ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-10715-2018Ice cloud microphysical trends observed by the Atmospheric Infrared SounderIce cloud microphysical trends observedKahnBrian H.brian.h.kahn@jpl.nasa.govTakahashiHaniiStephensGraeme L.YueQinghttps://orcid.org/0000-0002-3559-6508DelanoëJulienManiponGeraldManningEvan M.HeymsfieldAndrew J.https://orcid.org/0000-0003-4107-7533Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, 91109, USAJoint Institute for Regional Earth System Science and Engineering,
University of California – Los Angeles, Los Angeles, CA, 90095, USALATMOS/IPSL, UVSQ-CNRS-UPMC, 11 Boulevard D'Alembert, 78280 Guyancourt, FranceNational Center for Atmospheric Research, Boulder, CO, 80301, USABrian H. Kahn (brian.h.kahn@jpl.nasa.gov)26July20181814107151073922December201731January20187June20182July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/10715/2018/acp-18-10715-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/10715/2018/acp-18-10715-2018.pdf
We use the Atmospheric Infrared Sounder (AIRS) version 6 ice cloud property
and thermodynamic phase retrievals to quantify variability and 14-year trends
in ice cloud frequency, ice cloud top temperature (Tci), ice
optical thickness (τi) and ice effective radius
(rei). The trends in ice cloud properties are shown to be
independent of trends in information content and χ2. Statistically
significant decreases in ice frequency, τi, and ice water path
(IWP) are found in the SH and NH extratropics, but trends are of much smaller
magnitude and statistically insignificant in the tropics. However,
statistically significant increases in rei are found in all three
latitude bands. Perturbation experiments consistent with estimates of AIRS
radiometric stability fall significantly short of explaining the observed
trends in ice properties, averaging kernels, and χ2 trends. Values of
rei are larger at the tops of opaque clouds and exhibit
dependence on surface wind speed, column water vapour (CWV) and surface
temperature (Tsfc) with changes up to 4–5 µm but are only 1.9 %
of all ice clouds. Non-opaque clouds exhibit a much smaller change in
rei with respect to CWV and Tsfc. Comparisons between DARDAR
and AIRS suggest that rei is smallest for single-layer cirrus,
larger for cirrus above weak convection, and largest for cirrus above strong
convection at the same cloud top temperature. This behaviour is consistent
with enhanced particle growth from radiative cooling above convection or
large particle lofting from strong convection.
Introduction
While our understanding of ice cloud microphysics has greatly advanced from
targeted in situ campaigns during the past several decades (Baumgardner et
al., 2017), global distributions obtained from satellite observing systems
remain highly uncertain (e.g. Stubenrauch et al., 2013). Climate GCM
simulations of the radiative response of high clouds have a large
inter-model spread (e.g. Zelinka et al., 2013). Uncertainty in the ice
hydrometeor fall speed is cited as a leading contributor despite dozens of
modelling parameters contributing to this spread (e.g. Sanderson et al.,
2008). The ice particle size distribution (PSD) is closely correlated to the
magnitude of fall speed (Heymsfield et al., 2013; Mitchell et al., 2008).
The PSD is frequently summarised as an ice effective radius (rei)
(McFarquhar and Heymsfield, 1998) in most publicly available satellite
remote sensing data sets.
Global decadal-scale satellite-based estimates of rei are
available from two algorithm versions of the Moderate Resolution Imaging
Spectroradiometer (MODIS) (Platnick et al., 2017; Minnis et al., 2011) and
the Atmospheric Infrared Sounder (AIRS) (Kahn et al., 2014; Teixeira, 2013).
Another estimate of rei is derived from a combination of the
Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Imaging
Infrared Radiometer (IIR) instruments (Garnier et al., 2013) on the
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)
satellite (Winker et al., 2010). Additional active–passive algorithms are
also available. Deng et al. (2013) describe comparisons of rei
between the CloudSat level-2C ice cloud property product (2C-ICE),
radar–lidar profiles of rei (DARDAR; Delanoë and Hogan,
2010), and the CloudSat level-2B radar-visible optical depth cloud water
content product (2B-CWC-RVOD).
Satellite retrievals of rei have known sources of uncertainty that are
traced to the use of 1-D radiative transfer theory (e.g. Fauchez et al.,
2015), errors that arise from a lack of precision, accuracy, or incomplete
sampling in the atmospheric and surface state (e.g. Kahn et al., 2015),
variability in mixtures of ice crystal habits and PSDs within a single
satellite pixel (e.g. Kahn et al. 2008; Posselt et al., 2008), systematic
uncertainties and approximations taken in the forward model (e.g. Wang et
al., 2016; Irion et al., 2018), and instrument calibration drift (e.g.
Pagano et al., 2012; Yue et al., 2017a; Manning and Aumann, 2017). The
vertical heterogeneity of ice water content (IWC) and rei are known to
cause differences in rei derived from shortwave/near-infrared (SWIR)
and mid-infrared (MIR) bands for identical clouds and observing geometry
(Zhang et al., 2010). Kahn et al. (2015) compared pixel-scale retrievals
between MODIS Collection 6 (C6; Platnick et al., 2017) and AIRS Version 6
(V6; Kahn et al., 2014) and confirmed, for a small subset of homogeneous
clouds, that rei is typically 5–10 µm larger when derived from
MODIS SWIR bands compared to AIRS MIR bands.
Secular trends in water path, cloud amount, and cloud height (e.g. Wylie et
al., 2005; Dim et al., 2011; Bender et al., 2012; Marvel et al., 2015;
Norris et al., 2016; Manaster et al., 2017) suggest that climate change
signals may be observable within the satellite era. There is a notable
absence of published studies regarding trends in ice microphysics. Sherwood (2002)
inferred regional decreases of rei by ∼ 0.5 µm per decade using offline calculations of the Advanced Very High Resolution
Radiometer (AVHRR) radiances and argued for the importance of aerosols on
cloud microphysics. Chen et al. (2016) describe experiments with the
Nonhydrostatic ICosahedral Atmospheric Model (NICAM) that suggest an
increase of rei in a warming climate that may be related to a weaker
tropical circulation, and therefore implies changes in the dominant pathways
of ice nucleation, growth, and precipitation processes. The rapid
intensification of Earth's hydrological cycle is consistent with increased
convective aggregation (Mauritsen and Stevens, 2015), the narrowing and
intensification of the ITCZ (Su et al., 2017), and a lower end estimate of
climate sensitivity; the high cloud response is key to reconcile a strong
hydrological response and lower end climate sensitivity (e.g. Lindzen et
al., 2001; Mauritsen and Stevens, 2015). The areal extent of cloud anvils
are correlated to upper tropospheric lapse rate (Bony et al., 2016) and
additionally ice hydrometeor fall speed, with larger (smaller) anvils for
smaller (larger) rei (Satoh and Matsuda, 2009). The radiometrically
stable AIRS instrument (Pagano et al., 2012) may provide constraints on ice
cloud microphysical properties that are highly desired (Kärcher, 2017).
While not the focus of this investigation, aerosols modulate ice clouds
through numerous indirect and direct pathways (Liu et al., 2007; Gettelman
et al., 2010; Kärcher, 2017). Variable magnitudes of rei at
convective tops result from variations in cloud condensation nuclei (CCN) or
ice nuclei (IN); these responses are highly nonlinear because of complex
liquid and ice phase microphysical processes as a function of updraft
velocity, concentration and composition of CCN and IN (Phillips et al.,
2007; Van Weverberg et al., 2013). Morrison and Grabowski (2011) used a 2-D
cloud resolving model (CRM) to show that convection may subtly weaken, yet
cloud top height and anvil IWC may increase in polluted compared to clean
conditions, resulting in a reduction of rei by up to a factor of two.
Simultaneous observations of rei from MODIS and IWC from the Microwave
Limb Sounder (MLS) suggest that more intense convection (defined by positive
anomalies of IWC) increases rei while polluted air (defined by positive
anomalies of aerosol τ) decreases rei (Jiang et al., 2011).
However, whether convection is dynamically invigorated by increased aerosol
concentrations is highly debatable (Rosenfeld et al., 2008; Fan et
al., 2013; Grabowski, 2015). Improving our understanding of satellite-based
ice cloud microphysics will lay the groundwork for future observational
constraints of aerosol-ice microphysics interactions.
Stanford et al. (2017) show that the covariability of cloud temperature,
vertical velocity, and rei observations within tropical mesoscale
convective systems (MCSs) reveal very complex behaviour taken during the
High Altitude Ice Crystals-High Ice Water Content (HAIC-HIWC) field campaign
near Australia. HAIC-HIWC observations and simulations with the Weather
Research and Forecasting (WRF) model using multiple microphysical
parameterisations are compared. The observations show that larger (smaller)
rei are found in weaker (stronger) updrafts of MCSs, while the reverse
is generally true for IWC; however, the opposite behaviour was found for
observations taken within a tropical cyclone (Leroy et al., 2017). The
covariability of rei with cloud temperature and vertical velocity among
the WRF experiments strongly disagree in sign and magnitude compared to
HAIC-HIWC observations.
The aforementioned observational, theoretical, and numerical modelling
studies motivate the development of additional constraints on rei
and its covariability with other ice cloud, thermodynamic, and dynamic
fields. This investigation is a first attempt to quantify secular changes in
rei from AIRS over its decade and a half observational record
with well-characterised radiometric stability (Pagano et al., 2012). We
attempt to identify potential algorithmic, information content, calibration,
and sampling characteristics that induce nonphysical trends. Collocated AIRS
and Advanced Microwave Scanning Radiometer
(AMSR) data are used to glean insight among the connections between
rei and tropical convection/precipitation processes. Lastly,
DARDAR data are used to investigate increases in rei that occur
near the tops of deep convection. The collocation of pixel-scale data among
AIRS, AMSR, and DARDAR is a first step towards illuminating the potential
processes that may be responsible for secular changes in ice cloud
properties.
DataAtmospheric Infrared Sounder (AIRS)
The AIRS V6 cloud properties are used from 1 September 2002 until 31 August
2016 (Kahn et al., 2014; K14 hereafter). As this investigation addresses ice
microphysics, we focus on the 26.5 % of AIRS pixels containing ice
thermodynamic phase, and their ice cloud top temperature (Tci), ice
optical thickness (τi), and rei retrieval parameters (K14).
The identification of cloud thermodynamic phase is described in K14 and is
validated against CALIOP phase estimates in Jin and Nasiri (2014). A set of
four brightness temperature (Tb) thresholds and Tb differences
(ΔTb) in the 8–12 µm atmospheric window region identify
ice phase by leveraging the spectral dependence of the refractive index of
ice; the ice phase is identified correctly in excess of 90 % of the time
using CALIOP as truth. Ice clouds of convective origin typically have larger
τi (Krämer et al., 2016) and trigger more of the ice phase
tests, while tenuous ice clouds have smaller τi and trigger fewer
phase tests.
For each AIRS footprint identified as containing ice, the three-parameter
optimal estimation (OE) ice cloud property retrieval is performed (K14) and
minimises the following cost function:
C=y-F(x,b)Sε-12+x-xaSa-12,
where F(x) is the radiance vector that is forward modelled,
x is the state vector of retrieved parameters, b is the
vector containing fixed state parameters, y is the vector of AIRS
radiances (K14), xa is the prior guess of x,
Sa-1 is the inverse of the a priori covariance, and
Sε-1 is the inverse of the noise covariance of
AIRS radiances. The retrieval state vector x is restricted to
τi (ice_cld_opt_dpth in L2 Support file; defined at
0.55 µm), rei (ice_cld_eff_diam in L2 Support file;
De is converted to rei henceforth), and Tci
(ice_cld_temp_eff in L2 Support file). The bulk ice scattering models, and
the definition of ice De, follows from Baum et al. (2007, cf. Eq. 4).
The surface properties (temperature and emissivity) and atmospheric profiles
(temperature and specific humidity) in b, and
xa (e.g. upper level cloud top temperature
Tcld used as a prior guess for Tci) are taken from
the AIRS cloud-clearing product (L2 Standard file AIRX2RET for IR/MW and
AIRS2RET for IR only). The averaging kernel matrix A quantifies
the sensitivity of the retrieval with respect to changes in the true state:
A=(KTSε-1K+Sa-1)-1KTSε-1K.
Scalar averaging kernels (AKs; ice_cld_opt_dpth_ave_kern,
ice_cld_eff_diam_ave_kern, and ice_cld_temp_eff_ave_kern in L2
Support file) are reported for each of the three state vector retrieval
parameters as off-diagonal terms of A in Eq. (2) are not
considered in K14. The scalar AKs quantify the information content of the
retrieval with respect to x. The normalised χ2
(ice_cld_fit_reduced_chisq in L2 Support file) is defined as:
χ2=1N∑i=1Nyi-[Fx]iεi2,
where εi is the radiance error in channel i and N= 59.
The χ2 from Eq. (3) is calculated for 59 observed and simulated
8–14 µm channels (K14) and is used to determine the robustness of
the radiance fits.
We have also tested the fidelity of rei within 6 K of the cold point
tropopause. As AIRS observations are derived from IR thermal emission
spectra, biases may arise in proximity to the tropopause. Any uncertainty in
Tci, the height and magnitude of the cold point tropopause, or
temperature lapse rate may lead to increased uncertainty in rei as
small spectral changes translate to large geophysical retrieval changes.
Therefore, “filtered” sets of AIRS retrievals that remove ice clouds
within 6 K of the cold point tropopause are shown throughout the paper, then
are compared with “unfiltered” data when contrasted to DARDAR retrievals
(see Sect. 6).
The average 14-year climatology of rei, τi,
Tci, and ice cloud frequency based on the IR only retrieval
(AIRS2RET) is shown in Fig. 1. Retrievals with quality control (QC) flags
QC = 0 and QC = 1 (K14; ice_cld_opt_dpth_QC,
ice_cld_eff_diam_QC, and ice_cld_temp_eff_QC in L2 Support file) are
included and the χ2 is filtered using the QC flag specific to
rei. The climatology is restricted to ice free ocean between
54∘ S and 54∘ N as influences of surface heterogeneity
including mountainous terrain, low emissivity such as bare mineral soils, and
nocturnal and high latitude wintertime inversions on the ice parameter
retrieval are not fully understood and warrant further investigation. A
strict ad hoc QC may filter questionable data over land but the resulting
data sample may skew the geophysical signals toward opaque clouds at the
expense of non-opaque clouds that are more likely to be filtered out. The
AIRS sensitivity is maximised for optically thinner cirrus with τ≤ 5 (e.g. Huang et al., 2004), while MODIS sensitivity is maximised for
optically thicker cirrus (e.g. Kahn et al., 2015; Chang et al., 2017). The
AIRS sampling includes nearly all ice clouds with
τi > 0.1, while the maximum values of
τi asymptote to values near 6–8 (e.g. Kahn et al., 2015). The
rei is retrieved for the same sample although retrievals with
QC = 2 are not included. Kahn et al. (2015) describe pixel-level
comparisons between AIRS and MODIS ice cloud properties and show that
overlapping sensitivity for both τi and rei is
observed for optically thicker pixels containing four positive ice phase
tests with spatial maps resembling those described in King et al. (2013).
The global 14-year averages (1 September 2002–31 August 2016)
of Tci, rei, τi, and ice cloud frequency,
for the AIRS IR only retrieval (AIRS2RET) between 54∘ S and 54∘ N
over the oceans. Note that the maximum Tci value is
higher than indicated in the colour bar.
The well-documented (Wylie et al., 2005; King et al., 2013; Stubenrauch et
al., 2013) spatial distributions of ice clouds with maxima in the tropics
and extratropical storm tracks and minima in the subtropical gyres are shown
in Fig. 1. Higher magnitudes of rei are observed along the ITCZ where
deep convection is more dominant, while lower magnitudes are observed in the
western Pacific Warm Pool region where transparent cirrus is more dominant.
These patterns are consistent with previous observational and modelling
studies. Yuan and Li (2010) found that MODIS rei is a few µm
larger at the tops of tropical deep convection when compared to the
extratropics for the same cloud top temperatures and Tb. Barahona et
al. (2014) show an increase in rei of a few µm in the Goddard
Earth Observing System Model (GEOS-5) with improved comparisons against
MODIS. Eidhammer et al. (2017) describe simulations using the Community
Atmosphere Model (CAM5) for a consistent single ice species across cloud and
precipitating ice hydrometeors without thresholds for autoconversion. A
subtle but distinct double ITCZ is observed in the zonal averages, not
unlike AIRS rei and τi in Fig. 1.
Same as Fig. 1 except these are the global 14-year averages of Tci,
rei, and τi averaging kernels (AKs) and χ2
fitting parameter. Note that the minimum AK values may be lower than
indicated in the colour bar.
The loss of AMSU-A2 on 24 September 2016 led to the termination of the
operational AIRS/AMSU combined infrared and microwave (AIRX2RET/AIRX2SUP)
cloud clearing retrieval (Yue et al., 2017b). Since that time, the AIRS
infrared only (AIRS2RET/AIRS2SUP) cloud clearing retrieval is the current
operational version. The impacts of the loss of AMSU on retrieval parameters
are assessed in great detail in Yue et al. (2017b). While many subtle
changes were documented for vertical profiles of temperature and specific
humidity, differences were more substantive for particular cloud properties.
Further discussion regarding differences in Fig. 1 for AIRS2RET and AIRX2RET
is found in the Appendix and shown in Fig. A1.
Figure 2 details AKs and χ2 using identical QC as Fig. 1. Typically
the AKs are much lower and the χ2 fits much larger for QC = 2
(not shown) and indicate lower information content and much poorer radiance
fits. The AK and χ2 patterns are spatially coherent and exhibit small
variations between tropical convection, thin cirrus, and both extratropical
storm tracks. The AKs have maxima on the equatorial side of the storm tracks
with a poleward minimum and additional minima within the tropics. Multi-layer
clouds are more frequent in the tropics and extratropical storm tracks (Chang
and Li, 2005; Mace et al., 2009) and lower values of Tci AKs in
these areas is consistent with the single cloud layer assumption in the
forward model (Kahn et al., 2015). However, the rei AK
distributions strongly suggest that the single layer assumption is providing
high information content of ice cloud properties. Interestingly, the spatial
patterns of rei AKs are correlated to τi (compare
Figs. 1 and 2) in the low latitudes, where large values of τi
align with slightly reduced rei AK. An area of reduced
rei AK in the subtropical north Atlantic corresponds to the
Saharan air layer. Generally χ2 is about 3–4 between ±30∘ latitude with somewhat lower values (better fits) in the ITCZ
and extratropics. The spatial pattern of χ2 does not closely track
any of the AK patterns; thus for QC = [0, 1] retrievals, spatial
variations in information content do not correlate to the quality of the
spectral fit. Differences between the IR/MW and IR only retrievals are
generally minor but not negligible and are consistent with the loss of MW
information. Further discussion regarding differences in Fig. 2 for AIRS2RET
and AIRX2RET is found in the Appendix and depicted in Fig. A2.
AMSR-E/AMSR-2
Coincident satellite observations in the A-train are valuable for gaining
physical insight about the AIRS ice cloud properties. The Advanced Microwave Scanning
Radiometer–Earth Observing System (AMSR-E) on Aqua and AMSR-2 instrument on
GCOM-W1 provide several atmospheric and surface properties that coincide with
AIRS. The AMSR-E instrument was operational from 4 May 2002 until
4 October 2011, while AMSR-2 is currently operational since 18 May 2012. We
focus on the summer months July and August from 2003 to 2016 in this
investigation. The total column water vapour (CWV) cloud liquid water path
(LWP), rain rate (RR) (Hilburn and Wentz, 2008), sea surface temperature
(Tsfc) (Gentemann et al., 2010), and direction-independent near
surface wind speed (u) are obtained from AMSR-E (Wentz et al., 2014a) and
AMSR-2 (Wentz et al., 2014b) using Version 7 data.
DARDAR
We use the CloudSat, CALIPSO, and MODIS radar–lidar (DARDAR) combined ice
cloud property retrieval developed by Delanoë and Hogan (2008, 2010) to
investigate rei in a variety of ice clouds. This algorithm is based on
an OE algorithm that includes CALIOP's lidar backscatter and CloudSat's
radar reflectivity to retrieve vertical profiles of IWC, rei, and other
cloud variables. The ice cloud property retrievals are available at
CloudSat's 1.4 km horizontal resolution and CALIPSO's 60 m vertical
resolution and are matched to the nearest AIRS pixel. As the radar and lidar
have different sensitivities to a range of cloud characteristics, the
retrieval is functional when one of the instruments is unable to detect
clouds either because of small ice particles or strong attenuation. Deng et
al. (2013) showed that there is very good agreement in rei between
DARDAR, 2C-ICE, and in situ obtained observations from the Small Particles
in Ice (SPARTICUS) field campaign; therefore only DARDAR data are used in
this study.
DARDAR contains two products: DARDAR-MASK (Delanoë and Hogan, 2010;
Ceccaldi et al., 2013) and DARDAR-CLOUD (Delanoë and Hogan, 2008, 2010),
which are both available through the ICARE Thematic Center
(http://www.icare.univ-lille1.fr/archive, last access: 1 July
2017). DARDAR-MASK
provides the vertical cloud classification and a range of additional
categorisations (e.g. clear, aerosols, rain, supercooled and warm liquid,
mixed phase, and ice). DARDAR-CLOUD provides ice cloud properties such as
extinction, rei, and IWC by a variational radar–lidar ice-cloud
retrieval algorithm called VarCloud (Delanoë and Hogan, 2008). In this
product, normalised ice particle size distributions are used and
non-spherical particles leverage in situ measurements (Delanoë et al.,
2014).
MethodologyPixel matching
A pixel scale nearest neighbour matching approach (Fetzer et al., 2013) is
applied to AIRS/AMSU, AMSR-E and AMSR-2 from 1 September 2002 until 31
August 2016. By retaining the native spatial and temporal covariances in the
matched data, smaller scale and faster temporal processes are captured that
are otherwise lost to spatial gridding and temporal averaging (e.g. Kahn et
al., 2017). The same methodology is applied between AIRS/AMSU and DARDAR
from 1 July 2006 until 31 December 2008 within the subset of AIRS/AMSU
pixels along the CloudSat-CALIPSO ground track.
Secular trends
Trends and their statistical significance are calculated following Santer et
al. (2000) using a two-sided t-test and confidence intervals at the 95 %
significance level. The 95 % confidence intervals are also calculated for
a lag-1 autocorrelation in order to assess the sensitivity to highly
correlated time series (Cressie, 1980; Santer et al., 2000). Trends are
calculated for two different spatial averages. First, global monthly
anomalies at 1∘× 1∘ resolution over the
14-year observing period are calculated, then trends are reported at the same
spatial resolution. Second, monthly anomalies averaged between 54 and 18∘ S,
18∘ S and 18∘ N, and 18∘ S and 54∘ N are calculated
then trends with 95 % confidence
intervals with and without lag-1 autocorrelation are determined and
displayed as box and whisker diagrams. Three sets of AIRS properties are
described: combined IR/MW, IR only, and IR only for the third of pixels in the
swath nearest to nadir view. The purpose of contrasting results between IR
only and combined IR/MW is to highlight differences between the two
algorithms with further detail found in the Appendix. The purpose of showing
near nadir IR only is to demonstrate the overall lack of sensitivity in the
results to scan angle.
The global 14-year trends for the fields in Fig. 1. Note that the
minimum and maximum trends may exceed those indicated in the colour bar.
Joint histograms
Instantaneous pixel matches of AMSR-E and AMSR-2 variables vs. AIRS ice
cloud properties are used to construct joint histograms following the
approach described in Kahn et al. (2017). The joint histograms contain the
natural log of counts with rei and τi contours
superimposed. The histograms each contain one of several AMSR variables on
the x-axis and the AIRS upper layer Tcld on the y-axis. The intent of
these diagrams is to reveal the physical response of cloud top rei and
τi (if any) to precipitating and non-precipitating cloud types
and meteorological variability inferred from AMSR. The emphasis is on
rei vs. Tcld to facilitate comparisons with previous works that
describe temperature dependence of rei. Convective and non-convective
cloud types are shown separately in order to highlight the much larger
responses of rei to thermodynamic and dynamical variability in tropical
convection.
As discussed in Kahn et al. (2014), the Tci variable is included in the
retrieval state vector to improve the χ2 radiance fits and the
success rate of retrieval convergence. While there are strong similarities
between Tci and the upper level Tcld, some differences arise
within multi-layer clouds as expected, as Tci is based on the
assumption of a single-layer cloud (Kahn et al., 2014). Further discussion
on the reconciliation of the two cloud top temperatures is in progress and
will be presented in a separate paper.
Comparing DARDAR and AIRS rei
The retrievals of rei from AIRS and DARDAR are separately
compared for single-layer cirrus and convective clouds over ocean. We use the
CloudSat reflectivity, DARDAR-MASK, and DARDAR-CLOUD products to define
cirrus only (Ci), cirrus above weak convection (Weak conv), and cirrus above
strong convection (Strong conv). We begin with identifying the number of
cloud layers in each profile and the cloud top height (CTH) and cloud bottom
height (CBH) of each layer. Profiles with more than three layers of clouds
are not included in the statistics. Cirrus is defined as
CBH > 12 km (e.g. Keckhut et al., 2006) and deep convection is
defined as CTH > 10 km and CBH < 2 km (Takahashi and
Luo, 2014). Among deep convective clouds, weak and strong convection are
defined by the echo top height (ETH) of the 10 dBZ contour: the ETH of
10 dBZ > 10 km (Luo et al, 2008; Takahashi and Luo, 2012,
2017) for strong deep convection, while the ETH at 10 dBZ < 5 km
for weak deep convection. For the statistics described later, the upper layer
cirrus is chosen to have (1) CTH < tropopause height, (2) cloud
base temperature < 200 K, and (3) cirrus
geometrical thickness < 1 km. As mentioned in Sect. 3.3, the
coarse classification of convective intensity is useful for quantifying
differences in cloud top rei for non-convective, weakly, and
strongly convective scenes.
ResultsGlobal trends
The 14-year temporal trends in the cloud properties depicted in Fig. 1 are
shown in Fig. 3. The Tci decreases about 1–2 K in most areas, but a
few regions are found to increase. The trend in rei is increasing most
everywhere, from a few tenths to 1 to 2 µm, with the largest values in
proximity to tropical convection. While the upward trend in rei is
somewhat noisy at 1∘× 1∘, the increase is
notably consistent across the global oceans. In contrast, τi is
decreasing in most regions except for convectively active regions in the
ITCZ, parts of the tropical western Pacific Warm Pool, and southern Indian
Ocean. The trend in ice frequency is similar to τi but is
spatially smoother and the magnitude is much larger in the tropics. We
reiterate that the AIRS sensitivity to ice clouds is limited between
0.1 < τi < ∼ 6–8, thus the τi trends do not include contributions from clouds outside of this
sensitivity range.
The global 14-year trends for the fields in Fig. 2. Note
that the minimum and maximum trends may exceed those indicated in the colour
bar.
Trends in the AKs and χ2 are shown in Fig. 4. Generally
speaking, the trends in the retrieval parameters do not resemble the spatial
patterns of trends in their respective AKs. The trends for rei AKs in
Fig. 4 resemble τi trends in Fig. 3. This shows that changes in
the information content of rei are related to trends in τi.
However, the trends for rei do not resemble trends in either τi or τi AKs. We conclude that the upward trend in rei
is independent of the gain or loss of information in rei. The trends in
χ2 are quite subtle and are smallest when the ice frequency is
largest. There is an increased zonal symmetry in the Tci AK trend than
the Tci trend in Fig. 3 and no obvious correspondence is apparent
between the sign of the Tci trend in Fig. 3 and the Tci AK trend
in Fig. 4. The τi AK trend is generally upwards except in a few
tropical locations including the Saharan air layer. The spatial patterns in
τi AK trends do not appear to be related to other fields. In
conclusion, the observed trends in the ice cloud properties in Fig. 3 do not
appear to be caused by the gain or loss of information content, or the
fidelity of the radiance fitting in the retrieval.
Latitude bands
The anomaly time series of rei for three latitude bands and the IR/MW,
IR only, and IR nadir retrievals are shown in Fig. 5. While the anomaly time
series for most of the 1∘× 1∘ grid boxes are
not statistically significant (not shown), a statistically significant
signal is obtained for the broad oceanic latitude bands. The strong
similarity in the anomaly time series for IR/MW, IR only, and IR nadir is
apparent for rei and is also true for the other variables (not shown).
Despite some of the regional spatial differences in IR/MW and IR only
retrievals described in the Appendix, the algorithm differences are
minuscule with regard to the latitude band anomaly time series.
Monthly anomaly time series for rei for the IR/MW, IR only,
and IR nadir retrievals organised into three latitude bands: SH extratropics
(54–18∘ S), tropics 18∘ S–18∘ N,
and NH extratropics (18–54∘ N).
The 14-year trends for the tropics, SH and NH extratropics,
and IR/MW, IR only, and IR nadir retrievals. (a)Tci,
(b)rei, (c)τi,
(d)Tci AK, (e)rei AK,
(f)τi AK, (g) ice cloud frequency,
(h)χ2, and (i) IWP. The confidence for 95 %
statistical significance is shown as black lines with narrow tick marks and
the confidence for 95 % statistical significance with a lag-1
autocorrelation correction is shown as wider tick marks (see Santer et al.,
2000).
Figure 6 summarises the results for three latitudinal bands and IR/MW, IR
only, and IR nadir including 95 % confidence intervals with and without
lag-1 autocorrelation. The results in Fig. 6 have clouds within 6 K of the
tropopause filtered out; very similar results are obtained with no filtering,
and no material changes in sign, magnitude, and statistical significance are
found. The extratropical SH and NH upward trends in rei (Fig. 6b)
are steadier with time than the tropics, which appears initially flat but
then jumps upwards since 2013 (Fig. 5); however, all three regions and
retrievals exhibit statistically significant trends (Fig. 6). The
Tci trends (Fig. 6a) show about 0.5–1.0 K of cooling for the
three latitude bands and three data sets, with IR nadir exhibiting 95 %
confidence except for the lag-1 autocorrelation. The τi trends
(Fig. 6c) are downward by 0.025–0.075 (significant at 95 % in the SH and
NH, but not in the tropics). The AKs are slightly downwards for
Tci (Fig. 6d) and rei (Fig. 6e) and upwards for
τi (Fig. 6f); there is a mix of statistical significance that
depends on the retrieval algorithm and latitude band. The χ2 trends
(Fig. 6h) are essentially not statistically significant except for IR nadir
in the tropics, although the trend is slightly downwards. This is consistent
with an increasing thermal contrast with time as SSTs increase and ice clouds
slightly cool. Ice frequency (Fig. 6g) decreases by about 1 % relative to
the sum of all AIRS pixels in the SH and NH extratropics (significant at
95 %) and much less so in the tropics (not significant at 95 %).
Lastly, the ice water path (IWP) (Fig. 6i), which is calculated from
(2/3)ρireiτi, where
ρi is the density of ice (assumed to be 0.92 g cm-3),
decreased about 0.4–0.8 g m-2 in the SH (marginal significance at
95 %) and NH (significant at 95 %), and increased about
0.25 g m-2 in the tropics (not significant at 95 %). This
latitudinal pattern is similar to the CMIP5 zonal average ensemble mean
(Ceppi et al., 2016; cf. Fig. 1c) although the decrease of AIRS IWP appears
larger in magnitude than CMIP5 IWP at first glance. However, the statistical
significance of IWP is reduced compared to τi because of the compensating
increases in rei at all latitudes.
Potential impacts from radiometric drift
While the radiometric stability of AIRS is established to be approximately
4 ± 1 mK yr-1 (Pagano et al., 2012), the impacts of radiometric
drift on secular cloud property trends has, to date, not been assessed.
Manning and Aumann (2017) raise the possibility of channel-dependent drift
that could cause a systematic change in slope in the 8–14 µm region
especially for cold scenes with Tb < 200 K. However these
effects are subtle and are undetermined (but likely much smaller) for warmer
Tb that dominate the vast majority of AIRS pixels.
Six different tests
have been designed to estimate the potential effects of calibration drift on
the cloud products: (1) add +50 mK to all 59 cloud retrieval channels in
K14 after cloud clearing but before the ice cloud property retrieval; (3) add
+50 mK to cloud clearing channels and K14 channels but before cloud
clearing is performed; (3) increase the slope across the 59 channels by
reducing the longest wavelength channel by -50 mK, increasing the shortest
wavelength channel by +50 mK, and apply a linearly interpolated correction
to channels in between; (4) the same as (3) except decrease the slope by
reversing the radiometric perturbations; (5) different ice cloud properties
between v6 and a new version (v7j) of AIRS radiances that is updated with new
calibration estimates (Steve Broberg and Thomas Pagano, personal
communication, 2018); and (6) the same as (5) except restricted to the new
polarisation corrections only. Experiment (1) isolates radiometric drift on
ice cloud properties only, while (2) accounts for impacts from the full
geophysical retrieval. Experiments (3) and (4) are an approximate way to
estimate channel dependent drift but ignores the effects of individual
detector modules. Experiments (1) to (4) are performed for the focus day 6
September 2002. Experiment (5) assesses the differences in ice cloud
properties solely due to the updated radiance calibration (v7j) and
experiment (6) isolates the contributions from updates in the polarisation
corrections only. Experiments (5) and (6) are performed for the focus days
6 September 2002, 3 March 2007, 6 June 2007, 9 December 2007,
1 September 2012, and 12 September 2017. Further investigation into
radiometric drift scenarios on the AIRS Level 2 retrieval system that take
into account individual characteristics of each relevant detector module is
warranted.
The two radiance perturbation tests after cloud
clearing (+50 mK) and before cloud clearing (+50 mK
before CC); see text for description), with reported mean difference and
median absolute deviations of cloud properties, AKs, and χ2
for 54∘ S–54∘ N, over ocean, for
6 September 2002.
Mean difference Median absolute deviation (Perturbed – baseline IR/MW) (Perturbed – baseline IR/MW) +50 mK+50 mK before CC+50 mK+50 mK before CCrei (µm)-0.105-0.1310.7861.168rei AK-9.15 × 10-5+2.05 × 10-60.002780.00247τi+0.00534+0.01010.08120.149τi AK-0.00117-2.35 × 10-40.01080.0053Tice (K)+0.261+0.2351.1792.187Tice AK+4.73×10-4+0.001130.008520.00999χ2-0.0176-0.03150.540.648
T4he trends depicted in Fig. 6 for the IR only
retrieval, and the radiance perturbation sensitivity tests that adjust the
slope in the retrieval channels. The “steep” category indicates a steeper
slope with retrieval channel 1 at 692.76 cm-1
perturbed 50 mK cooler, retrieval channel 59 at 1133.91 cm-1 perturbed 50 mK warmer, and a linear
interpolation in between (see K14 for a list of the 59 retrieval channels).
The “shallow” category is reversed from the “steep” category.
Mean trend in Fig. 6 Mean difference for IR only (Perturbed – standard IR/MW) SHTropicsNHSteepShallowrei (µm)+0.41+0.54+0.33-0.228+0.22rei AK-0.0012-0.0024-0.0009+9.49×10-4-0.00101τi-0.066-0.019-0.079-0.0223+0.0233τi AK+0.0031+0.0017+0.0023+0.00154-0.00154Tice (K)-0.67-0.99-0.72-0.267+0.272Tice AK-0.0018-0.0032-0.0011-9.01 × 10-4+0.00105χ2-0.009-0.019-0.0005-0.0161+0.0204
The results of experiments (1) and (2) are summarised in Table 1 for one day
of retrievals on 6 September 2002 for ±54∘ latitude over
the oceans and for QC = [0, 1]. Both experiments indicate that differences
with respect to the IR/MW retrieval are substantially smaller than the
trends reported in Fig. 6. For the experiments before and after cloud
clearing, Δrei is -0.13 and -0.1 µm, Δτi is +0.01 and +0.005, and ΔTci is +0.24 and
+0.26 K, respectively. The AK trends for cloud variables for the
perturbation experiments are much less than depicted in Fig. 6. Furthermore,
the sign changes of the perturbations are not consistent with Fig. 6. (A
similar test with -50 mK that is not shown was performed and is nearly
symmetric but with the opposite result.) The results of the slope perturbation
experiments (3) and (4) are summarised in Table 2. The Δrei and
Δτi trends are somewhat larger than for experiments (1)
and (2) including a few of the AK trends, but still fall short of the
magnitudes reported in Fig. 6 (also listed in Table 2 for IR only). As with
experiments (1) and (2), the signs of the trends are not in agreement
between the observed trends and the slope perturbation experiments,
suggesting that the slope adjustment does not explain observed trends. While
the four highly simplified radiance perturbation experiments fall short of
explaining the sign and magnitude of the observed ice property trends, some
fractional contribution to observed trends cannot be ruled out.
Experiment (5) shows that the differences between the standard v6 retrieval
and that using v7j radiances are somewhat larger than the differences
obtained from experiments (1) to (4) and depicted in Tables 1 and 2, and are
closer to magnitudes obtained in the 14-year trends depicted in Fig. 6 (not
shown). However, the differences are nearly identical for all focus days
listed above that are dispersed throughout the length of the AIRS mission.
Therefore, the new v7j radiance calibration estimates indicate that the
update will not cause meaningful changes in the sign and magnitude of ice
cloud property trends. Rather, some small shifts in the magnitude of the mean
properties may occur that are similar in magnitude to the absolute value of
the trends themselves. This behaviour holds for the left, centre, and right
third of the AIRS swath, although a very slight scan dependence on the mean
difference between v6 and v7j is observed. Lastly, experiment (6) shows that
the polarisation effects are about an order of magnitude smaller than
experiment (5) (not shown). In summary, an eventual implementation of v7j
(or similar) radiances should have no material impact on the secular trends
derived in Fig. 6. However, this does not eliminate the possibility of a
partial contribution from radiometric drift itself as described in
experiments (1)–(4).
Definitions of the three ice cloud categories using the
AIRS two-layer effective cloud fraction (ECF) product, the total counts and
relative counts (%) of the three cloud categories. These results are
obtained over the tropical oceans (±18∘) during July and
August from 2003 to 2016.
Upper layer ECFLower layer ECFTotal counts% of total countsOpaque≥0.98Not specified890 7471.90 %Non-opaque< 0.98< 0.134 310 20973.42 %Multi-LayerNot specified≥0.111 533 23924.68 %Insight into convective processes with AMSR
Using ground-based retrievals at Darwin, Protat et al. (2011) found that
rei is ∼ 1–3 µm larger during active deep
convection compared to suppressed conditions. Using a simultaneous retrieval
of ice cloud properties from the MODIS and POLDER instruments, van
Diedenhoven et al. (2014) found that rei is larger in stronger
convective events compared to others at a given cloud top pressure. Van
Diedenhoven et al. (2016) used airborne remote sensing observations to show
that rei decreases with height, reaches a minimum around 14 km, and may
subsequently increase at higher altitudes. Barahona et al. (2014) obtained
an increase of a few µm for clouds colder than 195 K with GEOS-5, and
Hong and Liu (2015) show similar results with DARDAR data for the thickest
convective clouds above 10 km. Convective overshoots into the lower
stratosphere are 0.3–4.0 µm larger than non-overshooting deep
convection observed with DARDAR data (Rysman et al., 2017). Lawson et
al. (2010) use in situ aircraft probe data of ice particles in aged cirrus in
proximity to convection to show that particle size is more weakly dependent
on temperature below 215 K. Collectively, the aforementioned investigations
suggest that rei varies significantly at the tops of convective ice
clouds and motivates the synergistic use of AMSR and AIRS at the pixel scale
to capture convective-scale processes.
Differences in rei between opaque, non-opaque, and multi-layer
clouds
We show separate vertical profiles of AIRS rei with ice
Tcld for opaque, non-opaque, and multi-layer clouds in Fig. 7
with definitions listed in Table 3. The two-layer effective cloud fraction
(ECF) product is used to categorise the three cloud types and the threshold
of 0.98 used by Protopapadaki et al. (2017) is applied to the upper layer to
categorise opaque clouds. The ECF is a cloud product that represents the
convolution of cloud fraction and cloud emissivity. Nasiri et al. (2011)
showed that ECF from AIRS and effective emissivity from MODIS is in excellent
agreement for both single and multi-layered cloud configurations. Only
1.9 % of cloud tops are identified as opaque, and 98.1 % are
classified as non-opaque or multi-layer. The three categories of clouds are
further subdivided into three additional categories using AMSR-E/AMSR-2 LWP
and RR (Fig. 7): no liquid cloud or rain (LWP = 0 and RR = 0), liquid
cloud but no rain (LWP > 0 and RR = 0), and liquid cloud
with rain (LWP > 0 and RR > 0). Symbols show mean
rei over 10 K bins derived from IWC and extinction observations
for three tropical in situ field campaigns: the Cirrus Regional Study of
Tropical Anvils and Cirrus Layers–Florida Measurements for Area Cirrus
Experiment (CRYSTAL-FACE, diamonds), the Tropical Composition, Cloud, and
Climate Coupling Experiment (TC4, triangles), and the NASA African Monsoon
Multidisciplinary Analysis (NAMMA, squares) campaigns (e.g. Heymsfield et
al., 2014).
The mean (solid) and ±1σ values of AIRS IR only
rei vs. Tci for opaque, non-opaque and multi-layer
ice clouds over the tropical oceans during the months of July and August
within the 14-year time period of investigation. Refer to Table 3 for
definitions of the three categories. The AMSR-E/AMSR-2 estimates of LWP and
RR are used to divide categories into clear (LWP = 0) and no rain
(RR = 0), according to the passive microwave; cloudy
(LWP > 0) and no rain (RR = 0); and cloudy
(LWP > 0) and rain (RR > 0). The results in both
panels have all AIRS ice clouds filtered within 6 K of the cold point
tropopause; otherwise a much larger and potentially unphysical increase in
rei would be observed in the figures at the coldest temperatures.
The symbols are average rei from in situ field campaign data for
the CRYSTAL-FACE (diamonds), TC4 (triangles), and NAMMA (squares) campaigns;
please see Heymsfield et al. (2014) for more
details.
A maximum rei occurs near 230 K and decreases at warmer and colder
Tcld for non-opaque and multi-layer clouds in the tropics (Fig. 7).
About 23.0 % (17.3 %) of cases are clear for non-opaque (multi-layer)
according to AMSR but cloudy according to AIRS (Table 4). About 62.7 %
(53.3 %) have LWP > 0 and RR = 0 for non-opaque (multi-layer)
clouds. Both of these categories have nearly identical Tcld dependence
but multi-layer is several µm larger than non-opaque. The remaining
14.3 % (29.4 %) have LWP > 0 and RR > 0, and
rei is 0.5–2.5 µm larger than no rain, with the largest
differences for Tcld < 210 K. For opaque clouds in the tropics,
rei is substantially larger for all Tcld than non-opaque clouds.
There is three times the relative frequency of occurrence with raining
opaque clouds compared to raining non-opaque clouds. An increase in rei
is found for increasing Tcld unlike non-opaque and multi-layer clouds.
A weaker vertical dependence in rei for Tcld < 210 K occurs
and is similar to the results of van Diedenhoven et al. (2016). While this
region is sensitive to the 6 K cut-off used to filter questionable
retrievals near the tropopause, the profile in Fig. 7a is fairly robust for
∼ 4 K and larger. While the in situ rei estimates are
somewhat larger for Tcld > 220 K, this is expected as these
observations may be obtained well below cloud top in which the infrared
signal is not sensitive. Furthermore, the standard deviation of the in situ
observations (not shown) has a magnitude that is approximately as large as
the AIRS estimates of standard deviation, providing significant overlap
between satellite and in situ observations.
The relative occurrence frequencies of non-zero liquid
water path and rain rate according to coincident AMSR pixels for the three
ice cloud categories defined in Table 3. These results are obtained over the
Tropical oceans (±18∘) during July and August from
2003–2016.
LWP = 0LWP > 0LWP > 0RR = 0RR = 0RR > 0Opaque12.9 %44.3 %42.8 %Non-Opaque23.0 %62.7 %14.3 %Multi-Layer17.3 %53.3 %29.4 %Dependence of rei on near surface wind speed
Correlations between rain rate and near surface wind speed in passive MW
observations have suggested a tropical precipitation–convergence feedback
(Back and Bretherton, 2005). The AMSR low frequency (LF) wind is used to
quantify the response of rei to variability in near surface wind speeds
in the presence of convection. Figure 8 shows that opaque clouds
exhibit dependence on wind speed; the weakest (strongest) winds are
associated with the largest (smallest) rei. The change in rei is
∼ 2 µm for Tcld < 210 K but can be as large as
3–5 µm for Tcld > 230 K. The dependence for non-opaque
clouds is of opposite sign and lower magnitude when compared to opaque
clouds. Figure 8 was also calculated with NWP model winds and is nearly
identical with < 1 µm difference (not shown). The consistency
between NWP and AMSR winds provided confidence to partition the NWP winds
into the individual u- and v-components, and the u-component is shown in the
left column of Fig. 8. The largest values of rei are found for light
easterly winds around 5–10 m s-1 for opaque, consistent with
convectively active regimes generating larger rei (e.g. Protat et al.,
2011). Smaller values of a few µm are associated with westerly winds
during suppressed convection. Changes in rei due to wind direction
changes are largest for Tcld > 230 K and are lowest for the
coldest convective cloud tops. Very weak dependence is found for non-opaque
clouds.
AMSR-E/AMSR-2 low frequency (LF) wind speeds (m s-1)
(b, d, f) or NWP model u-component wind speeds (a, c, e)
vs. Tcld histograms. The log counts are shown as greyscale, the
AIRS IR only rei (µm) overlaid as coloured contours,
with opaque (a, b), non-opaque (c, d), and
multi-layered (e, f) clouds shown separately.
Multi-layer clouds exhibit the largest changes with wind speed (Fig. 8).
However, the reduced values of rei at higher wind speeds have low
occurrence frequencies (i.e. noted by the greyscale shading). The
contribution of retrieval biases that arise from an additional lower
layer(s) not accounted for in the forward model (Kahn et al., 2014) has not
been quantified. A firm conclusion on the realism of changes in multi-layer
cloud top rei to wind speed variability thus remains elusive and
warrants further investigation.
The response of τi to near surface wind speed is shown in Fig. 9
for the same cloud categories in Fig. 8. There is little, if any, dependence
of τi on wind speed except for slightly larger values during
light easterly winds. The values of τi are lowest for non-opaque
and confirm that these clouds are typically thin cirrus. The values of τi are a few factors larger for multi-layer and increase for stronger
winds.
AMSR-E/AMSR-2 low frequency (LF) wind speeds (m s-1) (b, d, f)
or NWP model u-component wind speeds (a, c, e)
vs. Tcld histograms. The log counts are shown as greyscale,
the AIRS IR only τi overlaid as colored contours, with opaque
(a, b), non-opaque (c, d), and multi-layered (e, f) clouds
shown separately.
Dependence of rei on CWV and Tsfc
The Tsfc and CWV also exhibit correlations with rei (Fig. 10).
Opaque clouds exhibit a 2–4 µm jump as CWV increases from 45 to 65 mm,
with the largest values for Tcld > 230 K; a similar pattern is
observed for Tsfc (Fig. 10). Little dependence of rei is observed
for non-opaque clouds. There is an increase in rei with increasing CWV
and Tsfc for multi-layer clouds. Overall the rei is lower in
non-opaque clouds compared to opaque clouds for all combinations of
Tci, CWV, and Tsfc. There is a subtle dependence of τi
on CWV for opaque clouds, although this dependence is absent in Tsfc
(Fig. 11). Both non-opaque and multi-layer clouds show increases in τi with CWV, but this only holds true for multi-layer clouds for
increasing Tsfc.
AMSR-E/AMSR-2 total CWV (mm) (a, c, e) or Tsfc
(K) (b, d, f) vs. Tcld histograms. The log counts are
shown as a greyscale, the AIRS IR only rei (µm) overlaid as
colored contours, with opaque (a, b), non-opaque (c, d), and
multi-layered (e, f) clouds shown separately.
Comparisons of AIRS and DARDAR rei
In the previous section, we showed that AIRS observes larger rei at the
tops of convection, and variations that depend on surface wind speed,
Tsfc, CWV, and precipitation rate. In this section, the rei at the
base of single-layer cirrus clouds, cirrus clouds above weak deep
convection, and cirrus clouds above strong deep convection will be shown
separately using the classification defined in Sect. 3.4. Stephens (1983)
argued for a radiative mechanism that leads to rei growth in cirrus
overlying lower-layer clouds from enhanced radiative cooling; particle
growth (decay) occurs in a radiatively cooled (heated) environment. DARDAR
data are used to test whether the mechanism of Stephens (1983) is operating
in observations, and also to determine the behaviour of AIRS in the same
clouds. As opacity increases, or the higher (colder) the lower layer cloud
occurs, the cooling from the cirrus layer is enhanced and larger rei
should therefore be observed. Systematic changes in the global circulation
and changes in convective clustering and cloud overlap may lead to a higher
frequency of overlapping cirrus on top of convection, and a reduced
frequency of thin cirrus with climate change evidenced by trends in the
Multi-angle Imaging SpectroRadiometer (MISR) derived cloud texture (Zhao et
al., 2016). Thus, we will assess if this mechanism is a viable contributor
to upward trends in rei.
Figure 12 shows the median (centre lines), mean (asterisks), and
interquartile range (bottom and top edges of boxes) for DARDAR, IR/MW, and
IR only AIRS retrievals, with filtered (non-filtered) versions of IR/MW and
IR only that have retrievals within 6 K of the cold point tropopause removed
(retained). For single-layer cirrus (Ci), AIRS is typically 2–3 µm
larger than DARDAR, with about 1 µm difference between IR/MW and IR
only. For cirrus above weak convection (Weak conv), AIRS remains about 2–3 µm
larger than DARDAR, as all AIRS retrievals increase about 2–3 µm
in the mean compared to single-layer cirrus. For cirrus above strong
convection (Strong conv), the differences between DARDAR and AIRS are
reduced, with IR only about 1–1.5 µm larger than IR/MW. The width of
the DARDAR quantiles increases from single-layer cirrus to cirrus over
strong convection indicating increased variability in rei; AIRS and
DARDAR have nearly identical variability for cirrus above strong convection.
AIRS and DARDAR exhibit an increase in rei as convective cloud appears
below cirrus layers suggesting that the mechanism described in Stephens (1983)
may be operating in these differences. Partitioning contributions to
the increase in rei from the Stephens (1983) radiative cooling
mechanism for particle growth, and lofting of large ice particles from
adjacent convection, however, cannot be easily differentiated; further
investigation is warranted in the context of rei trends shown in this
work.
AMSR-E/AMSR-2 total CWV (mm) (a, c, e) or Tsfc
(K) (b, d, f) vs. Tcld histograms. The log counts are
shown as a greyscale, the AIRS IR only τi overlaid as colored
contours, with opaque (a, b), non-opaque (c, d), and
multi-layered (e, f) clouds shown separately.
DARDAR (black), IR/MW (blue), and IR only (red) retrievals of
rei in single-layered cirrus (a), cirrus on top of weak
convection (b), and cirrus on top of strong convection (c);
see Sect. 3.4 for a description of the definition of
convective intensity. The AIRS retrievals are also shown for a filtered
version that removes retrievals within 6 K of the tropopause. The central
line is the median, the asterisk is the mean, and the bottom and top edges of
the boxes are the 25th and 75th percentiles.
Summary, conclusions, and outlook
While high-level ice clouds are a key component of the changing climate
system, there remains an absence of well-characterised observational
constraints of ice cloud microphysics that are desired for climate model
evaluation (Kärcher, 2017). The Atmospheric Infrared Sounder (AIRS)
instrument, launched in May of 2002, is radiometrically stable within ±3–4 mK yr-1 (Pagano et al., 2012). We use the AIRS version 6 ice
cloud property and thermodynamic phase retrievals (Kahn et al., 2014) to
quantify variability and trends in ice cloud frequency, ice cloud top
temperature (Tci), ice optical thickness (τi) and
ice effective radius (rei) from 1 September 2002 until 31 August
2016. We also investigate the scalar averaging kernels (AKs) associated with
each retrieval quantity to determine changes in information content; and the
χ2 fitting parameter, which quantifies the fidelity of the observed
and simulated radiance fits across cloud types. Differences are described
between the ice cloud properties
using the AIRS/AMSU combined cloud-clearing retrieval (IR/MW), the AIRS only
cloud-clearing retrieval (IR only), and a subset of IR only for the third of
pixels nearest to nadir view (IR nadir).
Spatial patterns in rei reflect differences in proximity to deep
convection, thin cirrus, and extratropical storm tracks. The averaging
kernels and χ2 patterns are spatially coherent and exhibit
variations between different cloud regimes with slight variations observed.
The spatial patterns of χ2 and averaging kernels do not
resemble each other. For the highest quality retrievals, we can conclude
that the spatial variations in information content do not correlate to the
spectral radiance fit in the retrievals of Kahn et al. (2014). The trends
are nominally 1–2 K of cooling in Tci, an increase of a few tenths to
∼ 1 µm in rei, with the largest values in proximity
to tropical convection. In contrast, τi is decreasing except for
regions with high frequencies of convection (ITCZ, W. Pacific Warm Pool, and
S. Indian Ocean). The trend in ice frequency is similar to τi but
is spatially smoother and the magnitude is much larger.
The statistical significance of three wide latitude bands in the tropics,
SH, and NH extratropics are calculated following the methodology of Santer
et al. (2000). Ice frequency decreases by about 1 % with respect to the
total frequency of all AIRS pixels in the SH and NH extratropics
(significant at 95 %), and much smaller in the tropics (not significant at
95 %). The ice water path (IWP) decreases by 0.4–0.8 g m-2 in the SH
(marginal significance at 95 %) and NH (significant at 95 %), and
increases about 0.25 g m-2 in the tropics (not significant at 95 %).
The statistical significance of IWP is lower than τi because of
compensating increases in rei at all latitudes (significant at 95 %).
Impacts of assumed radiometric drift on cloud property trends were
determined for a few perturbation experiments. The perturbation experiments
fall significantly short of explaining the magnitude of the observed ice
property, AKs, and χ2 trends, although some fractional
contribution to trends cannot be ruled out.
Surface based and aircraft in situ observations have demonstrated that
active periods of deep convection are associated with larger rei at
cloud top (e.g. Protat et al., 2011; van Diedenhoven et al., 2014). Values
of AIRS rei are plotted against Tcld for opaque, non-opaque, and
multi-layer clouds separately; only 1.9 % of ice cloud tops are identified
as opaque. Values of rei are 3–9 µm larger (depending on cloud
top temperature) for opaque clouds that are treated as a proxy for deep
convection. A weaker dependence of rei with Tcld < 210 K
occurs and shows similarity to van Diedenhoven et al. (2016). Opaque clouds
exhibit some dependence of rei on wind speed. Opaque clouds also
exhibit a 2–4 µm jump as CWV increases from 45 to 65 mm, and a similar
pattern is observed for Tsfc. Non-opaque clouds do not exhibit much of
a dependence on CWV and Tsfc.
The differences in rei at the tops of opaque, non-opaque and
multi-layer ice clouds due to thermodynamic and dynamic variability suggest
distinct cloud regime behaviour. The low frequency of occurrence of opaque
ice clouds (1.9 %) implies that temporal changes in deep convective
behaviour are likely overwhelmed by non-opaque and multi-layer behaviour.
Secular changes in the joint histograms themselves, and, furthermore, changes
in the frequency of occurrence within the joint histogram bins, must be
quantified to deduce if trends or variability in CWV, rain occurrence,
surface wind speed, and other relevant geophysical parameters such as
low-level convergence (e.g. Stephens et al., 2018) could explain in part or
whole secular trends in rei.
Comparisons between DARDAR and AIRS rei are made for single-layer
cirrus, cirrus above weak convection, and cirrus above strong convection.
AIRS is typically 1–3 µm larger than DARDAR with about 1 µm
difference between IR/MW and IR only. AIRS and DARDAR exhibit an increase in
rei as convective cloud appears below cirrus layers suggesting that the
mechanism described in Stephens (1983) may be operating in these
differences. However, secular trends in convective aggregation, convective
mode, the probability distribution of vertical velocity, and ice nucleation
and growth mechanisms that may change within the changing character of
convection (e.g. Chen et al., 2016), are highly complex and uncertain in
observations. The pixel-level collocations of AIRS, AMSR, and DARDAR are a
first attempt at identifying atmospheric processes that could be responsible
for secular trends in ice cloud properties. Further research is necessary to
quantify the links between trends in ice cloud microphysics shown in this
work with cloud responses in climate model simulations.
Current and future work will require the full AIRS and AMSR observational
record to be collocated at the pixel scale to derive secular trends within
the joint histograms. This will enable the assessment of whether there are
preferred cloud, thermodynamic, or dynamical regimes that exhibit either
strong or weak trends, or perhaps whether opposing trends among regimes
exist. Further research is necessary to determine if retrieval biases may
explain in part the variability exhibited in the multi-layer cloud category.
The results presented herein were restricted to the July–August time period
and ±18∘ latitude oceans. Cursory inspection of other
latitudes and months (not shown) can exhibit different behaviour and may
indicate fundamental differences in time changes of ice cloud properties
between tropical and extratropical cloud processes. Lastly, this
investigation does not include the analysis on cloud thermodynamic phase
differences (liquid vs. ice), cloud top temperature, and ECF, as those
results will be reported elsewhere.
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/, last access: 1 July 2017) (Teixeira,
2013). The AMSR-E/AMSR-2 Version 7 data sets were processed by and obtained
from Remote Sensing Systems (http://www.remss.com, 17 July 2017; Wentz
et al., 2014a, b). The AIRS and AMSR data were collocated together using the
data sets provided by Fetzer et al. (2013). The DARDAR data sets were
processed by and obtained from the ICARE Thematic Center
(http://www.icare.univ-lille1.fr/archive/). The data and code used in
this investigation are available upon request from the lead author
(brian.h.kahn@jpl.nasa.gov).
Figure A1 shows that the AIRS algorithm differences in IR only (AIRS2RET)
minus IR/MW (AIRX2RET) on the frequency of ice phase clouds over the global
oceans are less than 1 %. This implies that virtually the same sets of
pixels are identified as ice in the two retrievals. The impacts on Tci
are on the order of 1–2 K difference with IR only slightly cooler
especially in the tropics. Some subtle and spatially coherent changes in
τi of ∼ 0.1 are observed with IR only thinner. The
rei is larger by 0.5–1.0 µm in IR only compared to IR/MW but a
few of the extratropical storm track regions exhibit an opposite sign. To
summarise, for the IR only retrieval, τi is lower by 0.1,
rei is larger by 0.5–1.0 µm in most areas, and Tci is lower
by 1–2 K depending on the latitude.
The differences in IR only (AIRS2RET) minus IR/MW
(AIRX2RET) for the same time period as described in Fig. 1 for Tci,
rei, τi, and ice cloud frequency. Note that the minimum and
maximum differences may exceed those indicated in the colour bar.
Figure A2 shows the same differences as Fig. A1, except for the
Tci, rei, and τi AKs, and the
χ2 fitting parameter. The differences in AKs and χ2 are
generally small but some notable regional differences are observed. The
Tci AK is mostly lower for the IR only compared to IR/MW and is
consistent with added information provided by the microwave channels in the
AMSU instrument. The rei AK is larger for the IR only compared to
IR/MW but the difference is an order of magnitude smaller than the
Tci AK difference. The τi AK difference is small
and does not exhibit coherent cloud-regime dependence except for the Saharan
air layer. The difference in χ2 shows slightly worse fitting for the
IR only; this is also consistent with added information provided by microwave
channels in the AMSU instrument. Most of the globe is 0.1–0.2 higher for IR
only, which is about 5 % of the mean value of χ2= 3–4 for
retrievals restricted to QC = [0, 1].
Same as Fig. A1, except for the Tci, rei, and τi AKs, and the χ2 fitting parameter. Note that the minimum
and maximum differences may exceed those indicated in the colour bar.
BHK led the design of the research, performed data analysis,
and wrote the manuscript. HT led the DARDAR data analysis and helped write
the manuscript. GLS helped design the research with regard to investigating
cloud top microphysical responses to convection. QY helped design the data
analysis. JD provided DARDAR data and expertise on its use in this
investigation. GM provided collocated data sets used in the data analysis.
EMM performed offline retrievals of AIRS data using adjusted radiance
calibration and helped design the assessment of calibration drift on cloud
property trends. AJH provided in situ data and helped write the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
AMSR-E and AMSR-2 data products are produced by Remote Sensing
Systems and were sponsored by the NASA AMSR-E Science Team. The collocation methodology
matching AIRS/AMSU with AMSR-E/AMSR-2 and DARDAR used index files generated
through the NASA Earth Science Program NASA's Making Earth Science Data
Records for Use in Research Environments (MEaSUREs) program. 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. We thank two anonymous
reviewers for very constructive comments on the manuscript, George Aumann,
Steve Broberg, and Tom Pagano for detailed discussions about AIRS
calibration, and Noel Cressie for guidance about testing for statistical
significance. 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. Edited by: Qiang Fu Reviewed by: two
anonymous referees
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