NASA (CALIPSO), the ICARE Thematic Center (DARDAR-MASK), the ECMWF data server (ERA-Interim), the National Centers for Environmental Information (OISST), the National Climatic Data Center (ICOADS), and the University of Wyoming (Cheju and Naze soundings). This work is supported by the National Basic Research Program of China (2012CB955602), the Natural Science Foundation of China (NSFC; 41505003), key NSFC (41490641), and NASA. S.-P. Zhang was supported by NSFC (41576108).
REFERENCES
Albrecht, B. A., M. P. Jensen, and W. J. Syrett, 1995: Marine boundary layer structure and fractional cloudiness. J. Geophys. Res., 100, 14 209–14 222, doi:10.1029/95JD00827.
Bretherton, C. S., and M. Wyant, 1997: Moisture transport, lower-tropospheric stability, and decoupling of cloud-topped boundary layers. J. Atmos. Sci., 54, 148–167, doi:10.1175/1520-0469(1997)054<0148:MTLTSA>2.0.CO;2.
Caldwell, P., C. S. Bretherton, and R. Wood, 2005: Mixed-layer budget analysis of the diurnal cycle of entrainment in southeast Pacific stratocumulus. J. Atmos. Sci., 62, 3775–3791, doi:10.1175/JAS3561.1.
Ceccaldi, M., J. Delanoë, R. J. Hogan, N. L. Pounder, A. Protat, and J. Pelon, 2013: From CloudSat–CALIPSO to EarthCARE: Evolution of the DARDAR cloud classification and its comparison to airborne radar–lidar observations. J. Geophys. Res. Atmos., 118, 7962–7981, doi:10.1002/jgrd.50579.
Chelton, D., M. Schlax, M. Freilich, and R. Milliff, 2004: Satellite measurements reveal persistent small-scale features in ocean winds. Science, 303, 978–983, doi:10.1126/science.1091901.
Chen, T., W. B. Rossow, and Y. Zhang, 2000: Radiative effects of cloud-type variations. J. Climate, 13, 264–286, doi:10.1175/1520-0442(2000)013<0264:REOCTV>2.0.CO;2.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
Delanoë, J., and R. J. Hogan, 2010: Combined CloudSat–CALIPSO–MODIS retrievals of the properties of ice clouds. J. Geophys. Res., 115, D00H29, doi:10.1029/2009JD012346.
Delanoë, J., A. Protat, O. Jourdan, J. Pelon, M. Papazzoni, R. Dupuy, J.-F. Gayet, and C. Jouan, 2013: Comparison of airborne in situ, airborne radar–lidar, and spaceborne radar–lidar retrievals of polar ice cloud properties sampled during the POLARCAT campaign. J. Atmos. Oceanic Technol., 30, 57–73, doi:10.1175/JTECH-D-11-00200.1.
Gao, S., H. Lin, B. Shen, and G. Fu, 2007: A heavy sea fog event over the Yellow Sea in March 2005: Analysis and numerical modeling. Adv. Atmos. Sci., 24, 65–81, doi:10.1007/s00376-007-0065-2.
Hartmann, D. L., M. E. Ockert-Bell, and M. L. Michelsen, 1992: The effect of cloud type on Earth’s energy balance: Global analysis. J. Climate, 5, 1281–1304, doi:10.1175/1520-0442(1992)005<1281:TEOCTO>2.0.CO;2.
Hayes, S. P., M. J. McPhaden, and J. M. Wallace, 1989: The influence of sea-surface temperature on surface wind in the eastern equatorial Pacific: Weekly to monthly variability. J. Climate, 2, 1500–1506, doi:10.1175/1520-0442(1989)002<1500:TIOSST>2.0.CO;2.
Huang, Y., S. T. Siems, M. J. Manton, A. Protat, and J. Delanoë, 2012: A study on the low-altitude clouds over the Southern Ocean using the DARDAR-MASK. J. Geophys. Res., 117, D18204, doi:10.1029/2012JD017800.
Iacobellis, S. F., J. R. Norris, M. Tyree, and R. Daniel, 2009: Climate variability and California low-level temperature inversions. California Climate Change Center Final Paper, 48 pp.
Jones, C. R., C. S. Bretherton, and D. Leon, 2011: Coupled vs. decoupled boundary layers in VOCALS-REx. Atmos. Chem. Phys., 11, 7143–7153, doi:10.5194/acp-11-7143-2011.
Kacenelenbogen, M., and Coauthors, 2011: An accuracy assessment of the CALIOP/CALIPSO version 2/version 3 daytime aerosol extinction product based on a detailed multi-sensor, multi-platform case study. Atmos. Chem. Phys., 11, 3981–4000, doi:10.5194/acp-11-3981-2011.
Kawai, H., S. Yabu, Y. Hagihara, T. Koshiro, and H. Okamoto, 2015: Characteristics of the cloud top heights of marine boundary layer clouds and the frequency of marine fog over mid-latitudes. J. Meteor. Soc. Japan, 93, 613–628, doi:10.2151/jmsj.2015-045.
Klein, S., and D. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 1587–1606, doi:10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2.
Li, X., W. Zheng, and W. Pichel, 2004: A cloud line over the Gulf Stream. J. Geophys. Res., 31, L14108, doi:10.1029/2004GL019892.
Lindzen, R., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 2418–2436, doi:10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2.
Liu, J.-W., S.-P. Zhang, and S.-P. Xie, 2013: Two types of surface wind response to the East China Sea Kuroshio front. J. Climate, 26, 8616–8627, doi:10.1175/JCLI-D-12-00092.1.
Liu, J.-W., S. P. Xie, J. R. Norris, and S. P. Zhang, 2014: Low-level cloud response to the Gulf Stream front in winter using CALIPSO. J. Climate, 27, 4421–4432, doi:10.1175/JCLI-D-13-00469.1.
Liu, J.-W., S. P. Xie, and S.-P. Zhang, 2015: Effects of the Hawaiian Islands on the vertical structure of low-level clouds from CALIPSO lidar. J. Geophys. Res. Atmos., 120, 215–228, doi:10.1002/2014JD022410.
Medeiros, B., A. Hall, and B. Stevens, 2005: What controls the mean depth of the PBL? J. Climate, 18, 3157–3172, doi:10.1175/JCLI3417.1.
Minobe, S., and S. Takebayashi, 2015: Diurnal precipitation and high cloud frequency variability over the Gulf Stream and over the Kuroshio. Climate Dyn., 44, 2079–2095, doi:10.1007/s00382-014-2245-y.
Minobe, S., A. Kuwano-Yoshida, N. Komori, S.-P. Xie, and R. J. Small, 2008: Influence of the Gulf Stream on the troposphere. Nature, 452, 206–209, doi:10.1038/nature06690.
Myers, T. A., and J. R. Norris, 2013: Observational evidence that enhanced subsidence reduces subtropical marine boundary layer cloudiness. J. Climate, 26, 7507–7524, doi:10.1175/JCLI-D-12-00736.1.
Nicholls, S., 1984: The dynamics of stratocumulus: Aircraft observations and comparisons with a mixed layer model. Quart. J. Roy. Meteor. Soc., 110, 783–820, doi:10.1002/qj.49711046603.
Norris, J. R., 1998a: Low cloud type over the ocean from surface observations. Part I: Relationship to surface meteorology and the vertical distribution of temperature and moisture. J. Climate, 11, 369–382, doi:10.1175/1520-0442(1998)011<0369:LCTOTO>2.0.CO;2.
Norris, J. R., 1998b: Low cloud type over the ocean from surface observations. Part II: Geographical and seasonal variations. J. Climate, 11, 383–403, doi:10.1175/1520-0442(1998)011<0383:LCTOTO>2.0.CO;2.
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, doi:10.1175/2007JCLI1824.1.
Rogers, D., 1989: The marine boundary layer in the vicinity of an ocean front. J. Atmos. Sci., 46, 2044–2062, doi:10.1175/1520-0469(1989)046<2044:TMBLIT>2.0.CO;2.
Rozendaal, M. A., C. B. Leovy, and S. A. Klein, 1995: An observational study of diurnal variations of marine stratiform cloud. J. Climate, 8, 1795–1809, doi:10.1175/1520-0442(1995)008<1795:AOSODV>2.0.CO;2.
Samelson, R. M., E. D. Skyllingstad, D. B. Chelton, S. K. Esbensen, L. W. O’Neill, and N. Thum, 2006: On the coupling of wind stress and sea surface temperature. J. Climate, 19, 1557–1566, doi:10.1175/JCLI3682.1.
Sasaki, Y. N., S. Minobe, T. Asai, and M. Inatsu, 2012: Influence of the Kuroshio in the East China Sea on the early summer (baiu) rain. J. Climate, 25, 6627–6645, doi:10.1175/JCLI-D-11-00727.1.
Schubert, W. H., J. S. Wakefield, E. J. Steiner, and S. K. Cox, 1979: Marine stratocumulus convection. Part II: Horizontally inhomogeneous solutions. J. Atmos. Sci., 36, 1308–1324, doi:10.1175/1520-0469(1979)036<1308:MSCPIH>2.0.CO;2.
Skyllingstad, E. D., and J. B. Edson, 2009: Large-eddy simulation of moist convection during a cold air outbreak over the Gulf Stream. J. Atmos. Sci., 66, 1274–1293, doi:10.1175/2008JAS2755.1.
Small, R. J., and Coauthors, 2008: Air–sea interaction over ocean fronts and eddies. Dyn. Atmos. Oceans, 45, 274–319, doi:10.1016/j.dynatmoce.2008.01.001.
Tokinaga, H., Y. Tanimoto, S.-P. Xie, T. Sampe, H. Tomita, and H. Ichikawa, 2009: Ocean frontal effects on the vertical development of clouds over the western North Pacific: In situ and satellite observations. J. Climate, 22, 4241–4260, doi:10.1175/2009JCLI2763.1.
Tomita, H., S.-P. Xie, H. Tokinaga, and Y. Kawai, 2013: Cloud response to the meandering Kuroshio Extension front. J. Climate, 26, 9393–9398, doi:10.1175/JCLI-D-13-00133.1.
Vaughan, M. A., and Coauthors, 2009: Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements. J. Atmos. Oceanic Technol., 26, 2034–2050, doi:10.1175/2009JTECHA1228.1.
Wallace, J., T. Mitchell, and C. Deser, 1989: The influence of sea-surface temperature on surface wind in the eastern equatorial Pacific: Seasonal and interannual variability. J. Climate, 2, 1492–1499, doi:10.1175/1520-0442(1989)002<1492:TIOSST>2.0.CO;2.
Wang, B., and LinHo, 2002: Rainy season of the Asian–Pacific summer monsoon. J. Climate, 15, 386–398, doi:10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.
Warren, S. G., 1988: Global distribution of total cloud cover and cloud types over ocean. National Center for Atmospheric Research Tech. Note NCAR/TN-317+STR, 42 pp. + 170 maps.
Winker, D. M., M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 2310–2323, doi:10.1175/2009JTECHA1281.1.
WMO, 1975: Manual on the observation of clouds and other meteors. WMO Publ. 407, 155 pp.
Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 2373–2423, doi:10.1175/MWR-D-11-00121.1.
Wood, R., and C. S. Bretherton, 2006: On the relationship between stratiform low cloud cover and lower-tropospheric stability. J. Climate, 19, 6425–6432, doi:10.1175/JCLI3988.1.
Worley, S. J., S. D. Woodruff, R. W. Reynolds, S. J. Lubker, and N. Lott, 2005: ICOADS release 2.1 data and products. Int. J. Climatol., 25, 823–842, doi:10.1002/joc.1166.
Wyant, M. C., C. S. Bretherton, H. A. Rand, and D. E. Stevens, 1997: Numerical simulations and a conceptual model of the stratocumulus to trade cumulus transition. J. Atmos. Sci., 54, 168–192, doi:10.1175/1520-0469(1997)054<0168:NSAACM>2.0.CO;2.
Xie, S.-P., 2002: Bathymetric effect on the winter sea surface temperature and climate of the Yellow and East China Seas. Geophys. Res. Lett., 29, 2228, doi:10.1029/2002GL015884.
Xie, S.-P., 2004: Satellite observations of cool ocean–atmosphere interaction. Bull. Amer. Meteor. Soc., 85, 195–208, doi:10.1175/BAMS-85-2-195.
Xu, H., M. Xu, S.-P. Xie, and Y. Wang, 2011: Deep atmospheric response to the spring Kuroshio over the East China Sea. J. Climate, 24, 4959–4972, doi:10.1175/JCLI-D-10-05034.1.
Xu, M., and H. Xu, 2015: Atmospheric responses to Kuroshio SST front in the East China Sea under different prevailing winds in winter and spring. J. Climate, 28, 3191–3211, doi:10.1175/JCLI-D-13-00675.1.
Young, G. S., and T. D. Sikora, 2003: Mesoscale stratocumulus bands caused by Gulf Stream meanders. Mon. Wea. Rev., 131, 2177–2191, doi:10.1175/1520-0493(2003)131<2177:MSBCBG>2.0.CO;2.
Zhang, C., Y. Wang, A. Lauer, K. Hamilton, and F. Xie, 2012: Cloud base and top heights in the Hawaiian region determined with satellite and ground-based measurements. Geophys. Res. Lett., 39, L15706, doi:10.1029/2012GL052355.
Zhang, S.-P., S.-P. Xie, Q.-Y. Liu, Y.-Q. Yang, X.-G. Wang, and Z.-P. Ren, 2009: Seasonal variations of Yellow Sea fog: Observations and mechanisms. J. Climate, 22, 6758–6772, doi:10.1175/2009JCLI2806.1.
Zhang, S.-P., J.-W. Liu, and S.-P. Xie, 2011: The formation of a surface anticyclone over the Yellow and East China Seas in spring. J. Meteor. Soc. Japan, 89, 119–131, doi:10.2151/jmsj.2011-202.
Zhou, X., P. Kollias, and E. R. Lewis, 2015: Clouds, precipitation, and marine boundary layer structure during the MAGIC field campaign. J. Climate, 28, 2420–2442, doi:10.1175/JCLI-D-14-00320.1.
