ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-9451-2017An A-train and MERRA view of cloud, thermodynamic, and dynamic variability
within the subtropical marine boundary layerKahnBrian H.brian.h.kahn@jpl.nasa.govMatheouGeorgiosYueQinghttps://orcid.org/0000-0002-3559-6508FauchezThomashttps://orcid.org/0000-0002-5967-9631FetzerEric J.LebsockMatthewMartinsJoãohttps://orcid.org/0000-0003-4117-0754SchreierMathias M.SuzukiKentarohhttps://orcid.org/0000-0001-5315-2452TeixeiraJoãoJet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USANASA Goddard Space Flight Center, Greenbelt, MD, USADepartment of Meteorology and Geophysics, Instituto Português do Mar e da Atmosfera, Lisbon, PortugalDivision of Climate System Research, Atmosphere and Ocean Research Institute, The University of Tokyo,
Kashiwa, JapanBrian H. Kahn (brian.h.kahn@jpl.nasa.gov)7August201717159451946823January20176February20174July20176July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/9451/2017/acp-17-9451-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/9451/2017/acp-17-9451-2017.pdf
The global-scale patterns and covariances of subtropical marine
boundary layer (MBL) cloud fraction and spatial variability with atmospheric
thermodynamic and dynamic fields remain poorly understood. We describe an
approach that leverages coincident NASA A-train and the Modern Era
Retrospective-Analysis for Research and Applications (MERRA) data to quantify
the relationships in the subtropical MBL derived at the native pixel and grid
resolution. A new method for observing four subtropical oceanic regions that
capture transitions from stratocumulus to trade cumulus is demonstrated,
where stratocumulus and cumulus regimes are determined from
infrared-based thermodynamic phase. Visible radiances are normally
distributed within stratocumulus and are increasingly skewed away from the
coast, where trade cumulus dominates. Increases in MBL depth, wind speed, and
effective radius (re), and reductions in 700–1000 hPa moist
static energy differences and 700 and 850 hPa vertical velocity correspond
with increases in visible radiance skewness. We posit that a more robust
representation of the cloudy MBL is obtained using visible radiance rather
than retrievals of optical thickness that are limited to a smaller subset of
cumulus. The method using the combined A-train and MERRA data set has
demonstrated that an increase in re within shallow cumulus is
strongly related to higher MBL wind speeds that further correspond to
increased precipitation occurrence according to CloudSat, previously
demonstrated with surface observations. Hence, the combined data sets have
the potential of adding global context to process-level understanding of the
MBL.
Introduction
Much of the uncertainty in projections of future climate is directly or
indirectly related to clouds and their associated processes (IPCC, 2013),
including shallow marine cumuliform clouds (Bony and Dufrense, 2005). The
low-cloud–climate feedback is generally regarded to be positive (e.g., Clement et
al., 2009). Many studies, however, suggest that the sign and magnitude of the
feedback are cloud-type-dependent (e.g., Caldwell et al. 2013; Bretherton et
al., 2013; Dal Gesso et al., 2015; Stephens, 2005; Yue et al., 2017; Zelinka
et al., 2012).
Using large eddy simulation (LES) experiments forced with doubled CO2,
Bretherton et al. (2013) show that the gradient of relative humidity (RH) from the marine
boundary layer (MBL) to the
free troposphere is a key factor that controls the shortwave cloud radiative
feedback. Rieck et al. (2012) used LES forced by perturbed lower-tropospheric
temperature profiles with fixed RH to show that an increase in surface
moisture fluxes leads to a drying of the trade-cumulus-topped MBL. The drying
overwhelms the increased shortwave reflection from the liquid water lapse
rate feedback, thus leading to reduced cloudiness and a positive shortwave
cloud feedback. These mechanisms are also discussed by Nuijens and
Stevens (2012) in the context of bulk theory and clearly demonstrate that
free-tropospheric temperature and moisture gradients act as constraints for
climate-change-induced surface flux changes.
While the constant RH framework is a useful concept to investigate
cloud–climate feedback in simplified modeling experiments, an overall
reduction of RH in the subtropical free troposphere was found in the Coupled Model Intercomparison Project Phase 3
(CMIP3; Sherwood et al., 2010; Fasullo and Trenberth, 2012) and Phase 5 (CMIP5; Lau and Kim,
2015) archives with a non-negligible spread in the changing magnitude and
vertical structure of RH among the models. Therefore, the assumption that
constant RH might hold across the diversity of subtropical cloud regimes with
a changing climate is likely not valid. Medeiros and Nuijens (2016) showed
that the RH gradient between the MBL and lower free troposphere is widely
variable among the CMIP5 models within the trade cumulus regime. Therefore,
further examination of cloud variability and the vertical structure of RH
with present-day satellite and reanalysis observations is warranted.
A strong linkage between cloud amount and estimated inversion strength (EIS; Wood and Bretherton, 2004),
lower-tropospheric stability (LTS) (Klein and Hartmann, 1993), and moist
static energy differences (dMSE) between the free troposphere and surface
(Kawai and Teixeira, 2010; Chung et al., 2012; Kubar et al., 2015) is well
understood. Satellite observations of the MBL have revealed prodigious
variations of cloud organization that span orders of magnitude over spatial
and temporal scales (Cahalan et al., 1994; Wood and Hartmann, 2006; Muhlbauer
et al., 2014). Even for a fixed value of cloud fraction, a large diversity of
statistical variability may be observed (Kawai and Teixeira, 2012).
Correlations of cloud fraction to other environmental variables are highly
dependent on the timescale of comparison (e.g., Brueck et al., 2015). At
present, the relationships of cloud fraction and spatial variability to
larger-scale properties other than EIS/LTS remain poorly understood.
Furthermore, previous work has emphasized correlations of MBL cloud
properties to 500 hPa vertical velocity and RH that are averaged over
monthly, seasonal, or annual timescales. Kawai and Teixeira (2010) found
significant correlations for instantaneous observations of cloud
inhomogeneity and the skewness of liquid water path (LWP) to thermodynamic structure changes
between 850 and 1000 hPa; the correlations are larger for LWP than with
cloud fraction.
Modeling and observational studies have demonstrated that the vertical
structures of moments of conserved thermodynamic variables depend on the
cloud regime (e.g., Suselj et al., 2013; Ghate et al., 2016; Zhu and
Zuidema, 2009). Substantial differences exist between stratocumulus and
trade cumulus in the mean, variance, skewness, and kurtosis of equivalent
potential temperature θe, liquid water potential temperature
θl, and vertical velocity profiles, and they point to the importance
of a global perspective uniquely provided by satellite and reanalysis data.
The NASA A-train (Stephens et al., 2002) provides a wealth of remote-sensing
data about the microphysics and thermodynamics of the cloudy MBL. Reanalysis
data such as the Modern Era Retrospective-Analysis for Research and
Applications (MERRA; Rienecker et al., 2011) offer a complementary set of
thermodynamic and dynamic variables that help establish a larger-scale
perspective for coincident remote-sensing observations.
Our primary purpose is to investigate instantaneous relationships between
cloud microphysical and optical properties, dynamics, and thermodynamic
variables from the A-train and MERRA at the native temporal and spatial
resolution of the observations. The satellite and reanalysis data each
provide unique information that should ideally be combined together at the
native resolution rather than relying on one instrument or reanalysis alone,
or combining over time and space averages. The geophysical fields are
retained at the native spatial and temporal resolution such that the
instantaneous spatial “snapshots” of the cloud probability density function
(PDF) are preserved and are then conditioned by available thermodynamic and
dynamic variables. This approach removes the temporal variability in order to
focus on the spatial variability and covariances. The statistical behavior of
cloud properties, and how the thermodynamic and dynamic state variables are
related to them, is thus inferred using the finest temporal and spatial
resolutions available. The different instruments and reanalysis data sets are
treated as “building blocks” that construct a simultaneous view of the MBL,
playing on the strengths of each data set. This holistic synthesis of
multivariate and multimoment data sets may highlight aspects of MBL
structure that are otherwise overlooked. The MBL structures of interest are
summarized in Nuijens et al. (2009) using surface-based observations and
demonstrate testable relationships between clouds, wind, humidity, and
precipitation. Lastly, the approach taken herein may ultimately enhance our
ability to quantify the complex time, space, and cloud regime coupling of
clouds and circulation (Bony et al., 2015).
Section 2 describes the data sets used, while Sect. 3 details the
methodological approach taken in this investigation. Section 4 details the
regional maps, while Sect. 5 examines the joint PDFs. We conclude in Sect. 6.
Data
The Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU)
sounding suite located on board NASA's Earth Observing System (EOS) Aqua satellite has
obtained vertical profiles of temperature and water vapor at approximately
45 km horizontal resolution since September 2002 (Chahine et al., 2006).
While AIRS cannot capture the sharpness of the temperature and water vapor
mixing ratio gradients across the top of the MBL (Maddy and Barnet, 2008; Yue
et al., 2011), the coarse-resolution vertical gradients from the surface to
the lower free troposphere are obtained with high fidelity as shown in
validation studies using numerical weather prediction model data or radiosondes (Yue et al., 2013;
Kalmus et al., 2015). The AIRS operational products also provide numerous
cloud variables that include effective cloud fraction (ECF), cloud
thermodynamic phase (liquid, ice, and unknown categories), and others (Kahn
et al., 2014). A MBL depth estimate inferred from the height/pressure of
maximum RH gradient is described and validated with radiosondes launched
during the Rain in Shallow Cumulus over the Ocean (RICO) campaign in Martins
et al. (2010).
The AIRS version 5 channel 4 visible spectral radiance
(0.49–0.94 µm) (Gautier et al., 2003; Aumann et al., 2006) with a
nadir spatial resolution of 2.28 km is used and has units of
W m-2µm-1 sr-1. AIRS visible-band data are
co-registered to the AIRS IR footprint such that 72 visible pixels are
aligned within every footprint. A prototype AIRS visible cloud mask (Gautier
et al., 2003) that was developed to support earlier algorithm development
efforts is also used. Although the cloud mask has not been compared directly
against benchmarks such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud mask, manual inspection suggests
that this cloud mask tends towards clear-sky conservative and captures many
shallow, broken subpixel cumulus clouds.
The MODIS instrument on EOS
Aqua is capable of observing a wide variety of land, ocean, and atmospheric
variables (Platnick et al., 2017) that are collocated to the AIRS field of view (FOV). We use
the Collection 6 liquid phase cloud optical thickness τ and effective
radius re retrievals from the MYD06_L2 swath product and the
1 km cloud mask from the MYD035_L2 swath product. Platnick et al. (2017)
show that the re change between C5.1 and C6 is
±1–2 µm. We have tested the differences in the PDFs between C5.1
and C6 for a subset of the data investigated, and very little change in the
PDFs was observed (not shown). The MODIS liquid cloud re is used
as a proxy for precipitation and is verified with the CloudSat
2C-RAIN-PROFILE (Release 4) precipitation product (L'Ecuyer and Stephens,
2002).
The MERRA instantaneous, 6-hourly, native-resolution, gridded data sets at
1/2∘×2/3∘ (Rienecker et al., 2011) are used to
assess the thermodynamic profiles derived from AIRS, assign vertical profiles
of horizontal u and v wind components, and assign vertical profiles of pressure
velocity ω in the MBL and lower free troposphere. All of the
instantaneous MERRA data are spatially and temporally matched to the A-train
orbit using a nearest-neighbor matching approach with no time interpolation.
The matching approach uses a nearest-neighbor technique weighted by the
sensor spatial response function (Schreier et al., 2010). The mean, variance,
and skewness of MODIS cloud properties at 1 or 5 km resolution is retained
within a larger 45 km resolution AIRS/AMSU field of regard (FOR), while
MERRA's 1/2∘×2/3∘ resolution thermodynamic and
dynamic variables are matched to the nearest AIRS/AMSU FOR.
AIRS version 5 visible channel 4 radiance (0.49–0.94 µm)
at a nadir spatial resolution of 2.28 km (left), and AIRS cloud mask (binary
clear and cloudy) determined from visible channel thresholds (right). See
Gautier et al. (2003) for more details.
Methodology
Four subtropical oceanic regions that capture transitions from stratocumulus
to trade cumulus are investigated. The four regions are greatly expanded in
scale from those used in Klein and Hartmann (1993) to investigate the
stratocumulus-topped MBL and are listed in Table 1. While all available
daytime (ascending) orbits from 1 January 2009 to 31 December 2009 were used,
the remaining discussion is limited to the seasons that contain the observed
peak in cloud frequency listed in Table 1 (Klein and Hartmann, 1993).
The four regions investigated in this study are greatly expanded in
area from Klein and Hartmann (1993). The four columns with percentages and
total counts are defined at the AIRS/AMSU field-of-regard (FOR) spatial
scale. The three cloudy categories indicate whether clouds of that type
occur with any frequency within the AIRS/AMSU FOR. Clear sky is defined over the
entire AIRS/AMSU FOR and is therefore very infrequent.
RegionAbbrevSeasonLocation % Sc % Cu % Other % ClrCountsNortheastPacific OceanNEPJJA15–35∘ N 110–150∘ W23.364.810.71.3186 133NortheastAtlantic OceanNEAJJA15–35∘ N 10–50∘ W8.172.018.01.9183 798SoutheastPacific OceanSEPSON5–25∘ S 70–110∘ W25.569.63.91.0184 208SoutheastAtlantic OceanSEASON5–25∘ S 25∘ W–15∘ E31.862.14.41.7180 668
Figure 1a is an example visible image for a 6 min AIRS granule within
the southeast Atlantic Ocean (SEA). The visible band captures various spatial
structures of clouds. The cloud mask derived from AIRS visible bands for the
same granule is shown in Fig. 1b. The cloud mask is used to narrow down the
spatial sampling for the following analysis. The cloud mask likely includes
instances of clear sky, but the approach only requires a coarse masking
approach to filter out a majority of the clear-sky pixels. We will discuss
implications regarding the filtering process in Sect. 4.
Removal of pixels containing mid- and high-level clouds helps to reduce
ambiguities introduced by free-tropospheric clouds and also a portion of the
thermodynamic and dynamic variability associated with cloudy areas of
synoptic-scale waves. Figure 2 shows the AIRS infrared Tb within
a clean atmospheric window at 1231 cm-1, the cloud thermodynamic phase
mask, three constant pressure levels of AIRS RH (700, 850, and 925 hPa), and
the skewness of visible radiance for the same granule shown in Fig. 1. The
cloud thermodynamic phase identifies some scattered ice in the northern
portion of the granule. All pixels identified with ice are removed in the
following analysis. Jin and Nasiri (2014) showed that AIRS successfully
identifies the presence of ice within the AIRS FOV in excess of 90 % of
the time when compared to Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) thermodynamic phase estimates at the pixel
scale. A similar approach is taken in Nam et al. (2012) and Myers and
Norris (2015) to minimize impacts from convection and synoptic-scale weather
systems. Additional occurrences of Tb,1231 < 273 K
that potentially contain supercooled liquid phase mid-level clouds are also
removed.
AIRS (a) RH925 (%), (b) RH850 (%),
(c) RH700 (%), (d) 1231 cm-1Tb
(K), (e) cloud thermodynamic phase, and (f) radiance
skewness from visible channel 4. The granule is identical to the one shown in
Fig. 1.
As the AIRS cloud phase algorithm is based on a channel selection that
exploits differences in the index of refraction for liquid and ice, the cloud
amount observed in the AIRS pixel is frequently small enough that the
spectral signature does not trigger a positive liquid test (e.g., Jin and
Nasiri, 2014). The ECF for these unknown phase cases can simultaneously be
well above the sensitivity of cloud detection (validated using CALIPSO lidar;
see Kahn et al., 2014). As a result, none of the phase tests are triggered
even though cloud is observed within the AIRS pixel. These unknown cases line
up very well with the frequency of trade cumulus in the four regions selected
based on inspection of individual granule data (e.g., Figs. 1 and 2) and
gridded seasonal averages (Sect. 4).
The AIRS liquid detections coincide with uniform stratocumulus (Fig. 1) with
close to normally distributed visible radiances (lower right, Fig. 2), while
unknown detections correspond well to shallow cumulus with a distinctive
positively skewed visible radiance, very similar to previous results obtained
using LWP (Wood and Hartmann, 2006; Kawai and Teixeira,
2010). Previous investigations have used free-tropospheric vertical velocity
to separate cloud regime types (e.g., Bony and Dufresne, 2005; Medeiros and
Stevens, 2011; Nam et al., 2012). Henceforth, the two regimes defined
exclusively by liquid and unknown phase detections will be
generically referred to as stratocumulus and cumulus
regimes, respectively. An advantage of this instantaneous approach is that
the temporal and spatial variations of cumulus and stratocumulus cloud areas
are more precisely separated from each other.
For the AIRS/AMSU FORs containing MBL clouds, the coincident AIRS and MODIS
geophysical fields are collocated. The AIRS ECF is averaged over the entire
AIRS/AMSU FOR where clear sky is equal to a value of 0. The AIRS
thermodynamic phase is averaged over cloudy AIRS FOVs only. The individual
phase tests are summed and liquid is defined for values < -0.8,
unknown between -0.8 and +0.8, and ice for values > +0.8.
The MODIS cloud mask and τ are averaged over the entire AIRS/AMSU FOR.
The MODIS re is averaged only over the successful retrievals that
are a subset of MODIS pixels identified as containing cloud. The nearest
neighbor is matched for MERRA geophysical fields at a similarly sized
spatial resolution. The mean, standard deviation, and skewness of MODIS and
AIRS FOV properties are then calculated for each AIRS/AMSU FOR separately.
Therefore, multiple satellite instrument and reanalysis observations at
multiple spatial scales can be linked together through joint PDFs for a large
combination of statistical moments. These data serve as the basis of the
following investigation.
Regional spatial averages
Regional-scale, seasonal averages are calculated from the pixel-scale data
described in Sect. 3 for 90 daytime (130 pm Equator-crossing time) snapshots
and are then re-gridded to 1∘× 1∘ spatial
resolution and help facilitate comparisons to previous studies. Figure 3
shows the visible radiance skewness for JJA in the northeast Pacific (NEP)
and northeast Atlantic (NEA) regions, and SON in the southeast Pacific (SEP)
and SEA regions, with an overlay of AIRS total ECF. The coastal stratocumulus
radiances are distributed approximately normally, while the radiances are
positively skewed away from the coast, where disorganized cumulus dominates
(e.g., Wood and Hartmann, 2006). Contours of the magnitude of radiance
skewness closely align with the magnitude of ECF in cumulus but much less so
in proximity to the coast within stratocumulus. Very poor spatial
correspondence between radiance skewness and the mean value of MODIS cloud
fraction was found (not shown) and is consistent with low correlations
between Geostationary Operational Environmental Satellite (GOES)-derived
cloud fraction and LWP noted by Kawai and Teixeira (2010) in the SEP region.
Interestingly, the average radiance skewness is larger and ECF is smaller in
the NEA than the other three regions, which is consistent with other
satellite observations (Klein and Hartmann, 1993; Rossow and Schiffer, 1999)
and ship-based observations (Wood, 2012). The patterns of radiance skewness
shown in Fig. 3 also resemble typical climatological patterns of cloud sizes
reported in Wood and Field (2011) and cloud texture as viewed from the
Multi-angle Imaging SpectroRadiometer (MISR) (Zhao et al., 2016).
Radiance skewness for regions listed in Table 1: (a) NEP, (b) NEA,
(c), SEP, and (d) SEA. The AIRS ECF is overlaid as white contours.
Values of MODIS total water path (TWP) skewness do not show a clear
transition from normally distributed to positively skewed values in Weber et
al. (2011). This further motivates the removal of mid- and high-level cloud
occurrences using the AIRS phase mask that comprise anywhere from 4 to
18 % of the total number of FOVs depending on the region of study
(Table 1). The total number of collocated data points within each region is
roughly ∼ 180,000. However, the AIRS and MODIS cloud fields have
smaller spatial resolutions that are aggregated to the AIRS/AMSU field of
regard, and the raw counts for these fields number in the millions.
Oreopoulos and Cahalan (2005) show that the inhomogeneity parameter
calculated from MODIS LWP, rather than TWP, is most homogeneous near the
coast and indicates increasing heterogeneity that extends into the cumulus
regimes. We argue that the results of Oreopoulos and Cahalan (2005) are more
definitive than those shown in Weber et al. (2011) and more closely resemble
the gradients and magnitudes contained within Fig. 3.
There are several factors that contribute to relationships between ECF and
the various moments of radiance. A reduced ECF and increased radiance
skewness (Fig. 3) may indicate smaller cloud sizes, but this is probably not
universally true. If the cloud optical thickness is decreased, the ECF is
also decreased from reductions in cloud emissivity even though cloud coverage
itself may remain constant. (Recall that the ECF is a convolution of
emissivity and cloud fraction.) If the cloud optical thickness is fixed, the
cloud emissivity remains fixed even though the cloud coverage itself and ECF
could be decreased. The ECF could also be decreased (increased) if small
cloud elements become more widely spaced (packed together), assuming the cloud
sizes of the individual cumulus elements remain the same. With respect to the
visible radiances, the radiance is decreased if cloud elements become smaller
than the nominal 2.2 km pixel size, assuming the optical thickness of the
cloud elements does not change. Therefore, if an increased proportion of a
cloud population with normally distributed radiances becomes subpixel in
size, one would expect a shift towards positive skewness. If cloud
distributions are spatially resolved, an increased skewness radiance is still
entirely possible if the optical thickness of cloud distributions is skewed
itself. However, in this investigation, the skewness of the MODIS optical
thickness is less skewed at low ECF than visible radiance (not shown). This
suggests that the skewness in the visible radiance at low ECF at least
partially arises from smaller cloud sizes.
MBL depth (hPa) for regions listed in Table 1: (a) NEP, (a) NEA,
(c), SEP, and (d) SEA. The AIRS 1000–700 hPa dMSE is overlaid in white
contours (solid are for positive, and dashed for negative).
The mean MBL depth (Fig. 4) reaffirms a characteristic transition from
shallow MBLs (920–970 hPa) near the coast to deeper MBLs (830–880 hPa) to
the west and is a well-observed feature of the stratocumulus-to-cumulus
transition previously observed by Karlsson et al. (2010), Teixeira et
al. (2011), and others. Closest to the coast, the MBL is shallowest in the
NEA and slightly deeper in the NEP. The SEA and SEP are deeper than their Northern Hemisphere
(NH) counterparts, with SEP the deepest. The SEP MBL depths agree with VAMOS
Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx)
in situ radiosonde-derived temperature inversion base heights described by
Bretherton et al. (2010). Furthermore, the inter-regional differences in MBL
depth show consistency with Global Positioning System–radio occultation
(GPS-RO) data described by Chan and Wood (2013).
dMSE between 700 and 1000 hPa are
calculated following the approach outlined by Kubar et al. (2012) and are
also shown in Fig. 4. The dMSE is calculated from quality-controlled AIRS
soundings (PGood ≥ 1000 hPa) and is nearly identical to estimates
from ERA-Interim shown by Kubar et al. (2012). The magnitude of dMSE is
larger and positive near the coast in the Southern Hemisphere (SH) compared to the NH and is
somewhat reduced in the NEA region. Yue et al. (2011) showed that values of
EIS and LTS obtained from AIRS soundings are lower in the NEA compared to the
other three regions and are also consistent with Fig. 4.
AIRS RH700 (%) for regions listed in Table 1: (a)
NEP, (b) NEA, (c), SEP, and (d) SEA. The
MERRA–AIRS RH700 difference is shown as white contours (solid implies
MERRA is moister, and dashed implies AIRS is moister). The length and
direction of the arrows depict the 700 hPa wind vectors from MERRA.
Seasonal averages of AIRS RH700 with an overlay of the corresponding
MERRA–AIRS RH700 differences are shown in Fig. 5. Wind vectors depict
the mean horizontal flow. Overall, RH700 in the SH is lower than the NH,
while the NEA is the moistest of the four regions, and SEP the driest. MERRA
is on average moister than AIRS by ∼ 5 % in the NH, nearly
identical to AIRS in the SEA, and a much more spatially heterogeneous
difference is observed in the SEP from the coastal proximity westward between
8 and 12∘ S.
Bretherton et al. (2010) demonstrate that the free troposphere in the SEP
westward of 75∘ W is characteristically very dry (0.1 g kg-1)
with sporadic filaments of moist air (as high as 3–6 g kg-1) up to an
altitude of 2.5 km. In addition, these moist filaments have been observed
with GPS-RO refractivity profiles by von Engeln et al. (2007). The vertical
structure of RH obtained from VOCALS-REx radiosondes implies a well-mixed MBL
near the coast with MBL decoupling west of 80∘ W. Myers and
Norris (2015) showed that 700 hPa is drier in the SH subtropics than in
the NH using ERA-Interim data. When general circulation models are sampled for RICO-like conditions
using representative mid-tropospheric large-scale vertical velocities as in
Medeiros and Stevens (2011), a dry bias is obtained above the MBL in
comparison to a composite of RICO radiosondes.
The seasonal averages of AIRS RH850, and the corresponding MERRA–AIRS
RH850 differences, are larger than those found for RH700 (Fig. 6)
and are due to temperature and water vapor weighting function widths on the
order of 2–3 km (Maddy and Barnet, 2008).
AIRS RH850 (%) for regions listed in Table 1: (a)
NEP, (b) NEA, (c), SEP, and (d) SEA. The
MERRA–AIRS RH850 difference is shown as white contours (solid implies
MERRA is moister, and dashed implies AIRS is moister). The length
and direction of the arrows depict the 850 hPa wind vectors from MERRA.
In summary, the seasonal averages exhibit realistic three-dimensional spatial
morphologies and gradients and show consistency with MERRA RH in the
subtropical MBL. The MBL depth and seasonal variations (not shown) agree with
GPS-RO (Chan and Wood, 2013). The AIRS-derived dMSE between 700 and 1000 hPa
agrees with ERA-Interim (Kubar et al., 2012). The radiance skewness is
strongly related to dMSE (Kawai and Teixeira, 2012). The AIRS ECF
distributions closely correspond to well-established climatologies of cloud
amount (e.g., Klein and Hartmann, 1993; Rossow and Schiffer, 1999; Wood,
2012). The vertical structure of the horizontal wind flow well represents
known climatological patterns in the MBL and lower free troposphere. While
the variability within each region and between the four regions is consistent
with previous studies, the physical reasons for these differences are beyond
the scope of the current investigation.
Multivariate and multimoment PDFsDimensionality of PDFs
Choosing an ideal subset of variables and statistical moments to form the
basis of joint histograms is a challenge. Motivated in large part to link
cloud and thermodynamic properties derived from infrared and visible bands,
we describe six variable combinations. The natural log frequency of
occurrence is shown in gray scale from black to white, and MBL depth is
superimposed as contours (Fig. 7).
Shown are joint PDFs for six different combinations of variables
that are described in Sect. 4.2: (a) radiance versus AIRS ECF, (b)
radiance versus MODIS CF, (c) MODIS τ versus AIRS ECF, (d) MODIS τ versus MODIS CF, (e) radiance versus MODIS τ, and (f) MODIS CF
versus AIRS ECF. The gray scale is the natural log of total counts per bin.
All values in the PDFs shown are for the cumulus regime. The color contours
depict the MBL depth (hPa).
The MBL depth exhibits clearer patterns in the ECF dimension (Fig. 7a, c)
rather than the cloud fraction dimension (Fig. 7b, d). The latter is more
compressed, and the gradients are weaker in both dimensions. The MBL depth is
deepest for lower values of ECF, τ, and visible radiance. In addition,
the MBL depth also decreases for the most reflective clouds at a given value
of ECF, while this behavior is not observed for τ. An additional
population of subpixel cumulus clouds is captured within the radiance data
that is not captured in τ data. The two other panels (Fig. 7e, f)
highlight the challenges with the choice of dimensionality. In the case of
radiance versus τ, while there is a strong correlation in the
occurrence frequency within the more reflective clouds, the structure in the
MBL depth is much less clear. In the case of cloud fraction versus ECF, the
occurrence frequency is much more poorly correlated and scattered, while the
MBL depth shows less structure in either dimension.
We will use radiance versus ECF (Fig. 7a) in the remainder of this work. We
are not advocating that the dimensional choices made are optimal. Instead,
the results motivate the use of satellite and reanalysis data building from
native-resolution, pixel-scale, temporally instantaneous coincidences.
Joint PDFs of visible radiance versus ECF for the four spatial
regions listed in Table 1. The SEP in Fig. 7 is repeated here for clarity.
Regional similarity in MBL depth
The frequencies of AMSU FORs that contain stratocumulus and cumulus are
listed in Table 1. The largest differences in the gradients between
stratocumulus and cumulus are found in the NEP (Fig. 8a, e), while the
smallest differences are found in the NEA (Fig. 8c, g). The MBL depth is
several tens of hectopascals shallower in stratocumulus (Fig. 8a–d) than in
cumulus (Fig. 8e–h) in all four regions for almost every possible
combination of radiance and ECF. We can conclude that the cloud amount and
shortwave reflected radiation act independently of MBL depth. A small
population of shallow MBL depths for ECF > 0.9 is found in
cumulus (Fig. 8e–h) and is a consequence of a few stratocumulus clouds that
fail to exhibit a large enough Tb signature to trigger liquid
phase tests (e.g., Kahn et al., 2011, 2014). The two cloud regimes therefore
should not be considered mutually exclusive of each other.
A significant increase in MBL depth with increasing radiance is found in
cumulus with a stronger relationship in the NH than in the SH
(Fig. 8e–h) at a fixed value of ECF. This is partly a result of a deeper MBL
in the SEP and SEA near the coastline (Fig. 4). The exception is that the
NEP, SEP, and SEA show a decrease for the most reflective clouds except for
the NEA. Generally speaking the NEA is the largest outlier of the four
regions for all radiance moments shown for MBL depth in Fig. 8 and is
affected more by the midlatitudes than other regions. The MBL depth gradients
have an approximately linear relationship with the standard deviation of
radiance (Fig. 8i–l) unlike the average radiance (Fig. 8e–h). The MBL is
deepest for the largest values of the standard deviation at almost all values
of ECF in all four regions. This suggests that the largest values of average
radiance in Fig. 8e–h are uniform in spatial structure and have some of the
lowest standard deviations (Fig. 8e–h).
The radiance skewness is shown in Fig. 8m–p. There are several important
features to describe. First, the MBL depth is shallower for normally
distributed radiance, and a sharp increase in MBL depth with increasing
positive skewness is consistent with Figs. 3 and 4. Second, the change in MBL
depth is somewhat greater for an identical increase in radiance skewness when
compared to τ skewness (not shown). Third, the cumulus occurrences at
low ECF for positive skewness > 1 are mostly absent in the τ data (not shown) but are very common in radiance data. We argue that this
discrepancy has an important impact on the interpretation of the trade
cumulus climatology. The gradient of MBL depth in the dimension of increasing
positive skewness at low values of ECF is much greater in the radiance data
where the highest data counts are found. We posit that the radiance data
contain more subpixel cumulus, which is missing in the τ data. Fourth (not
shown), the AIRS cloud mask filter (Fig. 1b) is removed in order to retain
all values of radiance (clear and cloudy) in the joint PDF. While the
patterns of radiance skewness and MBL depth are not significantly altered
when applying the cloud mask filter, many more counts with normally
distributed radiances appear, which indicates some leakage of weak clear-sky
surface reflection. We conclude that there is a much bigger difference
between the cloud-mask-filtered radiance and τ than between the
filtered and non-filtered variants of radiance, implying a robust
interpretation. Fifth, the MBL depth contours change more rapidly with
skewness of τ or radiance than with the mean value of τ or
radiance, consistent with the findings of Kawai and Teixeira (2010), where a
tighter correlation with LWP skewness compared to average LWP was found.
Joint PDFs of visible radiance average (left) and skewness (right)
versus ECF for the SEP with dMSE depth as the overlay field. Other regions
are very similar and are not shown for reasons of brevity.
Figure 9 shows that the dMSE in the SEP is positive in sign and largest in
magnitude for larger values of ECF and normally distributed radiance (other
regimes are similar and are not shown). In the case of radiance skewness,
contours of constant dMSE track closely to the occurrence frequency through
much of the joint PDFs, with a reduction of dMSE to values less than 0 at
a fixed value of ECF as positively skewed radiances increases. This behavior
is similar to MBL depth (Fig. 8f) and suggests that instantaneous values of
dMSE correlate well with small-scale cloud variability. This is not
inconsistent with LTS and dMSE correlating well with larger-scale atmospheric
thermodynamic structure on much longer timescales. Kawai and Teixeira (2012)
showed that the skewness of LWP varies from +1 to +2 for cloud amounts of
90–100 %, and up to +1.5 to +3.5 for cloud amounts
< 30 %. Kawai and Teixeira (2010) showed that the highest correlations are found between
LWP homogeneity, skewness, and kurtosis with temperature and moisture differences
between the surface and 850 hPa; the correlations to EIS and LTS were not as large.
Relating meteorology and microphysical processes
Nuijens et al. (2009) describe RICO field
campaign observations that illustrate fundamental physical relationships
between cloud cover, wind speed and direction, the vertical structure of RH,
and precipitation frequency and intensity within precipitating shallow trade
cumulus. The observations can be grouped into three fairly distinct cumulus
regimes: (i) low cloud fraction with little to no precipitation characterized
by low values of u and a drier MBL; (ii) an increase in cloud fraction with
some light precipitation characterized by low values of u and elevated RH
between 800 and 1000 hPa; and (iii) a further increase in cloud fraction with light
precipitation and some isolated heavier events characterized by higher values
of u and a large increase in RH between 650 and 900 hPa. A key observational
difference among the three regimes is the variation of RH within the MBL
(800–1000 hPa) and near the top of the MBL extending into the lower free
troposphere (650–900 hPa). The width of these layers is similar to the AIRS
700 and 925 hPa temperature and specific humidity weighting functions. Even
though the RICO observations do not fall within any of the four regions
listed in Table 1, Medeiros and Nuijens (2016) show that the observational
site is applicable to the trade regime as a whole across the globe. Thus our
approach for the remainder of the investigation is to determine if similar
relationships shown in Nuijens et al. (2009) exist in cumulus for the regions
listed in Table 1.
Joint PDFs of visible radiance skewness versus ECF for the four
regions listed in Table 1; the overlay field is re.
Figure 10 shows the MODIS-derived re for stratocumulus
(Fig. 10a–d) and cumulus (Fig. 10e–p) that are limited to successful
retrievals (no PCL pixels are included). There are several prominent features
in the histograms. First, the stratocumulus re is about 11 to
12 µm throughout most of the PDF in all four regions. An exception
is the increase of re by several micrometers when average
radiance and ECF are reduced (Fig. 10a–d). While these particular MODIS
pixels were successful, cloud horizontal inhomogeneity causes larger
re within this population of clouds because of the plane-parallel
homogeneous bias (Cho et al., 2015; Zhang et al., 2016). Cloud inhomogeneity
may also lead to significant 3-D radiative transfer effects, but these tend to
cause both larger and smaller re in similar proportions (Zhang et
al., 2012). Second, the NEA region (Fig. 10g) is most dissimilar to the other
three regions for average (Figs. 10e–h), standard deviation (Fig. 10i–l),
and skewness (Fig. 10m–p). Third, re is largest along the axis
of maximum counts with values upwards of 16 to 20 µm in the SEP,
15–18 µm in the SEA, and 14–17 µm in the NEP. The
largest values in the NEA are confined to the most skewed radiances unlike
the other three regions. Fourth, in the cleaner SH, the values of
re appear to be more tightly coupled to cloud microphysical
processes that respond to changing wind speed and a deepening MBL. Fifth, the
variations of re with the standard deviation of radiance
(Fig. 10i–l) are more nonlinear than in the case of MBL depth (Fig. 8i–l).
This shows that the relationship between radiance moments and different
physical quantities is not the same.
(a) Same as Fig. 10n except that sampling is restricted to AMSU FORs that
contain the CloudSat ground track. (b) Samples of the data in (a) that
contain detected precipitation according to CloudSat.
One general interpretation of the larger re in cumulus
(Fig. 10e–h) than in stratocumulus (Fig. 10a–d) is that it is
caused by increased inhomogeneity of cumulus (Zhang et al., 2012), retrieval
failures and partly cloudy pixels (Cho et al., 2015), and view angle biases
(e.g., Liang et al., 2015) that are further coupled together with other
factors at play (Zhang et al., 2016). The aforementioned issues may still
impact a successful re retrieval. However, we offer evidence that
the increase in re is also consistent with environmental
variability, which in turn is consistent with droplet growth and precipitation.
The contours of re correspond very closely to the magnitude of
the u component of wind speed at 925 hPa (u925) (see Fig. 12) and other
levels in the MBL (not shown), suggesting a link between cloud droplet
growth, light rain, and dynamical variability. The somewhat larger
re in the SH is consistent with droplet growth in a cleaner
environment (Suzuki et al., 2010a, b). Successful retrievals may be more
frequently precipitating, either because of larger re in the
cloud or because the plane-parallel homogeneous bias is larger in
precipitating clouds.
Joint PDFs of 700 hPa θe, u925, ω700, and ω925 for the four regions listed in Table 1.
To determine if the elevated re along the axis of maximum counts
is associated with increased precipitation frequency, collocated matchups of
the CloudSat precipitation rate are used to determine which AMSU FOVs contain
occurrences of precipitation. Figure 11 shows results for the SEP region. The
radiance skewness for the full AIRS/AMSU/MODIS swath in Fig. 10n is
restricted to the CloudSat ground track in Fig. 11a. The counts are reduced
by a factor of ∼ 30 as expected. There are some subtle changes in the
re distribution showing an increase of 2–3 µm with
increasing skewness at a fixed value of ECF. Figure 11b shows the proportion
of the PDF that contains at a minimum the natural log(2) counts of
precipitation occurrence within each bin. About 20–50 % of the AMSU FOVs
are precipitating according to CloudSat within the PDF of Fig. 11a. The
precipitation frequency is consistent with Rapp et al. (2013), where up to
40 % of clouds precipitate in the cumulus regime. Little to no
precipitation occurs outside of the central portion of the PDF in Fig. 11a.
The highly skewed cumulus with ECF < 0.2 appears to be exhibiting
large re biases due to visible radiance inhomogeneity (Cho et
al., 2015; Zhang et al., 2016). We also point out that the population of
clouds detected by CloudSat that have ECF > 0.95 (Fig. 11b) is
associated with very little precipitation and is consistent with the spatial
distributions described by Rapp et al. (2013).
Figure 12 shows θe,700, u925, ω700, and
ω925. The θ700 (not shown) is nearly identical among all
regions with θ700= 314 K ± 1 K. Thus, the structure in
θe,700 (Fig. 12a–d) is driven by variations in specific humidity.
For a fixed value of ECF, the clouds with the lowest and highest values of
radiance are associated with moistening of the lower free troposphere. Using
climatological averages, Myers and Norris (2015) show that shortwave
observations from CERES, cloud fraction estimates from ISCCP and CALIPSO, and
RH700 and ω700 from ERA-Interim reflect aspects of Fig. 12
and, namely, that more reflected shortwave is associated with increased cloud
fraction and decreased ω700.
The highest values of θe,925 (not shown) occur along the axis of
highest counts, while reductions in θe,925 occur for the least and
most reflective clouds at a fixed value of ECF. This is the case for the NEP,
SEP, and SEA, but the NEA is an outlier and shows a constant increase as seen
with RH and MBL depth. Unlike 700 hPa, θ925 is more variable (not
shown) between the four regions but is generally 2 K or less.
The u925 is largest (Fig. 12e–h) when the PDF has the largest counts
and very closely resembles re in Fig. 10e–h. The subtle
differences in the contours in Figs. 10e–h and 12e–h align very well,
suggesting a tight correlation between the two parameters. The magnitude of
u925 is larger than u700 (not shown), consistent with RICO (Nuijens
and Stevens, 2009). The ω700 fields (Fig. 12i–l) exhibit minimal
correspondence with average radiance and ECF in the NH regimes with a weak
correspondence in the average radiance in the SH regions. The ω925
fields (Fig. 12m–p) show larger gradients in all four regions. The
ω925 decreases with increasing radiance in all regions similar to
that shown in Myers and Norris (2015), with a slightly noisier pattern in
ω925 observed in the NH regimes. The decrease of ω925
with increasing radiance is consistent with a deeper MBL (Fig. 8e–h) and
larger τ. Where u925 (Fig. 12e–h) increases, ω925
(Fig. 12m–p) decreases and RH925 increases (not shown). The largest
values of re (Fig. 10e–h) also correspond to the above
tendencies, consistent with the concept of more frequent precipitating clouds
within a windier and deeper MBL (Nuijens and Stevens, 2012).
The joint PDFs imply simultaneous increases in θe,700, θe,925 (and by extension RH700 and RH925), u925, and ECF in
three of the four regions investigated (NEP, SEP, and SEA) with a
particularly strong relationship between u925 and re. The
NEA is somewhat of an outlier, although this is based on one season's worth of
data during 2009. There is much variability across the trade cumulus regime
as it should not be treated as a single homogeneous entity.
Summary and conclusions
The global-scale relationships and coupling of cloud fraction and spatial
variability to thermodynamic and dynamic properties of the atmosphere remain
poorly understood (Bony et al., 2015). The NASA A-train (Stephens et al.,
2002) provides a wealth of remote-sensing data about the microphysics and
thermodynamics of the cloudy MBL. MERRA (Rienecker et al., 2011) offers a
complementary set of thermodynamic and dynamic variables that helps
establish context for coincident remote-sensing observations. The synergy
between satellite and reanalysis data at the native spatial and temporal
resolutions available has not been fully exploited to date. We describe a
new approach that leverages coincident reanalysis and remote-sensing data at
the native resolution of the observations. The spatial variability of
clouds, and the relationship to thermodynamic and dynamic state variables,
is thus inferred using the finest temporal and spatial resolutions
available.
Four subtropical oceanic regions that capture transitions from stratocumulus
to trade cumulus are investigated. We define two regimes based exclusively
on liquid and unknown cloud thermodynamic phase detections with the AIRS instrument, and
we generically refer to them as stratocumulus and cumulus regimes, respectively. The mean, standard
deviation, and skewness of MODIS and AIRS FOV cloud properties and visible
radiances are calculated for each AIRS and MERRA temperature and humidity
observation.
As with previous findings, coastal stratocumulus radiances are approximately
normally distributed, while the radiances are positively skewed away from the
coast, where disorganized cumulus dominates. The radiance skewness closely
aligns with the magnitude of AIRS ECF in cumulus
with less correspondence in stratocumulus. Strong (poor) spatial
correspondence between radiance skewness and AIRS ECF (MODIS cloud fraction)
was found, suggesting infrared-based ECF is a potentially valuable and
unappreciated diagnostic for MBL cloud characterization. The mean MBL depth
derived from AIRS (Martins et al., 2010) shows a characteristic transition
from shallow MBLs (920–970 hPa) near the coast to deeper MBLs
(830–880 hPa) away from the coast and is a well-observed feature of the
stratocumulus-to-cumulus transition (e.g., Teixeira et al., 2011). The
AIRS-derived dMSE between 700 and 1000 hPa
agree very well with ERA-Interim (Kubar et al., 2012). We find that the
radiance skewness is strongly related to the magnitude of dMSE as previously
found by Kawai and Teixeira (2012). The MBL depth is shallower for
stratocumulus than cumulus for almost all values of visible radiance and ECF.
The change in MBL depth is somewhat greater for an identical increase in
radiance skewness when compared to τ skewness. The population of
cumulus occurrences at low ECF for positive skewness > 1 is
mostly absent in the τ data but are very common in radiance data. This
highlights the importance of understanding the sampling from derived Level 2
products compared to Level 1 radiances that may capture a fuller range of the
geophysical state in different cloud regimes.
The re in stratocumulus is about 11 to 12 µm for most
values of radiance and ECF in all four regions of study. For cumulus,
re ranges anywhere from 12 to 20 µm, with larger
re for increasing positive skewness especially when ECF is small.
The values of re appear to be tightly coupled to cloud
microphysical processes that respond to changing MBL wind speed and a
deepening MBL. We argue that for these successful MODIS retrievals the
increase in re is consistent with increased droplet growth and
hence precipitation occurrence. This may be caused by larger re
in the cloud itself or by the association of precipitating clouds with an
increased subpixel inhomogeneity that leads to the plane-parallel homogeneous
bias; this topic warrants further investigation. In the SEP region, the
elevated values of re that correspond with the increased
u925 are more frequently precipitating according to CloudSat.
The RICO observations provide an important
multiparameter testing benchmark (Nuijens et al., 2009). These results are
generalized into three types of shallow precipitating cumulus regimes
observed during RICO. The joint PDFs imply simultaneous increases in θe,700, θe,925, u925, and ECF in three of the four regions
investigated (NEP, SEP, and SEA) with a strong correspondence between
u925 and re. The NEA less clearly follows these behaviors
and is an outlier, although this is based on one season's worth of data
during 2009.
Future work will expand to other cloud regimes, additional data sets, and
multiple years of data. A similar approach with numerical model output
should also be attempted using temporal snapshots of similar geophysical
fields. We expect that this approach will be especially useful for linking
cloud microphysics together with the thermodynamic and dynamic state of the
atmosphere at the process scale.
The AIRS version 6 data sets were processed by and obtained
from the Goddard Earth Services Data and Information Services Center
(http://daac.gsfc.nasa.gov/). The MODIS Collection 6 data sets were
processed by and obtained from the Level 1 and Atmosphere and Archive
Distribution System (http://ladsweb.nascom.nasa.gov). The MERRA data
sets were processed by and obtained from the NASA Goddard Global Modeling
and Assimilation Office (GMAO). CloudSat data were obtained through the
CloudSat Data Processing Center
(http://www.cloudsat.cira.colostate.edu/). The data and code used in this investigation are available
upon request from the lead author (brian.h.kahn@jpl.nasa.gov). All rights
reserved. Government sponsorship acknowledged.
The authors declare that they have no conflict of
interest.
Acknowledgements
Part of this research was carried out at the Jet Propulsion Laboratory (JPL),
California Institute of Technology, under a contract with the National
Aeronautics and Space Administration. Georgios Matheou and Brian H. Kahn were partially supported by
an R&TD project at JPL. Brian H. Kahn was partially supported by the AIRS project at
JPL and by the NASA Science of Terra and Aqua program under grant NNN13D455T.
Brian H. Kahn, Qing Yue, and Mathias M. Schreier were partially supported by NASA's Making Earth Science Data
Records for Use in Research Environments (MEaSUREs) program. The authors are
grateful to two reviewers for comments and suggestions that led to an
improved version of this paper. Edited
by: Johannes Quaas Reviewed by: two anonymous referees
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