Boundary Layer and Cloud Structure Controls on ... - AMS Journals

194

JOURNAL OF CLIMATE

VOLUME 24

Boundary Layer and Cloud Structure Controls on Tropical Low Cloud Cover Using A-Train Satellite Data and ECMWF Analyses TERENCE L. KUBAR, DUANE E. WALISER, AND J.-L. LI Jet Propuslion Laboratory, California Institute of Technology, Pasadena, California (Manuscript received 2 March 2010, in final form 26 August 2010) ABSTRACT The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat radar, and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud data on the A-Train constellation complemented with the European Centre for Medium-Range Forecasts (ECMWF) analyses are used to investigate the cloud and boundary layer structure across a 108 wide cross section starting at 58S near the international date line and extending to 358N near the California coast from March 2008 to February 2009. The mean large-scale inversion height and low-level cloud tops, which correspond very closely to each other, are very shallow (;500 m) over cold SSTs and high static stability near California and deepen southwestward (to a maximum of ;1.5–2.0 km) along the cross section as SSTs rise. Deep convection near the ITCZ occurs at a surface temperature close to 298 K. While the boundary layer relative humidity (RH) is nearly constant where a boundary layer is well defined, it drops sharply near cloud top in stratocumulus regions, corresponding with strong thermal inversions and water vapor decrease, such that the maximum (2›RH/›z) marks the boundary layer cloud top very well. The magnitude correlates well with low cloud frequency during March–May (MAM), June–August (JJA), and September–November (SON) (r2 5 0.85, 0.88, and 0.86, respectively). Also, CALIPSO and MODIS isolated low cloud frequency generally agree quite well, but CloudSat senses only slightly more than one-third of the low clouds as observed by the other sensors, as many clouds are shallower than 1 km and thus cannot be discerned with CloudSat due to contamination from the strong signal from surface clutter. Mean tropospheric v between 300 and 700 hPa is examined from the ECMWF Year of Tropical Convection (YOTC) analysis dataset, and during JJA and SON, strong rising motion in the middle troposphere is confined to a range of 2-m surface temperatures between 297 and 300 K, consistent with previous studies that show a narrow range of SSTs over which deep ascent occurs. During December–February (DJF), large-scale ascending motion extends to colder SSTs and high boundary layer stability. A slightly different boundary layer stability metric is derived, the difference of moist static energy (MSE) at the middle point of the inversion (or at 700 hPa if no inversion exists) and the surface, referred to as DMSE. The utility of DMSE is its prediction of isolated uniform low cloud frequency, with very high r2 values of 0.93 and 0.88, respectively, for the MODIS and joint lidar plus radar product during JJA but significantly lower values during DJF (0.46 and 0.40), with much scatter. To quantify the importance of free tropospheric dynamics in modulating the DMSE–low cloud relationships, the frequency as a function of DMSE of rising motion profiles (v , 20.05 Pa s21) is added to the observed low cloud frequency for a maximum hypothetical low cloud frequency. Doing this greatly reduces the interseasonal differences and holds promise for using DMSE for parameterization schemes and examining low cloud feedbacks.

1. Introduction It has long been known that marine boundary layer (MBL) clouds are pervasive over the ocean, covering nearly one-third of the global ocean surface (Charlson

Corresponding author address: Terence L. Kubar, Jet Propulsion Laboratory, MS 183-518, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109. E-mail: [email protected] DOI: 10.1175/2010JCLI3702.1 Ó 2011 American Meteorological Society

et al. 1987; Klein and Hartmann 1993). Their optical depth is directly proportional to the cloud liquid water path (LWP) and inversely to the effective radius. The liquid water path is a macrophysical quantity that tends to scale with cloud thickness (Kubar et al. 2009), which in turn is governed in part by the thermodynamic profile. Though the effective radius is a microphysical variable, it too depends at least partly on cloud-top height, as deeper clouds allow for more condensational growth of cloud droplets. Key in these relationships is how the

1 JANUARY 2011

KUBAR ET AL.

vertical boundary layer structure is connected to the underlying meteorology. In the subtropics, the largescale downwelling Hadley–Walker branches favor strong thermal inversions that limit vertical cloud growth to the boundary layer. The cloud-top temperature TTOP also determines the longwave cloud effect, which for low clouds is normally small since TTOP is not that much colder than the sea surface (Stevens and Brenguier 2010). For the global mean, the reduction in net radiation caused by low clouds is 15 and 18 W m22 during June–August (JJA) and December–February (DJF), respectively (Hartmann et al. 1992). In light of their large impact on the global energy balance, understanding how low cloud frequency relates to multiple meteorological factors, including SST, stability, and large- and smallscale circulations, is a legitimate climate question, particularly as these factors may change with global warming (Stevens and Brenguier 2010). Additionally, the transitions from stratiform to shallow cumuliform clouds and to deep convection continue to be active research areas and are connected not only to dynamical and static stability regimes but also have implications for the top of atmosphere (TOA) radiative forcing. Considerable work has been done in the study of low clouds in terms of their microphysical heteorgeneity, macroscale and horizontal spatial structure, as well as their underlying relationship and sensitivity to the largescale circulation, including variability ranging from seasonal to interannual time scales. Using six years of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) pixel-level data, Jensen et al. (2008) have performed a thorough analysis of the seasonal variability of low cloud cover and cloud optical properties of five global subtropical stratocumulus regions and additionally classified the spatial structure of low clouds by a useful quantity called the effective cloud diameter. Using a multitude of satellite data and in situ information, a comprehensive assessment of the northeastern Pacific marine boundary layer (MLB) structure and clouds, encompassing the stratocumulus regime, has been performed by Lin et al. (2009). This study examines the annual cycle of cloud fraction, MBL depth, lifting condensation level (LCL), LWP, and inversion properties. Some important take-home messages include more abundant shallow and homogeneous boundary layer clouds during JJA versus DJF, higher JJA in-cloud LWP, and stronger inversions during JJA. The reason for deeper DJF MBL clouds is likened to the stratocumulus-tocumulus transition, with the ‘‘deepening-warming mechanism’’ of Bretherton and Wyant (1997) cited—whereby the ratio of the cooling rate at the MBL top is less than the threshold of the surface latent heat flux, a requirement for MBL decoupling. We also examine the annual

195

cycle of low cloud tops and inversion properties in our cross section of study (defined shortly) and see to what extent these variations are true. Large-scale subsidence regimes in the subtropics, where low clouds are pervasive, continue to be the largest source of uncertainty among climate models, both due to the spread among the models and also because of the large differences in changes of clouds between models and observations as a function of changes in surface temperatures in these regimes (Bony and Dufresne 2005). Cloud feedbacks contribute 66% and 85% to intermodel variance of two GCM ensembles, respectively, analyzed by Webb et al. (2006), and positive low cloud feedbacks (decrease in low cloud cover and optical depth with warming) contribute most in one ensemble, while negative low cloud feedbacks dominate the other. This range among climate models in handling cloud feedbacks is an important motivation for studying low clouds, including the processes that control their vertical and horizontal structure as well as their abundance. The underlying thermodynamics and large-scale dynamics have been studied as being important controllers of low cloud abundance. In two different stratocumulus regimes, one off the west coast of Peru and the other off the west coast of Angola, Oreopoulos and Davies (1993) use the Earth Radiation Budget Experiment (ERBE) and the International Satellite Cloud Climatology Project (ISCCP) for cloud albedo and fraction, respectively, and find that low cloud albedo, and more weakly cloud fraction, are negatively correlated with SSTs in the SST ranges that tend to bound the stratocumulus regimes in each of the regions (198–258C near Peru and 228–278C near Angola). Similarly, Tselioudis et al. (1992) show with multiple years of ISCCP cloud information that, for warm maritime clouds, cloud optical thickness decreases with cloud temperature. More recently, Clement et al. (2009) have used a multidecade record of satellite and surface data to show significant correlations between low plus middle cloud cover variations in the northeast Pacific with several climatic variables, including negative correlations with SSTs and positive correlations with low-level stability, sea level pressure, and midtropospheric pressure vertical velocity (v). Extensive cloud information now offered by CloudSat/ CALISPO and other A-Train sensors, as well as collocated temperature and moisture profiles from ECMWF analyses, allow the opportunity to explore the vertical structure of marine low clouds, their relationship to various static stability metrics, and their vertical growth outside of the boundary layer into middle and deep clouds. Furthermore, large-scale vertical velocity information from ECMWF provides pertinent information about the circulation. We are interested in exploiting

196

JOURNAL OF CLIMATE

VOLUME 24

FIG. 1. JJA 2008 mean ECMWF–YOTC (a) sea level pressure (hPa), (b) SSTs (8C), (c) ECMWF–YOTC inversion frequency, and MODIS Level-3 low cloud fraction in 18 boxes with no middle or high clouds. The line represents the center of the cross section analyzed in the study.

these synergistic active and passive satellite and model analysis datasets to compile a vertical composite of cloud distributions and large-scale circulation in a cross section that encompasses a rich set of dynamical regimes and spans a wide range of SSTs. As a function of SST, we wish to quantify the frequency of low-level inversions and clouds and also to identify several simple independent measures of the boundary layer top. We furthermore derive a simple boundary layer stability metric—similar to but slightly different from those of Klein and Hartmann (1993) and Wood and Bretherton (2006) with our metric including both vertical temperature and moisture

information—and relate this to single-layer low cloud frequency at the satellite pixel-level scale. The annual cycle is examined, as has been done in Lin et al. (2009), and we furthermore quantify the role of large-scale dynamics in modulating stability and low cloud relationships. We focus on a cross section over the Pacific from just south of the equator at 58S at the date line northeastward to near the California coast at 358N, 1208W (with a longitudinal width of 108)—the same region studied recently by Kawai and Teixeira (2009) and Teixeira et al. (2010, manuscript submitted to J. Climate), and a region historically studied, which we show in Fig. 1.

1 JANUARY 2011

KUBAR ET AL.

A subtropical high often centered between 308 and 408N in the eastern Pacific is dominant especially during JJA (see Fig. 1a), which is also associated with frequent low-level temperature inversions (Fig. 1c) and extensive stratocumulus sheets (see Fig. 1d for MODIS level-3 low cloud fraction) to the east of the high. Farther south along the cross section, trade winds and warmer SSTs conspire to weaken the inversions and allow shallow cumuliform cloud development (see Fig. 1b for a map of SSTs). Farther south yet, very warm SSTs, converging winds, and reduced static stability allow for convection in the intertropical convergence zone. During DJF the subtropical jet is strongest, often centered near 308N, and is associated with transient frontal systems and forced synoptic-scale ascent, leading to a weaker climatological subtropical high and weaker mean subsidence (Hartmann 1994).

2. Observational data and collocated ECMWF temperature and humidity profiles a. A-Train data The Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS)–Working Group on Numerical Experimentation (WGNE) Pacific Cross Section Intercomparison (GPCI) encompasses multiple climatic regimes, with large swaths of stratocumuli to the north, cumulus clouds near Hawaii, and deep convection just north of the equator (Teixeira et al. 2010, manuscript submitted to J. Climate). Some of the primary objectives of the GPCI that we focus on include characterizing primary cloud systems in the tropics and subtropics as well as identifying transitions between different climate zones using A-Train observations and ECMWF analyses. While we examine possible dynamic and thermodynamic controlling factors for all cloud types, we mostly focus on low clouds in an attempt to quantify a possible simple metric and parameterization of isolated single-layer low-level clouds. For the observational half of this study, we analyze A-Train satellite data including CloudSat, the Cloud– Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and MODIS. The A-Train constellation (Stephens et al. 2002) has equatorial passing times of 0130 LT and 1330 LT, and we analyze one full year of data.

1) CLOUDSAT CloudSat 2B-Geoprof provides radar reflectivity and cloud mask, and its horizontal footprint is 2.5 km along track by 1.4 km across track with an effective vertical resolution of 240 m due to oversampling. CloudSat emits electromagnetic radiation in the microwave range, with

197

wavelengths on the order of a few millimeters (Stephens et al. 2002; Winker et al. 2010), allowing scattering by cloud liquid and ice particles. It has a sensitivity of 230 dBZ (where dBZ 5 10 log10Z), which prevents some optically thin clouds from being sensed because particle sizes are too small to be scattered by the emitted radiation of the radar. Based on computations of radar reflectivity and water content from droplet size spectra aircraft observations, a radar with a sensitivity such as CloudSat allows 80%–90% of stratocumulus clouds with relatively small liquid water paths between 1 and 20 g m22 to be sensed (Fox and Illingworth 1997). Another complicating factor for very shallow clouds is contamination of the signal by surface clutter, as the surface is usually much more reflective than hydrometeors. In fact, the surface clutter reflectivity is approximately 40 dBZ at the surface, decreases to 225 dBZ at around 960 m, and does not decrease to the nominal radar reflectivity sensitivity until the fifth radar gate of ;1200 m (Marchand et al. 2008). This suggests that most shallow clouds will be missed by the radar below 1 km, which is significant since many stratocumulus clouds have tops near or even less than 1 km. In section 3a, we quantify the percentage of clouds seen by CloudSat relative to more sensitive A-Train instruments in our cross section.

2) CALIPSO The CALIPSO Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP, referred to hereafter as CALIPSO) has a higher sensitivity for detecting tenuous hydrometeor layers since the laser transmitter wavelength is much shorter (;1 mm). Thus, thin liquid water clouds composed of small water droplets not visible to CloudSat are observed with CALIPSO. However, the lidar easily becomes attenuated when the visible optical depth exceeds five (Anselmo et al. 2006), whereas the radar penetrates dense nonprecipitating clouds (Winker et al. 2010). The two instruments thus complement each other, allowing a full perspective on most cloud types— the exception being cases of dense high-topped clouds over thin low-topped clouds in which both instruments will likely miss the low clouds. The CALIPSO lidar also has a considerably higher vertical resolution at 30 m below 8.2 km and 75 m above 8.2 km, a cross-track footprint of 0.3 km and a varying along-track resolution with height (1 km below 8.2 km, and 0.3 km above 8.2 km). Furthermore, the short wavelength of the lidar permits sensing clouds down to the earth’s surface.

3) LIDAR PLUS RADAR Merging the two instruments with different resolutions has been done by defining a footprint determined

198

JOURNAL OF CLIMATE

by the radar. This is the basis behind the 2B-Geoprof– lidar product, which we use extensively throughout this study, and is described in detail in Mace et al. 2007. Heights and bases for up to five cloud layers are reported by this merged product.

4) MODIS To further analyze the GPCI tropical–subtropical cross section, we also use cloud flags from the MODIS, a 36-band instrument aboard the Aqua satellite, about 1 min before CloudSat and CALIPSO. Extensive information about MODIS cloud mask, flags, and cloudtop and optical properties can be found in Platnick et al. (2003). Though it is part of the same constellation, MODIS contains swath data, whereas CloudSat and CALIPSO take a single sample suborbital track with vertical slices through the atmosphere. The CloudSat science team has collocated pertinent MODIS flags to the CloudSat footprint, including cloud confidence flags, scene characterization, and scene variability. The MODIS scene characterization partitions among eight different sky types including 1) clear sky, 2) high cloud, 3) very thin high cloud, 4) thin high cloud, 5) thick high cloud, 6) nonhigh cloud, 7) middle thick cloud, and 8) low cloud (Mace 2007). High clouds have top pressures lower than 500 mb and top temperatures colder than 273 K, middle clouds have pressure tops greater than 500 mb and top temperatures lower than 273 K, and low clouds top pressures and top temperatures greater than 500 mb and 273 K, respectively. The same high, mid, and low cloud categories are used by CloudSat ‘‘echo top’’ characterizations, which we also use to discriminate different cloud types for the merged lidar plus radar clouds. The MODIS scene variability flag uses 1-km MODIS pixels that comprise the CloudSat footprint and immediately adjacent 6–9 MODIS pixels to describe the uniformity or heterogeneity of the immediate MODIS pixels. We use this uniformity flag extensively, particularly in the low cloud section, and other sensors are used diagnostically on this flag as well.

VOLUME 24

horizontal resolution and approximately eight or nine vertical levels within the lowest kilometer. However, the CloudSat team has interpolated ECMWF–AUX onto the same horizontal (2.5 by 1.4 km) and vertical (240 m) grids as CloudSat. Since we also explore relationships between clouds and large-scale dynamics, we also use the ECMWF Year of Tropical Convection (YOTC) profiles of v (vertical velocity in pressure coordinates). This product has the same resolution as ECMWF–AUX and starts in June 2008 (Available online at http://data-portal.ecmwf.int. data/d/yotc_od/; YOTC Science Plan 2008), overlapping with available joint lidar–radar data through February 2009, at least at the time of this writing. For our analysis, we use the –four times daily data with horizontal resolution reduced to 0.58 and, though we do not explicitly collocate with the observations, we use output from the same cross section during JJA, SON, and DJF. Our primary interest is the role in which large-scale dynamics modulates seasonal low cloud and stability relationships. To clarify, we are using two different ECMWF datasets: one primarily in direct conjunction with the satellite observations and used for temperature, pressure, and specific humidity, which we refer to hereafter as ECMWF–AUX, and the other for pressure vertical velocity calculations, using the YOTC analyses, referred to hereafter as ECMWF–YOTC. We also use temperature, specific humidity, and pressure from ECMWF–YOTC simply for compositing purposes of v with various environmental parameters.

3. Relevant thermodynamic variables, a few definitions, and comparisons of sensors In our study, we extensively use moist static energy (MSE), which is the sum of sensible, potential, and latent energy as follows: MSE 5 cp T 1 gz 1 Lq,

(1)

b. ECMWF analyses European Centre for Medium-Range Weather Forecasts analyses of temperature, pressure, and specific humidity already collocated by the CloudSat science team are used to characterize the thermodynamic vertical structure of the atmosphere. We use temperature structure information not only to identify inversions, but also to confirm that the joint lidar plus radar low clouds are defined identically to CloudSat and MODIS. Subsequently, we refer to this dataset as ECMWF–AUX. ECMWF–AUX utilizes the full resolution T799 operational analysis with 91 vertical levels and 25-km

where cp is the specific heat at constant pressure (1004 J K21 kg21), g is standard gravity, L is the latent heat of vaporization at 08C (2.5 3 106 J kg21), and q is the mass mixing ratio of water vapor (Hartmann 1994). Under stably stratified conditions, MSE increases with height, making its vertical gradient in the lower troposphere a good indicator of the presence and strength of possible inversions. We define a parameter called DMSE as DMSE 5 MSEinv

MSEsfc ,

(2)

1 JANUARY 2011

199

KUBAR ET AL.

where MSEinv is the MSE at 700 mb or at the middle height of a low-level inversion below 700 mb, if ECMWF indicates one. The inversion height is defined if the temperature profile increases with height below 700 mb. In the relatively rare cases of multiple low-level inversions, only the lowest one is used to define DMSE. We choose the midpoint of the inversion since the lidar plus radar observational data suggest that low cloud-top heights correspond most closely to this level. Since DMSE is constrained to be in the lower troposphere and very frequently within the boundary layer in the subtropics as inversion frequency is high under the influence of large-scale subsidence, DMSE may often be a measure of boundary layer stability and, more generally, a measure of lower-level stability in the oftinversion-free tropics. Moist static energy decreases with height for a less stable lower atmosphere, though DMSE can also be strongly negative for a lower troposphere that dries very quickly with height and thus is less conducive for cloud formation. Multiple measures of stability in the lower troposphere have been defined before and are still widely utilized today, including lower tropospheric stability (LTS) as introduced by Klein and Hartmann (1993), the difference of potential temperature u between 700 mb and the surface, and estimated inversion strength (EIS) defined by Wood and Bretherton (2006), which also relies only on temperatures at the surface and 700 mb and uses the observation that freetropospheric temperatures are usually constrained to be nearly moist adiabatic. Though LTS is shown to be a good predictor of low cloud amount on seasonal time scales at multiple locations (Klein and Hartmann 1993), Wood and Bretherton show that EIS is a more general predictor under a wide range of temperatures including the tropics, subtropics, and even the midlatitudes. A multitude of different low-level stability measures are also statistically tested for correlation by Kawai and Teixeira (2009) against various low cloud properties such as liquid water path and cloud cover. We analyzed low cloud data against LTS and EIS but found a flattening of low cloud frequency for regimes of high stability during JJA despite fairly high coefficients of determination and, thus, tested DMSE with hopes of an improvement, especially since DMSE contains moisture information. Later, we will test how well a predictor DMSE is of single-layer homogeneous low-cloud frequency for the annual cycle. Kawai and Teixeira (2009) also develop a slightly different MSE parameter, which they call the corrected gap of low level moist static energy (CGLMSE) and quantify a similar correlation between low cloud amount and CGLMSE as between low cloud amount (from geostationary satellite data) and LTS and EIS. In

addition to focusing merely on seasonal means, we also look at the subseasonal variability of low cloud frequency by examining how low clouds correspond to a given DMSE within a season, and how and if the low cloud frequency–DMSE slope changes for different seasons. In section 4e of this study, we attempt to quantify the importance of large-scale dynamics in reducing both the slope and correlation between low clouds and DMSE, therefore demonstrating the relative importance of thermodynamics and dynamics in controlling low clouds. An unsaturated rising air parcel cools dry adiabatically until the saturation vapor pressure, which is solely dependent on temperature, is equal to the vapor pressure, which remains constant—at which point condensation occurs. This is the surface-based lifting condensation level and is normally a good estimate of cloud base or, at least a lower limit thereof. From Georgakakos and Bras (1984), the LCL is estimated as LCL( p) 5

[(T  TD 5 T 1

PSFC

, T D )/223.15 1 1]3.5  T ln(RHSFC /100) 1 , L/RV

(3)

where PSFC is the surface pressure, TD the dewpoint temperature in kelvin, RHSFC the surface relative humidity, and RV is the gas constant for vapor (5461 J K21 kg21). Only surface pressure, temperature, and RHSFC or surface specific humidity or vapor pressure are needed to calculate the LCL. We also employ a fairly strict definition for singlelayer uniform low clouds so that the comparison among the different sensors is as consistent as possible (next subsection). We are most interested in low clouds such as stratus or stratocumulus clouds, which are unlikely to be connected or adjacent to middle or high clouds, and we refer to these low clouds as isolated uniform low clouds. Our definition of uniform scenes are those identified with the MODIS flag, in which case the percent of MODIS scene characterizations that are the same (e.g., low clouds, clear) surrounding the central CloudSat footprint is equal to or greater than 90%. This potentially filters out some patchy small shallow cumulus clouds and focuses instead on more uniform and larger-scale shallow cumulus and stratiform low clouds.

Comparison of low cloud frequency and vertical structure from different sensors Having introduced three unique cloud datasets, we briefly assess how the different A-Train instruments compare to each other in low cloud frequency across the GPCI cross section. In Figs. 2a and 2b, we show isolated uniform

200

JOURNAL OF CLIMATE

VOLUME 24

FIG. 2. MODIS, radar only, lidar plus radar isolated low-cloud-top frequency, and inversion frequency vs latitude along the cross section for (a) JJA 2008 and (b) DJF 2008/09. Also, uniform and nonuniform low cloud-top height histograms for lidar plus radar and radar only for (c) JJA 2008 and (d) DJF 2008/09.

low-cloud frequency as a function of latitude (in our 108 wide boxes) for each of 48 latitude bins for MODIS, CloudSat only, and the merged lidar plus radar product for JJA 2008 and DJF 2008/09. Inversion frequency versus latitude is also shown. The entire annual cycle of single-layer uniform low-cloud frequency for all three instruments, shown in Table 1, shows that during DJF isolated low-cloud frequency is about half of the JJA maximum. The merged lidar plus radar compares remarkably well with MODIS, except that the lidar plus radar sees slightly more low clouds in the tropics (perhaps these are trade cumuli) and slightly fewer clouds than MODIS near 358N. The radar only, on the other hand, sees fewer than half of the low clouds seen by the other sensors in the heart of the stratocumulus regime.

Both MODIS and the joint lidar plus radar show a JJA maximum of single-layer uniform low-cloud frequency, reaching nearly 70% between 258–308N. Finally, the jump from small low cloud frequency in the tropics to large low cloud frequency in the subtropics corresponds with a marked increase of inversion frequency at ;108N. TABLE 1. Isolated uniform low cloud frequency for different instruments for the annual cycle.

Radar only Lidar1radar MODIS

MAM 2008

JJA 2008

11% 25% 25%

12% 34% 32%

SON 2008 9.3% 22% 22%

DJF 2008/09 9.7% 18% 17%

1 JANUARY 2011

KUBAR ET AL.

Given the reduced CloudSat sensitivity below 1.2 km, many of such clouds are missed. To show this more clearly, we next plot histograms of low cloud-top heights both for the radar only as well as for the joint product for JJA and DJF (Figs. 2c and 2d). As expected, the radar sees very few clouds with tops below 1 km, whereas a large fraction of low clouds with such tops are seen by the lidar. During MAM, JJA, and SON the lidar low cloud-top mode is roughly 500 m below the radar mode, though the peak is quite similar for both instruments during DJF when low clouds are deeper. Our cross section overlaps with Lin et al. (2009), who also find deeper clouds during DJF versus JJA. Finally, for comparison, nonuniform low cloud-top histograms are also shown, which indicate very small differences between seasons as seen by the lidar. These likely are patchy shallow cumulus clouds, and it is interesting that their mode is fixed at nearly 1 km.

4. Primary results a. Seasonal cloud-top histograms and relative humidity profiles To provide additional context and background of the cloud and moisture profiles in our tropical and subtropical Pacific cross section, we look at the annual cycle of cloud-top frequency histograms of all possible cloud tops. For the cloud top histograms in Fig. 3, there are no spatial uniformity or other restrictions. The low-cloud frequency color scale (cloud tops below 5 km) is different from the high cloud scale since low clouds are much more abundant relative to higher clouds, and we wish to most clearly highlight both modes on a single panel. Especially during JJA, we see that middle and deep convection is most prevalent between 08 and 108N. The boundary-layer cloud frequency increases sharply north of 108N, and this shallow mode is most isolated during JJA. From south to north, as SSTs decrease, the low-topped cloud depth decreases: we will see this more clearly later when we zoom in on the lower troposphere. Higher clouds are more pervasive farther north, especially during DJF (Fig. 3d), presumably because the transient midlatitude storm track is more active farther south during this season. The corresponding ECMWF–AUX relative humidity profiles are consistent with the cloud-top histograms in that regions of high mean relative humidity are collocated with greater cloud frequencies (Fig. 4). The largescale subsidence in the subtropics is also apparent in its relative humidity imprint, with a very dry free troposphere north of ;158N, particularly during JJA. This ‘‘dry layer’’ is smaller in depth during MAM and DJF,

201

when there are more high clouds over the subtropics. Finally, the boundary layer is very clearly seen in the relative humidity profiles, with a sharp decrease above the MBL from high RH to low RH values, and the BL depth decreases poleward north of the ITCZ. Visually, this jump in relative humidity also seems to be related to low cloud abundance, probably because inversions cap low clouds, and inversions are not only regions of increasing temperature but usually strongly decreasing water vapor as well, due to entrainment of drier environmental air from top-down vertical mixing. We also perform the analogous analysis of cloud-top frequency histograms now as a function of 2-m temperature (T2m) (Fig. 5). During JJA, middle and deep convection are confined to high surface temperatures, and the transition from shallow to deep convection occurs near T2m 5 298 K. This transition also occurs during the other seasons as well, but more high clouds occur over cold SSTs during MAM and especially DJF. As expected, the boundary layer cloud depth grows modestly as a function of T2m, which we will examine much more closely in section 4c. As a simple thought experiment, we also test whether simple stability calculations can capture the transition from shallow to deep convection, especially since shallow clouds seem to transition so abruptly to high clouds close to 108N and at T2m 5 298 K. To do this, we calculate a probability distribution function (pdf) of equilibrium levels (EL) as a function of T2m. Here, EL is simply defined as the level where the saturated equivalent potential temperature uES is the same as the uES at the LCL. Figure 6 shows that the EL during JJA 2008 is nearly always constrained to the boundary layer for T2m , 298 K and then often reaches the middle and upper troposphere above this temperature and keeps rising with increasing T2m. Interestingly, a secondary shallow mode is captured over warm SSTs, consistent with low clouds in this region. Thus, as a first cut, the thermodynamic stability of the atmosphere can explain the vertical structure of cloud frequency. We next examine seasonal composite plots of vertical velocity v from ECMWF–YOTC for the three seasons overlapping with the available joint lidar plus radar observations—JJA 2008, SON 2008, and DJF 2008/09. For all three seasons, we see in Fig. 7 that deep upward motion occurs over a narrow range of 297 K , T2m , 300 K, with subsidence over the warmest surface temperatures. Though not shown, we also note that we briefly analyzed NCEP reanalysis v profiles as a function of T2m during JJA 2008, and the features are generally qualitatively similar with deep ascent within a narrow range of T2m, though perhaps even slightly narrower than ECMWF–YOTC and over slightly higher T2m. Also,

202

JOURNAL OF CLIMATE

VOLUME 24

FIG. 3. Lidar plus radar all-cloud-top frequency histograms for (a) MAM 2008, (b) JJA 2008, (c) SON 2008, and (d) DJF 2008/09 vs latitude along the cross section. The high cloud frequency color scale is different from the low cloud frequency color scale so as to highlight both modes and allow them to be shown on one plot.

subsidence does not get progressively stronger with decreasing T2m as it does with ECMWF–YOTC. Over the global tropics, Waliser and Graham (1993) suggest that the forcing of the high SSTs is subsidence through reduced cloud cover since over active convection SSTs peak at 29.58C and optically thick cloud shading constrains SSTs. Our analyses showing rising motion over a narrow window of T2m is also consistent with Bony et al. (1997), whose assessment of tropicswide reanalyses indicate that the frequency of montly mean ascending motion (v500 , 0 Pa s21) peaks at SSTs of 29.58C. Our area of rising motion in the ITCZ in the central Pacific

(near the date line) is dynamically in-between vertical velocity profiles analyzed by Back and Bretherton (2006), where their west Pacific profiles are ‘‘top heavy’’ and east Pacific profiles ‘‘bottom heavy.’’ In our cross section, rising motion is strongest during SON. During DJF, mean subsidence is somewhat weaker over cold T2m compared to JJA and SON, likely because of the passage of transient frontal systems and associated dynamical ascent in the northern latitudes of our cross section. During all seasons, subsidence over the subtropics for T2m , 297 K tends to extend down to near the surface.

1 JANUARY 2011

KUBAR ET AL.

203

FIG. 4. ECMWF–AUX relative humidity profiles along the cross section.

b. Vertical velocity and cloud cover versus T2m and DMSE Instead of looking at vertical profiles of v, we now examine the free-tropospheric vertical velocity (vfree), which we define as the mean v between 300 and 700 hPa (fairly similar to v500 as defined in Bony et al. 1997) as a function of both T2m and DMSE. In Fig. 8, we see once again that rising motion is confined to a fairly narrow range of surface temperatures (297 K , T2m , 300 K), particularly during JJA and SON. These composites of vfree are broadly consistent with Williams et al. (2003), who calculate joint histograms of v500 and SSTs using ECMWF Re-Analysis (ERA) and show that the bulk of

rising motion bins are over higher SSTs (e.g., .268C). Furthermore, ascending motion seems to have little dependence on, or relation with, DMSE, suggesting that the free-tropospheric dynamics and boundary layer stability are largely disconnected from each other. Now, we also see more clearly that during DJF ascending motion extends to colder SSTs and high DMSE. Our stability parameter, DMSE, is somewhat similar to the saturated stability parameter defined by Williams et al. (2006), which is the difference between the saturated equivalent potential temperature from 700 hPa and the surface. Their joint histograms representing presentday climate variability of v500 and saturated stability show little coherent relationship among the two variables,

204

JOURNAL OF CLIMATE

VOLUME 24

FIG. 5. As in Fig. 3 but as a function of ECMWF–AUX 2-m temperature.

similar to our cross section as negative vfree tends to be found over a wide DMSE range, but only high SSTs, at least during JJA. In Fig. 9, we examine how high and isolated uniform low-cloud frequencies (low clouds not likely to be adjacent to higher-topped clouds, with uniformity of adjacent pixels determined by the MODIS uniformity flag) vary with T2m and DMSE. Generally we see that the largest low-topped cloud frequency is found over T2m , 295 K, and the largest high cloud frequency is over T2m . 298 K, though during DJF an abundance of high clouds exists over low T2m and high DMSE. Qualitatively, high cloud frequency tends to scale with vfree, except many high clouds are found over very high T2m

and tend to have weak vertical velocity or even subsidence. We do not differentiate here between optically thick convective cores and thin cirrus, but some of the high clouds over high T2m may be thin cirrus, perhaps detrained from convective systems. Isolated uniform lowcloud frequency decreases from JJA through DJF, and superimposed contours represent the distribution of data in T2m and DMSE space, with generally increasing DMSE with decreasing T2m, suggesting a DMSE–low cloud frequency connection, which we will quantitatively test later.

c. Boundary layer growth with temperature To more closely look at the boundary layer structure as a function of near-surface temperature, we zoom in

1 JANUARY 2011

KUBAR ET AL.

205

FIG. 6. Pdf of the level where the saturated equivalent potential temperature (uES) is the same as at the (a) LCL and (b) cloud-top frequency histograms as a function of T2m for JJA 2008.

on the lowest four kilometers of the atmosphere. In Fig. 10a, we show contours of ›MSE/›z, the zero line of ›MSE/›z, the median middle inversion height, the median uniform low-cloud-top height, and the LCL versus T2m. The size of the inversion (1) and cloud-top height symbols (D) correspond to the frequency for each of 25 equal (in terms of data) T2m bins. Over low temperatures, the stable layer above the clouds is especially sharp, whereas over all T2m the boundary layer seems to be well mixed, at least from the composite perspective, since ›MSE/›z , 0. The inversion height, cloud-top height, and ›MSE/›z ; 0 level generally correspond with each other, and their heights increase with T2m. Isolated uniform low clouds and inversions are frequent when T2m , 295 K and then decrease rather substantially for higher temperatures as the atmosphere destabilizes. There is also a slight increase of the LCL with T2m across the stratocumulus regime, but then a decrease near 298 K, likely because of a very moist near-surface layer there. An analogous plot is shown in Fig. 10b but with contours of relative humidity. The very sharp relative humidity gradient from high to low marks the boundarylayer cloud tops quite well, thus showing consistency between observations and model analyses for two independent datasets. Also, the jump in relative humidity seems to be related to low cloud abundance, as evidenced in Fig. 10c, which shows the largest values of 2›RH/›z over low surface temperatures. Though we do not show this here, 2›RH/›z is a bit weaker during MAM and SON and significantly weaker during DJF

over the lowest SSTs. This may help explain why uniform low cloud cover in isolation is less common during DJF in that weaker subsidence weakens the ability for moisture to be contained in the boundary layer. At T2m ; 298 K, the boundary layer is rather ill defined as the moist layer becomes very thick, and the thin layer of strong 2›RH/›z is no longer present. As a quick sensitivity study, we also note that we analyzed NCEP analysis of low-level relative humidity and ›RH/›z as a function of surface temperature, and qualitatively the features seen by ECMWF–AUX in Figs. 10b and 10c are also apparent with NCEP (not shown). We also test whether the magnitude of maximum (2›RH/›z) in the lower atmosphere is statistically correlated with uniform single-layer low cloud frequency, though only show JJA in Fig. 10d. The magnitude of maximum (2›RH/›z) is well correlated with low clouds during MAM, JJA, and SON with respective r2 values of 0.85 (0.85), 0.88 (0.89), and 0.86 (0.84) for lidar plus radar (MODIS) and with a drop to 0.66 (0.60) during DJF 2008/09. Despite the fairly good correlations, the linear relationship between maximum (2›RH/dz) and low cloud frequency breaks down somewhat when maximum (2›RH/dz) is very high, which is when the boundary layer is likely very shallow and perhaps sometimes even cloud free.

d. Relationship between inversion height and strength In regions of stronger subsidence, we would expect both stronger inversions and shallower clouds. To examine

206

JOURNAL OF CLIMATE

VOLUME 24

FIG. 7. ECMWF–YOTC profiles of vertical velocity in pressure coordinates (v) for (a) JJA 2008, (b) SON 2008, and (c) DJF 2008/09.

this, we simply examine the inversion height pdfs and single-layer low cloud-top height histograms as a function of normalized inversion strength, defined as inversion magnitude divided by inversion depth, both of which are shown in Fig. 11. Inversion heights and low clouds, indeed, become shallower with inversion strength, and low clouds are more abundant under stronger inversions. Except for very weak inversions, the inversion height pdfs and low cloud histograms resemble each other.

e. Single-layer uniform low cloud frequency versus DMSE As described earlier, DMSE is the difference of MSE at either 700 hPa or the midpoint inversion height (when

ECMWF identifies one) and at the surface. Since DMSE contains moisture and temperature information, its difference in the lower troposphere or within the boundary layer should be related to low-level stability and the presence of low clouds capped by either a stable layer or strong inversion. We attempt to quantitatively test how well isolated uniform single-layer low cloud frequency scales with DMSE. We perform linear regression analyses for all four seasons for both MODIS and the joint lidar plus radar and report coefficient of determination (r2) values and best linear fit lines in Table 2. Very high r2 values for JJA are apparent between DMSE and isolated uniform lowcloud frequency for both datasets, at 0.93 for MODIS and 0.88 for the joint set. Though not shown, MODIS

1 JANUARY 2011

KUBAR ET AL.

207

FIG. 8. ECMWF–YOTC free tropospheric v (mean v between 300 and 700 hPa) as a function of T2m and DMSE.

senses slightly fewer low clouds for conditions with highly negative DMSE values and slightly more over very strong inversions, making the best linear fit slope slightly higher than for lidar plus radar. During JJA, as DMSE is very well linearly correlated to single-layer uniform low-cloud frequency, this parameter shows promise as being a good predictor for low clouds. As a sensitivity study, we also performed a regression analysis against both Klein and Hartmann’s (1993) lower tropospheric stability (LTS) and Wood and Bretherton’s (2006) estimated inversion strength (EIS), and while both were fairly well correlated with low cloud frequency [r2 5 0.888 (0.801) for LTS for MODIS (lidar plus radar) and 0.896 (0.812) for EIS], cloud frequency flattened considerably over high

stability conditions, reducing r2 values slightly compared to DMSE. The correlations are slightly smaller during MAM and SON compared to JJA, as are the best-fit slopes and intercepts, likely due to more overlying middle and high clouds over colder SSTs during the the two transitional seasons (Fig. 5). In the next section we attempt to quantify the importance of large-scale dynamics. In particular, vfree may be decoupled from boundary layer stability, and DMSE is not designed and probably not able to capture this, particularly closer to the midlatitudes. As we saw earlier, low cloud frequency is greatly reduced during DJF, despite a fairly similar range of DMSE. The coefficient of determination values are only 0.46

208

JOURNAL OF CLIMATE

VOLUME 24

FIG. 9. Isolated uniform low cloud frequency for (a) JJA 2008, (c) SON 2008, and (e) DJF 2008/09, and high cloud frequency for (b) JJA 2008, (d) SON 2008, and (f) DJF 2008/09 vs T2m and DMSE. Black-labeled contours indicate distribution of data as a function of T2m and DMSE.

1 JANUARY 2011

KUBAR ET AL.

209

FIG. 10. Contours of ›MSE/›z as a function of (a) T2m, (b) relative humidity, and (c) ›RH/›z for JJA 2008. Also shown in (a) is the zero ›MSE/›z line. In (a) and (b) midpoint inversion height, isolated uniform low cloud-top height, and LCL are also shown. Isolated uniform low cloud-top height is shown in (c). (d) Isolated uniform low cloud frequency vs min (›RH/›z).

for MODIS and 0.40 for lidar plus radar. Inversions over the subtropics are somewhat less frequent during DJF compared to other seasons (see Fig. 2), which reduces the capacity for low cloud formation for a given surface temperature or DMSE. There are many more high clouds over low SSTs and high DMSE values during DJF (Fig. 9), and we propose that the annual cycle of low clouds in our cross section may be partly related to the annual cycle of mean large-scale dynamics.

f. Maximum potential low cloud frequency versus DMSE As a simple way to test for the relative importance of free-tropospheric dynamics in the seasonal differences in isolated uniform low clouds, we look at the frequency of rising motion profiles as a function of DMSE and then add this to observed low cloud frequency since high clouds and deep convection tend to scale well with

210

JOURNAL OF CLIMATE

VOLUME 24

FIG. 11. (a) Pdf of inversion midpoint vs inversion strength, and (b) histogram of isolated uniform low cloud tops vs inversion strength.

vertical velocity, as was suggested in Figs. 8 and 9. This represents the maximum hypothetical low-cloud frequency that would be possible if dynamics were filtered out. We consider strong rising motion to occur when the the vfree is less than 20.05 Pa s21, and our underlying gross assumption is that high cloud frequency for a given DMSE is the same as rising motion frequency. We do not show plots of % vfree , 20.05 Pa s21 versus DMSE, but rising motion is much more frequent under high DMSE conditions during DJF versus JJA (;40% versus ;5%). The observed low cloud frequency and the low cloud frequency plus percent vfree , 20.05 Pa s21 are shown in Fig. 12 for the lidar plus radar. Now the slope increases rather dramatically during DJF, and the seasonal differences are greatly reduced. The r2 values are about the same during JJA, increase a bit more during SON (0.86 versus 0.83), and substantially more during DJF (0.77 versus 0.40). Using t-test hypothesis testing with a null hypothesis of the same slopes, we compare the slopes for the different seasons for the lidar plus

radar. At the 99% confidence level the JJA and SON slopes are the same, as are the SON and DJF slopes; whereas the null hypothesis must be rejected for the JJA and DJF pair, though at the 95% confidence level the null hypothesis cannot be rejected. Overall, the hypothetical maximum low-cloud frequency curves are much more normalized among the three seasons. This implies that DMSE largely controls low clouds, and vfree high clouds. Since midtropospheric rising motion occupies more of the cross section during DJF, isolated low cloud frequency is much lower compared to JJA.

5. Discussion and possible climate feedbacks The results presented contain several important implications, including potential feedbacks. An obvious question is that, since isolated uniform low clouds tend to scale well with DMSE, particularly during JJA when large-scale subsidence in the subtropics is rarely disrupted by midlatitude frontal systems and free-tropospheric

TABLE 2. Summary of statistics for isolated uniform low cloud frequency–DMSE relationships. Best linear fit lines for cloud frequency(%) and DMSE (103 Jkg21).

2

MODIS r Best linear fit Lidar1radar r2 Best linear fit

MAM 2008

JJA 2008

SON 2008

DJF 2008/09

0.85 51.2% 1 2.45DMSE 0.68 43.8% 1 1.77DMSE

0.93 66.3% 1 3.27DMSE 0.88 59.9% 1 2.49DMSE

0.87 46.8% 1 2.28DMSE 0.83 40.1% 1 1.64DMSE

0.46 27.4% 1 0.96DMSE 0.40 26.2% 1 0.74DMSE

1 JANUARY 2011

211

KUBAR ET AL.

FIG. 12. Isolated uniform low-cloud frequency and maximum potential low-cloud frequency vs DMSE for (a),(b) JJA 2008, (c),(d) SON 2008, and (e),(f) DJF 2008/09. Maximum potential low-cloud frequency is the observed low cloud frequency plus rising motion frequency (vfree , 20.05). Also shown is best-fit line and linear fit 63 (standard error).

ascent as it is during DJF, how will DMSE respond to climate change? Also, can we attempt to better explain the annual cycle that we have presented for low clouds? Even though DMSE does not necessarily act in isolation from large-scale dynamics, considering it as such allows

for diagnoses of how stability may change with climate warming. We begin by examining the individual components of DMSE, and rewrite (2) in component form: DMSE 5 cp DT 1 gDz 1 LDq.

(4)

212

JOURNAL OF CLIMATE

VOLUME 24

FIG. 13. Potential (gDz), latent (LvDq), and sensible (cpDT ) energy vs DMSE for (a) JJA 2008, (b) SON 2008, and (c) DJF 2008/09. The units of the different energy forms are the same as DMSE (103 Jkg21). Also, (d) the difference in RH between inversion midpoint or 700 mb and the surface vs DMSE.

In the current climate, both DT and Dq increase as DMSE increases, whereas Dz decreases. As a function of DMSE, the behavior of Dq is the same during JJA and DJF (see Fig. 13), but DT is greater for a given DMSE during JJA, suggesting stronger inversions during JJA. Also, the boundary layer top is greater during DJF so that Dz is also larger for a given DMSE. A deeper boundary layer and weaker inversion during DJF is consistent with Lin et al. (2009), who cite the ‘‘deepeningwarming mechanism’’ of Bretherton and Wyant (1997), which leads to decoupling and a stratocumulus-tocumulus transition. The surface latent heat flux relative to cloud-top cooling rate is important, and calculations from Lin et al. (2009) indicate that the cloud-top radiative

cooling rate is smaller in their analyzed region in the northeast Pacific during DJF, so the surface latent heat flux requirement for decoupling is lower. This flux is not purely dependent upon the surface temperature annual cycle, but rather the surface temperature variations relative to free-tropospheric temperature variations. In our cross section, even though the mean T2m during JJA is higher than during DJF (296.9 versus 296.3 K), the DJF surface temperature relative to 700-mb potential temperature (u700) is warmer by 2 K, helping to explain a stronger surface latent heat flux consistent with deeper low clouds and a lower uniform cloud frequency during DJF. A pervasive signal in climate models is that of stonger free-tropospheric warming relative to the surface, resulting

1 JANUARY 2011

KUBAR ET AL.

in higher stability (Colman 2003), and even enhanced lower-tropospheric stability (Williams et al. 2006). This would thus increase DT, arguing for more low clouds or, in other words, as explained above, the surface temperature relative to u700 would be lower, decreasing the surface latent heat flux. This essentially follows increasing low cloud cover with LTS (Klein and Hartmann 1993) and is in accord with the vertical structure of moist adiabats with temperature, as discussed by Wood and Bretherton (2006). However, Wood and Bretherton also point out that if lower-tropospheric temperature changes with climate warming follow a moist adiabat, EIS changes would be small since the moist adiabatic lapse rate is part of the the definition of EIS. As mentioned earlier, low cloud frequency is also strongly correlated with EIS, implying a weak feedback. In addition, a negative correlation exists between low cloud frequency and SSTs in stratocumulus regimes, as found by Oreopoulos and Davies (1993), or the reduction of low cloud optical thickness with cloud temperature (Tselioudis et al. (1992)). Indeed, we also examine isolated single-layer uniform low-cloud frequency versus T2m, and the highest cloud frequencies are found at T2m at ;290–293 K, depending on the season, with then a strong decrease with T2m (not shown). This would tend to counter a possible cloud increase with DT. As in Oreopoulos and Davies, fewer low clouds are observed in our cross section over the lowest T2m, and also during DJF there is no coherent relationship between low cloud frequency and T2m. Another important question is how Dq might change with climate warming. Given the small seasonal differences in the current climate, one hypothesis is that Dq may not change. In the current climate, Dq becomes less negative as a function of DMSE to preserve the same mean boundary layer RH–DMSE relationship for each season, ranging from about RH 5 60% for strongly negative DMSE values to ;80% for near-zero or slightly positive DMSE (not shown). However, as seen in Fig. 13d, the relative humidity difference between mid-inversion or 700 mb and the surface (DRH) diverges for the three seasons around DMSE ; 0 J kg21, with increasingly more negative DRH for positive DMSE for JJA versus DJF. The DRH values of nearly 260% over strongly negative DMSE values are observed owing to a dry boundary layer that supports few low clouds (or high clouds, not shown). In considering possible boundary layer RH changes with climate warming, it is useful to consider how the large-scale circulation may change. Williams et al. (2006) have performed 2 3 CO2 model simulations, and v500 tends to increase near the equator close to the southern part of our cross section and decrease in the subsidence region toward the northern part, consistent with

213

a weakening of the Hadley circulation. If sinking motion were to weaken in the subtropics, then DRH in the stable regimes may be less negative, and the inversion layer RH gradient may be reduced, resulting in fewer low clouds as interpreted from Fig. 9d. Indeed, using multidecadal observations, Clement et al. (2009) identify a strong positive correlation of low cloud cover with v500, suggesting that a circulation weakening would correlate with fewer low clouds.

6. Summary In this study, we utilized a combination of A-Train satellite data and ECMWF analysis data to investigate the cloud structure, boundary layer characteristics, and thermodynamic versus dynamic controls on low clouds in a 108 wide cross section from just south of the equator northeastward to off the California coast. Joint lidar plus radar cloud-top histograms as a function of T2m indicate that the boundary layer cloud mode deepens with increasing temperature, with median cloud and inversion heights near 500 m near the California coast to ;1.5– 2.0 km just prior to the transition to deep convection at a T2m 5 298 K. The cloud top is also characterized by a marked negative vertical gradient of relative humidity (2›RH/›z), with much drier air above a boundary layer well mixed in terms of relative humidity, and the magnitude of 2›RH/›z is linearly correlated with isolated uniform low cloud frequency. Simple equilibrium level calculations indicate a clear transition from a shallow mode to middle and deep ones, qualitatively consistent with the cloud-top histograms. Vertical velocity profiles indicate large-scale subsidence for T2m , 297 K and then a narrow range of deep ascending motion for 297 K , T2m , 300 K, with sinking motion over the warmest SSTs near the equator, consistent with previous studies (e.g., Waliser and Graham 1993, Bony et al. 1997; Williams et al. 2003). Our comparision of low cloud frequency among the instruments shows good agreement between the joint lidar plus radar and MODIS, but CloudSat detects fewer than half of the low clouds in the cross section, most of which are in the subtropics in the stratocumulus areas associated with descending motion and a semipermanent high pressure system, as the sensitivity of CloudSat is reduced below 1.2 km. The lidar, on the other hand, senses many clouds shallower than 1 km, and the histogram of boundary layer cloud tops and an inversion height pdf are structurally similar, with decreasing heights with increasing inversion strength. A significant annual cycle also exists, with 34% isolated uniform low cloud frequency during JJA in our entire cross section and 18% during DJF (from the lidar plus radar joint product).

214

JOURNAL OF CLIMATE

We also attempt to improve upon prior low-level stability metrics for describing low cloud frequency by introducing DMSE, the difference in moist static energy at either 700 hPa or the midpoint inversion height (where an inversion exists) and at the surface. We use this to statistically test its relationship to isolated uniform low-cloud frequency. Since moist static energy tends to increase with height in a stably stratified atmosphere, boundary layer stability increases with DMSE. On the other hand, DMSE can also be strongly negative for a very dry boundary layer, inhibiting cloud formation. The coefficient of determination between DMSE and isolated uniform low cloud frequency is very high during JJA (r2 5 0.93 for MODIS, 0.88 for lidar plus radar) but substantially lower during DJF. To understand why, we see that the free-tropospheric vertical velocity (mean between 300 and 700 hPa) is confined to warm SSTs during JJA but extends over lower surface temperatures and high values of DMSE during DJF, likely because midlatitude frontal systems and the subtropical jet stream extend into the northern part of the cross section during this season. As a simple approach to account for dynamical differences and their impact on reducing the isolated uniform low cloud frequency–DMSE slope from JJA through DJF, we add the frequency of rising motion profiles (where vfree , 20.05 Pa s21) for a given DMSE regime, which represents the maximum hypothetical low cloud frequency if free-tropospheric dynamics were normalized. High cloud amount tends to scale with vertical velocity, with most high clouds occurring in regions of large-scale ascent, so that rising motion inhibits low clouds from being seen, even if they exist in the presence of a low-level inversion. The seasonal differences in maximum hypothetical low-cloud frequency are much smaller than the seasonal differences of observed isolated low cloud frequency, as rising motion during DJF is much more common over high values of DMSE. Therefore, controlling for dynamics makes DMSE a potential metric for low cloud parameterizations in climate models, and changes in the circulation with climate warming may thus influence the DMSE–low cloud relationships and should be accounted for. Acknowledgments. The research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Additionally, the authors thank the NASA Postdoctoral Program for its support of this work. The authors additionally thank Mark Zelinka for useful feedback, as well as Dr. Xianan Jiang for NCEP output data during the JJA 2008 season.

VOLUME 24 REFERENCES

Anselmo, T., and Coauthors, 2006: Cloud–Aerosol Lidar Infrared Pathfinder Satellite Observations—Data management system and data products catalog. NASA Langley Research Center Document PC-SCI-503, 97 pp. Back, L. E., and C. S. Bretherton, 2006: Geographic variability in the export of moist static energy and vertical motion profiles in the tropical Pacific. Geophys. Res. Lett., 33, L17810, doi:10.1029/ 2006GL026672. Bony, S., and J.-L. Dufresne, 2005: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 33, L17810, doi:10.1029/ 2005GL023851. ——, K.-M. Lau, and Y. C. Sud, 1997: Sea surface temperature and large-scale circulation influences on tropical greenhouse effect and cloud radiative forcing. J. Climate, 10, 2055–2077. Bretherton, C. S., and M. C. Wyant, 1997: Moisture transport, lower-tropospheric stability, and decoupling of cloud-topped boundary layers. J. Atmos. Sci., 54, 148–167. Charlson, R. J., J. E. Lovelock, M. O. Andreae, and S. G. Warren, 1987: Oceanic phytoplankton, atmospheric sulfur, cloud albedo, and climate. Nature, 326, 655–661. Clement, A. C., R. Burgman, and J. R. Norris, 2009: Observational and model evidence for positive low-level cloud feedback. Science, 325, 460–464. Colman, R., 2003: A comparison of climate feedbacks in general circulation models. Climate Dyn., 20, 865–873. Fox, N. I., and A. J. Illingworth, 1997: The potential of a spaceborne cloud radar for the detection of stratocumulus clouds. J. Appl. Meteor., 36, 676–686. Georgakakos, K. P., and R. L. Bras, 1984: A hydrologically useful station precipitation model. 1. Formulation. Water Resour. Res., 20, 1585–1596. Hartmann, D. L., 1994: Global Physical Climatology. Academic Press, 411 pp. ——, 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. Jensen, M. P., A. M. Vogelmann, W. D. Collins, G. J. Zhang, and E. P. Luke, 2008: Investigation of regional and seasonal variations in marine boundary layer cloud properties from MODIS observations. J. Climate, 21, 4955–4973. Kawai, H., and J. Teixeira, 2009: Probability density functions of liquid water path of marine boundary layer clouds: Geographical and seasonal variations and controlling meteorological factors. J. Climate, 23, 2079–2092. Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 1587–1606. Kubar, T. L., D. L. Hartmann, and R. Wood, 2009: Understanding the importance of microphysics and macrophysics for warm rain in marine low clouds. Part I: Satellite observations. J. Atmos. Sci., 66, 2953–2972. Lin, W., M. Zhang, and N. Loeb, 2009: Seasonal variation of the physical properties of marine boundary layer clouds off the California coast. J. Climate, 22, 2624–2638. Mace, G. G., cited 2007: Level 2 GEOPROF product process description and interface control document algorithm version 5.3. [Available online at http://www.cloudsat.cira.colostate.edu/ dataICDlist.php?go5list&path5/2B-GEOPROF.] ——, D. G. Vane, G. L. Stephens, and D. Reinke, 2007: Level 2 radar–lidar GEOPROF product version 1.0 process description and interface control document. NASA CloudSat, 20 pp.

1 JANUARY 2011

KUBAR ET AL.

Marchand, R., G. G. Mace, T. Ackerman, and G. Stephens, 2008: Hydrometeor detection using CloudSat – An Earth-orbiting 94GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519–533. Oreopoulos, L., and R. Davies, 1993: Statistical dependence of albedo and cloud cover on sea surface temperature for two tropical marine stratocumulus regions. J. Climate, 6, 2434–2447. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey, 2003: The MODIS cloud products, algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, 459–473. Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train—A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 1771–1790. Stevens, B., and J.-L. Brenguier, 2010: Cloud controlling factors— Low clouds. Clouds in the Perturbed Climate System. J. Heintzenberg and R. J. Charlson, Eds., Stru¨ngmann Forum Reports Series, MIT Press, 173–196. Tselioudis, G., W. B. Rossow, and D. Rind, 1992: Global patterns of cloud optical thickness variation with temperature. J. Climate, 5, 1484–1495. Waliser, D. E., and N. E. Graham, 1993: Convective cloud systems and warm-pool sea surface temperatures: Coupled

215

interactions and self regulation. J. Geophys. Res., 98, 12 881–12 993. ——, and M. Moncrieff, 2008: The Year of Tropical Convection (YOTC) science plan: A joint WCRP–WWRP/THORPEX international initiative. World Meteorlogical Organixation Rep. WMO/TD No. 1452, WCRP–130, WWRP/THORPEX–No. 9., 34 pp. [Available online at http://www.ucar.edu/yotc/documents/ YOTC_Science_Plan.pdf.] Webb, M. J., and Coauthors, 2006: On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Climate Dyn., 27, 17–38. Williams, K. D., M. A. Ringer, and C. A. Senior, 2003: Evaluating the cloud response to climate change and current climate variability. Climate Dyn., 20, 705–721. ——, and Coauthors, 2006: Evaluation of a component of the cloud response to climate change in an intercomparison of climate models. Climate Dyn., 26, 145–165. Winker, D. M., and Coauthors, 2010: The CALIPSO mission: A global 3D view of aerosols and clouds. Bull. Amer. Meteor. Soc., 91, 1211–1229. Wood, R., and C. S. Bretherton, 2006: On the relationship between stratiform low cloud cover and lower-tropospheric stability. J. Climate, 19, 6425–6432.