ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-14351-2018 Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 2: Controls on the ice crystal number concentrationControls on the NiGryspeerdtEdwarde.gryspeerdt@imperial.ac.ukhttps://orcid.org/0000-0002-3815-4756SourdevalOdranhttps://orcid.org/0000-0002-2822-5303QuaasJohannesDelanoëJulienKrämerMartinahttps://orcid.org/0000-0002-2888-1722KühnePhilippSpace and Atmospheric Physics Group, Imperial College London, London, UKInstitute for Meteorology, Universität Leipzig, Leipzig, GermanyLaboratoire Atmosphères, Milieux, Observations Spatiales/IPSL/UVSQ/CNRS/UPMC, Guyancourt, FranceForschungszentrum Jülich, Institut für Energie und Klimaforschung (IEK-7), Jülich, Germanynow at: Laboratoire d'Optique Atmosphérique, Université Lille 1, Villeneuve d'Ascq, FranceEdward Gryspeerdt (e.gryspeerdt@imperial.ac.uk)9October2018181914351143705January201831January20183September20184September2018This 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/18/14351/2018/acp-18-14351-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/14351/2018/acp-18-14351-2018.pdf
The ice crystal number concentration (Ni) is a key property of
ice clouds, both radiatively and microphysically. Due to sparse
in situ measurements of ice cloud properties, the controls on the
Ni have remained difficult to determine. As more advanced
treatments of ice clouds are included in global models, it is becoming
increasingly necessary to develop strong observational constraints on the
processes involved.
This work uses the DARDAR-Nice Ni retrieval described in Part 1
to investigate the controls on the Ni at a global scale. The
retrieved clouds are separated by type. The effects of temperature, proxies
for in-cloud updraft and aerosol concentrations are investigated.
Variations in the cloud top Ni (Ni(top))
consistent with both homogeneous and heterogeneous nucleation are observed
along with differing relationships between aerosol and
Ni(top) depending on the prevailing meteorological
situation and aerosol type. Away from the cloud top, the Ni
displays a different sensitivity to these controlling factors, providing a
possible explanation for the low Ni sensitivity to temperature
and ice nucleating particles (INP) observed in previous in situ studies.
This satellite dataset provides a new way of investigating the response of
cloud properties to meteorological and aerosol controls. The results
presented in this work increase our confidence in the retrieved
Ni and will form the basis for further study into the processes
influencing ice and mixed phase clouds.
Introduction
Clouds play a central role in the Earth's energy budget, as they are responsible
for large variations in the reflected shortwave and emitted longwave
radiation . The response of clouds to changing greenhouse
gases and aerosols remains one of the largest uncertainties in understanding
past and future climate changes . Significant advances have
been made into modeling and observing the role of aerosols in liquid clouds
e.g.,
especially through the use of retrievals of the cloud droplet number
concentration e.g., but the impact of
aerosols on high clouds remains uncertain .
A large part of this uncertainty comes from the difficulty
in retrieving cirrus cloud properties at a large enough scale to separate the
roles of individual factors controlling the ice crystal number concentration (Ni).
A key microphysical property of ice clouds, the Ni links the
aerosol environment to dynamic effects driving cloud updrafts and the
generation of supersaturation . Through changes in the
ice crystal size, changes in the Ni can have far-reaching
implications for a cloud, impacting the radiative ,
precipitation and cloud lifetime properties . The
Ni is often used as a prognostic variable in two-moment cloud
microphysics schemes e.g.. This highlights a
requirement to understand the controls on the Ni in order to improve
our understanding and parameterization of cloud processes. While aircraft
measurements of the Ni exist, they are restricted in space and
time. They can also be affected by shattering of ice crystals at the instrument
inlet and difficulties in measuring
the smallest crystals . In this paper, the new DARDAR-Nice
satellite dataset described in Part 1 allows the
processes that control the Ni to be investigated globally.
It is known that the temperature plays a strong role in determining the ice
crystal nucleation rate. The homogeneous nucleation rate is a strong function
of temperature and supersaturation , with atmospherically
relevant nucleation only taking place at temperatures colder than 235 K.
This strong temperature dependence in the nucleation rate does not
necessarily correspond to a strong temperature dependence in the Ni. A weak Ni temperature
dependence was found by . found similar
results, with a slight reduction in the Ni for the coldest
measurements. Higher Ni values have been observed at cold
temperatures during ATTREX than in
, leading to a weak combined temperature dependence. However,
using different datasets targeting different cloud types,
and both showed an increase in Ni with
decreasing temperature, demonstrating that there is still considerable
uncertainty regarding the Ni temperature dependence.
The in situ homogeneous nucleation of ice crystals is also dependent on the
supersaturation , which is often generated through
cooling due to vertical air motion. Large-scale updrafts cannot reproduce
observed cirrus properties on their own, the smaller scale variation in
updraft provided by gravity waves is necessary and is
occasionally able to produce cirrus in regions of large-scale subsidence
. These small-scale updrafts can produce
Ni values as high as 50 000 L-1, highlighting
the important role that vertical motion can play in determining the Ni.
Although ice can form by in situ nucleation, many ice crystals also form
through the freezing of liquid condensate. This liquid-origin cirrus often
originates from high updraft regions in mixed-phase clouds, forming thicker
cirrus than those composed of in situ ice (; ).
Synoptic-scale updrafts can also produce liquid-origin cirrus in the
midlatitudes . The Ni formed through these
liquid-origin processes is also strongly dependent on the cloud-scale
updrafts, with higher updrafts maintaining higher ice supersaturations
and producing larger Ni values .
Aerosol also plays a role in determining the Ni, although its
impact is complicated by variations in ice crystal nucleation pathways and
aerosol properties. While any particle can theoretically act as a homogeneous
nucleation center given a high enough supersaturation, in practice these
aerosols are often hydrophilic liquid aerosols . Increases in
the aerosol number can result in an increase in Ni through
increased homogeneous nucleation. However, in many situations, the
Ni is limited by dynamical concerns, which limits the impact of
aerosols .
A second class of aerosols, known as ice nucleating particles (INP) are able
to nucleate ice heterogeneously and can freeze liquid water droplets at
temperatures warmer than 235 K. At these warmer temperatures, the presence
of INP will often control the Ni near nucleation locations
, as they form the sites for creating an ice crystal,
although the nucleating ability of these INP is also a strong function of
temperature . As heterogeneous nucleation can take place at a
lower supersaturation than homogeneous nucleation, the introduction of INP
has the ability to prevent homogeneous nucleation by depressing the
supersaturation. As the Ni produced through homogeneous
nucleation events is typically higher than the INP concentration (and so the
Ni from heterogeneous nucleation), this implies that the
introduction of INP into a clean atmosphere can reduce the Ni. In situ and satellite studies
have provided some evidence for a possible decrease in Ni
with increasing aerosol based on regional and hemispheric
differences in ice crystal properties, although it has proved difficult to
conclusively link these Ni changes to a change in INP.
The relative role of heterogeneous and homogeneous nucleation in the
atmosphere is unclear, making it difficult to develop observational
constraints on the impact of aerosols on the Nie.g.. In addition,
changing conditions over the life cycle of a cloud can result in a switch
between nucleation mechanisms ; nucleation is also not the only
control on the Ni. The rarity of INP suggests that other
processes, such as ice multiplication, are required to explain the
Ni observed in the lower atmosphere .
These four factors (temperature, supersaturation/updraft, ice origin and
aerosol environment) are all thought to influence the Ni in high
clouds, but significant uncertainties remain in assessing the role of
these factors on the Ni. First, although they have been
investigated using aircraft and balloon measurements ,
the ice origin and in-cloud updraft are difficult to
determine from observations at a global scale and over a significant period
of time. A recent classification has shown some skill
at determining these quantities when compared to a convection permitting
model and is used in this work to account for this issue.
Second, the Ni is a difficult property to measure at a global
scale. Aircraft measurements are limited in space and time and have been
afflicted by shattering of crystals on the tips of measurement probes,
casting doubt on some earlier measurements of the Ni. Additionally, due to the highly
variable nature of cirrus clouds and their strong sensitivity to
environmental conditions, it can be difficult to separate the relative roles
of aerosol and dynamics .
The retrieval presented in Part 1 of this work has
demonstrated that the Ni can be retrieved using a combined
radar–lidar retrieval, and that this compares well to new in situ aircraft
measurements where shattering is accounted for. Combined with simultaneous
retrievals of the ice water content, this allows the global distribution of
the Ni and the factors that control it to be investigated. Using
reanalysis aerosol concentrations and a proxy for the INP concentration, the
impact of aerosols on high clouds is also investigated, highlighting avenues
for future research into cirrus cloud processes.
Methods
The Ni dataset used in this work (DARDAR-Nice) has been described
in detail in Part 1 of this work , so only the main
features are outlined here. The DARDAR-Nice product is based on the DARDAR
retrieval , a combined raDAR–liDAR retrieval of ice cloud
water content (IWC) and ice crystal effective radius using data from the
CloudSat and CALIPSO satellites at approximately 13:30 LST (local solar time). Only
daytime data from the period from 2006 to 2013 is used in this work due to
constraints in the reanalysis data availability. The properties are retrieved
at a horizontal resolution of 1.7 km and a vertical resolution of 60 m.
Both the DARDAR IWC and the DARDAR-Nice Ni retrieval compare
favorably to in situ aircraft data . The
best agreement is at temperatures below -30∘C, where the retrievals are
more accurate due to the dominance of unimodal ice crystal size
distributions and reduced ambiguity in the cloud phase.
To investigate the controls on ice crystal nucleation, a more in-depth study
is performed of the Ni near the cloud top (Ni(top)).
As the cloud top is the location of the
coldest temperature in the cloud, it has the highest theoretical nucleation
rates. Although the cloud top is close to the nucleation region in wave
clouds e.g., this is not always the case and the
Ni(top) can be rapidly reduced by differential
sedimentation and entrainment e.g.. However, as the
coldest temperature, it provides a limitation on the maximum nucleated Ni
within the cloud, restricting the impact of temperature
variability due to the vertical extent of the cloud. The cloud top is taken
to be the top 120 m of the cloud and only the uppermost cloud layer (in
multilayer situations) is used to avoid issues with ice being seeded by ice
crystals falling from overlying layers. The data are also restricted to
locations where the retrieval has gone through at least two iterations,
limiting the impact of prior assumptions about the cloud structure.
Four main controls on the Ni(top) are considered in
this work: temperature, cloud-scale updraft, ice origin and aerosol.
Temperature data in this study are taken from the ECMWF ERA-Interim reanalysis
. Information about the cloud-scale updraughts and the
ice-origin (liquid/ice) cannot yet be directly obtained at a global scale
using satellites. To provide an indication of these cloud properties, the
IC-CIR classification from is used. This classification is
based on the assumed cirrus source (orographic, frontal, convective or
synoptic) and determined at 13:30 LST using cloud retrievals
from the MODIS instrument and reanalysis data. This
classification selects orographic clouds by assuming the product of the
topographic variation and the wind speed is related to the in-cloud updraft,
similar to global climate model parameterizations e.g.. The
Oro 2 and Oro 1 regimes are the regimes with the respective highest and second highest
sextiles of the parameterized in-cloud updraft. Frontal and convective
regimes are selected as connected regions of high level cloud that intersect
with reanalysis fronts and regions of large-scale updraft, respectively.
Finally, the synoptic regime is taken as a residual, with clouds being
considered synoptic if they do not fit any of the other classes. Through
comparisons with convection permitting model data and classifications based
on reanalysis data, this classification has been shown to provide useful
information on the cloud scale updrafts and the ice origin. While not a
direct retrieval of these properties, it does allow some inferences to be made
regarding the response of the Ni to these factors. The results in
this paper are restricted to daytime data, which in turn restricts it to
13:30 LST due to the orbit of the satellites used to construct the DARDAR-Nice dataset.
To investigate a possible aerosol link to Ni, we use the
“monitoring atmospheric composition and climate” (MACC) aerosol reanalysis
product , which assimilates MODIS aerosol
optical depth (AOD) retrievals into the ECMWF integrated forecast system. The
MACC product provides altitude information for aerosols along with speciation
information. Although the MACC speciation has not yet been validated, the
MODIS cloud droplet number concentration shows a stronger sensitivity to
hydrophilic aerosol types than hydrophobic aerosols, suggesting that the MACC
speciation conveys useful information about the aerosol type
. Further from sources, where ageing and other
assumptions come into play, the speciation may be less accurate. In the upper
troposphere, liquid aerosol is thought to be the dominant aerosol component
, although recent studies have noted that glassy organic
aerosols are abundant in the upper atmosphere and can act as INP at
temperatures below -55∘C . This work takes
the MACC total mass concentration as a measure of the liquid aerosol
concentration – high (low) aerosol is more (less) than 6 µg m-3,
with the caveat that this measure of aerosol may shift towards a measure of
INP at the coldest temperatures considered in this work.
The response of the Ni to aerosol is likely to vary by aerosol
type . Although MACC provides a dust speciation, it is
not clear whether this can be used to determine the presence of INP. Previous
studies have suggested that high altitude aerosol may be responsible for
glaciating clouds between 0 and -35∘C .
Based on this previous work, the glaciation of clouds between 0 and
-35∘C is used as a proxy for the occurrence of INP.
Cloud phase is determined using the DARDAR phase detection algorithm
v1.1.4;. This algorithm uses the different response of lidar
backscatter and radar reflectivity to liquid and ice hydrometeors to identify
glaciated clouds. Clouds with a peak in lidar backscatter at the cloud top
are treated as liquid or mixed phase and those with only a strong radar
return are treated as glaciated. Experience suggests that the retrieved phase
can be unreliable for clouds less than 600 m thick, so these are excluded
from this part of the analysis.
Using this cloud top phase product, a “glaciation index” is developed using
the phase retrievals between 0 and -35∘C. As approximately half of
cloud tops are glaciated at -20∘C, glaciated clouds with a top
temperature warmer than -20∘C are identified as “warm ice”, and
liquid topped clouds colder than -20∘C are identified as “cold
liquid”. The warm ice pixels are taken to indicate the presence of INP
within 100 km the approximate spatial scale of aerosol variability
from, whilst the cold liquid pixels are taken to indicate a
relatively INP-free environment. If both (or neither) are detected within
100 km, that pixel is excluded from the analysis. The cloud phase is only
used for the uppermost cloud layer when determining this INP proxy to reduce
the impact of overlying ice clouds seeding ice in lower layers. In addition,
regions with nearby higher cloud layers (those within a 10:1 glide slope) are
also excluded from the glaciation index. To reduce the impact of random
errors in the phase retrieval, two or more neighboring pixels are required
for a detection of warm ice or cold liquid. This glaciation index
is produced for each DARDAR vertical profile and is then used as a proxy for
the occurrence of INP at temperatures colder then -35∘C in that
profile. The use of cloud glaciation as an INP proxy for colder temperatures
in the atmosphere assumes that cloud glaciation is correlated to INP between
0 and -35∘C and that there is a sufficient vertical correlation in INP
occurrence. These assumptions are discussed in Sect. .
This combination of reanalysis temperature and aerosol data, along with
previously determined clouds regimes and a proxy for INP are used globally
for daytime data over the period from 2006 to 2013 to investigate the role of
different processes on the Ni.
ResultsGlobal Ni(top) distribution
The global Ni(top) distribution for crystals larger
than 5 µm (Ni(top)5µm)
displays several features that highlight the role of different cloud
processes in controlling the
Ni(top)5µm. The zonal mean
Ni(top)5µm
(Fig. a) shows a strong temperature dependence, with
significant increases in the
Ni(top)5µm as the temperature
decreases from a mean of around 40 L-1 at -35∘C to almost
140 L-1 at -70∘C. This temperature dependence is particularly
strong at temperatures colder than -40∘C in both the northern and
the southern midlatitudes. There is also a strong
Ni(top)5µm increase in the
tropics, although the initial increase in
Ni(top)5µm at -40∘C is
weaker. Although the Ni produced by heterogeneous nucleation
should also increase as temperatures decrease due to increasing INP
concentrations , this strong increase in
Ni(top)5µm at -40∘C
along with a continuing Ni increase at colder temperatures is
indicative of homogeneous nucleation, which is only significant at temperatures
below around -35∘C.
At temperatures warmer than -35∘C, the mean
Ni(top)5µm is relatively small,
especially in the Northern Hemisphere where it averages less than
50 L-1. This is expected from heterogeneous nucleation, where the
Ni(top)5µm is limited by
available INP. The mean Ni(top)5µm is much larger in
the Southern Hemisphere and the tropics, although this is skewed by the long
tail of the Ni(top)5µm
distribution (Fig. ). A phase misclassification, with
liquid topped cloud being mistaken for ice cloud might explain this
hemispheric contrast, due to the large amounts of supercooled water in the
Southern Hemisphere . A strong lidar backscatter at the cloud
top would lead to a large retrieved
Ni(top)5µm (if it was
misclassified as an ice cloud). As liquid topped clouds at sub-zero
temperatures are more common in the Southern Hemisphere, this would result in
an erroneously large mean Ni in the Southern Hemisphere.
(a) The zonal mean DARDAR-Nice cloud top Ni
(Ni(top)5µm) for crystals
larger than 5 µm as a function of temperature from DARDAR-Nice data
for the period from 2006 to 2013. Temperatures warmer than -35∘C are in
greyscale. (b) The mean
Ni(top)5µm at
-50∘C. Grey indicates missing data. (c) The number of
cloud top retrievals at -50∘C. Zonal means and maps of
Ni are available in Part 1.
The conditional Ni(top) (L-1) for
5 ∘C temperature bins for each of the main cloud regimes
(orographic, frontal, convective and synoptic) from O2, F, C
and S. The top row shows the Ni(top) for
particles greater than 5 µm
(Ni(top)5µm) and the bottom row
shows the Ni(top) for particles greater than 100 µm
(Ni(top)100µm). The columns are
normalized so that they sum to 100 %. The vertical line is drawn at
-35∘C – approximately the homogeneous nucleation threshold
temperature. At temperatures warmer than -35∘C, the grid lines
show the INP numbers predicted by the parameterization
for 0.1, 1, 10 and 100 L-1 aerosols >0.5µm. The grid lines at
temperatures below -35∘C are the Ni values following
for 1, 10 and 100 cm s-1 updraft speeds for a mean
pressure and an aerosol particle number of 300 cm-3, following
. The regime names and definitions are given in Table 1 of
. Note the log scale for
Ni(top).
There are large geographical variations in
Ni(top)5µm. At -50∘C,
the Ni(top)5µm is strongly
affected by the topography (Fig. b). High
Ni(top)5µm values are retrieved
in mountainous regions over land and around the edge of the Antarctic ice
sheet, similar to results from orographic cirrus parameterizations in global
climate models e.g. and other satellite
retrievals . This is consistent with a high
Ni(top)5µm being generated
through orographic uplift, which can generate the strong updrafts and high
supersaturations required for homogeneous nucleation. While it is possible
that the increased Ni(top)5µm
is due to an increase in the vertical transport of INP, the lack of a similar
pattern in the cloud supercooled fraction at -20∘C
makes this explanation unsatisfactory. The
Ni(top)5µm in the tropics is
comparatively low, even in regions of significant topography such as the
Ethiopian highlands. This is due to low wind speeds in the tropics reducing
the in-cloud orographic updraft, similar to the GCM results of
. The high orographic
Ni(top)5µm also partially
explains the hemispheric asymmetry in Ni(top) in the
midlatitude and polar regions, due to the high
Ni(top)5µm generated by
orographic clouds over the Andes and around the edge of the East Antarctic
ice sheet. The Ni(top)5µm in
the tropics is significantly lower than the Ni5µm
at -50∘C (Part 1). This is partly due to the low number of cloud tops at this
temperature in the tropics, meaning that there is a clear sampling bias.
Additionally, the cloud top temperature plays an important role in
determining the Ni5µm, giving it a much weaker temperature
dependence. This temperature dependence is investigated further in the
following two subsections.
Cloud regime dependence
The location map and temperature dependence of the
Ni(top)5µm
(Fig. ) and the results from Part 1 hint that there may
be a significant regime dependence in the Ni(top), in
particular a strong role for orographic clouds and a possible role for
convective clouds, given the low Ni(top) in the
tropics. Separating the Ni(top) data by regime using
the classification of allows this dependence to be
independently investigated. Due to the strong temperature dependence and the
large variability of the Ni(top), joint probability
histograms, showing the probability of a Ni(top)
retrieval at a given temperature are shown in Fig. .
Following the results of Part 1, the Ni(top) is
investigated for crystals bigger than 5 µm
(Ni(top)5µm) and 100 µm
(Ni(top)100µm). With a minimum
size of 5 µm, Ni(top)5µm
typically lines up with the smallest sizes measured by in situ instruments,
while with a larger minimum size, Ni(top)100µm covers a size
range where less shattering is expected and where the normalized size
distribution performs well . As noted in the
previous section, the skewed Ni(top) distribution
makes a simple linear average complicated to interpret. For the remainder of
this work, normalized joint histograms are used, showing the probability of
finding a particular Ni, given that a certain temperature has
been observed.
There are a number of broad similarities between the regimes. Each regime
shows a very similar increase in Ni(top)5µm with decreasing
temperature, with the decrease becoming weaker at very colder temperatures,
rising to around 100 L-1 at -75∘C. While this is larger than
the Ni values reported from many measurements of tropical
tropopause layer cirrus e.g., this may be due to sampling
differences between the satellite and in situ measurements, with some of the
thinnest clouds being missed by the DARDAR retrieval. It is also possible
that uncertainties in the shape of the particle size distribution (PSD) can
lead to overestimations of Ni(top)5µm by as much as a
factor of 2 . The temperature dependence is similar to
that observed during the SPARTICUS and MACPEX campaigns ,
although the temperature dependence is stronger than that
observed in where the Ni was sampled in cloud,
rather than at the cloud top. However, if the satellite data are sampled in a
manner similar to previous work, it reproduces the in situ results
, giving confidence in the magnitude and temperature
dependence of the results presented here. There is evidence of possible
retrieval errors, as both the orographic and convective regimes have a small
number of retrievals of over 30 L-1, with some as high as 50 L-1,
around -15∘C. This suggests that the possible phase
misclassification and the high Ni(top) values observed
in the zonal mean are more common in certain regimes.
All of the regimes also show a peak in the highest
Ni(top)5µm percentiles at
temperatures just colder than -35∘C. The strength varies by regime,
with the orographic regime showing a stronger peak and only a weak peak being
observed in the frontal and convective regimes. The peak is barely present in
the synoptic regime. An increase in the largest Ni(top)5µm values at this
temperature is consistent with homogeneous nucleation, either through an
increase in the freezing of liquid droplets or by increased homogeneous
nucleation through the freezing of unactivated aqueous haze particles. At
these colder temperatures, the Ni(top) is roughly
parallel with the contours of Ni expected through homogeneous
nucleation at a constant updraft , especially in cases
where the peak in Ni(top)5µm at
-40∘C is small (Fig. ). Warmer than
-35∘C, the Ni(top)5µm
is broadly consistent with the number of INP predicted by the
parameterization, but the
Ni(top)5µm becomes increasingly
large compared to the number of INP as the temperature increases. As the
DARDAR-Nice retrieval has not been evaluated at this temperature, it is
unclear if this is a real effect, or if it is due to the possible phase
classification issue mentioned previously.
(a) The difference in the
Ni(top)5µm as a function of
temperature between the Oro 2 and Oro 1 regimes (highest and second highest
sextiles of estimated in-cloud updraft, respectively). Red above blue at a given
temperature indicates an increased
Ni(top)5µm in the Oro 2 regimes
compared to Oro 1. (b) The difference between the frontal and
synoptic regimes. Note the different color scale
from (a).
The variation in the strength of this peak is clearly seen when comparing the
Oro 2 and Oro 1 regimes (the highest and second highest sextiles of the
estimated in-cloud updraft) in Fig. a. While there is
little difference between the regimes at warmer temperatures, below
-35∘C there is a strong increase in the
Ni(top)5µm in the Oro 2 regime.
This increase peaks at about -50∘C, reducing and then almost
disappearing at the coldest temperatures studied. The high
Ni(top)5µm retrieved in these
clouds and the strong dependence on the in-cloud updraft explain the
geographical pattern shown in Fig. b, where high
Ni(top)5µm are observed in
mountainous regions. A high
Ni(top)5µm in these regimes is
supported by results from previous in situ studies, where large
Ni values were recorded in orographic clouds .
It is possible that this increased
Ni(top)5µm is the result of
increased aerosol concentrations carried to lower temperatures in the
stronger updrafts of the Oro 2 regime. However, the lack of a difference in
Ni(top)5µm between the regimes
at temperatures warmer than -35∘C indicates that an increase in
INP is not driving this change in
Ni(top)5µm, which in turn makes
it less likely that this change is due to a change in liquid aerosol. As the
Oro regimes are defined by the estimated in-cloud updraft
, the difference between the regimes shown in
Fig. is likely due to a change in the updraft
environment impacting freezing processes.
A change in the updraft environment could modify the
Ni(top)5µm by changing the
likelihood of homogeneous nucleation, either through allowing more
liquid droplets to reach temperatures where they can freeze homogeneously or
by increasing the nucleation of haze droplets . These processes
cannot be easily distinguished in the current study, although the lack of a
significant occurrence of liquid-topped cloud in orographic regions
suggests that an increased cloud droplet number concentration
is not the leading contributor to the increase in
Ni(top)5µm. This strong
response to updraft changes would support previous studies that highlighted
the updraft limited nature of many cirrus clouds .
A larger difference exists between the frontal and synoptic regimes
(Fig. b), indicating that the magnitude of this updraft
effect could be stronger than is shown here. However, the difference between
the frontal and synoptic regimes cannot be easily attributed to updraft variations.
The temperature dependence of crystals larger than 100 µm
(Ni(top)100µm) at the cloud top
displays a different pattern (Fig. , bottom row).
While Ni(top)100µm and
temperature are negatively correlated at warmer temperatures, the
Ni(top)100µm reaches a peak at
around -50∘C and there is a decrease in the
Ni(top)100µm as temperatures
reduce further, with the strongest decrease observed in the orographic
regime. This is consistent with a shift towards smaller ice crystals at the
cloud top with colder temperatures. The synoptic regime shows a weaker
decrease in Ni(top)100µm,
indicating a slightly larger role for larger ice crystals in this regime.
This shift towards smaller crystals at the cloud top is expected due to
slower depositional growth and aggregation of ice crystals at colder
temperatures resulting in crystals precipitating from the cloud top region
before they grow larger than 100 µm.
The Ni within clouds
The behavior of the Ni within clouds as a function of
temperature displays some significant contrasts to the
Ni(top)5µm
(Fig. ). While all of the regimes show an increase
in the Ni5µm with decreasing temperature, this increase is much
weaker than the increase in the
Ni(top)5µm. Similarly, although
the peak that is visible in the
Ni(top)5µm at about
-40∘C is still visible in Ni5µm in the orographic and
frontal regimes, it is much weaker than the peak observed at the cloud top.
The synoptic regime has the strongest temperature dependence of all of the
regimes. One explanation is the lower average cloud depth, such that the
Ni5µm retrieved is often closer to the cloud top than in the other
regimes. In all of the regimes, the Ni5µm is much larger in-cloud
than at the cloud top for temperatures warmer than -30∘C, with
values up to 100 L-1 being commonly observed. The smaller
Ni5µm values that are more typically observed in the synoptic regime than the
other regimes suggest that seasonal variations of the cloud classes
are likely responsible for the seasonal variations in
Ni5µm observed in Part 1.
As in Fig. , but using the Ni from
throughout the cloud, rather than just the cloud top. The temperature scale
is the temperature of the Ni retrieval, rather than that of the
cloud top.
Similar to the Ni5µm, the temperature dependence of the
Ni100µm is very different internally within clouds compared to at cloud tops.
The temperature dependence is much weaker, with almost no temperature
dependence at temperatures warmer than -50∘C. There is a decrease
in the Ni100µm at the lowest temperatures, similar to the decrease
in the Ni(top)100µm seen in
Fig. and is explained by the retrievals at these
temperatures being closer to the cloud top than at warmer temperatures. The
synoptic regime has the lowest Ni100µm at warmer temperatures,
which may again be due to the lower geometrical thickness of clouds in this
regime, such that the Ni100µm is typically located closer to the
cloud top, resulting in a lower
Ni(top)100µm for any given
temperature inside a cloud.
The larger Ni100µm values at warmer temperatures mean that larger
crystals comprise a larger proportion of the Ni5µm, with a reduced
contribution of small crystals to the Ni5µm. A weaker temperature
dependence of the Ni5µm, especially at temperatures colder than
-35∘C, is in better agreement with the results from
, although a temperature dependence remains. It is possible
that the weak temperature dependence in previous results could be due to a
lack of measurements near the cloud top, where the temperature dependence is
strongest. This may also explain the apparent mismatch between the INP and
Ni concentration in aircraft data e.g., as the
retrieved Ni(top) values are a much closer match to
the INP concentrations predicted by the parameterization than
the internal Ni at temperatures warmer than -35∘C
(Fig. ). Further sampling differences between the
satellite and in situ studies due to the detection limits of satellite
instruments and the structuring of flight campaigns may explain the remaining
differences between Ni determined using different methods.
Vertical structure of Ni
The Ni5µm, Ni100µm and ice water content (IWC) all change
significantly as a function of depth through the cloud (Fig. ).
For clouds with a top temperature between -40 and
-50∘C (Fig. ), the Ni5µm continues to
increase until about 500 m from the cloud top, at which point it starts to
decrease again (Fig. , top row panels). The Ni5µm
distribution width stays approximately constant from about 1 km into the
cloud until around 2–3 km from the cloud top, when it reaches a temperature
of around -30∘C where liquid water can form more easily. At this
point the Ni5µm distribution broadens significantly. Similar to the
Ni5µm, the Ni100µm also grows quickly when moving down
through the cloud, moving to a slower growth regime after the first 500 m
from the cloud top. This shift in the Ni100µm growth regime is
roughly coincident with the location of the Ni5µm peak. All of the
regimes also show an increase in the IWC (Fig. , bottom
row) with increasing depth in the cloud, which displays a sharp increase over 2.5 km
from the cloud top. This sharp increase is consistent with a possible
increase in ice through liquid water processes in warmer parts of the cloud.
Retrieved properties as a function of the distance from the cloud
top. This is for clouds with tops between -40 and -50∘C. Note
the nonlinear scale on the horizontal axis.
There are some differences between the regimes. The synoptic regime has a much
weaker peak in Ni5µm below the cloud top and a consequently lower
Ni5µm throughout the depth of the regime. Despite having similar
values at the cloud top to the other regimes, the Ni100µm and the
IWC in the synoptic regime remain lower than the other regimes through the
cloud, possibly due to lower in-cloud updrafts. At about 2.5 km from the
cloud top, both Ni100µm and IWC increase until they are roughly
comparable to the other regimes, suggesting that the signal from liquid water
swamps any signal based on ice nucleation.
The peak is also temperature dependent, almost disappearing in clouds with
colder tops (see supplementary information) and varying in size and location
between the regimes. When the peak occurs in the synoptic regime, it is
within 300 m of the cloud top in 67 % of cases, compared to only 48 % of
cases for the frontal regime. These distances are comparable to the thickness
of nucleation regions of between 20 and 500 m noted in . The
enhancement of the Ni5µm within this peak in the synoptic regime is
also smaller, with an average peak of 130 L-1, compared to
270 L-1 in the frontal regime and 325 L-1 for the orographic
regime. The increased strength of this peak in regimes expected to have a
stronger updraft along with its location close to the cloud top may
indicate homogeneous nucleation. Model studies of cirrus clouds
suggest that homogeneous nucleation can produce peaks in Ni cloud
to the cloud top , with an increased Ni
at higher updraft velocities. The disappearance of the peak
at colder temperatures gives it a similar temperature dependence to the peak
in the Ni(top)5µm
(Fig. ) providing further supporting evidence of the impact
of homogeneous nucleation on Ni in this temperature range.
The difference in the conditional histograms between cases with high
MACC total aerosol mass concentration (>6µg m-3) and low
total mass concentration (<6µg m-3) for the four main
regimes. The grid lines are the same as Fig. . The upper
set of plots show the difference in
Ni(top)5µm and the lower in
Ni(top)100µm. The changes sum
to zero vertically; red over blue indicates an increase in the
Ni(top)5µm/Ni(top)100µm
for a given temperature and regime.
It is possible that the varying sensitivities of the CloudSat radar and the
CALIOP lidar to crystal size and the attenuation of the CALIOP lidar in the
upper levels of the cloud could be generating this vertical structure. The
lower vertical resolution and sensitivity to small crystals of the radar
could result in it missing the cloud top, which would generate a peak in the
Ni5µm at the level where the retrieval starts to include radar information.
However, the results in Part 1 show no evidence of a bias in the
Ni retrieval as a function of the instruments contributing to the
retrieval . This is primarily due to the sensitivity of
the instruments to different ice crystal size distributions. Although the
lidar-only retrievals have a higher expected error, they usually only occur
in cases where there is a monomodal size distribution dominated by small
crystals that can be accurately constrained by the lidar alone. Additionally,
the disappearance of the peak at colder temperatures indicates that it is a
physical property of the clouds, rather than a property of the retrieval, as
the instrument sensitivities would not be expected to strongly vary with temperature.
The relationship to aerosolThe relationship to liquid aerosol
Figure shows how the
Ni(top)5µm distribution changes
as a function of temperature and MACC reanalysis aerosol (used to indicate
high concentrations of liquid aerosol). In most of the regimes, there is a
positive relationship between MACC aerosol and
Ni(top)5µm at temperatures
below -35∘C (shown by red above blue in
Fig. ). In the synoptic regime, this positive
aerosol–Ni(top)5µm relationship
only exists for temperatures warmer than -60∘C – at temperatures
colder than this, the relationship becomes weak and noisy. In the other
regimes, the positive relationship is maintained to very cold temperatures.
At temperatures warmer than -35∘C, the relationship becomes a lot
weaker, with almost no
aerosol–Ni(top)5µm relationship
existing in the orographic and convective regimes. In the frontal regime,
there is a slight negative relationship, with a stronger negative
relationship in the synoptic regime. It is possible that this negative
relationship is related to a misclassification of ice and liquid at these
warmer temperatures being a function of the MACC aerosol, particularly in
regions where INP rich aerosol constitute a majority of the aerosol population.
The aerosol–Ni(top)100µm
relationship shows a weaker pattern than the
aerosol–Ni(top)5µm
relationship, with the smaller enhancement of the
Ni(top)100µm at colder
temperatures in most regimes indicating a shift to smaller crystal sizes. The
change in the synoptic regimes is the strongest, likely related to the strong
relationship for the
Ni(top)5µm. A negative
relationship between the aerosol environment and the crystal size has been noted in
previous work and often corresponds to an increase in
Ni(top), although positive relationships have been
observed over the Indian Ocean .
It is difficult to demonstrate causality with observed aerosol–cloud
relationships, to the extent that it is not clear that this relationship is a
change in Ni(top) due to a change in aerosol. However,
this strong relationship between MACC aerosol and
Ni(top) is consistent with an increased ice crystal
nucleation through homogeneous nucleation, which can be sensitive to the
concentration of liquid aerosol e.g.. In situations
where the Ni(top) is primarily determined by the
freezing of liquid droplets, an increase in cloud droplets in high aerosol
regions could also lead to an increased Ni(top),
although the number of droplets frozen is relatively insensitive to the total
number of liquid droplets . As with the impact of in-cloud
updraft on Ni(top), further investigation is
required to determine if one of these mechanisms is dominant. As liquid water
has been found in clouds at temperatures as cold as -40∘C, increased
droplet freezing cannot be ruled out, even though many clouds are frozen
before reaching this temperature . At colder temperatures, it
seems likely that homogeneous nucleation plays a role, as liquid droplets
cannot form at these temperatures. In this case, the stronger updrafts in
the frontal and convective regimes are important for generating the high
supersaturations in which homogeneous nucleation can occur. Changing aerosol
types may also play a role at temperatures colder than -60∘C, where
the increasing impact of glassy aerosols may lead the aerosol to nucleate ice
heterogeneously. A combination of the weak expected updrafts and the
increasing ability of glassy aerosol to act as an INP at low temperatures may
explain the weak aerosol–Ni(top) relationship in the
synoptic regime below -60∘C. While there is a clear relationship in
Fig. , the change in the mean
Ni(top) is small, even for this large aerosol
perturbation. At -50∘C, the mean
Ni(top)5µm increases from
around 140 to 175 L-1, an increase of 25 %. Much of this change is
driven by changes in the high updraft tail of the distribution, and producing a
25 % change in Ni(top) at -50∘C would require
an updraft in excess of 1 m s-1. While plausible for
the convective and orographic regimes , the large
updrafts required to generate such a sensitivity may indicate that this
relationship is affected by an updraft mediated covariation.
The relationship to an INP proxy
The sparse nature of INP measurements e.g. and the high
sensitivity of the Ni to low INP concentrations means that it is
difficult to use retrieved aerosol properties to investigate the effect of
INP on the Ni(top). To avoid this issue, the glaciated
fraction of clouds lower in the atmosphere (-20∘C) is used as a
proxy for the presence of INP at other levels in the atmosphere.
(a) The DARDAR supercooled fraction at -20∘C,
defined as the fraction of the DARDAR cloud top phase retrievals between
-17.5 and -22.5∘C from 2006 and 2013 that are classed as
liquid. (b) The conditional probability of observing a daily mean
supercooled fraction, given a specified MACC dust mixing ratio for the
regions specified in (a). The black line shows the mean supercooled
fraction for each aerosol bin.
Cloud glaciation and INP
The addition of the CloudSat data in the DARDAR phase retrieval allows smaller
quantities of ice to be detected than in the lidar-only studies, but it produces a
very similar pattern of cloud glaciation (Fig. ) to the
previous CALIOP studies . The supercooled fraction is
calculated as the number of DARDAR liquid phase retrievals divided by the total
number of liquid and ice retrievals between -17.5 and -22.5∘C from 2006 to 2013.
Only the phase of the top cloud layer is considered and only where this layer is more than 600 m thick.
There is a strong hemispheric contrast with a higher glaciated fraction over
the Northern Hemisphere and a high supercooled fraction over the Southern
Ocean and Antarctica, as observed in previous aircraft and
satellite studies . High glaciated fractions are
observed over desert locations in central Asia and Iran, stretching across
the North Pacific to the Americas. This is consistent with previous studies
suggesting that dust is a good INP. Previous studies have found Asian dust
over California, suggesting that transport across the Pacific is not
unexpected . There is also a significant proportion of
glaciated cloud downwind of the Andes, which appears to originate near the
Altiplano and Patagonia. These are sources of high altitude dust
and would support the hypothesis that high altitude dust is
able to glaciate clouds. While glaciated cloud in this region has
previously been noted , the lower resolution of the previous study
made it difficult to determine the source of possible INP. The longer dataset
and increased spatial resolution of Fig. a make the source
in the upper Andes much clearer. Although southern Africa and Australia are
also sources of dust , this dust is emitted at lower
altitudes, which would explain the lower glaciated fractions downwind of these regions.
The origin of the glaciated region over the north Atlantic is less clear, as
there are not many local sources of high level dust in the region. It is
possible that the dust here has been transported across the Sahara and lofted
by cyclone systems crossing the Atlantic. Furthermore, it is possible that the black
carbon or ash from North America may act as an INP. This
might explain the lower supercooled fraction over Siberia, where black carbon
from fires typically occurs without the other aerosols that are found in
industrial pollution, allowing it to act as an INP
despite the low amounts of high level dust in this region.
The role of the ice nucleation impact of dust for driving the cloud glaciated fraction is
supported by comparing the cloud glaciated fraction to reanalysis aerosol
fields (Fig. b). Strong negative correlations between the
occurrence of supercooled liquid cloud at -20∘C and the mass
concentration of reanalysis dust (Fig. b) are observed in
some regions, with glaciated cloud dominating at high mass concentrations of
reanalysis dust. However, this correlation varies by region. A stronger
relationship is found in regions close to dust sources, such as over the
N. Pacific (B) and central Asia (D); the relationship is much weaker in the
N. Atlantic (A) and the Southern Ocean (C) where the dust is further from source (Fig. b).
The stronger dust–glaciation relationship close to the dust source, where the
MACC aerosol speciation is best, suggests that the supercooled fraction of
clouds at -20∘C is strongly related to the occurrence of INP. The
weaker relationship further from source suggests that although the MACC
speciation has been shown to provide useful information on aerosol type, this
speciation is less reliable further from source. This is supported by results
in liquid clouds, where the dust optical depth–cloud droplet number
concentration relationship becomes stronger further from dust sources .
Due to the reduced speciation skill from MACC far from dust sources, the
occurrence of glaciated cloud at -20∘C is used as a proxy for the
occurrence of INP instead of the reanalysis aerosol. This relies on the following two assumptions:
(1) cloud glaciation at -20∘C is related to INP at
-20∘C; and (2) INP at -20∘C are correlated to INP at other temperatures.
Based on the relationship to MACC dust aerosol, the first assumption holds in
many cases. Although the second assumption is tenuous, previous studies have
found similar relationships between cirrus cloud properties and both column
and layer aerosol optical depth (AOD) , similar to model results showing a significant
correlation between high altitude cloud condensation nuclei concentration and column AOD
. Significant vertical aerosol autocorrelation has also been
observed in global climate models . Additionally, there is
very unlikely to be a negative correlation between the INP at the two
temperature levels, with the worst case being no correlation. As such, the
relationship between the Ni and the INP proxy is unable to give a
quantitative result for the impact of INP on the
Ni(top)5µm, but it is able to
provide a qualitative indication of the sign of the INP impact.
The INP relationship to Ni(top)
The relationship of the
Ni(top)5µm to the proxy for INP
is shown in Fig. . There are a number of features that
are similar between the regimes, in particular the strong negative
relationship between INP occurrence and
Ni(top)5µm at temperatures
warmer than -35∘C. As with the large mean
Ni(top)5µm values shown in
Fig. a, this may be due to liquid clouds being
misclassified as ice, resulting in large
Ni(top)5µm values being
retrieved. The requirement for “warm-ice” means that supercooled liquid
occurs less frequently in the high INP cases, and as such it is less likely
to be misclassified as ice. The lower frequency of this misclassification
then reduces the Ni(top)5µm and
Ni(top)100µm in cases of high
INP. The weaker Ni(top)5µm
response in the synoptic and orographic regimes supports this, as the
misclassification in these regimes is weaker (Fig. ).
The warmer temperatures are shaded out in Fig. due to
the impact of this potential misclassification.
As in Fig. , but showing the difference in the
conditional histograms as a function of the INP proxy. Red indicates an
increase in the occurrence of a particular bin at a higher inferred INP and blue indicates a
decrease, meaning that red above blue indicates an increase in
Ni(top) with increased INP for a given temperature.
The shaded regions are likely affected by a phase misclassification at warmer
temperatures.
At colder temperatures, the
INP–Ni(top)5µm relationship
starts to vary between the regimes. All of the regimes show a decrease in the
Ni(top)5µm between around
-35 and -50∘C, the temperatures where the peak in
Ni(top)5µm is observed
connected with in-cloud updraft (Fig. ). This decrease
is strongest in the orographic regime and weakest in the synoptic regime,
similar to the Ni(top)5µm peak
observed in the different regimes (Fig. ).
At temperatures colder than -50∘C, the relationship becomes
different again. In all of the regimes, there is an increase in
Ni(top)100µm with increasing
INP. This is consistent with an increasing number of INP shifting the size
distribution towards a smaller number of larger ice crystals. In the
orographic and synoptic regimes, this increase also appears in the
Ni(top)5µm, generating a
positive relationship between the INP proxy and the occurrence of small ice crystals.
As with the previous section, the impact of meteorological covariations
cannot be ruled out when interpreting these plots. However, they are
consistent with a reduction in
Ni(top)5µm due to a
suppression of homogeneous nucleation by INP at around -50∘C. This
relationship has previously been found in satellite relationships between the
aerosol environment and the ice crystal size, with an increase in the crystal
radius in situations where heterogeneous nucleation controls the Ni. This would fit with the results in
previous sections, suggesting that the Ni(top) at this
temperature range just slightly colder than -35∘C is influenced by
homogeneous nucleation. This effect would only be expected in a narrow range
of updrafts , so further work is necessary to understand
the cause of this relationship.
The increase in large crystals at the coldest temperatures (below
-60∘C) is consistent with an INP effect on
Ni(top) if heterogeneous nucleation was dominant at
these temperatures. This would fit with the results from
Fig. , where at the coldest temperatures, there was a
relatively small response of the
Ni(top)5µm to MACC total
(liquid) aerosol, suggesting that homogeneous nucleation was not controlling
the Ni(top)5µm in synoptic
cirrus. At these coldest temperatures, dust can act as an INP at very low
supersaturations as low as 105 %; and organic aerosol
can occur in a glassy state allowing it to act as an INP. This may explain
relationships consistent with heterogeneous nucleation and a classical Twomey
effect at these temperatures. It is important to note that this proxy for INP
relies upon the correlation between cloud glaciation at -20 and
INP at -50∘C, but the absence of this correlation would produce no
relationship in Fig. , giving some confidence to the
qualitative nature of these results.
If the peak in Ni(top)5µm at
temperatures colder than -35∘C is primarily due to droplet freezing,
an increase glaciated fraction at warmer temperatures could also result in
this reduction of Ni(top)5µm
with increasing INP. As the number of INP and Ni warmer than
-35∘C is much lower than the cloud droplet number concentration, the
increase in cloud glaciation could result in a reduction in the number of
cloud droplets available to form ice crystals at -35∘C. This would
result in a negative relationship between cloud glaciation at -20∘C
and the Ni(top)5µm at colder
temperatures as observed in Fig. . As with the relationship
of Ni(top) to updraft (Fig. ) and
aerosol (Fig. ), the difference between an aerosol impact
on homogeneous nucleation, a change in droplet freezing or an
updraft-mediated covariation (no causal effect of aerosol) cannot be
distinguished by this analysis.
As in Fig. , but showing the difference in the
retrieved properties depending on the cloud top properties. Red over blue
indicates that clouds with above median properties at the cloud top
(Ni(top)5µm,
IWC(top)) have higher values of the retrieved properties at a
specified depth from the cloud top. Note the nonlinear scale on the
horizontal axis.
Vertical information propagation
The changes in Ni(top)5µm
observed in the previous section have impacts throughout the depth of the
cloud. Figure shows how Ni and IWC
information propagates vertically within a cloud. The cloud profiles are
split into two categories, based on whether they have above or below median
values of the cloud top properties (Ni(top),
IWC(top)). The difference in the vertical structure of the clouds
(in a similar manner to Fig. ) is shown, with red over
blue indicating an increase in the retrieved quantity at a given
distance from the cloud top for profiles that were above median in that
property at the cloud top.
The top row of Fig. shows that Ni5µm
information propagates a significant distance through the cloud. Clouds with
an increased Ni(top)5µm
maintain a higher Ni5µm at distances at least 3km from the cloud top
in all regimes. However, as shown in the second row, vertical information
about the Ni100µm does not propagate nearly as far through the
cloud. The vertical propagation is the highest in the synoptic regime. The
vertical propagation of IWC information is very similar to the Ni100µm,
with the relationship to the cloud top IWC being significantly reduced
more than 500 m from the cloud top.
The large vertical propagation of the Ni5µm indicates that the
changes in Ni5µm at the cloud top found in the previous section can
have considerable impacts at lower levels in the cloud. However, the lower
vertical propagation of the information about the larger crystals (Ni100µm,
IWC) would support the suggestion that the growth of the ice
crystals after nucleation is primarily controlled by meteorological factors
that do not play a large role in the nucleation processes that control
Ni100µm. Note that the temperature of the cloud top and the
distance from the cloud top can still play a large role in determining
the Ni (Figs. and ).
Discussion
These results show that the
Ni(top)5µm is strongly affected
by several factors including temperature (Fig. ), cloud
type (Fig. ) and updraft (Fig. ),
and that changes in the Ni(top)5µm can be maintained at
large distances from the cloud top (Fig. ). The
dependence of the Ni(top)5µm on
the in-cloud updraft and the relationship to reanalysis liquid aerosol
(Fig. ) at temperatures between -35 and
-60∘C is consistent with the impact of homogeneous nucleation
processes on the Ni(top)5µm.
This is supported by the relationship of the
Ni(top)5µm to the INP proxy
(Fig. ), where a reduction in
Ni(top)5µm with increasing INP
could be indicative of an INP suppression of homogeneous nucleation
. The relationship with INP is also consistent with
heterogeneous nucleation having a strong role to play in determining the
Ni(top)5µm in synoptic cirrus
clouds at temperatures colder than -60 ∘C.
Uncertainties in the retrieval have been covered in Part 1 of this work
. However, there are a few points to note with regards to
the relationship of the Ni to other cloud and meteorological
properties. Although there is significant uncertainty in the
Ni retrieval, many of these uncertainties are random errors and not systematic
functions of the meteorological properties investigated here. Even ice
crystal shape, which can be a major issue in ice cloud retrievals, is a
function of temperature (to first order) and so does not impact the majority
of the results which are presented in this work stratified by temperature.
The geographical variations in Fig. b show a similar
pattern to those from , with high
Ni(top) observed in mountainous regions and a reduced
Ni(top) in the tropics. The similarity of the results
from these two different retrieval products, each with a different physical
basis supports the conclusions drawn from these datasets regarding the global
distribution of Ni. There is also little evidence to suggest that
there are large biases caused by the retrieval only being able to use one
instrument (radar or lidar). Cases where only the lidar detects a cloud are
often characterized by monomodal ice distributions, which are well
represented by the parameterization. As such, these cases
are retrieved with similar accuracy to the full radar–lidar retrieval .
The cloud phase classification is of critical importance to the warmer clouds
included in this study and there is evidence of the misclassification of a
small number of cases at temperatures warmer than -35∘C
(Fig. ). This can make it difficult to interpret results
at these temperatures, so they are not a focus of this work. The change in
phase of these clouds as a function of aerosol is likely to dominate the
radiative response of clouds to aerosols at these temperatures.
There are a number of limitations of this study that could be addressed in
future work. The lack of information about the location of INP is a serious
issue when investigating the impact of aerosol on Ni. While the
INP proxy in this work is able to provide a qualitative estimate of the role
of INP in determining the Ni, for a quantitative estimate a
better proxy or measure of the global INP concentration is required.
Additionally, the impact of meteorological covariations makes it difficult to
assign causality to the aerosol–Ni(top) relationships
observed in Fig. . The lack of a complete picture of the
atmosphere makes it difficult to directly control for meteorological
variability. The causal link between aerosol and
Ni(top) is thought to be strong
e.g., but the lack of observations of in-cloud
updrafts also limits how accurately the impact of aerosol on the Ni
can be determined. Although the cloud regimes used have some
ability to constrain the cloud-scale updraft , the
updraft is a critical component in determining the Ni through
its influence on the supersaturation. The in-cloud updraft is assumed to
be largely independent of the aerosol properties in this work, but it is
possible that the reanalysis aerosol is related to the in-cloud updraft,
such that more aerosol is vertically transported in conditions with high
in-cloud updrafts. In this case, a positive correlation between the Ni
and MACC reanalysis aerosol could be generated. However, as
MACC does not explicitly simulate in-cloud updrafts, the impact of this
confounding issue is likely to be small.
It is also possible that using cloud glaciation as a proxy allows other
meteorological covariations, which could generate apparent relationships
between the INP and Ni. However, the in-cloud updraft is of
a second-order importance in determining the cloud top phase compared to the
INP concentration . The inclusion of a glide-slope test
when determining the INP proxy means that it is also unlikely that clouds are
being glaciated by undetected ice falling from higher cloud layers. The
separation into cloud regimes also limits the impact of these kind of
meteorological covariations, which might be expected between different
regimes, but would be weaker within them.
The behavior of the Ni retrieval in this work follows the
expected behavior of the Ni determined in several previous
studies based on satellite remote sensing, in situ, theoretical and modeling
results. This provides further evidence that the DARDAR-Nice Ni retrieval
described in is able to retrieve the Ni in a variety of situations.
Conclusions
Few global studies exist of the controls on the ice crystal number
concentration (Ni), especially regarding the role of aerosols. In this
study, the DARDAR-Nice Ni retrieval from Part 1
is used to investigate possible controls on the Ni
at a global scale for the period from 2006 to 2013. A special emphasis
is placed on the Ni at the cloud top
(Ni(top)), due to the close proximity to ice crystal
nucleation locations within many high clouds .
Strong relationships between the Ni(top) and
updraft, cloud type and particularly temperature are observed
(Figs. and ), with a higher
Ni(top) for crystals larger then 5 µm
(Ni(top)5µm) being found at
colder temperatures in all regimes, which is consistent with an increased nucleation
rate at lower temperatures. Fewer crystals larger than 100 µm,
(Ni(top)100µm) are found at the
coldest temperatures, possibly due to the reduced depositional growth rate
meaning that they sediment from the cloud top region before they can grow to
a sufficient size.
Many of the regimes show an increase in the Ni100µm and a decrease
in the Ni5µm with increasing distance from the cloud top
(Fig. ) due to the size sorting impact of sedimentation.
The rate of change of the Ni moving away from the cloud top
depends on the regime, with much slower changes in the synoptic regime
indicating a role of meteorological factors in determining ice crystal growth
rates. This is supported by the weaker temperature dependence of the Ni
within clouds compared to the Ni(top)
(Fig. ), which may also explain the apparent weak
dependence of Ni on temperature and INP
found in previous studies. Given the large difference between
the Ni(top) and the Ni deeper in the
cloud, this may suggest that the cloud top would make a good target for
future in situ campaigns examining the controls on ice nucleation.
There are indications of homogeneous nucleation or possibly the freezing of
liquid droplets determining the Ni(top). At
temperatures just colder than -35∘C, there is a peak in the upper
quantiles of the Ni(top)5µm
(Fig. ). This peak is related to the updraft
strength in the cloud, with the reliably high updrafts in the orographic
regime giving it the strongest peak (Fig. ). This is
further supported by the relationship between the
Ni(top)5µm and the MACC
reanalysis aerosol (Fig. ), with an increased
Ni(top)5µm being observed in
high aerosol environments. This indicates a possible dependence on the liquid
aerosol concentration, particularly for smaller crystals, although this
analysis cannot make a conclusive statement about the causality of this
relationship. An investigation into the covariances between the MACC
reanalysis aerosol, the DARDAR-Nice Ni and meteorological factors
is an important target for future work.
As previous work has suggested that INP occurrence is related to cloud
glaciation, the glaciated fraction at -20∘C is used as a qualitative
proxy for INP occurrence (Fig. ). At temperatures between
-50 and -35∘C, there is a reduction in
Ni(top)5µm with increasing
INP (Fig. ), which may indicate an INP suppression of
the homogeneous nucleation , providing further supporting
evidence for the role of homogeneous nucleation in determining the
Ni(top). At colder temperatures, some regimes show an
increasing Ni(top), and the
Ni(top)100µm in particular,
which may be evidence of heterogeneous nucleation controlling the
Ni(top) and shifting the size distribution towards
larger crystals. However, as with the relationship to liquid aerosol,
meteorological covariations could be generating these relationships. Further
studies are required to separate the role of these different mechanisms in
controlling the Ni(top) and to isolate the role of
aerosols in these relationships.
While changes to the Ni(top) are important for
radiative considerations, changes in the Ni(top) can
have implications for the cloud many kilometers below the cloud top
(Fig. ). This far reaching impact into the life cycle
of ice and mixed-phase clouds demonstrates the importance of developing
strong observational constraints on the controlling factors of the Ni.
The results presented in this work provide a global context
for existing theory and in situ measurement based hypotheses about cloud
properties, highlighting areas for future research to further constrain ice
and mixed-phase cloud processes.
The DARDAR data product was retrieved from
the ICARE data center (http://www.icare.univ-lille1.fr, ).
The IC-CIR cloud classification is available at (http://catalogue.ceda.ac.uk/uuid/cddfe3093be247d7bac56c9fa9edb3d5, ).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-14351-2018-supplement.
EG, OS and JQ conceived the study, EG, OS and PK performed
the analysis, and EG and OS wrote the paper. All of the authors assisted with the
interpretation of the results and commented on the paper.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by funding from the European Research Council under the European
Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement
no. FP7-306284 (“QUAERERE”); the Bundesministerium für Bildung und
Forschung, grant numbers 01LK1210D, 01LK1503A and 01LK1505E; and the Deutsche
Forschungsgemeinschaft, grant number QU 311/14-1. Edward Gryspeerdt is supported
by an Imperial College Junior Research Fellowship.
Edited by: Matthias Tesche
Reviewed by: two anonymous referees
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