High-altitude cirrus clouds are climatically important: their formation
freeze-dries air ascending to the stratosphere to its final value, and their
radiative impact is disproportionately large. However, their formation and
growth are not fully understood, and multiple in situ aircraft campaigns have
observed frequent and persistent apparent water vapor supersaturations of
5 %–25 % in ultracold cirrus (T<205 K), even in the presence of ice
particles. A variety of explanations for these observations have been put
forth, including that ultracold cirrus are dominated by metastable ice whose
vapor pressure exceeds that of hexagonal ice. The 2013 IsoCloud campaign at
the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud and
aerosol chamber allowed explicit testing of cirrus formation dynamics at
these low temperatures. A series of 28 experiments allows robust estimation
of the saturation vapor pressure over ice for temperatures between 189 and
235 K, with a variety of ice nucleating particles. Experiments are rapid
enough (∼10 min) to allow detection of any metastable ice that may
form, as the timescale for annealing to hexagonal ice is hours or longer over
the whole experimental temperature range. We show that in all experiments,
saturation vapor pressures are fully consistent with expected values for
hexagonal ice and inconsistent with the highest values postulated for
metastable ice, with no temperature-dependent deviations from expected
saturation vapor pressure. If metastable ice forms in ultracold cirrus
clouds, it appears to have a vapor pressure indistinguishable from that of
hexagonal ice to within about 4.5 %.
Introduction
As air rises into the stratosphere, it is freeze-dried by condensation as it
passes through the coldest regions of the upper troposphere and lower
stratosphere (UT/LS). The temperature-dependent saturation vapor pressure
over ice therefore plays a strong role in setting the water vapor
concentration of the stratosphere as a whole
e.g.,, and in determining the abundance and
characteristics of radiatively important tropical cold cirrus. Inadequate
understanding of saturation vapor pressure, or incomplete relaxation of air
to saturation, would result in excess stratospheric water and errors in both
chemistry models and radiative forcing calculations. For example, an apparent
supersaturation of 20 % at 190 K over expected values (from the
Murphy–Koop parametrization, henceforth MK; )
corresponds to a difference of about 0.7 ppmv H2O. If uniformly
distributed, this additional stratospheric water would increase global
surface radiative forcing by about 0.2 W m-2. Incomplete dehydration would also change the
radiative effect of the cirrus produced by freeze-drying ascending air, but
the magnitude and even sign of this effect are not well known. Reduced cirrus
ice content would reduce longwave and shortwave cloud forcing, with opposing
cooling and warming effects. Modeling studies show effects that are of
comparable magnitude to the direct effect of water but disagree on the sign
. Furthermore, some explanations for
observed supersaturations invoke novel forms of ice that may have
intrinsically different radiative properties than those of hexagonal ice
. It is therefore important to understand the physics
of ice nucleation and growth at the cold temperatures found in this region.
High apparent supersaturations within ice clouds have been measured in
several in situ campaigns in the UT/LS, most frequently in the coldest ice
clouds, with temperatures at or below 205 K . From
observations in the 2002 CRYSTAL-FACE campaign,
reported supersaturations of 13 % above 202 K and 35 % below 202 K in
cold cirrus and persistent contrails. Using water and particle measurements
from the 2006 CR-AVE campaign, found supersaturations
in excess of 50 % in cirrus clouds. found frequent
supersaturations above 25 % in cirrus clouds near the cold point using
CALIOP data and balloon-borne chilled-mirror hygrometer
measurements taken during the 2007 and 2008 SOWER campaign
. Some degree of supersaturation has also been
observed in cirrus at warmer temperatures. show that
hygrometer data from IAGOS-CORE, a campaign using
instrumented commercial aircraft reaching minimum temperatures of 205 K,
exhibit most probable values of supersaturation over ice within cirrus of
5 %–10 %. A laboratory experiment in the AIDA (Aerosol Interaction and
Dynamics in the Atmosphere) cloud chamber spanning a wide temperature range
(243–185 K) showed values close to saturation, but with a systematic
increase of ∼6 % with decreasing temperatures .
Numerous explanations have been proposed for these observations. Many studies
interpret them as true “anomalous supersaturation”, i.e., resulting from
errors in our understanding of saturation vapor pressure and impossible to
explain with standard microphysics. Explanations involving anomalous
supersaturation include organic coatings on ice crystals
, glassy states
, surface uptake
interference due to ice binding with HNO3, temperature- and
supersaturation-dependent accommodation coefficients ,
multi-component aerosols , and metastable forms of ice
. Other studies suggest that no anomaly is necessary,
and that measured supersaturations result only from dynamics, i.e., they
occur when uptake rates on ice crystals are slow enough that the timescales
of relaxation to saturation are long. Long timescales to achieve saturation
may result from low particle numbers and small particle sizes found at low
temperatures , or strong
updrafts that lead to relaxation only to a dynamical equilibrium value
. The ATTREX campaigns of 2013–2014 provided examples
of apparent supersaturations due to low particle numbers: supersaturations of
up to 70 % were observed in low-concentration cirrus (<100 L-1),
but not in those with high concentrations (up to 10000 L-1), even
at cold temperatures (190 K; ). These measurements
demonstrate that saturations consistent with MK are at least possible in cold
cirrus. Finally, instrumental error could explain all or part of the observed
anomalies .
Metastable ices with non-hexagonal crystal structure and elevated saturation
vapor pressures could provide an explanation that encompasses the diverse
body of field measurements. Laboratory measurements have identified
metastable ices with saturation vapor pressures of as much as 10.5 % higher
than hexagonal ice (Ih) at temperatures below 200 K
. The properties of metastable ice are, however,
determined by its crystal structure, which can take different forms that may
have different vapor pressures. In the conditions found in Earth's
atmosphere, ice forms layers of puckered hexagonal rings referred to as
Ice I, which can be stacked in different ways: as
mirror images of each other (hexagonal ice; ice Ih), shifted by
half the ring width (cubic ice; ice Ic), or in a combination of
both stacking sequences (stacking-disordered ice; ice Isd;
). Note that much of the literature on cubic ice is
now thought to have been measuring ice Isd. The vapor pressure over metastable ice is poorly
understood, and some modeling studies suggest that it depends less on the
crystal's cubicity (fraction of cubic stacking sequences) than on the number
and type of imperfections within the crystal .
Laboratory measurements and computer simulations suggest that stacking
disordered ice could form in the UT/LS, which experiences the coldest
temperatures found in Earth's atmosphere. Measurements by multiple groups
have shown ice Isd forming in supercooled droplets, by both
homogeneous and heterogeneous nucleation. Homogeneous nucleation of
ice Isd was seen by ,
, , and in
micrometer-sized water and solution droplets suspended in oil at temperatures of
170–240 K, and by in nanodrops frozen during
expansion of N2 carrier gas. observed
heterogeneous nucleation of ice Isd in water containing solid
inclusions, and reported that pure hexagonal ice
formation was never observed below 190 K. The cubicity in
laboratory-generated ice Isd samples is variable and depends on
factors such as the freezing temperature, droplet size, and aerosol type and
content, but can be as high as 75 % in atmospherically relevant temperature
ranges. Simulations agree that ice frozen at 180 K should form
ice Isd, with producing two cubic ice
layers for each hexagonal ice layer, i.e., a cubicity of 67 %.
No experimental studies to date have measured the resulting influence of
ice Isd on the saturation vapor pressure expected in cirrus
clouds. Many of the ice Isd nucleation experiments provide no
means of measuring the vapor pressure over ice (i.e., those experiments
involving droplets suspended in oil). show that ice
formed by crystallization from amorphous solid water shows a significantly
higher vapor pressure than MK at temperatures below 190 K, but it is unclear
if this is ice Isd. Studies that report the free energy
difference between metastable and hexagonal ices vary widely in their
estimates, likely because the types of imperfections and defects that affect
vapor pressure are strongly influenced by experimental conditions. No
experiments have addressed the ice that is subsequently grown through vapor
deposition onto ice Isd crystals. Observations of cirrus clouds
are inevitably made after at least some growth has occurred, and no
ongoing experiments address how
these crystals behave as new ice layers are added. Recent modeling work on
depositional ice growth at UT/LS temperatures suggests that ice should grow
exclusively in hexagonal layers, regardless of nucleation method, as long as
supersaturation levels are moderate and temperatures are above 200 K
, while below 200 K some stacking disorder can occur. The
properties of metastable ices nucleated and grown in real atmospheric
conditions remain only poorly understood.
Any metastable ice formed in the cold UT/LS region should persist long enough
to be relevant for cirrus microphysics. Observed transformation times for
metastable ice into ice Ih depend strongly on the surface area of
the samples , but for low-surface-area samples such
as frozen droplets, the time can be quite long. ,
, , and
report annealing times of tens of minutes to hours over the UT/LS temperature
range, and observe that by the termination of their experiments the
transformation to ice Ih is often still not complete, especially
at lower temperatures. Observations of secondary indicators like crystal
habit suggest that metastable forms of ice may nucleate and persist for some
time in the coldest parts of Earth's atmosphere.
observed that cubic sequences in an otherwise hexagonal structure would yield
crystals with threefold rotational symmetry, and work by
and suggest that
ice Isd should form crystals with trigonal structure. Field
observations suggest that trigonal crystals may be quite common. In
measurements by in the equatorial Pacific at about
16.5 km, about 50 % of the crystals between 5 and 50 µm exhibit threefold symmetry. review
collected images of atmospheric ice crystals with threefold symmetry
(including those from ) and show that all are
consistent with trigonal crystal structure.
The IsoCloud experimental campaign allows us to characterize saturation vapor
pressure during post-nucleation growth of ice crystals in conditions
characteristic of the UT/LS. The campaign consists of a series of cooling
experiments with sample temperatures from 185 to 235 K, homogeneous
nucleation of sulfuric acid (SA) and secondary organic (SOA) aerosols, and
heterogenous nucleation on Arizona test dust (ATD), with number densities
high enough to ensure that vapor can reach an equilibrium over the duration
of each experiment. To reconstruct the saturation vapor pressure over ice, we
use a box model and the observed properties of the ice cloud and chamber gas
as cirrus grow and dissipate.
Methods
The 2012–2013 IsoCloud campaigns at the AIDA (Aerosol Interaction and
Dynamics in the Atmosphere) cloud chamber involve a series of cirrus
formation experiments designed to probe anomalous supersaturation. The
chamber's pressure and temperature can be varied to replicate conditions
throughout the UT/LS, and rapid pumping on the chamber simulates updrafts and
can initiate nucleation. The chamber can be seeded with a variety of liquid
aerosols and ice nucleating particles, and houses a variety of instruments
that make useful measurements supporting the study of gas-phase water vapor,
such as ice particle number and total water concentration. An in-depth
discussion of the experiments, methods, and instruments used at AIDA and in
the campaign can be found in and .
In the IsoCloud campaigns, 28 pseudo-adiabatic expansion experiments at
temperatures between 185 and 235 K and pressures between 300 and 170 hPa
were suitable for analysis. In these experiments, the chamber was seeded with
Arizona test dust (ATD), sulfuric acid (SA), and secondary organic
aerosols (SOA) which allowed the study of both heterogeneous and homogeneous
nucleation. We use measurements of water vapor and total water
(vapor + ice) and an ice growth model to estimate the saturation vapor
pressure over ice (ei) for clouds from 185 to 235 K, with vapor
pressure measurements in the coldest temperature regime (<205 K) provided
by the new Chicago Water Isotope Spectrometer (ChiWIS). The remainder of this
section is divided into subsections discussing the characteristics of the
instruments used in the analysis, the experiments included, the criteria for
their inclusion, and the model used to retrieve the saturation vapor
pressure.
Instruments
Determining the saturation vapor pressure over ice requires measurements from
three water instruments, an optical particle counter, and temperature and
pressure sensors (Fig. ). Each of these measurements is
described in the following sections, along with the instruments taking them,
typical accuracies and precisions, and limitations. Instrumental
uncertainties are used to generate bounds on the retrieved saturation vapor
pressures.
Layout of the instruments used in this analysis during the IsoCloud
campaigns at the AIDA chamber. ChiWIS and SP-APicT, both open-path tunable
diode laser absorption spectroscopy (TDLAS) instruments, provided water vapor
measurements. APeT, an extractive TDLAS instrument with a heated inlet,
provided total water (ice + vapor) measurements, and Welas provided ice
particle concentrations. The difference between total water and water vapor
measurements was used to calculate ice mass in the chamber. Gas temperature
is taken as the average of thermocouples 1 through 4. Thermocouple 5 is
excluded from the analysis due to the presence of a region of warm air at the
top of the chamber. The whole chamber is within a thermally controlled
housing that sets the base temperature of an experiment. The pumps draw gas
out of the chamber in a pseudo-adiabatic expansion.
Our primary source of information is ChiWIS, a mid-infrared tunable diode
laser instrument operated in open-path mode using one of AIDA's White cell
mirror systems. See and for
instrument details. The instrument has a typical precision of 22 ppbv in
H2O in analyzed IsoCloud experiments for 1 Hz measurements at
299.2 hPa and 204.2 K, corresponding to relative precisions of 5 % and
0.02 % at 0.45 and 100 ppmv, respectively. Measured quantities are
retrieved by fitting spectra calculated from line parameters in the HITRAN
database to raw spectra, rather than by empirical
calibration. Fitting is done using ICOSfit, a non-linear, least-squares
fitting algorithm. Uncertainties in the spectroscopic parameters for the
(413–524) line at 3789.63481 cm-1 in the ν1 band from which H2O measurements are derived contribute an additional ±2.5 %–3 % systematic uncertainty. The uncertainty due to pressure broadening values varies slightly with temperature across the experimental range, but for simplicity we apply the maximal value of ± 3 % to all experiments. Errors in the linearization of the wavelength scale contribute another 0.2 % of systematic uncertainty to all experiments. Day- and
experiment-specific systematic uncertainties due to the fit routine and
in the path length contribute another ±1.1 %. See Sect. for a
complete discussion of uncertainty.
The SP-APicT (single-pass AIDA PCI in cloud TDL; )
water vapor instrument is used to provide water vapor measurements in the
case of thick ice clouds, which form in some IsoCloud experiments above
210 K (13 of 28 experiments). During warmer experiments that form very dense
ice clouds, ChiWIS simultaneously experiences signal attenuation of up to
95 % and backscattering of light off the cloud into the detector, producing
artifacts that affect retrieved concentrations. During these intervals, we
rely on the SP-APicT instrument to provide water vapor measurements because
that instrument's single-pass optical arrangement is much less sensitive to
backscatter. At temperatures above 205 K, SP-APicT reports mixing ratios
during ice-free periods about 1.5 % lower than ChiWIS. For consistency
across all experiments, we arbitrarily scale up the substituted SP-APicT
measurements by that factor (details can be found in ).
The resulting composite water vapor record uses ChiWIS measurements for 15 of
28 experiments, and scaled SP-APicT measurements for the remaining
13 experiments.
Total water measurements are provided by APeT (AIDA PCI extractive TDL), an
extractive, tunable diode laser instrument . In previous
comparisons of AIDA instruments , APeT measurements
were found to be delayed by 17 s with respect to open-path in situ TDLAS
ones. As is standard practice, we take the chamber ice content to be the
difference between total water and vapor-phase measurements. To take
advantage of the high precision ChiWIS affords at temperatures below 205 K,
we use the composite water vapor record described above to calculate ice
mass. However, APeT total water measurements also require harmonization with
ChiWIS. APeT and SP-APicT derive their measured concentrations from the same
H2O spectral feature, and both instruments report values 1.5%
below ChiWIS during ice free periods. We therefore scale up the APeT
measurements by 1.5 % as well. After applying this time-invariant scaling
factor, if there is still an offset between the water vapor record and APeT
total water prior to the expansion (when there should be no ice cloud in the
chamber), that offset is subtracted from the whole experiment. These offsets
are most significant below 200 K where they are typically between
+0.05 and +0.25 ppmv. Potential causes could include parasitic water
absorption inside the instrument or outgassing from ice in the inlet of APeT
(e.g., ). See Supplement for details of instrument
comparisons, and Table S3 for instrument offsets prior to pumping.
Ice particle concentrations are measured by the Welas 1 instrument. Ice
particle number concentration is used to estimate the average radius of
particles in the chamber and the average, per-particle growth rate. One
component of this instrument's uncertainty comes from counting errors, which
follow Poisson statistics and are proportional to 1/n. However,
experiments included in this analysis have high ice particle densities and
small counting errors. We therefore neglect the counting errors in this
analysis. The conversion of this instrument's count rate into a number
concentration has a 10 % uncertainty. In experiments where particles are
very small, the Welas 1 instrument likely undercounts them since its
efficiency drops sharply for particles below 0.7 µm in diameter
. We address the steps taken to characterize this
undercounting in the following sections.
We assume a single chamber temperature at each point in time, and construct a value from the average of four thermocouples suspended at different heights in the chamber. The fifth thermocouple is not included in the analysis to avoid the introduction of bias from a known warm region at the top of the chamber. These measurements have an apparent precision of 0.3 K during pumpdowns and 0.15 K during static conditions between pumpdowns . A mixing fan at the bottom of the chamber is always operational and enhances the uniformity of the chamber, with a mixing time constant of about 1 min. Figure shows the positions of the instruments used during IsoCloud in the AIDA chamber, as well as the locations of the thermocouples.
Experiments
IsoCloud expansion experiments were designed to nucleate, grow, and maintain
cold cirrus clouds with the goal of testing for the presence of anomalous
supersaturation under conditions similar to the coldest parts of the
atmosphere. To be a suitable test for anomalous supersaturation, an expansion
experiment must satisfy two basic criteria. First, its duration must be
significantly longer than the vapor relaxation times associated with cirrus
growth. Relaxation times depend on experimental conditions, and are longer in
the cases where particle number densities are small and diffusion limitation
is strong. Second, cirrus cloud growth must continue for long enough to allow
for the retrieval of the saturation vapor pressure over ice. In practice,
this means that the chamber's ice-covered walls must serve as a source of
vapor from which the cirrus cloud can continue to grow throughout the
experiment. The remainder of this section describes a typical expansion
experiment, addresses the consequences of running experiments in the presence
of wall ice, and discusses the criteria for exclusion from analysis.
In a typical IsoCloud experiment, ice clouds are formed by pumping on a
chamber filled with water vapor near saturation. Adiabatic expansion causes
rapid cooling, which in turn leads to nucleation of ice. Air is kept close to
saturation before pumping by preparing the walls with a thin coating of ice.
In practice, chamber water vapor pressures are 80 %–90 % of
MK saturation before the expansions, which suggests that the wall ice is
0.5–2 K colder than the chamber air. Pumping and adiabatic expansion cool
the chamber air below the wall temperature, and given the presence of ice
nucleating particles, the now-supersaturated chamber air will nucleate an ice
cloud. Ice growth then draws the chamber vapor pressure below the saturation
vapor pressure at wall temperature, and the walls become an additional source
of water for the growing cirrus cloud. The transfer of mass from the walls is
often large enough that chamber total water is greater at the end of pumping
than at the beginning, despite loss to the pumps. Once the pumps cease, the
chamber warms and the cirrus cloud dissipates and part of its mass is
transferred through the vapor back to the walls. The total amount of cooling
in an experiment varies from 5 to 9 K, depending on pump speed, and occurs
primarily during the first ∼100 s of pumping when the chamber air
behaves nearly adiabatically. Subsequently, heat flux from the walls becomes
large enough to balance the adiabatic cooling. Cooling rates during the early
stages of pumpdowns are equivalent to effective atmospheric updraft speeds of
several meters per second, much faster than those typically associated with
cold cirrus in the natural atmosphere.
Of the 48 IsoCloud experiments in March of 2013, six were reference
expansions, and four others lacked measurements of one of the physical
quantities required for analysis. Of the remaining 38 experiments, this
analysis uses 28 and excludes 10. We include all experiments conducted with
standard protocol in which the ice cloud can be reasonably expected to
approach saturation. Five experiments are excluded for overly long relaxation
times, and five for non-standard protocol. See Tables S3 and S4 for
characteristics of included and excluded experiments, respectively. We
estimate vapor relaxation times for each point in each experiment using the
expression of , which takes into account cooling rate
(effective updraft speed), ice particle number, and particle size to estimate
the timescale for achieving a dynamical equilibrium value (see Sect. S2.3 for
expression). For each experiment, we determine τmin, the
minimum relaxation time at any point during the experiment,
and texp, the time interval over which the calculated relaxation
time is within a factor of two of τmin. We consider that an
experiment should reasonably approach dynamical equilibrium if
texp/τmin>4.
The five non-standard experiments excluded are Experiment 1, which had an abnormally short pumping time, and Experiments 40–43, where the chamber was prepared with dry walls. Pumping in Experiment 1 lasted only 250 s, vs. 400–750 s in all other experiments; we would expect more inhomogeneities in the resulting ice cloud. Experiments with dry walls pose a problem for our analysis because the lack of an ice source means that these experiments do not involve extended periods of ice growth near saturation.
Examples of experiments in which vapor is controlled by cirrus
uptake (a–c) and wall flux (d–f).
Experiment 16 (a-c) is a heterogeneous nucleation experiment onto
ATD, and Experiment 30 (d–f) is a homogeneous nucleation experiment
with SA aerosol. The top panels of each plot show the pressure (green) and
temperature (red) evolution. The action of the chamber pumps results in a
pseudo-adiabatic expansion that cools the chamber gas rapidly at first, then
more slowly until the cooling is balanced by heat flux from the chamber
walls. The chamber gas warms as soon as the pumps have stopped. The middle
panels show Murphy–Koop (MK) saturation (red dashed line), measured
saturation (black), ice particle number (light blue), and the cloud's ice
water content (dark blue). Ice nucleation starts at the peak in saturation,
and is followed by a sharp increase in particle number and rapid cloud
growth. Saturation relaxes back to a constant value, where it stays until the
pumps turn off. In the cirrus-dominated experiment, that value is the
saturation vapor pressure over ice. In the wall flux-dominated experiment,
that value of about 6 % supersaturation is what is required to drive enough
ice growth to balance the wall outgassing. When the pumps stop, the vapor
pressure returns to the wall-controlled value in the cirrus-controlled
experiment, but in the wall flux-controlled experiment the decay rate is
limited by wall uptake. The bottom panels show total water (green) and water
vapor (black). The small data gap in Experiment 30 at around 1100 s is due
to realignment of the chamber's White cell mirrors.
The 28 experiments used in this analysis still show a range of
characteristics, and can be grouped into two broad categories. In
“cirrus-dominated” experiments (see Fig. , left, for
example), the wall flux is comparable to ice uptake driven simply by the
change in saturation vapor pressure on cooling. In these experiments water
vapor concentrations draw down quickly to saturation. In the colder
experiments, however, wall flux is generally far more substantial. In these
“wall-dominated” experiments (Fig. , right), peak total water
rises to many times greater than initial water vapor, water vapor remains
supersaturated during the growth phase of the experiment, and then becomes
subsaturated during evaporation. This deviation complicates analysis and
requires a growth model to determine saturation vapor pressure. For
consistency, we treat all experiments the same, and extract saturation vapor
pressure using the same method.
The two examples shown in Fig. illustrate the key features of
each type of experiment. In the cirrus-dominated experiment
(Fig. , left), the onset of nucleation produces rapid ice
growth and a corresponding drawdown of vapor pressure to a value close to
saturation. The ice cloud then grows slowly for the remainder of the
expansion experiment, with water provided by the ice-covered chamber walls.
In this particular case, a heterogeneous nucleation experiment with abundant
ice nucleating particles, ice nucleation occurs at a relatively low
supersaturation, and the ice particle number reaches ∼400 cm-3
before decreasing nearly in proportion to the action of the pump. After the
pumping stops, the ice cloud decays over a roughly 200 s period, and the
chamber vapor pressure returns to the wall-controlled value.
In the wall-dominated experiment (Fig. , right), significant
supersaturation with respect to MK persists throughout the experiment. In
this particular case, initial supersaturation is quite high since the chamber
was prepared with only sulfuric acid droplets to study homogeneous
nucleation. The onset of nucleation again produces a drawdown of
supersaturation, but only to a value of about 6 %, which remains fairly
constant for the duration of pumping. This is the value required to drive the
strong continuing ice growth that balances the wall flux. Once the expansion
stops, the chamber air warms, the walls become a water sink rather than a
source, and chamber vapor pressure drops to RHice∼95 %, the
value required to drive enough evaporation to balance wall uptake. After the
ice cloud has nearly dissipated, the chamber vapor pressure again returns to
the wall-controlled value.
These chamber dynamics mean that saturation vapor pressure in IsoCloud
experiments cannot be determined simply by measuring the water vapor content
in the chamber after an ice cloud has developed. The colder the experiment,
the more wall dominated it typically becomes, so that experiments show
steadily increasing long-term supersaturations with respect to Murphy–Koop
saturation (MK) as temperature decreases, rising by approximately 5 % over
the temperature range 225–185 K (Fig. ). This rise should not
be interpreted as the result of a temperature-dependent saturation vapor
pressure, but instead as a temperature-dependent balance between wall flux
and diffusional ice growth. The wall flux contribution is relatively larger
for colder experiments because saturation vapor pressure falls sharply with
reduced temperature and the diffusional ice growth rate drops with vapor
concentration. The result is that the colder the temperature, the larger the
supersaturation over ice required to produce steady-state relative humidity.
For this reason we use an ice growth model to retrieve saturation vapor
pressure by modeling vapor pressure evolution during each experiment. The
model, fitting procedure, and uncertainty analysis are described in
Sect. .
Average measured saturation of the 28 IsoCloud experiments after
relaxation back to a near-constant value. Supersaturations plotted are the
average value of the final 200 s of pumping. Experiments are colored by
aerosol/IN type: Arizona test dust (ATD, black), liquid sulfuric acid
droplets (SA, red), secondary organic aerosol (SOA, green), and experiments
containing both ATD and SA (blue). Warm, cirrus-dominated experiments
(T≥195 K) typically show vapor pressures close to MK, with saturations
from 0.98 to 1.00. Cold, wall-dominated experiments (below ∼195 K) show
saturations that rise with decreasing temperature. Higher supersaturation is
necessary for the cirrus growth rate to match the mass flux off the chamber
walls. This effect means that an ice growth model is necessary to extract the
saturation vapor pressure. Water vapor retrievals during the pumping interval
in Experiment 33 are quite subsaturated with respect to MK.
Analysis
We model the vapor pressure evolution during each experiment assuming
diffusional growth to a sphere (Eq. ). Model inputs are all
measured or derived from measured quantities – ice mass, particle number,
growth rate, pressure, and temperature – with saturation vapor pressure as
the single free parameter. That is, we assume saturation vapor pressure over
ice is esat=xei, where ei is the
Murphy–Koop saturation vapor pressure and x is a
constant scale factor separately fit for each experiment. The model predicts
the evolving chamber vapor pressure, and we fit that prediction to the
observed H2O vapor pressure, minimizing the difference between
observed and calculated values.
Ice growth model
The model is obtained by rearranging an expression for the diffusional growth rate over ice to calculate the far-field water vapor pressure:
e=xei1+m˙Li4πr‾ka*T∞LiMwRT∞-1+m˙RT∞4πr‾αDv*Mw.
Measured and derived quantities here are m˙, the per-particle growth
rate (change in total ice mass/time/particle number); r‾, the
average particle radius; T∞, the gas temperature in the chamber;
and we identify the far-field vapor pressure e as the measured vapor
pressure. Parameters are Mw, the molar mass of water;
Li, the latent heat of sublimation; Dv*, the
diffusivity of water in air with kinetic corrections; α, the
accommodation coefficient; and ka*, the thermal accommodation
coefficient, which is taken here to be unity . The average
radius of the ice particles, r‾, is calculated from the total ice
water mass and particle number counts described previously, and a
temperature-dependent ice density. The bulk density of ice varies by about
1 % between -10 and -100∘C; the values used in this work are from a quadratic fit
to data from , which are based on the X-ray
diffraction measurements of . We assume the particles
are spherical, which is a reasonable approximation for small, micrometer-sized
particles. The diffusivity of water vapor in air, Dv*, is also
temperature dependent, and is evaluated using the functional form of
, which includes kinetic corrections
. Note that one limitation of this
method is that it can yield only a bulk value, and is not sensitive to
situations in which a small subset of ice crystals are metastable.
Confounding issues and corrections
We apply sensitivity tests or corrections to three issues that might confound
analysis: loss of ice crystals by pumping, uncertainty in the accommodation
coefficient, and undercounting of particles. The issues are sufficiently
unproblematic that they are not included in the formal uncertainty analysis
of Sect. .
The pseudo-adiabatic expansion procedure during experiments results in a loss of ice mass as air is removed from the chamber. This loss must be accounted for in order to accurately estimate ice mass change by sublimation/deposition to/from the vapor. The pumps remove a constant volume of gas from the chamber in each time interval, and we assume that they act in the same manner upon the small ice particles found in our experiments. With this assumption, we correct the ice growth rate by subtracting the assumed pumping losses from the derivative of the cirrus ice mass.
The accommodation coefficient α in Eq. () is not well constrained, with significant variation in the literature. α can be thought of as the probability that a molecule of water vapor that strikes the surface of an ice particle is incorporated into the ice matrix, and can be sensitive to experimental conditions. For similar chamber experiments, studies have shown that the accommodation coefficient can be treated as a constant during an experiment with values close to 1 . We test the sensitivity of our vapor pressure model to uncertainty in the accommodation coefficient by running the model with different values of α, and find that the derived results for saturation vapor pressure are quite insensitive to the exact values of α in the range of 0.2 to 1 (Fig. S8 in the Supplement). We therefore use a value of 1 throughout this work, but note that if the true α value is below 0.2, then this assumption will result in an overestimate of the saturation vapor pressure.
Undercounting of particles may occur because the Welas 1 optical particle
counter has a size cutoff for small particles of 0.7 µm in
diameter. In these experiments, we never see evidence of very large
particles, but very small particles are common at the beginnings and ends of
experiments, immediately after nucleation or towards the end of sublimation,
respectively. For some experiments at the coldest temperatures, where initial
water vapor and final ice mass are small, we also expect undercounts
throughout the experiment. Mean particle size in these experiments is
strongly temperature dependent, ranging from ∼1µm at 189 K
to ∼5µm at 235 K. Failure to account for undercounting would
lead to an overly large average radius, and could produce a low bias in
retrieved saturation vapor pressures.
We deal with the undercounts in two ways: we exclude all time periods in
which the calculated average radius is less than 0.85 µm, and we
conduct sensitivity tests on the resulting analyses. In several of the colder
experiments, however, the mean calculated radius remains below 1 µm
throughout the experiments. In these marginal cases assuming a log-normal
distribution produces estimated undercounts of up to 50 % throughout the
experiment (see Supplement for details of this calculation). We therefore
conduct sensitivity analyses on all experiments of uncertainty due to
potential undercounting by increasing ice particle counts by factors of 1.5,
2, and 5 (Fig. S9). Undercounting can result in underestimation of saturation
vapor pressure, but most IsoCloud experiments show a sensitivity of less than ±0.5 % in retrieved saturation vapor pressure, even in the unrealistic case of undercounting by a factor of 5. Maximum sensitivity to undercounting occurs in the three homogeneous nucleation experiments, where particle sizes are smallest, but still remains under +2.5 % even in the most extreme case tested.
Ability to diagnose saturation vapor pressure
Before fitting our experimental data, we conduct a preliminary
proof-of-concept exercise to evaluate whether the vapor pressure model is
indeed sensitive to assumptions about saturation vapor pressure. We calculate
evolution of the chamber vapor pressure during selected representative
experiments under three different assumptions of saturation vapor pressure
values: MK saturation, and MK multiplied by factors of 1.1 and 0.9. Comparing
these calculations to the observed values, we see that even small changes in
the assumed saturation vapor pressure result in significant deviations from
the measured chamber water vapor in both cirrus-dominated and wall-dominated
experiments (Fig. ). Results suggest that experiments are
sufficiently sensitive to resolve differences in saturation vapor pressure of
a few percent. This test establishes that observations of chamber vapor
pressure during ice growth can in fact constrain the saturation vapor
pressure in all the IsoCloud experiments.
Model output for experiments used in the previous example: the
cirrus-dominated Experiment 16 (a) and wall-dominated
Experiment 30 (b). Each output is calculated using observed
quantities under three different assumptions about the “true” saturation
vapor pressure of cirrus. The red line assumes the true value is 10 % lower
than the MK saturation vapor pressure, the blue line assumes it is 10 %
higher, and the green line assumes MK is the true saturation vapor pressure.
Calculated saturations are smoothed by 30 points (∼30 s). Measured
saturations are unsmoothed. Experiments are very sensitive to the assumed
vapor pressure, although wall-controlled experiments are noisier overall
since they are typically at lower temperatures. Experiment 16 shows spikes in
measured water which are due to real sampling of different air masses during
the turbulent period when the pumps are on and the cirrus cloud is growing.
Experiment 30 shows oscillations in the model output due to in-mixing of
warmer air from the top of the chamber. This type of temperature fluctuation
is not captured by the model.
Fitting procedure and region choice
The model is fit to the observed chamber vapor pressure using least-squares optimization. We use MPFIT , a Levenberg–Marquardt least-squares minimization routine written in IDL, based on the MINPACK algorithm . This routine attempts to minimize the difference between the model and observation by varying the scaling factor x in Eq. (), which multiplies the Murphy–Koop parametrization of the saturation vapor pressure over ice. The fit routine yields a single value of x for each experiment, which is multiplied by MK saturation to best fit the observations.
The fit region for each experiment is selected using three criteria. (1) The
fit region must start after the maximum ice particle number count has been
attained. In most experiments, the maximum particle count is achieved within
about 50 s of the peak in saturation associated with the onset of
nucleation. During the preceding brief period of rapid ice growth,
significant particle undercounts are likely. (2) We exclude all time periods
when the Welas 1 instrument reports fewer than 12 ice particles per cm3.
This criterion typically excludes the late portions of experiments when the
ice cloud has almost completely decayed. (3) We exclude all the time periods
in which the average particle radius is less than 0.85 µm, as
described in Sect. , again because particle undercounts are likely.
This criterion becomes relevant for cold experiments, in which vapor
pressures are low and particles grow slowly and remain small (in IsoCloud
experiments at temperatures below 195 K, average particle radius remains
under 1.5 µm at all times). These criteria result in an average fit
region length of ∼700 s. The shortest fit region is 259 s
(Experiment 9) and the longest fit region is 1647 s (Experiment 21). Colder
experiments tend to have longer fit regions, since in these experiments the
ice cloud can linger for tens of minutes after pumping has ceased.
Uncertainty analysis
We calculate error bars for each experiment that reflect uncertainties from
several sources: intrinsic measurement precision for water vapor and other
key observables, uncertainty due to experimental artifacts (e.g., chamber
inhomogeneities that affect model fits), and systematic offsets (e.g., line
strength errors that produce multiplicative errors in derived vapor
pressures). We group the first two categories, measurement precision and
chamber artifacts, under the term “instrumental uncertainty”.
Instrumental uncertainty for each experiment is calculated using the Monte
Carlo method: for each experiment, we generate 2000 parameter sets in which
each physical parameter used in the calculation is randomly drawn from its
estimated distribution, and then we run the fit routine on each set. See
Table S1 for the list of parameters varied and their assumed distributions.
AIDA chamber temperature uncertainty of 0.3 K is included in this table as a
random variable, although the temporal correlation of temperature errors is
not well known. Resulting error bars may therefore be slightly
underestimated. The resulting distribution of saturation vapor pressure
values is nearly normal in each experiment, and we take its standard
deviation to be the component of the error bar associated with instrumental
uncertainty. estimate that mechanical vibrations and chamber inhomogeneities result in optical path length fluctuations of about 0.1 %. Due to the rapid timescales of these fluctuations, their associated uncertainty is added directly into the instrumental uncertainty budget.
The primary source of systematic offsets is uncertainty in the spectroscopic
parameters used to retrieve water vapor concentrations from the observed
spectral features. All spectroscopic parameters are taken from the
HITRAN 2016 database , which provides uncertainty estimates
for several parameters used in this analysis, namely line strength (S),
air-broadened half-width (γair), and the
temperature-dependence coefficient (nair)
of γair. Line strength errors arise in two ways: through raw
uncertainty of the measured line strength at the reference temperature of
296 K, and through uncertainty in the measured experimental gas temperature
that propagates to uncertainty in the calculated temperature-dependent line
strength. The ChiWIS instrument uses the H2O line at
3789.63481 cm-1, which has a stated 1σ uncertainties in S,
γair, and nair of ±1 %, ±1 %, and
±10 %, respectively, in HITRAN 2016. Uncertainty in S propagates
directly into a ±1% systematic uncertainty in retrieved concentrations.
The uncertainties in γair and nair correspond to
uncertainties in concentration retrieval of 0.5 % and 1.5 %,
respectively, in the typical temperature and pressure range of the IsoCloud
experiments. AIDA chamber temperature uncertainty is assumed to be randomly
distributed, and contributes an additional line strength uncertainty of
0.1 % (note that typical temperature declines of
5–9 K during expansion experiments
are automatically incorporated in the retrievals). Systematic errors due to
uncertainty in spectroscopic parameters are added directly to the calculated
error bars, and in all but the coldest experiments are the dominant source of
uncertainty. A final contribution to systematic uncertainty comes from
uncertainty in the length of the ChiWIS free space etalon, which propagates
directly into the wavelength scale and contributes an estimated 0.2 % to
all experiments.
Several other factors produce uncertainties on timescales of days or shorter
and are treated here as contributing to the instrumental uncertainty budget.
Uncertainty in White cell optical path length due to mechanical vibrations
and chamber inhomogeneities occurs at rapid timescales and is estimated at
0.1 % . Thermal expansion and contraction of the
whole chamber over the experimental temperature range produces uncertainty of
0.1 % in the White cell path length and is day specific, since the chamber
is run at a constant base temperature on each experimental day. Finally, the
fit routine itself involves intrinsic uncertainty that is also likely day
specific, since fits for each experimental day are typically done as a group
and share typical temperature and water vapor concentrations. Sensitivity
tests suggest that the choice of fit parameters (baseline, fit region, etc.)
over a reasonable set of values may alter retrieved concentrations by about
1 %.
Because the final output of the analysis is the relationship of saturation
vapor pressure to temperature x(T), we must consider a final source of
uncertainty, that each experiment produces a single value for x but spans
several degrees of cooling. We therefore construct horizontal error bars to
acknowledge the spread in T, assigning them the standard deviation of
chamber temperatures during the experimental fit period. These error bars are
typically smaller in warmer experiments, since fit regions in that regime lie
almost completely within the time interval when the wall heat flux balances
the adiabatic cooling. Horizontal error bars are larger in colder experiments
since the ice cloud often persists for some time after the pumps turn off and
the chamber begins to warm back to its base temperature.
Results
Results of fitting the IsoCloud experiments show a saturation vapor pressure consistent with MK, with no increase in retrieved saturation vapor pressures at low temperatures (Fig. ). estimate the vapor pressure of cubic ice to be 3 %–11 % above that of hexagonal ice, and our measurements are consistent with the lowest postulated values for its vapor pressure. All experiments are inconsistent with the range of vapor pressures given for metastable ice by . Following the work of , which suggests that the entropies of cubic and hexagonal ices are nearly identical, we assume the same is true of ice Isd and extrapolate the measured values of to temperatures higher than their measurement range of 181–191 K (Fig. , blue dashed curve). The properties of metastable ice I are likely dependent on its method of preparation, and it is thus possible that ice grown through deposition from the vapor may have a different vapor pressure than ice prepared by annealing amorphous ice, as is done by .
Retrieved saturation vapor pressures for the 28 IsoCloud experiments
fitted, expressed as a fraction of MK saturation, and plotted against mean
experiment temperature. The red dashed line represents MK saturation. The
blue dashed line represents the value for the vapor
pressure of metastable ice, and the blue dotted lines show the errors of that
measurement. Experiments are colored by aerosol/IN type: Arizona test dust
(ATD, black), liquid sulfuric acid droplets (SA, red), secondary organic
aerosol (SOA, green), and experiments containing both ATD and SA (blue). All
experiments that include solid dust (black, blue) undergo heterogeneous
nucleation; those with SA or SOA only (red, green) undergo homogeneous
nucleation. Horizontal error bars are the standard deviation of the
temperatures over the fit region. Vertical error bars show both the 1σ
instrumental uncertainty (thick width), which is greater at colder
temperatures, and the larger systematic linestrength uncertainty (thin
width), which is identical for all experiments. Derived saturation vapor
pressures in general are consistent with MK (to within systematic
uncertainty), and exhibit no trend with temperature for each aerosol type (to
within instrumental uncertainty). Note that the fitting procedure means that
Experiment 33, the outlier in Fig. , yields a saturation vapor
pressure consistent with other experiments. Experiments do show lower values
in the cases where liquid aerosols are present. Compare to Fig. A2 in
.
Some differences are apparent between experiments with different ice
nucleating particles, so we focus first on those with only solid particles
(Arizona test dust, black points in Fig. ). These experiments
cover a temperature range from 235 to 193 K and have low 1σ
instrumental uncertainty of less than +0.5 % (see Fig. S6, which shows
model results labeled by experiment number and plotted with only instrumental
uncertainties). They show no temperature-dependent effects that could explain
anomalous supersaturations observed in field experiments. Derived saturation
mixing ratios throughout the experimental temperature range are all
consistent to within 2σ instrumental uncertainty (i.e., 1 % of MK).
To test more carefully for any trend in saturation vapor pressure with
temperature, we also perform a total least-squares two-parameter fit on the
ATD model results. Since we intend here to examine differences between
different types of experiments, we only consider instrumental, day-, and
experiment-specific uncertainties, and leave out the systematic uncertainties
which are uniform across all experiments. This line-fitting method takes into
account uncertainty in both variables (in our case, experimental temperatures
and instrumental uncertainties) rather than ascribing uncertainty only to a
dependent variable. The fit yields an intercept of 98.6 % ± 0.3 %
of MK at the mean temperature of 209.3 K, and a slope of -0.027 %
K-1±0.031 % K-1. The experiments are consistent with MK to
well within their ∼±4.5 % systematic uncertainty. The fitted trend
with temperature is not significant, equivalent to a change over the
40∘ K IsoCloud range of 1.1 % ± 1.2 % of MK saturation.
The experiments performed at the highest temperatures in IsoCloud also
demonstrate that the ice growth model used in this work does not introduce
artifacts into the retrieved saturation vapor pressures. In these experiments
(numbers 3–17, at T=205–235 K), ice particle number is high and ice
cloud growth dominates, so the chamber vapor pressure should draw down
quickly to saturation. The values to which these experiments relax (shown in
Fig. ) are effectively identical to those derived in our more
complex analysis procedure: 98.0 %–100.0 % in the simple calculation of
Fig. , and 97.5 %–99.5 % in the fits of
Fig. . This similarity confirms that the use of an ice growth
model does not bias the derived saturation vapor pressure values.
Experiments in which liquid aerosols are present result in derived saturation
vapor pressures on average lower than those with only ATD, although only a
single experiment is inconsistent given the instrumental uncertainty
(Fig. , which shows experiments below 205 K and
instrumental-only error bars). All experiments with sulfuric acid aerosols
present show slightly lower vapor pressures than experiments with only solid
ice nucleating particles (black). Heterogeneous nucleation experiments show a
slight effect (blue) and homogeneous nucleation experiments with only liquid
aerosols show a stronger one (red, green). ATD experiments are on average
1.4 % below MK; ATD-SA points are on average 2.7 % below; and the three
homogeneous nucleation experiments are 4 %–10 % below MK (note that
these experiments have large instrumental uncertainty, as they are cold and
dry). Total least-squares fits to the ATD and ATD-SA experiments show that
they are not significantly different from each other. The intercept at the
ATD-SA mean temperature of 196.0 K is 97.3 % ± 0.8 %, which is
lower than but overlaps with the expected ATD value at that temperature of
98.2 % ± 0.5 %. ATD-SA experiments show no significant temperature
dependence in deviation from MK saturation vapor pressure (fitted
slope =-0.03 % K-1±0.2 % K-1).
Zoomed in view of the experiments below 205 K. Linestrength errors result in the same shift for all experiments, so they are not included in the error bars here. Experiments containing sulfuric acid are ∼2.6 % lower on average than those containing pure ATD experiments. Experiments are colored by aerosol/IN type and MK saturation line is included for reference.
One possible explanation is that liquid aerosols may introduce some additional factor that depresses the implied saturation vapor pressure in our analyses. These experiments take place at cold temperatures and are probably subject to undercounting, but the tests of sensitivity to ice particle number described in Sect. suggest that undercounting cannot completely account for the observed differences. A more plausible mechanism may be water uptake by hygroscopic SA and SOA aerosols that remain unfrozen during the experiments. Such a population of aerosols would have a vapor pressure lower than that of ice. This effect would not be captured by the ice growth model, and would systematically lower retrieved saturation vapor pressures.
Discussion and conclusions
We see no evidence of anomalous supersaturation greater than ∼4.5 % in
ultracold cirrus formation, which cannot account for the largest field
observations. Our results show no temperature-dependent changes in retrieved
saturation vapor pressure that could explain field observations. Those colder
experiments that show anomalies show low rather than high saturation vapor
pressures, which are likely artifacts resulting from the presence of liquid
aerosols. For experiments with only solid ice nucleating particles, retrieved
saturation vapor pressures are essentially identical throughout the
185–235 K experimental temperature range. Results are consistent with the
MK parametrization throughout, with a mean value of MK -1.4 %, well within the ∼4.5 % systematic uncertainty. Scatter in experiments is small, and all experiments are inconsistent with the parametrization given by Shilling.
These results suggest that field measurements of anomalous supersaturation at
low temperatures are most likely either the consequence of dynamical effects
or of experimental error, or some combination of both. In heterogeneously
nucleated cirrus with sparse nuclei, ice growth times may be so slow as to
leave persistent observable supersaturation on the timescales of natural
temperature fluctuations. For example, report in situ
observations during the ATTREX campaign of thin cirrus with low particle
number densities (∼0.01 cm-3), supersaturations up to 70 %, and
estimated relaxation timescales of hours or longer.
summarize 20 high-altitude aircraft flights and report frequent
supersaturation in cirrus, but also low number densities
(∼0.01 cm-3) and estimated relaxation timescales of hours to
days
Note that in this work, where we use particle number densities
on the order of 50 cm-3, our need for an ice growth model stems not
from long relaxation times, but from the additional wall ice source not
present in atmospheric cirrus.
. The possibility that experimental error
contributes to some observations of anomalous supersaturation cannot be
entirely eliminated. Measurements of water vapor in the UT/LS are notoriously
difficult due to the cold temperatures found there, and just 1 ppmv of
contaminating H2O in an instrument (due to inlet icing or
outgassing) could lead to an anomalous supersaturation signal of ∼25 %
in a 190 K cold cirrus cloud at ∼17 km in the Tropical Tropopause Layer (TTL).
The IsoCloud experiments suggest that metastable forms of ice need not be considered in cloud models since they either do not form or do form but do not exhibit a vapor pressure significantly different from ice Ih. Many studies have suggested
that metastable ice should nucleate and persist in cirrus at these
temperatures. While suggest from modeling studies that
vapor-deposited ice should be hexagonal above 200 K, their work leaves open
the possibility that metastable ice could form at colder temperatures. The
experiments discussed here imply that if metastable ice does form, it must be
free of the defects and imperfections that are assumed to result in higher
vapor pressures than hexagonal ice Ih. The experiments shown here place strong constraints on ice
formation in the atmosphere, because the rapid cooling times in IsoCloud
should be maximally favorable to creating these defects, and the short
experimental timescales mean that we should detect its effects before any
annealing to hexagonal ice. These results suggest that even if metastable ice
does form in the UT/LS region, its effect on vapor pressure and on transfer
of water to the stratosphere would be small, less than ∼4.5 %.
Although these results suggest that ice Isd cannot induce
anomalous supersaturation in the UT/LS, ice Isd may nevertheless
be of climatic importance if its radiative properties differ from those of
hexagonal ice. Preliminary findings by suggest that
trigonal crystals, which are associated with ice Isd, have a
lower absorption efficiency than hexagonal ones, and that for column crystals
in particular over a broad range of sizes, trigonal column crystals have a
significantly larger single-scattering albedo than do scalene column crystals
or hexagonal column crystals. Since saturation vapor pressure seems not to
provide an indication of the presence of ice Isd, further
experiments would be needed to determine the conditions under which
ice Isd may nucleate and grow under deposition in the ultracold
regions of the UT/LS. High time-resolution diffraction measurements paired
with observations of atmospherically relevant observables like water vapor
pressure and crystal habit offer one possible method of probing the presence
of ice Isd. Moreover, the type of ice that first nucleates may
influence crystal habit even if subsequent deposition is of purely hexagonal
ice . Exploring the conditions in which
ice Isd and metastable ices can form in real atmospheric
conditions may then also be important for understanding their radiative
importance and possible future changes.
Data availability
The IsoCloud data sets can be found at
10.6082/uchicago.2132.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-1089-2020-supplement.
Author contributions
BWC and EJM led the data analysis; EJM directed the construction and operation of ChiWIS; LCS led the design of ChiWIS; KDL, BWC, and LCS built and operated ChiWIS; KDL, BWC, LCS, and AN analyzed raw ChiWIS data to produce water vapor measurements; HS provided and operated multipass optics; HS and OM operated AIDA during the IsoCloud campaign; HS and OM provided and interpreted AIDA instrument data; VE provided SP-APicT and APeT data; and BWC and EJM wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge the many individuals who contributed to the IsoCloud
project, including Stephanie Aho, Naruki Hiranuma, Erik Kerstel,
Benjamin Kühnreich, Janek Landsberg, Eric Stutz, and Steven Wagner, as
well as the AIDA technical staff and support team who made this work
possible. Eric Jensen, Martina Krämer, and Andrew Gettelman provided
helpful discussions and comments. The authors thank the two anonymous
referees for their useful comments. Kara D. Lamb acknowledges support from a National Defense Science and Engineering Graduate Fellowship and an NSF Graduate Research Fellowship and Laszlo C. Sarkozy acknowledges support from a Camille and Henry Dreyfus Postdoctoral Fellowship in Environmental Chemistry.
Financial support
This research has been supported by the National Science
Foundation (grant no. CHEM1026830) and the Deutsche Forschungsgemeinschaft
(grant nos. MO 668/3-1 and EB 235/4-1). This work was supported by the National Science Foundation (NSF) and the Deutsche Forschungsgemeinschaft (DFG) through the International Collaboration in Chemistry program (NSF grant #CHEM1026830 and DFG grants MO 668/3-1 and EB 235/4-1) and by the NSF through the Partnerships in International Research and Education program (grant #OISE-1743753).
Review statement
This paper was edited by Hang Su and reviewed by two anonymous referees.
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