ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-543-2019Lagrangian simulation of ice particles and resulting dehydration in the polar winter stratosphereSimulation of polar stratospheric ice particlesTritscherInesi.tritscher@fz-juelich.dehttps://orcid.org/0000-0001-5285-7952GrooßJens-Uwehttps://orcid.org/0000-0002-9485-866XSpangReinholdhttps://orcid.org/0000-0002-2483-5761PittsMichael C.https://orcid.org/0000-0001-8240-7223PooleLamont R.MüllerRolfhttps://orcid.org/0000-0002-5024-9977RieseMartinhttps://orcid.org/0000-0001-6398-6493Institute of Energy and Climate Research: Stratosphere (IEK-7), Forschungszentrum Jülich, 52425 Jülich, GermanyNASA Langley Research Center, Hampton, Virginia 23681, USAScience Systems and Applications, Inc., Hampton, Virginia 23666, USAInes Tritscher (i.tritscher@fz-juelich.de)14January201919154356329March201815May201813December201818December2018This 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/19/543/2019/acp-19-543-2019.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/19/543/2019/acp-19-543-2019.pdf
Polar stratospheric clouds (PSCs) and cold stratospheric aerosols drive
heterogeneous chemistry and play a major role in polar ozone depletion. The
Chemical Lagrangian Model of the Stratosphere (CLaMS) simulates the
nucleation, growth, sedimentation, and evaporation of PSC particles along
individual trajectories. Particles consisting of nitric acid trihydrate
(NAT), which contain a substantial fraction of the stratospheric nitric acid
(HNO3), were the focus of previous modeling work and are known for
their potential to denitrify the polar stratosphere. Here, we carried this
idea forward and introduced the formation of ice PSCs and related dehydration
into the sedimentation module of CLaMS. Both processes change the simulated
chemical composition of the lower stratosphere. Due to the Lagrangian
transport scheme, NAT and ice particles move freely in three-dimensional
space. Heterogeneous NAT and ice nucleation on foreign nuclei as well as
homogeneous ice nucleation and NAT nucleation on preexisting ice particles
are now implemented into CLaMS and cover major PSC formation pathways.
We show results from the Arctic winter 2009/2010 and from the Antarctic
winter 2011 to demonstrate the performance of the model over two entire PSC
seasons. For both hemispheres, we present CLaMS results in comparison to
measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization
(CALIOP), the Michelson Interferometer for Passive Atmospheric Sounding
(MIPAS), and the Microwave Limb Sounder (MLS). Observations and simulations
are presented on season-long and vortex-wide scales as well as for single PSC
events. The simulations reproduce well both the timing and the extent of PSC
occurrence inside the entire vortex. Divided into specific PSC classes, CLaMS
results show predominantly good agreement with CALIOP and MIPAS observations,
even for specific days and single satellite orbits. CLaMS and CALIOP agree
that NAT mixtures are the first type of PSC to be present in both winters.
NAT PSC areal coverages over the entire season agree satisfactorily. However,
cloud-free areas, next to or surrounded by PSCs in the CALIOP data, are often
populated with NAT particles in the CLaMS simulations. Looking at the
temporal and vortex-averaged evolution of HNO3, CLaMS shows an uptake
of HNO3 from the gas into the particle phase which is too large and
happens too early in the simulation of the Arctic winter. In turn, the
permanent redistribution of HNO3 is smaller in the simulations than
in the observations. The Antarctic model run shows too little denitrification
at lower altitudes towards the end of the winter compared to the
observations. The occurrence of synoptic-scale ice PSCs agrees satisfactorily
between observations and simulations for both hemispheres and the simulated
vertical redistribution of water vapor (H2O) is in very good
agreement with MLS observations. In summary, a conclusive agreement between
CLaMS simulations and a variety of independent measurements is presented.
Introduction
The representation of polar stratospheric clouds (PSCs) in global models is often poor despite their
importance for ozone chemistry in polar winter and spring. In the lower
stratosphere, PSCs provide surfaces for heterogeneous reactions activating
chlorine reservoir species and thus accelerating ozone loss
. Even though the importance
of liquid particles with respect to chlorine activation and ozone depletion
has been shown in recent publications , solid particles influence heterogeneous chemistry substantially
. Especially sedimentation of solid PSC particles
irreversibly changes the chemical composition of the lower stratosphere and
alters the process causing ozone depletion through denitrification
and dehydration .
Further, also the uptake of nitrogen-containing species in PSCs changes the
chemical composition of the lower stratosphere under PSC conditions
substantially, with important impact for ozone loss chemistry
. Finally, model results
for different types of particles are not additive in a simple way as shown by
. Therefore, particle surface areas in models should be
described as precisely and realistically as possible.
Thanks to the Montreal Protocol and its amendments and adjustments,
concentrations of ozone-depleting substances are now decreasing continuously
and now present evidence that
the healing of the Antarctic ozone layer has actually started. However,
recent years showed new record ozone losses above the Arctic winter pole
, a crucial reason to still step up efforts in
understanding and modeling PSC formation on global scales better. Facing
climate change, an in-depth understanding of atmospheric processes becomes
even more important and a complete and comprehensive knowledge of processes
affecting stratospheric ozone is required to reliably predict the future
evolution of the stratospheric ozone layer.
PSCs are supposed to consist of liquid supercooled ternary solution (STS)
droplets, solid nitric acid trihydrate (NAT) particles, and/or solid ice
particles . Their formation mechanisms are still a focus of
research, newly motivated by global, high-resolution satellite observations
. Due to unknown processes in the formation of
solid PSC particles, large differences in the parameterization of PSCs in
global models exist. Further, a detailed PSC formation scheme may require
large computing times and therefore is not applicable in every model. Using
PSC schemes of different complexity, the representation of PSCs in models
varies as well. Most current global models use a simplified PSC scheme that
prescribes number densities and particle radii and assumes thermodynamical
equilibrium . Some chemistry climate models (CCMs)
like SD-WACCM and EMAC (ECHAM5/MESSy Atmospheric Chemistry model) offer
submodels with more detailed PSC schemes. Those can be coupled to the
standard model setup for intensive PSC studies, as done by
with EMAC and by and with SD-WACCM. However, as presented recently by
, comparisons of measured PSC properties with
corresponding EMAC results show deficiencies. Before , the
microphysical model for ice particles within SD-WACCM was missing and the
fact that NAT nucleation in SD-WACCM is still based on the homogeneous
surface nucleation scheme by is a matter of debate
. Non-satisfying agreement between models and observations
as well as fundamental differences, e.g., in the NAT nucleation, exist even in
advanced PSC schemes, which further motivated the research presented in this
paper.
Here, we present new developments extending the sedimentation module of the
Chemical Lagrangian Model of the Stratosphere (CLaMS). We added ice PSC
particles to complete the Lagrangian PSC scheme, which allows comparisons to
PSC measurements and simulations of de- and rehydration in the Arctic and
Antarctic to be performed. To demonstrate the performance of the new CLaMS
ice sedimentation module, we have chosen two distinct winters, one Arctic and
one Antarctic winter. The Arctic winter 2009/2010 shows widespread ice PSCs
during mid-January and is therefore ideally suited to test our new ice
scheme. Additionally, the Arctic winter has been the focus of the intensive
RECONCILE aircraft campaign, which took place from January until March 2010
. The 2011 Antarctic winter is amongst the colder Antarctic
winters but still representative for other Antarctic
winters see Fig. 3.5 in. Global satellite data from the
Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)
and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)
are available for both winters and allow a comprehensive
evaluation of our model results to be conducted.
CLaMS model description and setup
The Chemical Lagrangian Model of the Stratosphere (CLaMS) is a global,
three-dimensional chemical transport model (CTM) based on the Lagrangian
principle . CLaMS is
structured into modules, which can individually be switched on and off as
needed. The principal CLaMS modules are the trajectory module, the mixing
module, and the chemistry module. Within the CLaMS trajectory module, air
parcels are advected forward in time based on prescribed wind fields. This
study makes use of wind and temperature fields from ERA-Interim analyses
provided by the European Centre for Medium-Range Weather Forecasts (ECMWF)
. Total diabatic heating rates are also taken from
ERA-Interim and used to determine vertical velocities .
CLaMS uses a hybrid vertical coordinate (ζ). At pressure levels
lower than 300 hPa, ζ can be interpreted as potential
temperature (θ). Towards higher pressure levels, ζ
transforms from an isentropic to a pressure-based coordinate
. Mixing is induced where the underlying wind field
shows large shear, diagnosed by the Lyapunov exponent .
Wherever the Lyapunov exponent exceeds a critical value, mixing between air
parcels is introduced either by adding or by merging air parcels in the case
of divergence or convergence, respectively. Using this method, a critical
Lyapunov exponent of 1.5 day-1 ensures an approximate equally
distributed grid and appropriate mixing strength . The
sensitivity of simulated trace gas distributions in the upper
troposphere and lower stratosphere to the value of the critical Lyapunov coefficient is
further discussed in . Stratospheric chemistry within the
CLaMS chemistry module is an updated version of with
additional reactions listed in . The chemical composition
of the whole atmosphere is described by the individual air parcels in the
shown setup. Each air parcel represents a certain volume of its surrounding
atmosphere with about 380 000 air parcels in total. Here, simulations were
carried out with a horizontal resolution of 100 km in the polar
regions. Vertically, the model is divided into 32 levels between 320 and
900 K, resulting in a vertical resolution of 400 m at
10 km up to about 800 m between 12 and 24 km
altitude. The chemical initialization of the hemispheric runs starting on
1 December 2009 and 1 May 2011, respectively, was based on satellite data
and observed tracer correlations . Additionally, we used
data from a multi-annual CLaMS simulation with simplified chemistry
. The details for the individual species are given in
for the Arctic winter 2009/2010 and in
for the Antarctic winter 2011.
The CLaMS cirrus module is used in the regular CLaMS setup to calculate the
dehydration of air masses at the tropopause level. This mechanism can be
implemented by either using a temperature-dependent parameterization for
heterogeneous ice freezing or by a fixed value of
100 % for saturation over ice. Water ice is removed if the inferred
particle fall speed exceeds a prescribed threshold value. This
parameterization also allows dehydration in the stratosphere to be simulated
e.g.,. However, it does not allow for simulations of
vertical redistribution of water, since water is irreversibly removed once a
critical fall speed is exceeded. Therefore, we restricted the cirrus module
to the troposphere (ζ<380K and PV<2) within
this study with an ice freezing threshold of 100 %. Within the
stratosphere, the new ice parameterization of the sedimentation module takes
over.
Sedimentation module
The CLaMS sedimentation module offers the possibility to enhance simulations
of the polar winter stratosphere by PSC cloud formation and corresponding
particle sedimentation. The module was developed by
and has been so far limited to the formation of NAT particles. Within this study,
we expand the sedimentation module to the simulation of ice PSC particles.
This step enables the simulation of the water redistribution. Moreover it
provides the opportunity for detailed comparisons of simulated PSC properties
with various observations. Finally, NAT and ice particle surface areas are
calculated within the CLaMS sedimentation module and are now also transferred to
and used within the chemistry module. First applications are shown in a paper
by .
Sedimenting particles in CLaMS are also described by a Lagrangian approach.
So-called particle parcels are initialized in addition to CLaMS air parcels
and move independently within the three-dimensional space. Every particle
parcel represents a number of NAT or ice particles, equally distributed over
a certain volume of air. The given number density remains constant during the
particle's lifetime. Growth, sedimentation, and evaporation of the particles
are carried out following the procedure described in detail in
. Vapor pressures of HNO3 are calculated
following ; H2O vapor pressures are calculated
according to . Uptake and release of H2O and
HNO3 is carried out by taking into account a weighted distance to the
three nearest neighbors each above and below . To draw
contour lines of frost and NAT point temperatures, we used total abundances
of H2O and HNO3 from CLaMS and ERA-Interim temperatures.
Further details to the module's fundamentals can be found in
.
Visualization of the heterogeneous ice nucleation parameterization
derived from . The sum of foreign nuclei initiating
heterogeneous ice nucleation (equal to a certain contact angle) resulting
from a combination of temperature and supersaturation (color-coded).
Currently, the sedimentation module comprises the following nucleation pathways:
Heterogeneous NAT nucleation
started the sedimentation module using a constant NAT
nucleation rate taken from . With a rate of 7.8×10-6cm-3h-1, NAT formed instantaneously as soon as
temperatures dropped below TNAT. In , the
heterogeneous NAT nucleation was updated motivated by results obtained in the
RECONCILE field campaign and new scientific findings about heterogeneous PSC
nucleation. According to , a saturation-dependent,
non-constant nucleation rate of NAT particles was formulated and used to improve
the simulations. The active site theory represents the
basis for this approach. The idea behind this is that particles may offer a
certain probability to nucleate NAT or ice. The probability
differs from particle to particle, which leads to nucleation events over a
broad temperature range as observed by . So-called
active sites, particle surface inhomogeneities, are assumed to initiate
nucleation. A particle might carry several of these sites but only the best
active site is of importance and triggers the nucleation event. For the use
within CLaMS, the number of particles carrying a particular contact angle is
tabulated in steps of 0.1∘ and described by a combination of
temperature and saturation ratio. The nucleation rate is calculated by the
sum over all bins up to the actual temperature and saturation ratio. Further
particle nucleation takes place only if the temperature drops and/or the
saturation ratio increases.
Compared to , we changed two details in the calculation.
(1) Information related to the number of activated contact angles is stored
for each particle parcel and is now also exchanged with the surrounding air
parcels. Increasing saturation ratios increases the number of activated
contact angles. Mixing is considered and, as soon as PSC particles evaporate,
the value for activated contact angles is reset to zero. (2) Up to now,
particle formation took place every 24 h . For this
reason, SNAT/ICE and Tmin are traced along each air
parcel trajectory to make use of the daily minimum temperature and maximum
saturation ratio. We kept the possibility to nucleate PSC particles only once
per day, e.g., to save computing time, but also introduced the possibility to
use an hourly nucleation timestep. In this case, hourly resolved values of
temperature and SNAT/ICE are taken to calculate nucleation rates
of NAT and/or ice particles.
Heterogeneous ice nucleation
Heterogeneous ice nucleation has been implemented within CLaMS in an
analogous manner as heterogeneous NAT nucleation. Vapor pressures for ice are
calculated following . Depending on temperature and
supersaturation with respect to ice (Sice), a fixed number of ice
particles may nucleate. Ice particle number densities are determined with the
help of a look-up table, as done in for heterogeneous NAT
nucleation. The look-up table is illustrated in Fig.
and based on the same parameterization for heterogeneous ice nucleation as
defined in . A combination of temperature (x axis) and
supersaturation (color-coded) defines the number of foreign nuclei initiating
ice particle nucleation, which is therefore equal to the number of nucleated
ice particles (y axis on the right side of the figure). With decreasing
temperature and increasing supersaturation, more contact angles can be
activated (y axis on the left side of the figure) and the number density of
nucleated ice particles increases. The size of the nucleated ice particles is
often determined by equilibrium conditions. While nitric acid uptake by
micron-sized particles needs hours, water is in equilibrium on the timescales
of seconds . Water equilibrium depends on gas-phase
water partial pressure and water vapor pressure of the aerosol particles. The
water fraction in the ice particles itself is calculated as the difference of
the total partial pressure of water and the saturation vapor pressure of
water over ice, depending on pressure and temperature .
The look-up table, as well as the parameterization from ,
requires the existence of small-scale temperature fluctuations. Those have
been introduced into CLaMS as described in Sec. . The use of
synoptic-scale temperatures only, as provided by ERA-Interim, would require a
reduction of the nucleation barrier. We performed several sensitivity runs
with different starting contact angles (not shown) and concluded that values
of Sice need to be lowered by about 0.17 to compensate for missing
temperature fluctuations and to achieve similar results for PSC occurrence
and dehydration. However, higher cooling rates as provided by smaller-scale
temperature fluctuations resolve individual PSCs better. The source of these
small-scale temperature fluctuations in the atmosphere is often related to
gravity waves as described in Sect. .
Homogeneous ice nucleation
Homogeneous nucleation of ice crystals from supercooled aqueous solution
droplets was described by . To calculate the freezing
threshold within CLaMS, we introduced the critical supersaturation
Scr following :
Scr=2.583-T[K]207.83.
This approximation is based on and saves additional
computation time. The particle radius of 0.25 µm given by
agrees well with the mean radius of STS droplets
. Homogeneous ice nucleation requires a supercooling of 3
to 4 K compared to the ice frost point and therefore high supersaturations at stratospheric polar
winter conditions (Scr is nearly constant at about 1.7 in the
temperature range from ∼180 to 190 K, ).
Such conditions can be found, for example, in an orographic lift above mountains that
can induce high cooling rates that freeze the entire background aerosol
population of 10 cm-3 particles e.g.,. Therefore, we assume that homogeneous nucleation within
CLaMS results in n(ice)=10cm-3.
NAT nucleation on preexisting ice particles
NAT nucleation on preexisting ice particles is an accepted and often
confirmed pathway of NAT formation . Downstream of mountain waves, NAT supersaturations are high and
clouds with NAT number densities of up to 1 cm-3 have been
observed seeand references therein. In this study, we
allow 50 % of the existing ice particles to serve as NAT nucleus
with an upper limit of n(NAT)=1cm-3 per nucleation
event. NAT particle radii and volume are determined assuming thermodynamical
equilibrium. This calculation is a first and easy attempt to include NAT
nucleation on preexisting ice particles in a global model and may need
refinements in later studies.
Parameterization of temperature fluctuations
It has been shown in several studies that small-scale
temperature fluctuations are ubiquitous in the atmosphere and play an
important role in ice cloud formation
e.g.,. Even if
synoptic-scale temperatures are above PSC formation thresholds, negative
temperature excursions may trigger ice formation and rapid cooling rates may
change cloud characteristics. Orographically induced temperature fluctuations
are the focus of several Arctic e.g.,
and Antarctic research studies e.g., on PSCs. quantified the
proportion of PSCs due to orographic gravity wave forcing in both
hemispheres. Observations from the Atmospheric Infrared Sounder (AIRS) were
used by to evaluate explicitly resolved temperature
fluctuations due to gravity waves in high-resolution meteorological analyses,
also in both hemispheres. Even though wave amplitudes are typically
underestimated, found that observed gravity wave
patterns agree well with those simulated in the ECMWF operational analysis.
Due to a resolution of 1∘× 1∘ of the
underlying wind and temperature fields from ERA-Interim used in this study,
we do not expect to explicitly catch wave patterns such as mountain wave ice
events see for example. However, a persistence of gravity wave
activity from background winds at subgrid scales needs to be parameterized
somehow in order to mimic PSC formation in general.
For CLaMS, we make use of parameterizations by for the
Northern Hemisphere (NH) and for the Southern Hemisphere
(SH). showed that mesoscale temperature
fluctuations increase with altitude in a systematic way. They are greatest
over mountainous terrain and towards polar latitudes during winter. These
dependencies are expressed in the following equations to calculate mesoscale
fluctuation amplitudes (MFAs), that are not present on synoptic scales:
MFA (NH)=(112-1.21Latitude+2.20W⋅Latitude+29.0Topography)⋅Pressure [hPa]58.85-0.4,MFA (SH)=(114-0.42Latitude+0.84W⋅Latitude+29.0Topography)⋅Pressure
[hPa]58.85-0.4.
Taking negative latitudes for the SH, we assumed a mean topographic parameter
of 0.5. Equation () represents a corrected version of the
original Eq. (3) of (Bruce Gary, private
communication). Consequently, the parameter W is calculated for each day of
the year (DOY) and both hemispheres in the following way:
W=0.5⋅1+sin2π⋅DOY-295365.
MFA is first converted from full-width to half-width amplitude and also from
its original altitude unit m to the temperature unit K by a
simple conversion assuming a dry adiabatic temperature behavior of
1K=100m. MFA is further scaled by random numbers
originating from a normal distribution, with MFA being the standard
deviation. The resulting temperature is added to the ERA-Interim
synoptic-scale temperature and used for the calculations of particle
nucleation for both NAT and ice particles.
Comparison of PSC areal coverage between CALIOP (v2) (a, d, g),
MIPAS (v1.2.8) (b, e, h), and CLaMS (c, f, i) from 1 December 2009
until 31 March 2010. Total PSC areal coverage (APSC) in
106km2(a, b, c) as well as further classified ice (d, e, f) and
NAT PSCs (g, h, i) are presented as a function of time and altitude
throughout the 2009/2010 NH winter. MIPAS observations and CLaMS simulations
are restricted to latitudes <82∘N. PSC thresholds for CLaMS
simulations are as follows: STS, 3.3 µm2 cm-3; NAT,
0.25 µm2 cm-3; ice, 0.5 µm2 cm-3. Black
triangles in the time series of the measurements indicate data gaps.
Please note that the color code is always identical except for the maximum value
of the top color bin.
Comparison to measurements
To evaluate simulations from the new ice PSC scheme within the CLaMS
sedimentation module, we compare our results with satellite measurements from
the Cloud-Aerosol Lidar with Orthogonal Polarization, the Microwave
Limb Sounder (MLS), and the Michelson Interferometer for Passive Atmospheric
Sounding. Since April 2006, CALIOP has been flying on the CALIPSO satellite
measuring high-resolution backscatter profiles from which information about
PSC composition can be inferred. MLS was launched in July 2004 on the Aura
spacecraft and delivers profiles of HNO3 and H2O. As part of
the NASA–ESA A-Train constellation, both instruments closely follow each
other along the same track describing a sun-synchronous polar orbit with a
global coverage ranging from 82∘N to 82∘S. MIPAS operated on board the Envisat
satellite from July 2002 to April 2012 measuring limb infrared (IR) spectra
in the wavelength range from 4 to 15 µm. The
satellite operated also in a sun-synchronous orbit and allowed geographical
coverage up to both poles due to additional poleward tilt of the primary
mirror (usually 87∘S to 89∘N). MIPAS measured
PSCs at day- and nighttime.
CALIOP
During the last decade, CALIOP was the basis of various studies on PSCs
e.g.,. Within this
study, we make use of the CALIPSO lidar level 2 polar stratospheric cloud mask version 2.0 (v2) data product,
which was recently introduced in . PSCs are detected
as statistical outliers relative to the background stratospheric aerosol
population (at T<200K) in perpendicular backscatter
(βperp) or scattering ratio (R) at 532 nm. The
background threshold values (Rthreshold and
βperp, threshold) are defined as median plus one median
absolute deviation. The thresholds are daily values that vary with potential
temperature and are included in the CALIOP v2 PSC data files. A CALIOP
measurement sample is defined to be a PSC if either βperp or
R exceeds the background threshold plus an uncertainty (σ); i.e.
βperp>βperp, threshold+σ(βperp) or R>Rthreshold+σ(R). An
outlier in βperp is assumed to contain detectable
non-spherical particles. The optical space for non-spherical particles is
divided into two general regimes. A dynamical boundary (RNAT|ice)
separates NAT mixtures from ice. RNAT|ice is computed from
estimates of cloud-free MLS HNO3 and H2O vapors to account
for effects of dehydration and denitrification. A data point is classified as
ice if R>RNAT|ice and R<50, and it is further classified
as wave ice if R>50. NAT mixtures are defined to be the opposite of the dynamical
boundary with values of R<RNAT|ice. Any NAT mixture with
R>2 and βperp>2×10-5km-1sr-1
belongs to the subclass named “enhanced NAT mixtures”. Each data point
that is not an outlier in βperp, but is an outlier in R, is
classified as STS. A visualization of the PSC classification can be found in
the lower panels of Figs. and .
Please refer to for more details of the v2 PSC
classification.
CALIPSO orbit track 2010-01-18T01-58-53Z on 18 January 2010. CALIOP
measurements are shown in (a, c, e, g) and corresponding model results in (b, d, f, h). (a, b) CALIOP PSC
classification v2 with overlaid
temperature contours for Tfrost (solid line) and TNAT
(dashed line); (c, d) aerosol backscatter ratio (R-1); (e, f) perpendicular backscatter
signal (βperp); (g, h) inverse backscatter ratio (1/R) vs. perpendicular backscatter signal
(βperp), with data color-coded by temperatures relative to
Tfrost, and overlaid CALIOP v2 PSC composition classification
scheme. Dashed lines are dynamical thresholds
(βperp, threshold, Rthreshold, and
RNAT|ice) compare Fig. 4 in.
Horizontal distribution of MIPAS (a) and CLaMS (b) PSC
composition classes for 18 January 2010 at an altitude level of 500 K
(±20K) potential temperature.
A comparison of individual PSC clouds simulated within CLaMS and measured by
CALIOP requires a conversion of model results into optical parameters. For
every CALIOP data point, we compute a size distribution from CLaMS PSC
particles within a radius of 50 km around the point of measurement
and attribute this to the observation. As in and
, we make use of Mie theory and T-matrix calculations to
compute scattering of light by STS, NAT, and ice particles as a function of
wavelength e.g.,. Prolate spheroids for both ice
and NAT particles with aspect ratios of 0.9 (diameter-to-length ratio) and a
refractive index of 1.31 for ice and 1.48 for NAT were chosen
. To fully adopt the procedure of PSC classification, we
calculated σ also for modeled values using the CALIOP noise equation
as follows:
σ(β)=CNF⋅β.
The CALIOP noise factor (CNF) combines different scaling factors into a
single value for each CALIOP horizontal averaging scale . We
used a CNF of 0.00102 for all our calculations, which corresponds
to an horizontal average of 135 km and therefore a best case for
detection. The calculations were done with the assumption that the
parallel and perpendicular components of molecular backscatter are 0.99634
and 0.00366 times the total molecular backscatter, respectively. Finally, the
following relationship is used for σ(R):
σ(R)=|R|⋅σ2(βperp)+σ2(βparallel)(βperp+βparallel)2+(0.03)2.
The last term accounts for assumed 3 % relative uncertainty in molecular backscatter.
The procedure described above is essential to compare individual optical
properties on a cloud by cloud basis. Comparisons between CLaMS, CALIOP, and
MIPAS showing PSC areal coverage have been performed using CLaMS PSC surface
areas. Information about the surface area density of ice, NAT, and STS
particles per volume of air is available for every CLaMS air parcel. The quantity is derived
from CLaMS particle parcels and interpolated onto neighboring CLaMS air parcels using
an inverse-distance-weighting method at every time step during the simulation.
This step saves computing time and allows an easier post-processing of the model results for comparisons, which do not
require individual size distributions. As lower boundaries, in accordance
with the CALIOP detection thresholds, we use 3.3 µm2 cm-3 for STS droplets , 0.25 µm2 cm-3 for NAT,
and 0.5 µm2 cm-3 for ice particles. Values exceeding those
thresholds are counted as PSCs and as a specific composition class,
respectively.
MIPAS
The MIPAS PSC detection and classification approach is based on the
combination of the well-known two-color ratio method for IR limb measurements
, the cloud index, and multiple 2-D brightness temperature
difference probability density functions . The so-called
Bayesian classifier combines the information content of various correlation
diagrams of color ratios and brightness temperature differences covering
several atmospheric window regions. Finally, the classifier estimates the
most likely probability that either one of the three PSC types (ice, NAT, or
STS) dominates the spectral characteristics of MIPAS or defines mixed-type
clouds with intermediate probabilities (40 %–50 %). The MIPclouds
processor for detection and cloud parameter retrieval is presented in detail
in . introduced the methodology of the
Bayesian classifier (v1.2.8) for PSC cloud types. The classification method
has been applied to the complete MIPAS dataset .
Within this paper, we compared horizontal distributions of PSC composition
classes for both hemispheres on single days at constant levels of potential
temperature. MIPAS data were processed as described above. For CLaMS, we
calculated trajectories from the model results to map those onto the MIPAS
measurement locations. As done for the comparison of PSC areal coverage (see
Sect. ), we used information about the surface area density
of ice, NAT, and STS particles from CLaMS. The same detection thresholds
(3.3 µm2 cm-3 for STS droplets,
0.25 µm2 cm-3
for NAT, and 0.5 µm2 cm-3 for ice particles) were applied
to classify CLaMS model results for the comparison with MIPAS. Please note
that vertical sampling differences exist between MIPAS (1.5–3 km)
and CLaMS (∼800m) and that measured and sampled air volumes
do not perfectly match.
MLS
MLS provides atmospheric profiles of temperature and composition (including
H2O and HNO3) via passive measurement of microwave
thermal emission from the limb of the Earth's atmosphere .
Those measurements are done almost simultaneously to measurements by CALIOP
since the Aura satellite flies together with CALIPSO in the A-Train satellite
constellation. We use version 4.2 of MLS measurements. Information about
vertical and horizontal along-track resolutions as well as precision and
accuracy of the data can be found in . In short, MLS
version 4.2 measurements have typical single-profile precisions (accuracies)
of 4 %– 15 % (4 %–7 %) for H2O and 0.6 ppbv (1–2 ppbv) for HNO3. Vertical and horizontal along-track resolutions are
3.1–3.5 km and 180–290 km for H2O and 3.5–5.5 km and
400–550 km for HNO3. For comparisons with CLaMS, we
interpolated MLS parameters onto ζ levels and calculated daily
averages for certain levels and equivalent latitude bins with areas of equal
size.
The MLS averaging kernel has also been taken into account for comparisons of
CLaMS model data to satellite observations. As for the comparison with MIPAS,
we calculated trajectories to transfer the model results to the measurement
locations. Afterwards, we applied a weighting defined by the satellite's
averaging kernels using pressure as the vertical coordinate and the logarithm
of H2O and HNO3 mixing ratios .
Further details about the MLS averaging kernels are discussed in
.
Results
Within this section, we present CLaMS results for the 2009/2010 Arctic winter
as well as for the 2011 Antarctic winter in comparison to satellite
observations (Sect. ). We show plots of daily
averaged PSC areal coverage over the entire winter. We compare simulations of
single clouds to satellite observations and simulated H2O and
HNO3 concentrations to MLS measurements.
The 2009/2010 Arctic winter was on average relatively warm; however, an
exceptional cold period from mid-December until the end of January led to the
formation of widespread PSCs. At that time, the RECONCILE field campaign took
place and intensive PSC studies followed from this
campaign. NAT and ice parameterizations by and
, respectively, are based on PSC observations during this
particular winter. To demonstrate that both parameterizations are working in
CLaMS, as well as in the original Lagrangian Zurich Optical and Microphysical
box Model (ZOMM), we selected 18 January 2010 for a single cloud comparison
because this day was analyzed in detail by . Moreover, this
day is in the middle of the 1-week period of intensive ice cloud coverage
in the NH vortex with the largest areas covered by ice PSCs .
The 2011 Antarctic winter is amongst the colder Antarctic winters with a
pronounced ozone hole . Observations from MIPAS,
CALIOP, and MLS are available throughout this entire winter. This is the
first time that CLaMS simulations of dehydration and denitrification are
shown in detail for a SH winter. For this reason, the Antarctic winter 2011
is presented with a series of cloud comparisons throughout the whole PSC
season.
Temporal evolution of water vapor (H2O) and nitric
acid (HNO3) are shown as an average inside the core of
the polar vortex (equivalent latitudes >70∘N) from 1 December 2009
until 31 March 2010. MLS measurements are presented in (a, d), CLaMS model results accounting for the MLS averaging
kernel in (b, e), and the difference between MLS and CLaMS in (c, f).
Regions not affected by any changes in the CLaMS sedimentation module are
hatched.
Same as Fig. but for the 2011 SH winter from
1 May 2011 until 31 October 2011. Here, MIPAS observations and CLaMS
simulations are restricted to latitudes <82∘S. Please note
that the color code is always identical except for the maximum value of the top
color bin.
2009/2010 Arctic winter: comparison with observations
We start the presentation of CLaMS results with a season-long and vortex-wide
comparison of PSC areal coverage. Daily, height-resolved values of PSC areal
coverage in the 2009/2010 Arctic winter are shown in
Fig. . Between 55 and 90∘, we
defined eight latitude bands of equal area with widths that vary in latitude
from 2.3∘ (250 km) up to 12.2∘ (1340 km). The
occurrence frequencies (number of PSC detections divided by the total number
of observations) were calculated for each band and altitude grid box. The
PSC areal coverage is estimated as the sum of the occurrence frequency
multiplied by the total area. This approach has also been used by
and and bypasses the caveat of the
irregular sampling density due to the orbit geometry. MIPAS observations and
CLaMS simulations are only considered at
latitudes <82∘N/S to mimic the latitudinal sampling
coverage of CALIOP. The model performance at PSC altitudes for the overall
winter is remarkably good (Fig. ). PSC occurrence starts
in mid-December and lasts until the end of January in both independent
measurements and in the simulation. CLaMS shows two maxima in PSC
areal coverage similar to CALIOP. Also the vertical extent agrees well
between all three panels (CALIOP, MIPAS, and CLaMS). Looking in particular at
ice PSCs, CLaMS misses ice clouds in the first 2 weeks of January that are
seen by both CALIOP and MIPAS. Those ice clouds are mountain-wave-induced
events as shown by , caused by wave-driven temperature
minima which are missing in the ERA-Interim data see Fig. 8
in. Starting in mid-January, as synoptic-scale temperatures
fell below the frost point, large areas of ice PSCs also develop within CLaMS
with an extension comparable to the observations. The disagreement between
CALIOP, MIPAS, and CLaMS at altitudes below 15 km is noticeable.
CALIOP observes cirrus clouds throughout the entire 2009/2010 season at
altitudes below 15 km. Further, CALIOP NAT at low
altitudes is likely cirrus that has been misclassified. Volcanic aerosol from
the Sarychev (48.1∘N, 153.2∘E) eruption in June
2009 were transported into the polar region producing an enhancement in the
background aerosol at altitudes below about 18 km. MIPAS is highly
sensitive to the presence of this volcanic aerosol which biases the MIPAS PSC
detection causing a striking maximum of PSC areal coverage
at low altitudes in the early winter period. The widespread volcanic aerosol
does produce an enhancement in the CALIOP estimate of the background levels,
but will not significantly affect the PSC product since it is based on
outlier detection. Both cirrus and volcanic aerosols introduce a bias in the
MIPAS PSC detection and are misclassified as NAT . In
CLaMS, we do not simulate clouds other than PSCs. The origin of the larger
PSC area at low altitudes seen in the CLaMS PSC area panel can be explained
by the altitude-independent fixed detection threshold
of 3.3 µm2 cm-3 for STS droplets. At altitudes around 12 km, the
stratospheric aerosol layer becomes visible as well. To reduce the large
PSC area in CLaMS at low altitudes, we introduced the same temperature
threshold as to this plot. Only data points with
temperatures less than 200 K are considered. This temperature
threshold reduces the maximum values of PSC areal coverage slightly.
Same as Fig. but for 25 June 2011: CALIPSO
orbit track 2011-06-25T10-34-25Z.
A detailed look on individual clouds illustrates the performance of the CLaMS
ice PSC scheme. In the middle of the 1-week period of synoptic-scale ice
PSCs in January 2010, we selected 18 January 2010 for a single cloud
comparison (Fig. ). This particular orbit was the focus of to adjust the heterogeneous nucleation
rates for ice. Whereas used the microphysical box model
ZOMM, run on single trajectories and starting at most 10 days before the
point of observation, we are using a CTM in this study, initialized
on 1 December 2009 and running for the whole winter 2009/2010. Despite those
fundamental model differences, the result is convincing and the agreement
between both models suggests a robust parameterization of heterogeneous ice
nucleation. The cloud classification and the individual parameters of
aerosol backscatter ratio (R-1) and βperp agree well with
the CALIOP observations. Areas classified as ice lie predominantly within
the Tfrost contour line and areas classified as NAT within the
TNAT contour line. The maximum values of R-1 and
βperp are in the same order of magnitude and the vertical and
horizontal extent of the observed PSC agrees with the model result. The lower
panels of Fig. illustrate the CALIOP v2 PSC
classification scheme. The dashed lines are dynamical thresholds as mentioned
in Sect. . Plotted are daily maximum values for
βperp, threshold and Rthreshold and the daily mean
for RNAT|ice. The spread in the CALIOP data is caused by
measurement noise. Although measurement noise is mimicked and added to the
modeled data, the spread in the modeled data is slightly less than for the
observed values. Those data points are still more confined and do not fill
the whole space of the diagram. Enhanced NAT mixtures represent PSCs
heterogeneously nucleated in wave ice PSCs. The CALIOP criteria defining
enhanced NAT mixtures are conservative and, therefore, the enhanced NAT
mixtures subclass is not all-inclusive . On this particular
day, we expect no NAT PSCs downstream of wave ice clouds. Whereas this area
is not populated in the CLaMS data, single scattered measurement points from
CALIOP fall into this class, likely due to measurement noise.
Same as first row in Figs. and
but for 4 certain days in SH winter 2011:
25 May (2011-05-25T12-56-29Z, a, b), 6 July (2011-07-06T15-12-24Z, c, d),
21 August (2011-08-21T03-52-24Z, e, f), and 8 September (2011-09-08T08-36-49Z, g, h).
A comparison to MIPAS is also beneficial to validate the new CLaMS PSC
scheme. A different measurement technique as well as the fact that MIPAS data
were not used to adjust the nucleation rate allows an independent quality
check. Figure presents the daily PSC distribution of
the Bayesian classifier for 18 January 2010. The colored symbols represent
the PSC classes, where in addition to the three main classes (ice, STS, NAT)
a mixed type of STS and NAT (NAT_STS) particles is shown. As described in Sect. , CLaMS surface area densities were
mapped onto the MIPAS profile locations. The classification of CLaMS results
is based on surface area densities, which need to be large enough to exceed
an empirical detection threshold (STS: 3.3 µm2 cm-3, NAT:
0.25 µm2 cm-3, ice: 0.5 µm2 cm-3). The mixed
type is chosen such that the simulated surface areas for both, STS and NAT,
need to lie above the threshold. Even though ice formation is highly
temperature dependent, the spatial pattern of ice PSC occurrence between
MIPAS and CLaMS agrees well (Fig. ). The model simulates
ice PSCs over Spitsbergen and in the center of the cold pool above the
Russian Arctic. These are the same locations where MIPAS observed ice. NAT
observations downstream of ice PSCs are also reproduced by the model.
However, CLaMS produces more NAT PSCs over the North Pole than observed by
MIPAS. A possible explanation for this discrepancy lies in the detection
threshold of MIPAS. MIPAS may classify a volume of air as STS even though NAT
particles are present with volume densities smaller than about 0.3 µm3cm-3 or NAT particles with radii larger than 3 µm
. Moreover, NAT clouds are
mixtures of NAT and STS or even NAT, STS, and ice particles as emphasized
by and , leading to the
conclusion that the discrepancy between NAT and STS as seen in
Fig. might be a caveat in the MIPAS classification.
However, as seen in the results for the Antarctic winter 2011, CLaMS also
tends to overestimate NAT occurrences (compare Sect. ).
Same as Fig. but for certain days in SH winter
2011: 25 May (a, b), 25 June (c, d), 6 July (e, f), 21 August (g, h), and 8 September (i, j).
Figure demonstrates the effect of PSCs on the
distribution of gas-phase HNO3 and H2O of the polar winter
stratosphere. First, PSCs lead to a temporary removal of HNO3 and
H2O from the gas phase by condensation onto NAT and ice particles,
respectively, and uptake by STS droplets. This temperature removal from the
gas phase can nonetheless be very important for ozone-destroying chlorine
chemistry. In cases of sedimentation, a permanent redistribution of the gas-phase components takes place. The temporal evolution of Arctic H2O is
presented in Fig. as function of potential temperature
and time for the average vortex core (equivalent
latitude >70∘N). It shows predominantly the dynamically
forced diabatic descent of air inside the polar vortex (compare
Fig. , upper left panels). Areas of low H2O mixing
ratios due to ice PSCs are too limited in the Arctic to be clearly seen in a
vortex average. A tiny sign of water uptake by PSCs is visible mid-January
at approximately 500 K in the MLS data. This effect is even smaller
in the model simulation. In contrast, the pattern of HNO3 shows a
clear layer of denitrified air in both observations and simulations at the
end of December 2009 (compare Fig. , lower panels).
Additionally, a layer of renitrified air forms between approximately 400 and
450 K. The redistribution of NOy in the Arctic has already
been presented but not compared to MLS observations by .
The panels on the right-hand side of Fig. show the
differences between CLaMS and MLS. Bluish colors mean that CLaMS values of
H2O and HNO3 are too small compared to MLS, meaning model
dehydration and denitrification was too efficient. Reddish colors mean the
opposite, too little dehydration and denitrification; therefore CLaMS values of
H2O and HNO3 are too large compared to MLS. Hatched areas mean
that CLaMS simulations with and without the sedimentation module have equal
results. Differences to MLS within the hatched areas cannot be improved by
changes in the ice or NAT particle nucleation and sedimentation scheme and
have reasons beyond the scope of this study. The assumption that CLaMS tends
to overestimate NAT occurrences is supported by the deviations in
HNO3 between the model and MLS. CLaMS shows an uptake of HNO3
from the gas into the particle phase which is too large and happens too early
in the season. Mid-January, this tendency turns around. The permanent
redistribution of HNO3 is smaller compared to the observations.
Same as Fig. but for the 2011 SH
winter from 1 May 2011 until 31 October 2011 and for equivalent latitudes >70∘S.
2011 Antarctic winter: comparison with observations
Figures – show the results for the
Antarctic winter 2011 corresponding to the figures above shown for the Arctic
winter 2009/2010. In addition, we present a number of days throughout the
winter to demonstrate the evolution of PSCs and corresponding model
simulations (Figs. , , and
). Figure shows the daily, height-resolved values of PSC areal coverage and gives an overview of the PSC season
in the SH 2011. Starting in the second half of May, CLaMS and CALIOP agree
well with both, showing NAT PSCs to be the first type of PSC present in the
season. In contrast, the MIPAS classifier detects STS clouds first, with NAT
PSCs following a few days later (see also Fig. ).
Unfortunately, there are gaps in the MIPAS data record at the beginning of
the PSC season. Going further in time, a large fraction of the vortex is
covered by ice PSCs, which is typical for Antarctic winters. Also the
agreement between CALIOP, MIPAS, and CLaMS is satisfying at PSC altitudes
(Fig. ). At altitudes below 15 km, large areas of
NAT can again be seen in the MIPAS data. Those values can be attributed to
cirrus clouds, which are misclassified as NAT due to a bias in the PSC
detection . CALIOP indicates large areas of ice and some
NAT mixtures below 15 km, with the NAT mixtures at these lower
altitudes likely being misclassified as ice.
Throughout the PSC season, we also looked at single clouds on individual days
in detail (see Figs. , , and ). As
highlighted in the paragraph above, NAT
clouds are mixtures of NAT and STS particles and MIPAS might misclassify NAT
particles with radii larger than 3 µm. This discrepancy is visible
in Fig. and can be seen even more clearly by comparing a
single day right in the beginning of the PSC season. The upper panel of
Fig. shows a predominantly NAT cloud seen by CALIOP on
25 May, whereas Fig. shows STS droplets classified by
MIPAS for the entire day. Size distributions from CLaMS simulations on 25 May
2011 point in the same direction, namely that simulated NAT number densities
do not exceed 10-2cm-3 with particle radii larger than
5 µm (not shown). Discrepancies between the model and both
satellite datasets appear to be related to the relative proportion of STS and
NAT PSCs with CLaMS overestimating NAT occurrences.
Figure shows one such example where CLaMS produces
widespread NAT particles in a region where CALIOP observes few or no NAT
particles. Moving the focus back to the simulation of ice PSCs, the agreement
on 25 June 2011 is convincing. The simulation of ice is confined to areas
with temperatures below Tfrost. The spread in the modeled data
shown in the lower panels (1/R vs. βperp) is slightly
less than for the observed values. However, observations and model results
lie close together. Overall, over the entire season, CLaMS simulations
somewhat underestimate ice occurrences on several occasions (e.g.,
Fig. , July and August). However,
Fig. gives the impression that the areal coverage of ice
PSCs is at least as large as in the observations. In general, the seasonal
evolution with variations in dominating PSC types, vertical PSC occurrences,
and spatial patterns is reproduced by the simulations.
The temporal evolution of gas-phase water vapor and nitric acid as measured
by MLS and simulated by CLaMS is presented in Fig. .
Dehydrated and denitrified areas are clearly seen in the MLS measurements and
in the CLaMS simulations. Water vapor mixing ratios as low as 1.6 ppm
(vortex core average) are observed. HNO3 mixing ratios in the vortex
core are extremely low and reach values of 200 ppt. Evaporation of
sedimenting PSC particles produces layers of enriched H2O
(rehydrated) and HNO3 (renitrified) air. They appear below the
depleted regions as seen in both MLS observations and CLaMS. The results
highlight the new capability of CLaMS to simulate the vertical redistribution
of H2O in good agreement with observations. The cirrus module of
CLaMS would irreversibly remove the water in the dehydrated areas. In
contrast, the new sedimentation module conserves H2O. The signal of
sedimentation with subsequent rehydration below is visible until the end of
July in the observations as well as in the simulations. Thereafter, a
diabatic descent of the rehydrated layer causes water that had originally
sedimented from higher latitudes to accumulate in the tropopause region.
Vertical velocities are deduced from ERA-Interim diabatic heating rates. In
October and November 2011, ERA-Interim indicates positive ascent
rates, causing the behavior seen in the simulations in spring. The temporal
evolution of both species agrees reasonably well between MLS and CLaMS. The
differences between measurements and simulations are quantified in the right
panels (Fig. ). The minimum values of H2O match very
well. The layer of rehydrated air around 350 K potential temperature
is slightly lower than in the observations, meaning that H2O mixing
ratios are smaller in the simulation than in the observations. A comparison
between MLS and CLaMS HNO3 mixing ratios is acceptable but reveals
differences. CLaMS HNO3 gas-phase mixing ratios around 500 K
potential temperature are lower than the observations for the whole season.
However, from August on, a layer of high HNO3 values below
500 K points to the possibility that the redistribution of
HNO3 is not efficient enough in the simulation and needs to extend
down to lower altitudes. This might explain the simulation of NAT particles
in areas which are almost cloud-free in the observations as seen in
Fig. . Even though CLaMS gas-phase mixing ratios of
HNO3 might be even lower than observed at that time, HNO3 in
the model could still be present in the particle phase and could not be
redistributed correctly to lower altitudes.
Conclusions
We present CLaMS simulations based on a new Lagrangian ice sedimentation
scheme focusing on the 2009/2010 Arctic and 2011 Antarctic winters. Previous
CLaMS studies solely simulated NAT PSCs and the corresponding
denitrification. Here, we extended the model by adding ice PSC particle
nucleation, growth, sedimentation, and evaporation. Heterogeneous and
homogeneous ice nucleation is included, as well as NAT nucleation on
preexisting ice particles. Heterogeneous ice nucleation rates are based on
; homogeneous ice nucleation occurs if the ice saturation
ratio exceeds a temperature-dependent, critical saturation
. In addition, the implementation of ice particle
nucleation requires small-scale temperature fluctuations to be added to the synoptic-scale temperature, here from the
ERA-Interim reanalysis.
The agreement between CLaMS simulations and the CALIOP, MIPAS, and MLS
observations on different temporal and spatial scales is convincing. CLaMS
PSC areal coverage presented for both seasons is in good agreement with MIPAS
and CALIOP. Similar comparisons between satellite observations and model
simulations have been performed in the past
e.g.,. However,
this is to our knowledge the first study presenting detailed results of
individual PSCs simulated by a global model in comparison to high-resolution
satellite observations. In general, CLaMS tends to overestimate the
occurrence of NAT PSCs. In comparison to MIPAS, CLaMS simulates NAT particles
at locations where MIPAS observes liquid PSC particles. At the onset of
Antarctic PSC occurrence in May, CLaMS is in agreement with CALIOP
observations indicating that NAT clouds were first observed. MIPAS first
observes STS, but is not sensitive to the presence of larger NAT particles
. However, the comparisons
with CALIOP also shows differences regarding NAT occurrence. Cloud-free
areas, next to or surrounded by PSCs in the CALIOP data, are often populated
with NAT particles in the CLaMS simulations. Looking at the temporal and
vortex-averaged evolution of HNO3, CLaMS shows an uptake of
HNO3 from the gas into the particle phase, which is somewhat too large
and happens too early in the NH season. The permanent redistribution of
HNO3 in the NH is smaller compared to the observations. Also the
Antarctic model run shows too little denitrification at lower altitudes
towards the end of the winter compared to the observations. These findings
point to shortcomings in the simulation of NAT particle sizes in combination
with number densities, namely that NAT particle sedimentation should be more
efficient in CLaMS. Further studies should try to find better combinations of
NAT number densities and sizes with the potential to denitrify the
stratosphere more precisely. Heterogeneous NAT nucleation on foreign nuclei
and preexisting ice particles has already been implemented and covers most
currently discussed routes to form NAT. However, NAT clouds downstream of
mountain waves may act as “mother clouds” and individual NAT particles
falling out of these clouds in low number densities can grow to large sizes
of up to 10 µm . So far, CLaMS comprises
the development of high-number-density NAT clouds on ice surfaces. No
attention has been paid to the mother cloud theory, which could be a step
forward to resolve deviations seen in the NAT simulations.
Simulated ice PSC coverages, in turn, are almost in agreement with the
observations as seen in the comparison between CALIOP, MIPAS, and CLaMS.
CLaMS simulations miss wave ice clouds as seen in the beginning of the
2009/2010 Arctic winter, likely because ERA-Interim temperature fields are
too coarse to resolve the required low values. The small-scale fluctuations
added in this study are ubiquitous and not related to specific gravity wave
sources such as mountains. A great leap forward would be a change from
ERA-Interim to the higher-resolution ERA5 data. This dataset may resolve
mountain waves and, once high supersaturations with respect to ice are
reached, a revision of the homogeneous ice nucleation parameterization is
meaningful. The modeled extent of the dehydration signal fits with the MLS
H2O observations. To bring this to even better agreement, the level
of dehydration in CLaMS could be slightly larger, to lower the minimum values
of H2O in the simulations by about 0.5 ppm. So far, ice
nucleation on preexisting NAT particles has not been considered in our CLaMS
simulations. This pathway will be included in the next CLaMS PSC study.
Recently, speculated about the existence of this specific
pathway, which was mentioned in the literature two decades ago
e.g.,. Discussions about the importance of ice
nucleation on NAT particles, e.g., for denitrification, are controversial
and this topic is likely to be a focus of
CLaMS studies in the future.
Despite deficiencies, the overall agreement between CLaMS and different PSC
and trace gas measurements is convincing. The advanced microphysical PSC
scheme, which includes now STS, NAT, and ice PSCs, therefore represents a
major improvement of the representation of cloud physics in CLaMS. Further
studies will benefit from this development and may provide more insights into
the occurrence and importance of PSC types and their formation mechanisms.
CLaMS code and data are available on request to the
first author. Observational datasets from CALIOP, MIPAS, and MLS can be found
on the following web pages (last accessed in March 2018). MIPAS/Envisat
Observations of PSCs: https://datapub.fz-juelich.de/slcs/mipas/psc/.
CALIPSO lidar level 2 polar stratospheric cloud mask version 2.0 (v2) data
product:
https://eosweb.larc.nasa.gov/project/calipso/lidar_l2_polar_stratospheric_cloud_table.
Aura MLS H2O product:
https://mls.jpl.nasa.gov/products/h2o_product.php. Aura MLS
HNO3 product:
https://mls.jpl.nasa.gov/products/hno3_product.php.
IT developed the extension for
ice particles within the CLaMS sedimentation module. JUG prepared the
initialization of both hemispheric CLaMS runs and processed data from MLS and
ERA-Interim. RS processed and provided MIPAS data. MCP and LRP processed and
provided CALIOP data and assisted with the new v2 PSC composition
classification. IT performed CLaMS simulations, wrote the manuscript, and
produced the figures. Together with RM and MR, all co-authors made
substantial contributions to the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank Beiping P. Luo for sharing his Mie and T-matrix calculations with
us. We also thank Felix Ploeger and Paul Konopka for providing the data of
the multi-annual CLaMS simulation. Nicole Thomas provided excellent support
for our programming activities. We gratefully acknowledge the European Centre
for Medium-Range Weather Forecasts (ECMWF) for providing ERA-Interim data. We
thank Michelle Santee and the MLS team for providing their high-quality datasets. Computing time has been granted on the supercomputer JURECA at the
Jülich Supercomputing Centre (JSC) under the VSR project ID JICG11. Ines
Tritscher was funded by the Deutsche Forschungsgemeinschaft (DFG) under
project number 310479827. We finally acknowledge the Stratosphere-troposphere
Processes And their Role in Climate (SPARC) project and the International
Space Science Institute (ISSI) for supporting the Polar Stratospheric Cloud
Initiative (PSCi). The article processing charges
for this open-access publication were covered by a Research Centre of the
Helmholtz Association. Edited by: Farahnaz Khosrawi
Reviewed by: three anonymous referees
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