A comprehensive ice nucleation parameterization has been
implemented in the global chemistry-climate model EMAC to improve the
representation of ice crystal number concentrations (ICNCs). The
parameterization of Barahona and Nenes (2009, hereafter BN09) allows for the
treatment of ice nucleation taking into account the competition for water
vapour between homogeneous and heterogeneous nucleation in cirrus clouds.
Furthermore, the influence of chemically heterogeneous, polydisperse aerosols
is considered by applying one of the multiple ice nucleating particle
parameterizations which are included in BN09 to compute the heterogeneously
formed ice crystals. BN09 has been modified in order to consider the
pre-existing ice crystal effect and implemented to operate both in the cirrus
and in the mixed-phase regimes. Compared to the standard EMAC
parameterizations, BN09 produces fewer ice crystals in the upper troposphere
but higher ICNCs in the middle troposphere, especially in the Northern
Hemisphere where ice nucleating mineral dust particles are relatively
abundant. Overall, ICNCs agree well with the observations, especially in cold
cirrus clouds (at temperatures below
Clouds play an important role in the Earth system by affecting the global
radiative energy budget, the hydrologic cycle, the scavenging of gaseous and
particulate substances, and by providing a medium for aqueous-phase chemical
reactions. Nevertheless, clouds remain one of the less understood components
of the atmospheric system, and their representation in models (including
processes like cloud droplet formation, ice nucleation, cloud phase
transitions, secondary ice production, and aerosol–cloud interactions) is one of
the major challenges in climate studies
Cirrus clouds form at high altitudes and very low temperatures (below
Ice crystal formation takes place via homogeneous and heterogeneous
nucleation, depending on environmental conditions (e.g. temperature,
supersaturation, vertical velocity) and aerosol populations (i.e. aerosol
number concentrations and physicochemical characteristics)
Overall, two different regimes for ice crystal formation are distinguished:
the
As heterogeneous nucleation takes place at lower supersaturation than
homogeneous nucleation, the available water vapour and the degree of
supersaturation decrease, reducing or inhibiting the formation of ice
crystals from homogeneous nucleation. This competition between homogeneous
and heterogeneous nucleation for water vapour drastically affects the ICNC in
the cirrus regime, even at low INP concentrations
Cloud schemes in atmospheric and climate models have evolved from using only
macrophysical properties, like cloud cover, to representing the microphysics
explicitly, e.g. formation, evolution, and removal of cloud droplets and ice
crystals
In this study the parameterization of
The EMAC model is a numerical chemistry-climate model which describes
tropospheric and middle-atmosphere processes and their interactions with
ocean, land, and human influences. Such interactions are simulated via
dedicated submodels in the MESSy framework
The EMAC model has been extensively described and evaluated against in situ
observations and satellite data, e.g. aerosol optical depth, acid deposition,
and meteorological parameters
The CLOUD submodel describes the evolution of the prognostic variables which
undergo all cloud microphysical processes (e.g. precipitation, deposition,
and evaporation/sublimation). As far as the formation of new ice crystals is
concerned, they are computed via two independent parameterizations, as shown
in black in Fig.
Scheme of the new ice crystal formation in the CLOUD submodel:
different ice nucleation schemes can be used in the cirrus and in the
mixed-phase regimes.
In the cirrus regime (
In the mixed-phase regime (
In the CLOUD submodel, a single updraught velocity (
The BN09 parameterization is computationally efficient and suitable for large-scale atmospheric models. It explicitly considers the competition for water vapour between homogeneous and heterogeneous nucleation in the cirrus regime, the influence of chemically heterogeneous, polydisperse aerosols acting as INPs, and allows us to use different heterogeneous nucleation parameterizations.
The BN09 algorithm can be divided into three subsequent parts. First, the
limiting number of INPs (
Third, the total concentration of new ice crystals formed in the cirrus
regime (
It is important to stress that the BN09 code actually includes five INP
parameterizations to deal with heterogeneous nucleation (as mentioned before)
and these are described by (i)
To summarize (see Fig.
In order to account for subgrid variabilities, the output variables of BN09,
which depend on the vertical velocity (
The BN09 parameterization has been added in the MESSy framework in order to
compute the newly formed ice crystals in the cirrus regime and/or in the
mixed-phase regime. The input variables of BN09 are the following: temperature (
A schematic overview of how BN09 has been implemented in EMAC through the
CLOUD submodel is shown in Fig.
In this study EMAC simulations have been carried out with the T42L31ECMWF
resolution, which corresponds to a spherical truncation of T42 (i.e.
quadratic Gaussian grid of approx.
In all experiments, contact nucleation is computed according to LD06, while
thermophoresis contact nucleation is not considered since its contribution is
negligible (as remarked in Sect.
All experiments carried out in this study.
BN09 improves the ice nucleation representation in EMAC by taking into account processes (e.g. water vapour competition, influence of polydisperse aerosols, PREICE effect) which were previously neglected by KL02 and LD06. In this section we investigate the changes and the effects obtained by using BN09 in the different regimes.
The annual zonal means of ICNC and ice water content (IWC) are shown as a
function of latitude and altitude in Fig.
The IWC pattern (Fig.
Annual zonal means of (grid-averaged) ice crystal number
concentration (ICNC,
Figure
IWC at
Annual means of (grid-averaged) ice crystal number concentration
(ICNC,
Annual means of (grid-averaged) ice water content (IWC,
Table
The ice water path (IWP) decreases by almost 7 % when BN09 is used in the
cirrus regime, similarly to what has been found in
The absolute values of the shortwave cloud radiative effect (SCRE) and
longwave cloud radiative effect (LCRE) are higher than those derived from
satellite data, especially when KL02 is employed in the cirrus regime.
However, when the net cloud radiative effect (NCRE) is computed, the same
simulations with KL02 in the cirrus regime are closer to the observations.
Looking at the absolute changes and the global distributions in the
Supplement (Fig. S5) it is evident that the cloud radiative effects are
sensitive to the ice nucleation scheme used for cirrus clouds. Indeed, SCRE
with BN09 becomes weaker (more than 5 %) because of the less efficient
scattering of shortwave radiation by fewer and larger crystals. More
importantly, LCRE decreases up to 15 % in BN
The total cloud cover (TCC) is slightly overestimated by the model (likely
explaining why the cloud radiative forcing is high despite IWP being half of
the observed values). However,
The annual zonal means of vertically integrated number concentration of ice
crystals and cloud droplets, ice water path, liquid water path, shortwave and
longwave cloud radiative effects, and total cloud cover are shown in Fig. S6
(in the Supplement) and are comparable with the literature cited before. The
annual zonal mean profiles clearly show that the simulations using the same
ice nucleation scheme in the cirrus regime are very close to each other, i.e.
KL
Overall, the model performs well with respect to observations and the literature. Mostly, the experiments do not yield evident differences among each other at the global scale, as regional variations may cancel out; however, there are clear effects on SCRE and LCRE from changing the cirrus ice nucleation scheme. As there is not a clear indication which simulation performs better, in the next subsection we extend our analysis including a statistical comparison with aircraft measurements.
Global annual means for simulations and observations. Shown are
grid-averaged vertically integrated cloud droplet number concentration
(CDNC
In-cloud ice crystal number concentrations
(ICNC
The validation of climate models with measurements from field experiments or
aircraft campaigns is always limited by the fact that the models have
difficulties to capture individual meteorological events. Nevertheless, here
we consider a large collection of aircraft measurements recorded over 15 years,
between 1999 and 2014 (Martina Krämer, personal communication, not yet
published, 2017). Eighteen field campaigns (in total, 113 flights with about 127 h
in cirrus clouds) covered Europe, Australia, Africa, Seychelles, Brazil, the USA,
Costa Rica, and the tropical Pacific (i.e. between
Overall, the simulations BN
For further information, in Fig.
Besides the flight measurements, the recent ICNC estimates from lidar–radar
satellite retrievals must be mentioned, e.g.
In this study we have implemented the ice nucleation scheme of
Focusing on the ice-related results, e.g. ICNC and IWC, we found that using
BN09 in the cirrus regime strongly reduces the total ICNC worldwide because
of the competition and PREICE effects, but increases ICNC along the
tropics. In contrast, BN09 in the mixed-phase regime produces slightly higher
ICNCs, especially in the NH where mineral dust particles are more abundant.
We found that changing the ice nucleation scheme in the cirrus regime
generates larger differences in ICNC and IWC than changing parameterization
in the mixed-phase regime, that is the simulations using the same
parameterization in the cirrus regime (e.g. BN
Overall, all modelled results agree well with global observations and the
literature data. The comparison made with flight measurements has pointed out
that ICNCs are overestimated by KL02 in the cirrus regime. BN09 agrees well
with the observations in cold cirrus clouds, but the PREICE effect is
likely overestimated causing the underestimation of ICNCs between
As BN09 takes into account additional processes which were previously
neglected by the standard version of the model, without consuming extra
computational resources, we recommend to apply this ice nucleation scheme in
future EMAC simulations. We also suggest to select P13 among the INP
parameterizations available in BN09, since it incorporates the ice-nucleating
ability of different aerosol species (dust, soot, bioaerosols, and soluble
organics) and simulates both deposition and immersion/condensation
nucleation. By using the configuration BN
The Modular Earth Submodel System (MESSy) is
continuously developed and applied by a consortium of institutions.
The usage of MESSy and access to the source code is licensed to all
affiliates of institutions, which are members of the MESSy consortium.
Institutions can become a member of the MESSy consortium by signing the MESSy
Memorandum of Understanding. More information can be found on the MESSy
consortium website (
In this appendix we provide some additional technical information about the
implementation of BN09 into the EMAC model. The BN09 parameterization has
been added as a Fortran95 module in the submodel core layer (SMCL) of MESSy
(named as
Other changes made during the implementation are the following.
The supplement related to this article is available online at:
SB wrote the paper with contributions from all coauthors. VAK, APT, and JL proposed the development of the CLOUD submodel. AN and DB provided the ice nucleation parameterization. SB – together with SCS, AP, and HT – performed the implementation in EMAC. MK provided the flight measurements. All authors were involved in discussions during the analyses.
The authors declare that they have no conflict of interest.
We would like to thank Mattia Righi from the German Aerospace Center (DLR)
for the discussion on the modelled results. We acknowledge the usage of the
Max Planck Computing and Data Facility (MPCDF) for the simulations performed
in this work. Sylvia C. Sullivan and Athanasios Nenes acknowledge funding
from a NASA Earth and Space Science Fellowship (NNX13AN74H), a NASA MAP grant
(NNX13AP63G), and a DOE EaSM grant (SC0007145). Moreover, Athanasios Nenes
acknowledges funding by the European Research Council Consolidator Grant
726165 (PyroTRACH), Vlassis A. Karydis acknowledges support from an FP7 Marie
Curie Career Integration Grant (project reference 618349), Holger Tost
acknowledges funding from the Carl-Zeiss Foundation, and Alexandra P.
Tsimpidi acknowledges support from a DFG individual grand programme (project
reference TS 335/2-1). Finally, we acknowledge the use of the programmes Ferret
(product of the NOAA's Pacific Marine Environmental Laboratory,