Clouds are highly variable in time and space, affecting climate sensitivity and
climate change. To study and distinguish the different influences of clouds on
the climate system, it is useful to separate clouds into individual cloud
regimes. In this work we present a new cloud classification for liquid water
clouds at cloud scale defined using cloud parameters retrieved from combined
satellite measurements from CloudSat and CALIPSO. The idea is that cloud
heterogeneity is a measure that allows us to distinguish cumuliform and
stratiform clouds, and cloud-base height is a measure to distinguish cloud
altitude. The approach makes use of a newly developed cloud-base height
retrieval. Using three cloud-base
height intervals and two intervals of cloud-top variability as an inhomogeneity parameter
provides six new liquid cloud classes. The results show a smooth transition
between marine and continental clouds as well as between stratiform and
cumuliform clouds in different latitudes at the high spatial resolution of
about 20 km. Analysing the micro- and macrophysical cloud
parameters from
collocated combined MODIS, CloudSat and CALIPSO retrievals shows distinct
characteristics for each cloud regime that are in agreement with expectation
and literature. This demonstrates the usefulness of the classification.
Introduction
Clouds affect the climate system in a wide variety of ways. They influence
outgoing solar and terrestrial radiation and therefore the Earth's temperature, produce
precipitation, transport heat and moisture, and interact with the surrounding
atmosphere including aerosols on different time and spatial scales. They exhibit a high
variability from minutes to days in time and metres to thousands of kilometres
in space. Because of their complexity, the response of clouds to perturbations
remains one of the largest uncertainties in climate prediction
e. g.,. Different cloud
regimes have different impacts on climate. Low clouds and optically thick clouds
contribute to cooling the climate system because their high-albedo effect
dominates their effect on emitted longwave radiation back to space
, whereas thin medium- and high-altitude clouds
contribute to warming the climate system .
Consequently, since the early start of meteorological research, clouds have been classified
. A fundamental distinction is usually made by
cloud altitude often in three classes of low, middle and high
tropospheric clouds; as well as the separation of stratiform
and cumuliform clouds .
Cloud types are often defined using the dynamical state of the atmosphere, or, alternatively,
using cloud parameters themselves, or a mix of both. Dynamical regimes are
often based on large-scale, mid-tropospheric vertical velocity
(ω500hPa) derived from meteorological model reanalysis
e.g.. Also the lower-tropospheric
stability LTS; or, alternatively, the estimated inversion
strength EIS; or estimated low-level cloud fraction have been used to characterise
low-level clouds; some studies have used a combination of mid-tropospheric
vertical velocity and LTS or EIS .
use the sea level pressure to define three different dynamical cloud types in
the northern midlatitudes, and combine sea surface
temperature and ω500hPa. As a prime example of the other
method, the International Satellite Cloud
Climatology Project (ISCCP) cloud classification uses cloud optical thickness,
τc, and cloud-top pressure ptop to separate 49 or, in a simplified version, nine cloud types . By applying a clustering algorithm to these
ISCCP cloud classes, defined four cloud regimes in the
tropical western Pacific using τc, ptop and the
total cloud cover ftot. Extending and simplifying this approach
for climate model evaluation, selected different
cloud regimes in particular geographical regions using cloud albedo,
ptop, and total cloud cover, ftot. Such a regime definition was also found useful in the
context of the analysis of aerosol optical depth–cloud droplet concentration using satellite data in
the study of .
We are interested in statistically analysing aerosol–cloud interactions in
satellite data beyond the aerosol–droplet concentration relationship. In order
to identify aerosol–cloud interactions, suggested
that it is necessary to do so for individual cloud regimes see also. However, a
dynamical regime definition is hampered by the problem of a rather coarse
resolution of the reanalysis data (50–100 km currently) and the problem that
thus such
cloud regimes are not able to separate clouds at the scale of individual cloud
regimes . In turn, the approach of using the ISCCP cloud
definition e.g. is not useful to analyse
aerosol–cloud interactions if one is interested in analysing how cloud
fraction and cloud albedo co-vary with the aerosol since these quantities are
fixed by the clustering method.
In this work we present a new cloud classification at cloud scale using the
cloud-base height indicating meteorological conditions and separating cloud
altitude and the cloud-top variability as an inhomogeneity parameter
separating between stratiform and cumuliform clouds. The collocated satellite data and the high spatial resolution defined as the Clouds and the Earth's Radiant Energy System (CERES) footprint size of about 20 km allow a cloud-class-based analysis of cloud parameter reflecting the high spatial and temporal variability.
Satellite data
Our studies rely on retrievals of two active satellite instruments, the Cloud
Profiling Radar CPR; on board CloudSat and the Cloud-Aerosol LIdar with
Orthogonal Polarization CALIOP; on board Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observations (CALIPSO), as well as the passive
Moderate Resolution Imaging Spectroradiometer MODIS; instrument on board
Aqua. These satellites are part of the A-Train satellite constellation
, a group of satellites flying along nearly the same polar orbital track crossing the Equator at about 13:30 local time and
providing a global data coverage between 82∘ N and 82∘ S
. The sun-synchronous polar orbit repeats the same
ground track every 16 d, retaining its size and shape
.
The CALIPSO CloudSat CERES and MODIS merged product (CCCM dataset) contains
collocated data from CALIOP, CPR, MODIS and the broadband radiometer CERES, providing comprehensive
information about clouds, aerosols and radiation fluxes in high vertical and
horizontal resolution , merged to the CERES
footprint of about 20 km horizontal size. It is this combined
product that is the basis for our analysis. The collocation of these various
retrievals with different spatial resolution requires a two step process.
In the first step the vertical cloud profiles as provided in the vertical
feature mask from CALIPSO and the 2B-CLDCLASS product
from CloudSat are collocated on a horizontal
1 km × 1 km grid. Each grid point contains three vertical cloud
profiles from CALIPSO and one from CloudSat; these are used to derive cloud-top heights and cloud-base heights . With this merging
procedure about 85 % of the cloud-top heights and 77 % of the cloud-base heights are derived from CALIPSO measurements. The second step starts
with horizontally collocating the merged vertical cloud profiles with CERES
footprints of about 20 km in size by selecting the CERES footprints with maximum
overlap with the CALIPSO–CloudSat ground track. Because the horizontal
resolution of CERES is much coarser than the horizontal resolution of the
combined CloudSat–CALIPSO vertical cloud profiles, at each grid box,
CloudSat–CALIPSO clouds groups are defined to retain the statistical cloud
geometric information.
The temperature profiles included in the CCCM dataset are derived at
computational levels from the CERES Meteorological, Ozone, and Aerosol (MOA)
analysis. They come from the Global Modeling and Assimilation Office (GMAO)
Goddard Earth Observing System (GEOS)-4 data
assimilation system reanalysis before November 2007 and GEOS-5
thereafter
with a temporal resolution of
6 h and a spatial resolution of 1∘×1∘.
A key parameter used in this paper is cloud-base height,
Hbase. This relies on a new retrieval on the basis of
CALIPSO lidar described by that assumes that
Hbase is constant in a scene and that the lowest lidar
return within columns that do not fully attenuate the lidar beam is
representative for Hbase.
Besides Hbase, cloud-top height,
Htop, is also used as derived from the CALIPSO in the merged
vertical cloud profiles. thoroughly examined the
cloud-base altitude using ground-based ceilometer data as reference. The
root-mean-square error on retrieved cloud-base height was in the range of 400
to 700 m and biases much lower at 5 to 50 m. Both parameters are defined here with
respect to the surface altitude.
Cloud-top temperature Ttop is taken from MODIS and
CloudSat–CALIPSO as derived from their respective Htop assigned
to Ttop using the temperature profile. Further, cloud optical
thickness, τc, and, cloud droplet effective radius,
reff, as derived from MODIS measurements are
analysed. We use retrievals that apply the 3.7 µm channel .
Daytime data are used, and high latitudes (polewards of 60∘) are
excluded to avoid biases in the retrieved cloud optical properties from MODIS .
Our studies investigate only liquid water clouds. These are defined as clouds
where Ttop values derived from both MODIS and CloudSat–CALIPSO are
larger than 273K.
Definition of the cloud classes
The liquid cloud classes are defined at the scale of the CERES footprint size
of about 20 km as horizontal resolution, at which the CCCM
dataset is generated. This results in one liquid cloud type per footprint
after the cloud classification process described in the following.
Cloud-base height
The first cloud parameter selected to define the cloud classes is cloud-base
height over ground Hbase. This is consistent with the WMO
definition of cloud altitude . Hbase is used
from the retrieval approach of , applied to the CCCM
dataset. Hbase of multilayer clouds is defined as the lowest
Hbase in this cloud group. In Fig.
the global distribution of the averaged Hbase of the four
completely available years of CCCM data from 2007 to 2010 is shown. One can
see a clear contrast between land and ocean and between higher and lower
latitudes. The lowest Hbase values are located over the ocean in the
storm track regions in mid-latitudes, whereas the highest Hbase
can be found over land for example over the Amazon rain forest or
Australia.
To separate different cloud-base height classes, the probability density
function (PDF) of the global spatio-temporal distribution of
Hbase shown in Fig. is used. Three cloud-base height classes are selected which are the round numbers that
approximately correspond to the terciles of the
distribution, which is the median ±300 m. With this definition,
low clouds are defined as those with Hbase≤350m, middle clouds 350m<Hbase≤950m, and high (liquid)
clouds Hbase>950m.
Time-average distributions
of daily values for the 2007–2010 period of CALIPSO retrievals as
reported in the CCCM dataset for liquid-water clouds for (a) cloud-base
altitude using the retrieval method of and
(b) cloud-top height
variability.
Global distribution of cloud parameters
PDFs of the spatio-temporal distributions of (a) of cloud-base
height (m) and (b) cloud-top height variability (%), for daily data for the 4-year period 2007–2010. The red lines indicate the median of the PDFs, and the two blue lines
in (a) represent the borders of the cloud-base classes.
Cloud-top variability as inhomogeneity parameter
Only using Hbase one cannot distinguish between cumuliform and
stratiform clouds as proposed by the WMO. We propose using an inhomogeneity
parameter and define stratiform clouds as homogeneous and cumuliform
clouds as inhomogeneous clouds. Cloud optical thickness, τc,
is often used to describe the inhomogeneity of a cloud or cloud field and to
separate clouds into homogeneous and inhomogeneous clouds. The ISCCP cloud
classification uses τc itself to classify stratiform and
cumuliform clouds, defining clouds with high τc as
stratiform . In a more advanced approach, the
horizontal variability of τc derived from MODIS measurements
is defined as the cloud inhomogeneity parameter to distinguish stratiform and
cumuliform clouds .
With the definition proposed here for cloud regimes, however, we aim to
analyse adjustments to aerosol–cloud interactions
e.g., i.e. the response of cloud liquid water
path to perturbations in cloud droplet concentrations
e.g.. It is thus impossible to use τc to
define cloud regimes, since this would constrain liquid water path.
We thus propose defining cloud inhomogeneity based on the cloud-top height
variability. Cloud-top height is related to τc and also Hbase, but its
variability is independent of it. The idea is that clouds with horizontally
homogeneous top heights are more stratiform, and those with horizontally
inhomogeneous top heights are more cumuliform. Cloud-top height variability is
defined here as the average relative deviation of cloud-top height from its
footprint mean. Preliminary analysis of the
cloud-top height variability at the scale of a CERES footprint, given the MODIS
resolution, often is not well defined, at least in broken-cloud
situations. This is due to the too low number of MODIS retrievals within a
CERES footprint. Thus, the variability in the two adjacent footprints
(adjacent along the A-Train ground track), in addition
to the footprint at nadir below the satellite, is used, and the average
cloud-top height variability weighted by cloud occurrence in the three
footprints is used.
In Fig. , the global distribution of the mean cloud-top
variability from 2007 to 2010 is shown. No clear land–ocean contrast is seen,
but the distribution is characterised by a latitudinal gradient with the
highest values of cloud-top variability in the tropics in the shallow cumulus
regions and along the Intertropical Convergence Zone (ITCZ). In the shallow
cumulus regions towards the western parts of the oceans in the sub-tropics,
the variability is – with values between 20 % and 30 % – about as large as in
the tropics for the Indian and Pacific oceans. It is, however, somewhat lower
in particular in the southern Atlantic Ocean. At mid-latitudes to high
latitudes the mean cloud-top variability decreases in general, compared to the
tropical regions. Although at low latitudes the stratocumulus covers western
southern Africa, South America and North America show the smallest mean cloud-top
variabilities. These features are consistent with the hypothesis that the heterogeneity
metric is useful to distinguish stratiform and cumuliform clouds. However, it
is not a perfect classification into stratiform and cumuliform clouds, e.g. for
suppressed shallow convection where the cloud-top altitude is dictated by
subsidence. The PDF of
the cloud-top variability shown in Fig. is used to make this
distinction. The median at about 11 % separates the more stratiform (homogeneous) clouds from more cumuliform (inhomogeneous) clouds, creating two inhomogeneity cloud classes.
Relative frequency of occurrence of the six cloud classes separated
using the three classes of cloud-base height and two classes of cloud-top
height heterogeneity. (a, b) Low, (c, d) mid-latitudes and (e, f) high clouds and
(a, c, e) homogeneous (stratiform) and (b, d, f) heterogeneous
(cumuliform) clouds.
Geographical distribution of the cloud regimes
The three cloud-base classes and two inhomogeneity cloud classes are now
combined to define six new liquid water cloud types or cloud regimes. The
global distribution of relative frequencies of occurrence (RFOs) of these cloud
classes is shown in Fig. . Clouds with low and middle base altitude tend to be more
heterogeneous, and clouds with high base altitude more are homogeneous. Low and
middle clouds tend to occur over ocean rather than land, although the contrast
is less strong for stratiform clouds.
PDFs of the spatio-temporal distribution of 4 years (2007–2010) of MODIS
retrievals as reported in the CCCM dataset at the 20 km × 20 km
horizontal resolution at nadir below the A-Train satellite constellation,
between 60∘ S and 60∘ N. Liquid clouds are selected. Light green – low, homogeneous
clouds; dark green – low, heterogeneous clouds; red – middle, homogeneous
clouds; purple – middle, heterogeneous clouds; light blue – high,
homogeneous clouds; dark blue – high, heterogeneous clouds. (a) Droplet number concentration, Nd (cm-3); (b) liquid water
path, L (g m-2).
Average (bold) and median (italic) values of Nd and L
for the six particular cloud classes derived from their PDFs (Fig. ).
Most of the liquid water clouds with low Hbase are located over
the ocean in the storm track regions in middle to high latitudes. Only a small
amount of the low clouds occur in low latitudes. Homogeneous low clouds
concentrate in mid-latitudes, especially in the Southern Hemisphere and in
narrow coastal stripes west of North and South America and north and southern
Africa, indicating parts of the typical stratocumulus clouds in these regions
with very low Hbase. The occurrence is highest over regions with
relatively low sea surface temperatures. The inhomogeneous clouds in this cloud-base class occur mainly in the mid-latitudes in both hemispheres, though a small amount can be found in tropical regions, especially along the ITCZ in the east Pacific.
Almost all mid-level-base clouds are marine clouds located over the oceans in low
latitudes. Especially in the tropics along the ITCZ in the Indian Ocean and in
the west Pacific, this cloud class is frequent. Inhomogeneous clouds in this cloud-base class extend in low latitudes around the entire globe, leaving out the stratocumulus decks and concentrating mainly in shallow cumulus regions and along the ITCZ.
In contrast to the cloud-base classes of lower Hbase, most of the
high clouds occur over land. However, a non-negligible amount can be found
over the ocean in low latitudes. Only in higher latitudes over the ocean and
in the stratocumulus regions in the east Pacific and east Atlantic are almost no
clouds with Hbase>950m found. A significant
amount of homogeneous clouds in this cloud-base class are located over land
with maxima over southern Africa, Australia and northwest Asia. Over the ocean
they cover two bands in both hemispheres at around 30∘, leaving out
roughly the areas covered by the inhomogeneous mid-level-base clouds. The
inhomogeneous high-base clouds occur mainly over land with maxima over rain forest regions in South America and middle Africa. Over ocean these clouds can be found equally distributed to inhomogeneous clouds in low latitudes except in the stratocumulus decks.
Cloud properties in the six cloud regimes
The key reason to define cloud regimes is that clouds are supposed to show
different characteristics in these regimes. The hypothesis is that their
response to perturbations, e.g. of aerosol concentrations, can possibly be identified more clearly in analyses
when focusing on individual regimes . The goal of this
section is to demonstrate the usefulness of the separation in cloud regimes
according to the six classes defined in the previous section. To this end, the
two main bulk cloud quantities are investigated, namely the cloud liquid water
path, L, and the cloud droplet number concentration, Nd. Both are
computed on the basis of the MODIS bi-spectral retrievals as reported in the
CCCM dataset. Nd is computed from the retrieved cloud optical
thickness and cloud-top droplet effective radius following
and the parameters defined in .
Cloud droplet number concentration is a key quantity when assessing
aerosol–cloud interactions and cloud radiative effects
e.g.. It depends on chemical composition and size
distribution of the precursor aerosol, as well as cloud-base vertical velocity
. It is also very much influenced by cloud and
precipitation microphysical processes as well as cloud-top and
cloud-side entrainment.Figure shows the global PDF of Nd for the
six cloud regimes, and Table summarises the mean and
median values. The values are clearly distinct between the six classes. One
key feature is that for all cloud-base heights, the homogeneous clouds contain
fewer droplets than the heterogeneous ones. This is consistent with the
expectation that heterogenous, convective clouds are driven by stronger
updraughts. In terms of the altitude classes, low-base clouds show more
droplets than mid-level-base clouds. This is a feature of the geographical
distribution: both types occur mostly over oceans, but the mid-level-base clouds
are more prevalent over the pristine parts of the oceans. The highest Nd is observed for the high clouds, due to the fact that high clouds mostly
occur over continents.
Also in terms of liquid water path, L, the clouds in the six classes are
distinct. Homogeneous, i.e. more stratiform, clouds, are thinner than the
heterogeneous counterparts in each altitude class. This is consistent with the fact that
convective clouds tend to develop more in the vertical, compared to stratiform
clouds. Among the cloud altitude classes, L is smallest for mid-level-base clouds
and largest for high clouds. Note that these clouds are only the liquid-water
clouds, so that the vertical development is limited by the 0 ∘C level in
our definition. Here, low clouds have the largest potential to develop in the
vertical and yet remain liquid. Over land, where high cloud bases are
prevalent, the 0∘ level is reached at higher altitudes, allowing these
clouds to develop further in the vertical. Due to the choice made here to
investigate only liquid water clouds, the behaviour is different in different
latitudes and seasons, due to the fact that the freezing level is at lower
heights in higher latitudes and winter. More detail of the geographical
variation in the cloud regimes is provided in the Appendix, where the cloud
properties for the different regimes are compared for land vs. ocean and
tropics vs. extratropics.
Summary and conclusions
The goal of the present study was to overcome limitations in the definition of
cloud regimes. Such a definition is desirable, for example, in the context of studying
aerosol–cloud interactions. Previous approaches were at the
comparatively very coarse resolution of either meteorological re-analyses or used
cloud parameters that are, however, the ones to study in aerosol–cloud
interaction and thus cannot be used to stratify the data. Also, previous
approaches were not very compatible with the standard WMO definitions. Here,
we propose six cloud regimes for liquid clouds, separated by
(i) cloud-base height and (ii) cloud-top height variability as an inhomogeneity
parameter. Both parameters are derived from active remote sensing satellite
measurements and are thus available at the scale of satellite retrievals.
They are evaluated using a 4-year (2007–2010) dataset of combined A-Train satellite
data in the CCCM dataset. A new approach to retrieve cloud-base altitude from
spaceborne lidar has recently been developed and applied here. The
geographical distributions of the frequency of occurrence of the six cloud
regimes shows desirable features: oceanic and continental clouds are smoothly
separated, and typical cloud regimes such as stratocumulus decks are readily
identified. In order to demonstrate the usefulness of the cloud regimes, cloud
parameters not used to define the regimes, but useful to study,
e.g. aerosol–cloud interactions, have been analysed. The selected parameters
are cloud droplet concentration and cloud liquid water path. From the analysis
it is evident that the cloud regimes show different characteristics in both
quantities; i.e. the cloud types are clearly distinct. In particular, expected
features of homogeneous (interpreted as stratiform) and heterogeneous
(interpreted as cumuliform) clouds appear, as do features related to
predominant aerosol sources and boundary-layer dynamics.
In future work, the study could be enhanced to study all clouds and not just
the liquid-water ones as done in the present study. While the cloud
classification method could be adapted in a straightforward way, this would
require a new analysis of how the classes differ in their characteristics. The
current study is limited by the fact that it can only be applied to the
ground track below the A-Train lidar and radar retrievals. However, approaches
exist to infer cloud-base altitude from passive, multi-angle measurements as well
. An adaptation of our method to these swath data would allow the
analysis of much larger data volumes.
Regime analysis by large-scale region
More detail about the characterisation of Nd and L by cloud regime
is provided in Figs. and
and summarised in
Table . The PDFs of Nd
(Fig. ) and L
(Fig. ) are separated into oceanic and
continental surfaces and between the tropics and extratropics, respectively. The
droplet concentrations are somewhat lower over ocean compared to the global
mean but are by factors of 2 to 4 higher over land (there are many more data
points for liquid-water cloud retrievals over ocean than over land) in all
cloud regimes,
consistent with the expectation. The result of smaller Nd for
inhomogeneous vs. homogeneous clouds holds for all categories over both land
and ocean. Liquid water path on average is slightly
lower over ocean and slightly larger over land, except for the high clouds where
things are rather similar. That inhomogeneous clouds have higher L holds
true over both land and ocean, with the exception of the low clouds over
land. Clouds in the tropics have larger Nd than in the extratropics,
and they also have larger L (except for those with high cloud bases).
As Fig. a, but separately for (a) ocean, (b) land, (c) the tropics (20∘ S–20∘ N) and (d) the extratropics (40∘ S–60∘ S and 40–60∘ N).
As Fig. b, but separately for (a) ocean, (b) land, (c) the tropics (20∘ S–20∘ N) and (d) the extratropics (40–60∘ S and 40–60∘ N).
Average (bold) and median (italic) values of Nd and L for the six particular cloud classes derived from their PDFs, but separated, but separated for ocean, land, the tropics (20∘ S–20∘ N) and the extratropics (40–60∘ S and 40–60∘ N).
All analyses are based on the publicly available CCCM
dataset .
Author contributions
CU and JQ designed the research with input from all
authors. OS and JM prepared the satellite data. CU and KB performed the data
analysis with support from all other authors. CU and JQ wrote the manuscript
with input from all authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors are grateful to the satellite retrieval science
teams for the CALIPSO, CloudSat and MODIS instruments and in particular Seiji Kato and his coworkers at NASA Langley for
compiling the CCCM dataset and helping with questions. The authors wish to
thank the two anonymous reviewers for their constructive comments. We acknowledge support from Leipzig University for Open
Access Publishing.
Financial support
This research has been supported by the European Research Council (grant no. QUAERERE (306284)).
Review statement
This paper was edited by Armin Sorooshian and reviewed by two anonymous referees.
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